{ "papers": [ { "title": "CounselBench: A Large-Scale Expert Evaluation and Adversarial Benchmarking of Large Language Models in Mental Health Question Answering", "authors": [ "Yahan Li", "Jifan Yao", "John Bosco S. Bunyi", "Adam C Frank", "Angel Hsing-Chi Hwang", "Ruishan Liu" ], "year": 2026, "venue": "ICLR", "abstract": "Medical question answering (QA) benchmarks often focus on multiple-choice or fact-based tasks, leaving open-ended answers to real patient questions underexplored. This gap is particularly critical in mental health, where patient questions often mix symptoms, treatment concerns, and emotional needs, requiring answers that balance clinical caution with contextual sensitivity.\nWe present CounselBench, a large-scale benchmark developed with 100 mental health professionals to evaluate and stress-test large language models (LLMs) in realistic help-seeking scenarios. The first component, CounselBench-EVAL, contains 2,000 expert evaluations of answers from GPT-4, LLaMA 3, Gemini, and online human therapists on patient questions from the public forum CounselChat. Each answer is rated across six clinically grounded dimensions, with span-level annotations and written rationales. Expert evaluations show that while LLMs achieve high scores on several dimensions, they also exhibit recurring issues, including unconstructive feedback, overgeneralization, and limited personalization or relevance. Responses were frequently flagged for safety risks, most notably unauthorized medical advice. Follow-up experiments show that LLM judges systematically overrate model responses and overlook safety concerns identified by human experts. To probe failure modes more directly, we construct CounselBench-Adv, an adversarial dataset of 120 expert-authored mental health questions designed to trigger specific model issues. Expert evaluation of 1,080 responses from nine LLMs reveals consistent, model-specific failure patterns. Together, CounselBench establishes a clinically grounded framework for benchmarking LLMs in mental health QA.", "source": "openreview", "url": "https://openreview.net/forum?id=8MBYRZHVWT", "decision_type": "Oral", "avg_rating": 6.7, "relative_path": "2026/ICLR/Oral/6.7_CounselBench_ A Large-Scale Expert Evaluation and Adversarial Benchmarking of Large Language Mo_2026.pdf" }, { "title": "Omni-Reward: Towards Generalist Omni-Modal Reward Modeling with Free-Form Preferences", "authors": [ "Zhuoran Jin", "Hongbang Yuan", "Kejian Zhu", "Jiachun Li", "Pengfei Cao", "Yubo Chen", "Kang Liu", "Jun Zhao" ], "year": 2026, "venue": "ICLR", "abstract": "Reward models (RMs) play a critical role in aligning AI behaviors with human preferences, yet they face two fundamental challenges: (1) Modality Imbalance, where most RMs are mainly focused on text and image modalities, offering limited support for video, audio, and other modalities; and (2) Preference Rigidity, where training on fixed binary preference pairs fails to capture the complexity and diversity of personalized preferences. To address the above challenges, we propose Omni-Reward, a step toward generalist omni-modal reward modeling with support for free-form preferences, consisting of: (1) Evaluation: We introduce Omni-RewardBench, the first omni-modal RM benchmark with free-form preferences, covering nine tasks across five modalities including text, image, video, audio, and 3D; (2) Data: We construct Omni-RewardData, a multimodal preference dataset comprising 248K general preference pairs and 69K instruction-tuning pairs for training generalist omni-modal RMs; (3) Model: We propose Omni-RewardModel, which includes both discriminative and generative RMs, and achieves strong performance on Omni-RewardBench as well as other widely used reward modeling benchmarks.", "source": "openreview", "url": "https://openreview.net/forum?id=9C4gVbPqSy", "decision_type": "Oral", "avg_rating": 6.5, "relative_path": "2026/ICLR/Oral/6.5_Omni-Reward_ Towards Generalist Omni-Modal Reward Modeling with Free-Form Preferences_2026.pdf" }, { "title": "What's In My Human Feedback? Learning Interpretable Descriptions of Preference Data", "authors": [ "Rajiv Movva", "Smitha Milli", "Sewon Min", "Emma Pierson" ], "year": 2026, "venue": "ICLR", "abstract": "Preference data is widely used for aligning language models, but remains largely opaque. While prior work has studied specific aspects of annotator preference (e.g., length or sycophancy), automatically inferring preferences without pre-specifying hypotheses remains challenging. We introduce *What's In My Human Feedback* (WIMHF), a method that produces human-interpretable, natural language features from preference data using sparse autoencoders. We show that a sparse set of interpretable features can account for two-thirds of the preference signal achieved by black-box models. Applying WIMHF to 7 widely-used datasets, we precisely characterize both (1) which preferences are even possible to measure from each dataset and (2) which preferences humans actually display. WIMHF surfaces preferences that are unintentional or even actively harmful, like a preference for toxic outputs in Chatbot Arena. We show how these findings enable *interpretable data curation*: re-labeling the examples that contain the harmful preference yields large safety gains (+37%) with no cost to general performance. We also demonstrate a new approach to *personalization*: on the Community Alignment dataset, we identify preferences that are subjective across annotators, and use the features as interpretable knobs to adjust model behavior along these axes.", "source": "openreview", "url": "https://openreview.net/forum?id=sC6A1bFDUt", "decision_type": "Oral", "avg_rating": 6.5, "relative_path": "2026/ICLR/Oral/6.5_What's In My Human Feedback_ Learning Interpretable Descriptions of Preference Data_2026.pdf" }, { "title": "One for Two: A Unified Framework for Imbalanced Graph Classification via Dynamic Balanced Prototype", "authors": [ "Guanjun Wang", "Binwu Wang", "Jiaming Ma", "Zhengyang Zhou", "Pengkun Wang", "Xu Wang", "Yang Wang" ], "year": 2026, "venue": "ICLR", "abstract": "Graph Neural Networks (GNNs) have advanced graph classification, yet they remain vulnerable to graph-level imbalance, encompassing class imbalance and topological imbalance. To address both types of imbalance in a unified manner, we propose UniImb, a Unified framework for Imbalanced graph classification. Specifically, UniImb first captures multi-scale topological features and enhances data diversity via learnable personalized graph perturbations. It then employs a dynamic balanced prototype module to learn representative prototypes from graph instances, improving the quality of graph representations. Concurrently, a prototype load-balancing optimization term mitigates dominance by majority samples to equalize sample influence during training. We justify these design choices theoretically using the Information Bottleneck principle. Extensive experiments on 19 datasets-including a large-scale imbalanced air pollution graph dataset AirGraph released by us and 23 baselines demonstrate that UniImb has achieved dominant performance across various imbalanced scenarios. Our code is available at GitHub.", "source": "openreview", "url": "https://openreview.net/forum?id=MraQM41SNS", "decision_type": "Oral", "avg_rating": 5.5, "relative_path": "2026/ICLR/Oral/5.5_One for Two_ A Unified Framework for Imbalanced Graph Classification via Dynamic Balanced Proto_2026.pdf" }, { "title": "P-GenRM: Personalized Generative Reward Model with Test-time User-based Scaling", "authors": [ "Pinyi Zhang", "Ting-En Lin", "Yuchuan Wu", "Jingyang Chen", "Zongqi Wang", "Hua Yang", "Xu Ze", "Fei Huang", "Yongbin Li", "Kai Zhang" ], "year": 2026, "venue": "ICLR", "abstract": "Personalized alignment of large language models seeks to adapt responses to individual user preferences, typically via reinforcement learning. A key challenge is obtaining accurate, user-specific reward signals in open-ended scenarios. Existing personalized reward models face two persistent limitations: (1) oversimplifying diverse, scenario-specific preferences into a small, fixed set of evaluation principles, and (2) struggling with generalization to new users with limited feedback. To this end, we propose **P-GenRM**, the first **P**ersonalized **Gen**erative **R**eward **M**odel with test-time user-based scaling. P-GenRM transforms preference signals into structured evaluation chains that derive adaptive personas and scoring rubrics across various scenarios. It further clusters users into User Prototypes and introduces a dual-granularity scaling mechanism: at the individual level, it adaptively scales and aggregates each user’s scoring scheme; at the prototype level, it incorporates preferences from similar users. This design mitigates noise in inferred preferences and enhances generalization to unseen users through prototype-based transfer. Empirical results show that P-GenRM achieves state-of-the-art results on widely-used personalized reward model benchmarks, with an average improvement of ~2.31\\%, and demonstrates strong generalization on an out-of-distribution dataset. Notably, Test-time User-based scaling provides an additional ~3\\% boost, demonstrating stronger personalized alignment with test-time scalability.", "source": "openreview", "url": "https://openreview.net/forum?id=hXNApWLBZG", "decision_type": "Oral", "avg_rating": 4.7, "relative_path": "2026/ICLR/Oral/4.7_P-GenRM_ Personalized Generative Reward Model with Test-time User-based Scaling_2026.pdf" }, { "title": "Not All Clients Are Equal: Collaborative Model Personalization on Heterogeneous Multi-Modal Clients", "authors": [ "Minhyuk Seo", "Taeheon Kim", "Hankook Lee", "Jonghyun Choi", "Tinne Tuytelaars" ], "year": 2026, "venue": "ICLR", "abstract": "As AI becomes more personal, e.g., Agentic AI, there is an increasing need for personalizing models for various use cases.\nPersonalized federated learning (PFL) enables each client to collaboratively leverage other clients' knowledge for better adaptation to the task of interest, without privacy risks. Despite its potential, existing PFL methods remain confined to rather simplified scenarios where data and models are the same across clients. To move towards realistic scenarios, we propose FedMosaic, a method that jointly addresses data and model heterogeneity with a task-relevance-aware model aggregation strategy to reduce parameter interference, and a dimension-invariant module that enables knowledge sharing across heterogeneous architectures without huge computational cost.\nTo mimic the real-world task diversity, we propose a multi-modal PFL benchmark spanning 40 distinct tasks with distribution shifts over time. The empirical study shows that FedMosaic outperforms the state-of-the-art PFL methods, excelling in both personalization and generalization capabilities under challenging, realistic scenarios.", "source": "openreview", "url": "https://openreview.net/forum?id=0g5Dk4Qfh0", "decision_type": "Poster", "avg_rating": 7.0, "relative_path": "2026/ICLR/Poster/7.0_Not All Clients Are Equal_ Collaborative Model Personalization on Heterogeneous Multi-Modal Cli_2026.pdf" }, { "title": "INO-SGD: Addressing Utility Imbalance under Individualized Differential Privacy", "authors": [ "Xiao Tian", "Jue Fan", "Rachael Hwee Ling Sim", "Bryan Kian Hsiang Low" ], "year": 2026, "venue": "ICLR", "abstract": "Differential privacy (DP) is widely employed in machine learning to protect confidential or sensitive training data from being revealed. As data owners gain greater control over their data due to personal data ownership, they are more likely to set their own privacy requirements, necessitating individualized DP (IDP) to fulfil such requests. In particular, owners of data from more sensitive subsets, such as positive cases of stigmatized diseases, likely set stronger privacy requirements, as leakage of such data could incur more serious societal impact. However, existing IDP algorithms induce a critical utility imbalance problem: Data from owners with stronger privacy requirements may be severely underrepresented in the trained model, resulting in poorer performance on similar data from subsequent users during deployment. In this paper, we analyze this problem and propose the INO-SGD algorithm, which strategically down-weights data within each batch to improve performance on the more private data across all iterations. Notably, our algorithm is specially designed to satisfy IDP, while existing techniques addressing utility imbalance neither satisfy IDP nor can be easily adapted to do so. Lastly, we demonstrate the empirical feasibility of our approach.", "source": "openreview", "url": "https://openreview.net/forum?id=HMapYMkcrl", "decision_type": "Poster", "avg_rating": 6.7, "relative_path": "2026/ICLR/Poster/6.7_INO-SGD_ Addressing Utility Imbalance under Individualized Differential Privacy_2026.pdf" }, { "title": "Beyond Markovian Drifts: Action-Biased Geometric Walks with Memory for Personalized Summarization", "authors": [ "Parthiv Chatterjee", "Asish Joel Batha", "Tashvi patel", "Sourish Dasgupta", "Tanmoy Chakraborty" ], "year": 2026, "venue": "ICLR", "abstract": "Document summarization helps readers focus on the \"content-of-interest\", a *subjective* and *time-variant* quantity. Capturing this *dynamic subjectivity* requires modeling how user preferences evolve over time, thereby demanding *personalized summarization*. Recent news recommendation and summarization models often assume that preferences follow a *memoryless or short-memory random walk* on interaction graphs, i.e., a Markovian diffusion seeded at the latest interaction or compressed into a short hidden state or prompt. We ask whether such a hypothesis also holds for personalized summarization. To test this, we propose **Walk2Pers**, a lightweight encoder–decoder framework that extends the walk view with *action-conditioned geometric steps*, decomposed into (i) a *magnitude* controlling shift strength and (ii) an *orientation* capturing continuity vs. novelty. The process is mediated by dual memory lanes that reinforce consistent interests while suppressing disinterest, and is augmented with a drift term for summary requests. We show theoretically that such structured walks approximate first-order action-conditioned kernels, and empirically validate the hypothesis on PENS, OpenAI-Reddit, and PersonalSum. Using PerSEval, a personalization metric with strong human correlation, Walk2Pers outperforms specialized personalized summarizers by an average of $0.41 \\uparrow$, and strong LLM baselines (DeepSeek-R1-14B, LLaMA-2-13B, Mistral-7B, Zephyr-7B) by $0.22 \\uparrow$. Analyses further confirm cross-domain robustness ($0.19 \\uparrow$ over the best LLM) and stability on long histories. Together, these results support viewing personalized summarization as an *action-biased geometric walk with memory*, offering both interpretability and efficiency.", "source": "openreview", "url": "https://openreview.net/forum?id=HvOKarTubb", "decision_type": "Poster", "avg_rating": 6.5, "relative_path": "2026/ICLR/Poster/6.5_Beyond Markovian Drifts_ Action-Biased Geometric Walks with Memory for Personalized Summarizati_2026.pdf" }, { "title": "CIMemories: A Compositional Benchmark For Contextual Integrity In LLMs", "authors": [ "Niloofar Mireshghallah", "Neal Mangaokar", "Narine Kokhlikyan", "Arman Zharmagambetov", "Manzil Zaheer", "Saeed Mahloujifar", "Kamalika Chaudhuri" ], "year": 2026, "venue": "ICLR", "abstract": "Large Language Models (LLMs) increasingly use persistent memory from past interactions to enhance personalization and task performance. However, this memory creates critical risks when sensitive information is revealed in inappropriate contexts. We present CIMemories, a benchmark for evaluating whether LLMs appropriately control information flow from memory based on task context. CIMemories uses synthetic user profiles with 100+ attributes per user, paired with various task contexts where each attribute may be essential for some tasks but inappropriate for others. For example, mental health details are necessary for booking therapy but inappropriate when requesting time off from work. This design enables two forms of compositionality: (1) flexible memory composition by varying which attributes are necessary versus inappropriate across different settings, and (2) multi-task composition per user, measuring cumulative information disclosure across sessions. Our evaluation reveals frontier models exhibit between 14%-69% attribute-level violations (leaking inappropriate information), and that higher task completeness (sharing necessary information) is accompanied by increased violations, highlighting critical gaps in integrity-aware memory systems.", "source": "openreview", "url": "https://openreview.net/forum?id=YnNIp38v1M", "decision_type": "Poster", "avg_rating": 6.5, "relative_path": "2026/ICLR/Poster/6.5_CIMemories_ A Compositional Benchmark For Contextual Integrity In LLMs_2026.pdf" }, { "title": "DragFlow: Unleashing DiT Priors with Region-Based Supervision for Drag Editing", "authors": [ "Zihan Zhou", "Shilin Lu", "Shuli Leng", "Shaocong Zhang", "Zhuming Lian", "Xinlei Yu", "Adams Wai-Kin Kong" ], "year": 2026, "venue": "ICLR", "abstract": "Drag-based image editing has long suffered from distortions in the target region, largely because the priors of earlier base models, Stable Diffusion, are insufficient to project optimized latents back onto the natural image manifold. With the shift from UNet-based DDPMs to more scalable DiT with flow matching (e.g., SD3.5, FLUX), generative priors have become significantly stronger, enabling advances across diverse editing tasks. However, drag-based editing has yet to benefit from these stronger priors. This work introduces DragFlow, the first framework to effectively harness FLUX’s rich prior via region-based supervision, enabling full use of its finer-grained, spatially precise features for drag-based editing and achieving substantial improvements over existing baselines. We first show that directly applying point-based drag editing to DiTs performs poorly: unlike the highly compressed features of UNets, DiT features are insufficiently structured to provide reliable guidance for point-wise motion supervision. To overcome this limitation, DragFlow introduces a region-based editing paradigm, where affine transformations enable richer and more consistent feature supervision. Additionally, we integrate pretrained open-domain personalization adapters (e.g., IP-Adapter) to enhance subject consistency, while preserving background fidelity through gradient mask-based hard constraints. Multimodal large language models (MLLMs) are further employed to resolve task ambiguities. For evaluation, we curate a novel Region-based Dragging benchmark (ReD Bench) featuring region-level dragging instructions. Extensive experiments on DragBench-DR and ReD Bench show that DragFlow surpasses both point-based and region-based baselines, setting a new state-of-the-art in drag-based image editing. Code and datasets will be publicly available upon publication.", "source": "openreview", "url": "https://openreview.net/forum?id=Zhckizkww1", "decision_type": "Poster", "avg_rating": 6.5, "relative_path": "2026/ICLR/Poster/6.5_DragFlow_ Unleashing DiT Priors with Region-Based Supervision for Drag Editing_2026.pdf" }, { "title": "Enhancing Persona Following at Decoding Time via Dynamic Importance Estimation for Role-Playing Agents", "authors": [ "Yuxin Liu", "Mingye Zhu", "Siyuan Liu", "Bo Hu", "Lei Zhang" ], "year": 2026, "venue": "ICLR", "abstract": "The utility of Role-Playing Language Agents in sociological research is growing alongside the adoption of Large Language Models. For realism in social simulation, these agents must adhere to their personas defined by character profiles, yet existing strategies—static prompt engineering or costly fine-tuning—fail to adapt personas to dynamic scenarios. Psychological theories, such as the Cognitive-Affective Personality Systems, provide a crucial explanation for this failure: a persona's influence on behavior is not static but varies with the scenarios. This context-dependence highlights the critical need for adaptive persona management. To address this gap, we propose a novel, theory-driven method that dynamically estimates context-dependent persona importance and integrates it into weighted reward-guided decoding, enabling inference-time persona following. Specifically, we introduce Persona Dynamic Decoding (PDD) framework that consists of two key components:\n(1) Persona Importance Estimation (PIE) module, which dynamically quantifies the contextual importance of persona attributes without requiring ground-truth supervision; and (2) Persona-Guided Inference-Time Alignment (PIA) paradigm, which leverages these importance scores to construct weighted multi-objective rewards and modulate generation probabilities during inference. Extensive experiments show the effectiveness of our method in utterance consistency and behavioral fidelity.", "source": "openreview", "url": "https://openreview.net/forum?id=lVE8H8QNcx", "decision_type": "Poster", "avg_rating": 6.5, "relative_path": "2026/ICLR/Poster/6.5_Enhancing Persona Following at Decoding Time via Dynamic Importance Estimation for Role-Playing_2026.pdf" }, { "title": "Fair in Mind, Fair in Action? A Synchronous Benchmark for Understanding and Generation in UMLLMs", "authors": [ "Yiran Zhao", "Lu Zhou", "Xiaogang Xu", "Zhe Liu", "Jiafei Wu", "Liming Fang" ], "year": 2026, "venue": "ICLR", "abstract": "As artificial intelligence (AI) is increasingly deployed across domains, ensuring fairness has become a core challenge. However, the field faces a \"Tower of Babel'' dilemma: fairness metrics abound, yet their underlying philosophical assumptions often conflict, hindering unified paradigms—particularly in unified Multimodal Large Language Models (UMLLMs), where biases propagate systemically across tasks. To address this, we introduce the IRIS Benchmark, to our knowledge the first benchmark designed to synchronously evaluate the fairness of both understanding and generation tasks in UMLLMs. Enabled by our demographic classifier, ARES, and four supporting large-scale datasets, the benchmark is designed to normalize and aggregate arbitrary metrics into a high-dimensional \"fairness space'', integrating 60 granular metrics across three dimensions—Ideal Fairness, Real-world Fidelity, and Bias Inertia & Steerability (IRIS). Through this benchmark, our evaluation of leading UMLLMs uncovers systemic phenomena such as the \"generation gap'', individual inconsistencies like \"personality splits'', and the \"counter-stereotype reward'', while offering diagnostics to guide the optimization of their fairness capabilities. With its novel and extensible framework, the IRIS benchmark is capable of integrating evolving fairness metrics, ultimately helping to resolve the \"Tower of Babel'' impasse.", "source": "openreview", "url": "https://openreview.net/forum?id=NYphgYTloq", "decision_type": "Poster", "avg_rating": 6.5, "relative_path": "2026/ICLR/Poster/6.5_Fair in Mind, Fair in Action_ A Synchronous Benchmark for Understanding and Generation in UMLLM_2026.pdf" }, { "title": "From Five Dimensions to Many: Large Language Models as Precise and Interpretable Psychological Profilers", "authors": [ "Yi-Fei Liu", "Yi-Long Lu", "Di He", "Hang Zhang" ], "year": 2026, "venue": "ICLR", "abstract": "Psychological constructs within individuals are widely believed to be interconnected. We investigated whether and how Large Language Models (LLMs) can model the correlational structure of human psychological traits from minimal quantitative inputs. We prompted various LLMs with Big Five Personality Scale responses from 816 human individuals to role-play their responses on nine other psychological scales. LLMs demonstrated remarkable accuracy in capturing human psychological structure, with the inter-scale correlation patterns from LLM-generated responses strongly aligning with those from human data (R² > 0.88). This zero-shot performance substantially exceeded predictions based on semantic similarity and approached the accuracy of machine learning algorithms trained directly on the dataset. Analysis of reasoning traces revealed that LLMs use a systematic two-stage process: First, they transform raw Big Five responses into natural language personality summaries through information selection and compression, analogous to generating sufficient statistics. Second, they generate target scale responses based on reasoning from these summaries. For information selection, LLMs identify the same key personality factors as trained algorithms, though they fail to differentiate item importance within factors. The resulting compressed summaries are not merely redundant representations but capture synergistic information—adding them to original scores enhances prediction alignment, suggesting they encode emergent, second-order patterns of trait interplay. Our findings demonstrate that LLMs can precisely predict individual participants' psychological traits from minimal data through a process of abstraction and reasoning, offering both a powerful tool for psychological simulation and valuable insights into their emergent reasoning capabilities.", "source": "openreview", "url": "https://openreview.net/forum?id=JXFnCpXcnY", "decision_type": "Poster", "avg_rating": 6.5, "relative_path": "2026/ICLR/Poster/6.5_From Five Dimensions to Many_ Large Language Models as Precise and Interpretable Psychological _2026.pdf" }, { "title": "Towards Better Optimization For Listwise Preference in Diffusion Models", "authors": [ "Jiamu Bai", "Xin Yu", "Meilong Xu", "Weitao Lu", "Xin Pan", "Kiwan Maeng", "Daniel Kifer", "Jian Wang", "Yu Wang" ], "year": 2026, "venue": "ICLR", "abstract": "Reinforcement learning from human feedback (RLHF) has proven effectiveness for aligning text-to-image (T2I) diffusion models with human preferences. Although Direct Preference Optimization (DPO) is widely adopted for its computational efficiency and avoidance of explicit reward modeling, its applications to diffusion models have primarily relied on pairwise preferences. The precise optimization of listwise preferences remains largely unaddressed. In practice, human feedback on image preferences often contains implicit ranked information, which conveys more precise human preferences than pairwise comparisons. In this work, we propose Diffusion-LPO, a simple and effective framework for Listwise Preference Optimization in diffusion models with listwise data. Given a caption, we aggregate user feedback into a ranked list of images and derive a listwise extension of the DPO objective under the Plackett–Luce model. Diffusion-LPO enforces consistency across the entire ranking by encouraging each sample to be preferred over all of its lower-ranked alternatives. We empirically demonstrate the effectiveness of Diffusion-LPO across various tasks, including text-to-image generation, image editing, and personalized preference alignment. Diffusion-LPO consistently outperforms pairwise DPO baselines on visual quality and preference alignment.", "source": "openreview", "url": "https://openreview.net/forum?id=ippWaS9PG9", "decision_type": "Poster", "avg_rating": 6.5, "relative_path": "2026/ICLR/Poster/6.5_Towards Better Optimization For Listwise Preference in Diffusion Models_2026.pdf" }, { "title": "VoxPrivacy: A Benchmark for Evaluating Interactional Privacy of Speech Language Models", "authors": [ "Yuxiang Wang", "HongYu Liu", "Dekun Chen", "Xueyao Zhang", "Zhizheng Wu" ], "year": 2026, "venue": "ICLR", "abstract": "As Speech Language Models (SLMs) transition from personal devices to shared, multi-user environments such as smart homes, a new challenge emerges: the model is expected to distinguish between users to manage information flow appropriately. Without this capability, an SLM could reveal one user’s confidential schedule to another—a privacy failure we term **interactional privacy**. Thus, the ability to generate speaker-aware responses becomes essential for SLM safe deployment. Current SLM benchmarks test dialogue ability but overlook speaker identity. Multi-speaker benchmarks check who said what without assessing whether SLMs adapt their responses. Privacy benchmarks focus on globally sensitive data (e.g., bank passwords) while neglecting contextually sensitive information (e.g., a user’s private appointment). To address this gap, we introduce **VoxPrivacy**, the first benchmark designed to evaluate interactional privacy in SLMs. VoxPrivacy spans three tiers of increasing difficulty, from following direct secrecy commands to proactively protecting privacy. Our evaluation of nine SLMs on a 32-hour bilingual dataset reveals a widespread vulnerability: most open-source models perform close to random chance (around 50\\% accuracy) on conditional privacy decisions, while even strong closed-source systems still fall short on proactive privacy inference. We further validate these findings on Real-VoxPrivacy, a human-recorded subset, confirming that the failures observed on synthetic data persist in real speech. We also demonstrate a viable path forward: by fine-tuning on a new 4,000-hour training set, we improve the model’s privacy-preserving capabilities while achieving fair robustness. To support future work, we are releasing the VoxPrivacy benchmark, the large-scale training set, and the fine-tuned model to help the development of safer and more context-aware SLMs.", "source": "openreview", "url": "https://openreview.net/forum?id=GNo1qMqgPD", "decision_type": "Poster", "avg_rating": 6.5, "relative_path": "2026/ICLR/Poster/6.5_VoxPrivacy_ A Benchmark for Evaluating Interactional Privacy of Speech Language Models_2026.pdf" }, { "title": "AMemGym: Interactive Memory Benchmarking for Assistants in Long-Horizon Conversations", "authors": [ "Cheng Jiayang", "Dongyu Ru", "Lin Qiu", "Yiyang Li", "Xuezhi Cao", "Yangqiu Song", "Xunliang Cai" ], "year": 2026, "venue": "ICLR", "abstract": "Long-horizon interactions between users and LLM-based assistants necessitates effective memory management, yet current approaches face challenges in training and evaluation of memory. Existing memory benchmarks rely on static, off-policy data as context, limiting evaluation reliability and scalability. To address these gaps, we introduce AMemGym, an interactive environment enabling on-policy evaluation and optimization for memory-driven personalization.\nAMemGym employs structured data sampling to predefine user profiles, state-dependent questions, and state evolution trajectories, enabling cost-effective generation of high-quality, evaluation-aligned interactions. LLM-simulated users expose latent states through role-play while maintaining structured state consistency.\nComprehensive metrics based on structured data guide both assessment and optimization of assistants.\nExtensive experiments reveal performance gaps in existing memory systems (e.g., RAG, long-context LLMs, and agentic memory) and corresponding reasons. AMemGym not only enables effective selection among competing approaches but also can potentially drive the self-evolution of memory management strategies.\nBy bridging structured state evolution with free-form interactions, our framework provides a scalable, diagnostically rich environment for advancing memory capabilities in conversational agents.", "source": "openreview", "url": "https://openreview.net/forum?id=sfrVLzsmlf", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2026/ICLR/Poster/6.0_AMemGym_ Interactive Memory Benchmarking for Assistants in Long-Horizon Conversations_2026.pdf" }, { "title": "Batch and Sequential Unlearning for Neural Networks", "authors": [ "Nhung Bui", "Xinyang Lu", "Rachael Hwee Ling Sim", "See-Kiong Ng", "Bryan Kian Hsiang Low" ], "year": 2026, "venue": "ICLR", "abstract": "With the increasing deployment of machine learning models trained on personal data, machine unlearning has become crucial for data owners to exercise their \"right to be forgotten\" and protect their privacy. While model owners can retrain the models without the erased data to achieve this goal, this process is often prohibitively expensive. Previous works have shown that Newton's method can be applied to linear models to unlearn multiple data points in batch (batch unlearning) with minimal iterations. However, adapting this method to non-linear models, such as neural networks, poses significant challenges due to the presence of degenerate Hessians. This problem becomes more pronounced when unlearning is performed sequentially (sequential unlearning). Existing techniques that tried to tackle this degeneracy often 1) incur unlearning updates with excessively large norm that yield unsatisfactory unlearning performance and 2) may require manual tuning of regularization hyperparameters. In this work, we propose new unlearning algorithms that leverage cubic regularization for Newton's method to address both challenges. We discuss the theoretical benefits of our method and empirically show that our algorithms can efficiently achieve competitive performance in both batch and sequential unlearning on real-world datasets.", "source": "openreview", "url": "https://openreview.net/forum?id=dHz2LBCyTh", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2026/ICLR/Poster/6.0_Batch and Sequential Unlearning for Neural Networks_2026.pdf" }, { "title": "DP-Fusion: Token-Level Differentially Private Inference for Large Language Models", "authors": [ "Rushil Thareja", "Preslav Nakov", "Praneeth Vepakomma", "Nils Lukas" ], "year": 2026, "venue": "ICLR", "abstract": "Large language models (LLMs) do not preserve privacy at inference-time. The LLM's outputs can inadvertently reveal information about the model's context, which presents a privacy challenge when the LLM is augmented via tools or databases containing sensitive information. Existing privacy-preserving methods at inference-time have significant limitations since they (i) lack provable guarantees or (ii) have a poor utility/privacy trade-off. We propose DP-Fusion, a Differentially Private Inference (DPI) mechanism for LLMs that provably bounds the influence a set of tokens in the context can have on the LLM's output. DP-Fusion works as follows: (1) label a subset of sensitive tokens, (2) infer the LLM without any sensitive tokens to obtain a baseline, (3) infer the LLM with the sensitive tokens, and (4) blend distributions so that the final output remains within a bounded distance of the baseline distribution. While this per-token influence bound also mitigates jailbreak-style prompt injection, we focus on document privatization, where the goal is to paraphrase a document containing sensitive tokens, e.g., personally identifiable information, so that no attacker can reliably infer them from the paraphrased document while preserving high text quality. The privacy/utility trade-off is controlled by $\\epsilon$, where $\\epsilon=0$ hides sensitive tokens entirely, while higher values trade off privacy for improved text quality. We show that our method creates token-level provably privatized documents with substantially improved theoretical and empirical privacy, achieving $6\\times$ lower perplexity than related DPI methods.", "source": "openreview", "url": "https://openreview.net/forum?id=WLK37mn0El", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2026/ICLR/Poster/6.0_DP-Fusion_ Token-Level Differentially Private Inference for Large Language Models_2026.pdf" }, { "title": "Directional Textual Inversion for Personalized Text-to-Image Generation", "authors": [ "Kunhee Kim", "NaHyeon Park", "Kibeom Hong", "Hyunjung Shim" ], "year": 2026, "venue": "ICLR", "abstract": "Textual Inversion (TI) is an efficient approach to text-to-image personalization but often fails on complex prompts. We trace these failures to embedding norm inflation: learned tokens drift to out-of-distribution magnitudes, degrading prompt conditioning in pre-norm Transformers. Empirically, we show semantics are primarily encoded by direction in CLIP token space, while inflated norms harm contextualization; theoretically, we analyze how large magnitudes attenuate positional information and hinder residual updates in pre-norm blocks. We propose Directional Textual Inversion (DTI), which fixes the embedding magnitude to an in-distribution scale and optimizes only direction on the unit hypersphere via Riemannian SGD. We cast direction learning as MAP with a von Mises-Fisher prior, yielding a constant-direction prior gradient that is simple and efficient to incorporate. Across personalization tasks, DTI improves text fidelity over TI and TI-variants while maintaining subject similarity. Crucially, DTI's hyperspherical parameterization enables smooth, semantically coherent interpolation between learned concepts (slerp), a capability that is absent in standard TI. Our findings suggest that direction-only optimization is a robust and scalable path for prompt-faithful personalization. Code is available at https://github.com/kunheek/dti.", "source": "openreview", "url": "https://openreview.net/forum?id=6wA4qpyyU9", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2026/ICLR/Poster/6.0_Directional Textual Inversion for Personalized Text-to-Image Generation_2026.pdf" }, { "title": "Evoking User Memory: Personalizing LLM via Recollection-Familiarity Adaptive Retrieval", "authors": [ "Yingyi Zhang", "Junyi Li", "Wenlin Zhang", "Pengyue Jia", "Xianneng Li", "Yichao Wang", "Derong Xu", "Yi Wen", "Huifeng Guo", "Yong Liu", "Xiangyu Zhao" ], "year": 2026, "venue": "ICLR", "abstract": "Personalized large language models (LLMs) rely on memory retrieval to incorporate user-specific histories, preferences, and contexts. Existing approaches either overload the LLM by feeding all the user's past memory into the prompt, which is costly and unscalable, or simplify retrieval into a one-shot similarity search, which captures only surface matches. Cognitive science, however, shows that human memory operates through a dual process: Familiarity, offering fast but coarse recognition, and Recollection, enabling deliberate, chain-like reconstruction for deeply recovering episodic content. \nCurrent systems lack both the ability to perform recollection retrieval and mechanisms to adaptively switch between the dual retrieval paths, leading to either insufficient recall or the inclusion of noise.\nTo address this, we propose RF-Mem (Recollection–Familiarity Memory Retrieval), a familiarity uncertainty-guided dual-path memory retriever. \nRF-Mem measures the familiarity signal through the mean score and entropy. High familiarity leads to the direct top-$K$ Familiarity retrieval path, while low familiarity activates the Recollection path. In the Recollection path, the system clusters candidate memories and applies $\\alpha$-mix with the query to iteratively expand evidence in embedding space, simulating deliberate contextual reconstruction.\nThis design embeds human-like dual-process recognition into the retriever, avoiding full-context overhead and enabling scalable, adaptive personalization. Experiments across three benchmarks and corpus scales demonstrate that RF-Mem consistently outperforms both one-shot retrieval and full-context reasoning under fixed budget and latency constraints. Our code can be found in the Supplementary Materials.", "source": "openreview", "url": "https://openreview.net/forum?id=f7p0F2X6XN", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2026/ICLR/Poster/6.0_Evoking User Memory_ Personalizing LLM via Recollection-Familiarity Adaptive Retrieval_2026.pdf" }, { "title": "Federated Graph-Level Clustering Network with Dual Knowledge Separation", "authors": [ "Xiaobao Wang", "Renda Han", "Ronghao Fu", "Di Jin" ], "year": 2026, "venue": "ICLR", "abstract": "Federated Graph-level Clustering (FGC) offers a promising framework for analyzing distributed graph data while ensuring privacy protection.\nHowever, existing methods fail to simultaneously consider knowledge heterogeneity across intra- and inter-client, and still attempt to share as much knowledge as possible, resulting in consensus failure in the server.\nTo solve these issues, we propose a novel **F**ederated **G**raph-level **C**lustering **N**etwork with **D**ual **K**nowledge **S**eparation (FGCN-DKS). \nThe core idea is to decouple differentiated subgraph patterns and optimize them separately on the client, and then leverage cluster-oriented patterns to guide personalized knowledge aggregation on the server.\nSpecifically, on the client, we separate personalized subgraphs and cluster-oriented subgraphs for each graph. Then the former are retained locally for further refinement of the clustering process, while pattern digests are extracted from the latter for uploading to the server.\nOn the server, we calculate the relation of inter-cluster patterns to adaptively aggregate cluster-oriented prototypes and parameters. Finally, the server generates personalized guidance signals for each cluster of clients, which are then fed back to local clients to enhance overall clustering performance.\nExtensive experiments on multiple graph benchmark datasets have proven the superiority of the proposed FGCN-DKS over the SOTA methods.", "source": "openreview", "url": "https://openreview.net/forum?id=FwKFjBX0PK", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2026/ICLR/Poster/6.0_Federated Graph-Level Clustering Network with Dual Knowledge Separation_2026.pdf" }, { "title": "Generative Value Conflicts Reveal LLM Priorities", "authors": [ "Andy Liu", "Kshitish Ghate", "Mona T. Diab", "Daniel Fried", "Atoosa Kasirzadeh", "Max Kleiman-Weiner" ], "year": 2026, "venue": "ICLR", "abstract": "Past work seeks to align large language model (LLM)-based assistants with a target set of values, but such assistants are frequently forced to make tradeoffs *between* values when deployed. In response to the scarcity of value conflict in existing alignment datasets, we introduce ConflictScope, an automatic pipeline to evaluate how LLMs prioritize different values. Given a user-defined value set, ConflictScope automatically generates scenarios in which a language model faces a conflict between two values sampled from the set. It then prompts target models with an LLM-written ``user prompt'' and evaluates their free-text responses to elicit a ranking over values in the value set. Comparing results between multiple-choice and open-ended evaluations, we find that models shift away from supporting protective values, such as harmlessness, and toward supporting personal values, such as user autonomy, in more open-ended value conflict settings. However, including detailed value orderings in models' system prompts improves alignment with a target ranking by 14%, showing that system prompting can achieve moderate success at aligning LLM behavior under value conflict. Our work demonstrates the importance of evaluating value prioritization in models and provides a foundation for future work in this area.", "source": "openreview", "url": "https://openreview.net/forum?id=RXCRKAcv3B", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2026/ICLR/Poster/6.0_Generative Value Conflicts Reveal LLM Priorities_2026.pdf" }, { "title": "Knowledgeable Language Models as Black-Box Optimizers for Personalized Medicine", "authors": [ "Michael S Yao", "Osbert Bastani", "Alma Andersson", "Tommaso Biancalani", "Aicha BenTaieb", "Claudia Iriondo" ], "year": 2026, "venue": "ICLR", "abstract": "The goal of personalized medicine is to discover a treatment regimen that optimizes a patient's clinical outcome based on their personal genetic and environmental factors. However, candidate treatments cannot be arbitrarily administered to the patient to assess their efficacy; we often instead have access to an *in silico* surrogate model that approximates the true fitness of a proposed treatment. Unfortunately, such surrogate models have been shown to fail to generalize to previously unseen patient-treatment combinations. We hypothesize that domain-specific prior knowledge—such as medical textbooks and biomedical knowledge graphs—can provide a meaningful alternative signal of the fitness of proposed treatments. To this end, we introduce **L**LM-based **E**ntropy-guided **O**ptimization with k**N**owledgeable priors (**LEON**), a mathematically principled approach to leverage large language models (LLMs) as black-box optimizers without any task-specific fine-tuning, taking advantage of their ability to contextualize unstructured domain knowledge to propose personalized treatment plans in natural language. In practice, we implement LEON via 'optimization by prompting,' which uses LLMs as stochastic engines for proposing treatment designs. Experiments on real-world optimization tasks show LEON outperforms both traditional and LLM-based methods in proposing individualized treatments for patients.", "source": "openreview", "url": "https://openreview.net/forum?id=w025bYRVkO", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2026/ICLR/Poster/6.0_Knowledgeable Language Models as Black-Box Optimizers for Personalized Medicine_2026.pdf" }, { "title": "Learning to summarize user information for personalized reinforcement learning from human feedback", "authors": [ "HyunJi Nam", "Yanming Wan", "Mickel Liu", "Peter F. Ahnn", "Jianxun Lian", "Natasha Jaques" ], "year": 2026, "venue": "ICLR", "abstract": "As everyday use cases of large language model (LLM) AI assistants have expanded, it is becoming increasingly important to personalize responses to align to different users' preferences and goals. While reinforcement learning from human feedback (RLHF) is effective at improving LLMs to be generally more helpful and fluent, it does not account for variability across users, as it models the entire user population with a single reward model, meaning it assumes that everyone's preferences are the same.\nWe present a novel framework, **P**reference **L**earning **U**sing **S**ummarization (**PLUS**), that uses reinforcement learning (RL) to learn to produce text-based summaries of each user's preferences, characteristics, and past conversations. These summaries condition the reward model, enabling it to make personalized predictions about the types of responses valued by each user. Both the user-summarization model and reward model are trained simultaneously, creating an online co-adaptation loop. We show that in contrast to the standard Bradley–Terry model, summaries produced by PLUS capture diverse aspects of user preferences, achieving a 11–77\\% improvement in reward model accuracy. Key strengths of PLUS are: (1) robust performance with new users and conversation topics, achieving a 25\\% improvement over the best personalized reward model technique used for RLHF; (2) zero-shot personalization with state-of-the-art proprietary models like GPT-4 (e.g., PLUS-summary-conditioned responses achieved a 72\\% win rate compared to 28\\% for default GPT-4o); (3) learning from flexible user contexts beyond preference labels, and (4) interpretable representation of users, enabling greater transparency and user control in pluralistic LLM alignment.", "source": "openreview", "url": "https://openreview.net/forum?id=Ar078WR3um", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2026/ICLR/Poster/6.0_Learning to summarize user information for personalized reinforcement learning from human feedb_2026.pdf" }, { "title": "LumosX: Relate Any Identities with Their Attributes for Personalized Video Generation", "authors": [ "Jiazheng Xing", "Fei Du", "Hangjie Yuan", "Pengwei Liu", "Hongbin Xu", "Hai Ci", "Ruigang Niu", "Weihua Chen", "Fan Wang", "Yong Liu" ], "year": 2026, "venue": "ICLR", "abstract": "Recent advances in diffusion models have significantly improved text-to-video generation, enabling personalized content creation with fine-grained control over both foreground and background elements. However, precise face–attribute alignment across subjects remains challenging, as existing methods lack explicit mechanisms to ensure intra-group consistency. Addressing this gap requires both explicit modeling strategies and face-attribute-aware data resources. We therefore propose $\\textbf{\\textit{Lumos{X}}}$, a framework that advances both data and model design. On the data side, a tailored collection pipeline orchestrates captions and visual cues from independent videos, while multimodal large language models (MLLMs) infer and assign subject-specific dependencies. These extracted relational priors impose a finer-grained structure that amplifies the expressive control of personalized video generation and enables the construction of a comprehensive benchmark. On the modeling side, Relational Self-Attention and Relational Cross-Attention intertwine position-aware embeddings with refined attention dynamics to inscribe explicit subject–attribute dependencies, enforcing disciplined intra-group cohesion and amplifying the separation between distinct subject clusters. Comprehensive evaluations on our benchmark demonstrate that $\\textit{LumosX}$ achieves state-of-the-art performance in fine-grained, identity-consistent, and semantically aligned personalized multi-subject video generation.", "source": "openreview", "url": "https://openreview.net/forum?id=r5o6PWgzav", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2026/ICLR/Poster/6.0_LumosX_ Relate Any Identities with Their Attributes for Personalized Video Generation_2026.pdf" }, { "title": "PerFit: Exploring Personalization Shifts in Representation Space of LLMs", "authors": [ "Jiahong Liu", "Wenhao Yu", "Quanyu Dai", "Zhongyang Li", "Jieming Zhu", "Menglin Yang", "Tat-Seng Chua", "Irwin King" ], "year": 2026, "venue": "ICLR", "abstract": "Personalization has become a pivotal field of study in contemporary intelligent systems. While large language models (LLMs) excel at general knowledge tasks, they often struggle with personalization, i.e., adapting their outputs to individual user expectations. Existing approaches that steer LLM behavior to meet users’ implicit preferences and behavior patterns, primarily relying on tune-free methods (e.g., RAG, PAG) or parameter fine-tuning methods (e.g., LoRA), face challenges in effectively balancing effectiveness and efficiency. Moreover, the mechanisms underlying personalized preferences remain underexplored. To address these challenges, we first uncover key patterns of user-specific information embedded in the representation space. Specifically, we find that (1) personalized information lies within a low-rank subspace represented by vectors, and (2) these vectors demonstrate both a collective shift shared across users and a personalized shift unique to each individual user. Building on these insights, we introduce PerFit, a novel two-stage solution that directly fine-tunes interventions in the hidden representation space by addressing both collective and user-specific shifts, thereby achieving precise steering of LLM with minimal parameter overhead. Experimental results demonstrate that \\perfit delivers strong performance across six datasets while \\cutting the number of parameters by an average of 92.3% compared to the state-of-the-art method.", "source": "openreview", "url": "https://openreview.net/forum?id=Lwn67fk9e1", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2026/ICLR/Poster/6.0_PerFit_ Exploring Personalization Shifts in Representation Space of LLMs_2026.pdf" }, { "title": "PersonaX: Multimodal Datasets with LLM-Inferred Behavior Traits", "authors": [ "Loka Li", "Wong Yu Kang", "Minghao Fu", "Guangyi Chen", "Zhenhao Chen", "Gongxu Luo", "Yuewen Sun", "Salman Khan", "Peter Spirtes", "Kun Zhang" ], "year": 2026, "venue": "ICLR", "abstract": "Understanding human behavior traits is central to applications in human-computer interaction, computational social science, and personalized AI systems. Such understanding often requires integrating multiple modalities to capture nuanced patterns and relationships. However, existing resources rarely provide datasets that combine behavioral descriptors with complementary modalities such as facial attributes and biographical information. To address this gap, we present PersonaX, a curated collection of multimodal datasets designed to enable comprehensive analysis of public traits across modalities. PersonaX consists of (1) CelebPersona, featuring 9444 public figures from diverse occupations, and (2) AthlePersona, covering 4181 professional athletes across 7 major sports leagues. Each dataset includes behavioral trait assessments inferred by three high-performing large language models, alongside facial imagery and structured biographical features.\nWe analyze PersonaX at two complementary levels. First, we abstract high-level trait scores from text descriptions and apply five statistical independence tests to examine their relationships with other modalities. Second, we introduce a novel causal representation learning (CRL) framework tailored to multimodal and multi-measurement data, providing theoretical identifiability guarantees. Experiments on both synthetic and real-world data demonstrate the effectiveness of our approach. By unifying structured and unstructured analysis, PersonaX establishes a foundation for studying LLM-inferred behavioral traits in conjunction with visual and biographical attributes, advancing multimodal trait analysis and causal reasoning. \nThe code is available at https://github.com/lokali/PersonaX.", "source": "openreview", "url": "https://openreview.net/forum?id=x446ASYlCt", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2026/ICLR/Poster/6.0_PersonaX_ Multimodal Datasets with LLM-Inferred Behavior Traits_2026.pdf" }, { "title": "PluriHarms: Benchmarking the Full Spectrum of Human Judgments on AI Harm", "authors": [ "Jing-Jing Li", "Joel Mire", "Eve Fleisig", "Valentina Pyatkin", "Anne Collins", "Maarten Sap", "Sydney Levine" ], "year": 2026, "venue": "ICLR", "abstract": "Current AI safety frameworks, which often treat harmfulness as binary, lack the flexibility to handle borderline cases where humans meaningfully disagree. To build more pluralistic systems, it is essential to move beyond consensus and instead understand where and why disagreements arise. We introduce PluriHarms, a benchmark designed to systematically study human harm judgments across two key dimensions—the harm axis (benign to harmful) and the agreement axis (agreement to disagreement). Our scalable framework generates prompts that capture diverse AI harms and human values while targeting cases with high disagreement rates, validated by human data. The benchmark includes 150 prompts with 15,000 ratings from 100 human annotators, enriched with demographic and psychological traits and prompt-level features of harmful actions, effects, and values. Our analyses show that prompts that relate to imminent risks and tangible harms amplify perceived harmfulness, while annotator traits (e.g., toxicity experience, education) and their interactions with prompt content explain systematic disagreement. We benchmark AI safety models and alignment methods on PluriHarms, finding that while personalization significantly improves prediction of human harm judgments, considerable room remains for future progress. By explicitly targeting value diversity and disagreement, our work provides a principled benchmark for moving beyond \"one-size-fits-all\" safety toward pluralistically safe AI.", "source": "openreview", "url": "https://openreview.net/forum?id=u7lXflJQX9", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2026/ICLR/Poster/6.0_PluriHarms_ Benchmarking the Full Spectrum of Human Judgments on AI Harm_2026.pdf" }, { "title": "PreferThinker: Reasoning-based Personalized Image Preference Assessment", "authors": [ "Shengqi Xu", "Xinpeng Zhou", "Yabo Zhang", "Ming Liu", "Tao Liang", "Tianyu Zhang", "Yalong Bai", "Zuxuan Wu", "Wangmeng Zuo" ], "year": 2026, "venue": "ICLR", "abstract": "Personalized image preference assessment aims to evaluate an individual user's image preferences by relying only on a small set of reference images as prior information. Existing methods mainly focus on general preference assessment, training models with large-scale data to tackle well-defined tasks such as text-image alignment. However, these approaches struggle to handle personalized preference because user-specific data are scarce and not easily scalable, and individual tastes are often diverse and complex. To overcome these challenges, we introduce a common preference profile that serves as a bridge across users, allowing large-scale user data to be leveraged for training profile prediction and capturing complex personalized preferences. Building on this idea, we propose a reasoning-based personalized image preference assessment framework that follows a \\textit{predict-then-assess} paradigm: it first predicts a user's preference profile from reference images, and then provides interpretable, multi-dimensional scores and assessments of candidate images based on the predicted profile. To support this, we first construct a large-scale Chain-of-Thought (CoT)-style personalized assessment dataset annotated with diverse user preference profiles and high-quality CoT-style reasoning, enabling explicit supervision of structured reasoning. Next, we adopt a two-stage training strategy: a cold-start supervised fine-tuning phase to empower the model with structured reasoning capabilities, followed by reinforcement learning to incentivize the model to explore more reasonable assessment paths and enhance generalization. Furthermore, we propose a similarity-aware prediction reward to encourage better prediction of the user's preference profile, which facilitates more reasonable assessments exploration. Extensive experiments demonstrate the superiority of the proposed method. Our code and dataset will be publicly released.", "source": "openreview", "url": "https://openreview.net/forum?id=iFrdyBKMff", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2026/ICLR/Poster/6.0_PreferThinker_ Reasoning-based Personalized Image Preference Assessment_2026.pdf" }, { "title": "ProPerSim: Developing Proactive and Personalized AI Assistants through User-Assistant Simulation", "authors": [ "Jiho Kim", "Junseong Choi", "Woosog Chay", "Daeun Kyung", "Yeonsu Kwon", "Yohan Jo", "Edward Choi" ], "year": 2026, "venue": "ICLR", "abstract": "As large language models (LLMs) become increasingly integrated into daily life, there is growing demand for AI assistants that are not only reactive but also proactive and personalized. While recent advances have pushed forward proactivity and personalization individually, their combination remains underexplored. To bridge this gap, we introduce ProPerSim, a new task and simulation framework for developing assistants capable of making timely, personalized recommendations in realistic home scenarios. In our simulation environment, a user agent with a rich persona interacts with the assistant, providing ratings on how well each suggestion aligns with its preferences and context. The assistant’s goal is to use these ratings to learn and adapt to achieve higher scores over time. Built on ProPerSim, we propose ProPerAssistant, a retrieval-augmented, preference-aligned assistant that continually learns and adapts through user feedback. Experiments across 32 diverse personas show that ProPerAssistant adapts its strategy and steadily improves user satisfaction, highlighting the promise of uniting proactivity and personalization.", "source": "openreview", "url": "https://openreview.net/forum?id=RV2aeCgxdB", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2026/ICLR/Poster/6.0_ProPerSim_ Developing Proactive and Personalized AI Assistants through User-Assistant Simulatio_2026.pdf" }, { "title": "Supporting High-Stakes Decision Making Through Interactive Preference Elicitation in the Latent Space", "authors": [ "Michael Eichelbeck", "Tim Voigt", "Matthias Althoff" ], "year": 2026, "venue": "ICLR", "abstract": "High-stakes, infrequent consumer decisions, such as housing selection, challenge conventional recommender systems due to sparse interaction signals, heterogeneous multi-criteria objectives, and high-dimensional feature spaces. \nThis work presents an interactive preference elicitation framework that couples preferential Bayesian optimization (PBO) with two complementary components: (i) large language models (LLMs) that interpret natural language input to produce personalized probabilistic priors over feature utility weights to mitigate cold start, and (ii) an autoencoder (AE)-based latent representation that reduces effective dimensionality for sample-efficient exploration. The framework learns a latent utility function from user pairwise comparisons observed and integrated in real-time.\nWe evaluate the developed method on rental real estate datasets from two major European cities. The results show that executing PBO in an AE latent space improves final pairwise ranking accuracy by 12%. For LLM-based preference prior generation, we find that direct, LLM-driven weight specification is outperformed by a static prior, while probabilistic weight priors that use LLMs only to rank feature importance achieve 25% better pairwise accuracy on average than a direct approach.", "source": "openreview", "url": "https://openreview.net/forum?id=ra7CSHcVCv", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2026/ICLR/Poster/6.0_Supporting High-Stakes Decision Making Through Interactive Preference Elicitation in the Latent_2026.pdf" }, { "title": "Swap-guided Preference Learning for Personalized Reinforcement Learning from Human Feedback", "authors": [ "Gihoon Kim", "Euntai Kim" ], "year": 2026, "venue": "ICLR", "abstract": "Reinforcement Learning from Human Feedback (RLHF) is a widely used approach to align large-scale AI systems with human values. However, RLHF typically assumes a single, universal reward, which overlooks diverse preferences and limits personalization. Variational Preference Learning (VPL) seeks to address this by introducing user-specific latent variables. Despite its promise, we found that VPL suffers from posterior collapse. While this phenomenon is well known in VAEs, it has not previously been identified in preference learning frameworks. Under sparse preference data and with overly expressive decoders, VPL may cause latent variables to be ignored, reverting to a single-reward model. To overcome this limitation, we propose Swap-guided Preference Learning (SPL). The key idea is to construct fictitious swap annotators and use the mirroring property of their preferences to guide the encoder. SPL introduces three components: (1) swap-guided base regularization, (2) Preferential Inverse Autoregressive Flow (P-IAF), and (3) adaptive latent conditioning. Experiments show that SPL mitigates collapse, enriches user-specific latents, and improves preference prediction. Our code and data are available at https://github.com/cobang0111/SPL", "source": "openreview", "url": "https://openreview.net/forum?id=nc28mSbyVG", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2026/ICLR/Poster/6.0_Swap-guided Preference Learning for Personalized Reinforcement Learning from Human Feedback_2026.pdf" }, { "title": "The Forecast After the Forecast: A Post-Processing Shift in Time Series", "authors": [ "Daojun Liang", "Qi Li", "Yinglong Wang", "Jing Chen", "Hu Zhang", "Xiaoxiao Cui", "Qizheng Wang", "Shuo Li" ], "year": 2026, "venue": "ICLR", "abstract": "Time series forecasting has long been dominated by advances in model architecture, with recent progress driven by deep learning and hybrid statistical techniques. However, as forecasting models approach diminishing returns in accuracy, a critical yet underexplored opportunity emerges: the strategic use of post-processing. In this paper, we address the last-mile gap in time-series forecasting, which is to improve accuracy and uncertainty without retraining or modifying a deployed backbone. We propose $\\delta$-Adapter, a lightweight, architecture-agnostic way to boost deployed time series forecasters without retraining. $\\delta$-Adapter learns tiny, bounded modules at two interfaces: input nudging (soft edits to covariates) and output residual correction. We provide local descent guarantees, $O(\\delta)$ drift bounds, and compositional stability for combined adapters.\nMeanwhile, it can act as a feature selector by learning a sparse, horizon-aware mask over inputs to select important features, thereby improving interpretability.\nIn addition, it can also be used as a distribution calibrator to measure uncertainty. Thus, we introduce a Quantile Calibrator and a Conformal Corrector that together deliver calibrated, personalized intervals with finite-sample coverage. \nOur experiments across diverse backbones and datasets show that $\\delta$-Adapter improves accuracy and calibration with negligible compute and no interface changes.", "source": "openreview", "url": "https://openreview.net/forum?id=syfWdclGE1", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2026/ICLR/Poster/6.0_The Forecast After the Forecast_ A Post-Processing Shift in Time Series_2026.pdf" }, { "title": "AttriCtrl: A Generalizable Framework for Controlling Semantic Attribute Intensity in Diffusion Models", "authors": [ "Die Chen", "Zhongjie Duan", "Zhiwen Li", "Cen Chen", "Daoyuan Chen", "Yaliang Li", "Yinda Chen" ], "year": 2026, "venue": "ICLR", "abstract": "Diffusion models have recently become the dominant paradigm for image generation, yet existing systems struggle to interpret and follow numeric instructions for adjusting semantic attributes. \nIn real-world creative scenarios, especially when precise control over aesthetic attributes is required, current methods fail to provide such controllability. \nThis limitation partly arises from the subjective and context-dependent nature of aesthetic judgments, but more fundamentally stems from the fact that current text encoders are designed for discrete tokens rather than continuous values. \nMeanwhile, efforts on aesthetic alignment, often leveraging reinforcement learning, direct preference optimization, or architectural modifications, primarily align models with a global notion of human preference. While these approaches improve user experience, they overlook the multifaceted and compositional nature of aesthetics, underscoring the need for explicit disentanglement and independent control of aesthetic attributes.\nTo address this gap, we introduce AttriCtrl, a lightweight framework for continuous aesthetic intensity control in diffusion models. \nIt first decomposes relevant aesthetic attributes, then quantifies them through a hybrid strategy that maps both concrete and abstract dimensions onto a unified $[0,1]$ scale. A plug-and-play value encoder is then used to transform user-specified values into model-interpretable embeddings for controllable generation.\nExperiments show that AttriCtrl achieves accurate and continuous control over both single and multiple aesthetic attributes, significantly enhancing personalization and diversity.\nCrucially, it is implemented as a lightweight adapter while keeping the diffusion model frozen, ensuring seamless integration with existing frameworks such as ControlNet at negligible computational cost.", "source": "openreview", "url": "https://openreview.net/forum?id=oyDe8cNXt6", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2026/ICLR/Poster/5.5_AttriCtrl_ A Generalizable Framework for Controlling Semantic Attribute Intensity in Diffusion _2026.pdf" }, { "title": "Cancer-Myth: Evaluating Large Language Models on Patient Questions with False Presuppositions", "authors": [ "Wang Bill Zhu", "Tian-qi Chen", "Xinyan Velocity Yu", "Ching Ying Lin", "Jade Law", "Mazen Jizzini", "Jorge J. Nieva", "Ruishan Liu", "Robin Jia" ], "year": 2026, "venue": "ICLR", "abstract": "Cancer patients are increasingly turning to large language models (LLMs) for medical information, making it critical to assess how well these models handle complex, personalized questions. \nHowever, current medical benchmarks focus on medical exams or consumer-searched questions and do not evaluate LLMs on real patient questions with patient details. \nIn this paper, we first have three hematology-oncology physicians evaluate cancer-related questions drawn from real patients. \nWhile LLM responses are generally accurate, the models frequently fail to recognize or address false presuppositions} in the questions, posing risks to safe medical decision-making.\nTo study this limitation systematically, we introduce Cancer-Myth, an expert-verified adversarial dataset of 585 cancer-related questions with false presuppositions.\nOn this benchmark, no frontier LLM---including GPT-5, Gemini-2.5-Pro, and Claude-4-Sonnet---corrects these false presuppositions more than $43\\%$ of the time.\nTo study mitigation strategies, we further construct a 150-question Cancer-Myth-NFP set, in which physicians confirm the absence of false presuppositions.\nWe find typical mitigation strategies, such as adding precautionary prompts with GEPA optimization, can raise accuracy on Cancer-Myth to $80\\%$, but at the cost of misidentifying presuppositions in $41\\%$ of Cancer-Myth-NFP questions and causing a $10\\%$ relative performance drop on other medical benchmarks.\nThese findings highlight a critical gap in the reliability of LLMs, show that prompting alone is not a reliable remedy for false presuppositions, and underscore the need for more robust safeguards in medical AI systems.", "source": "openreview", "url": "https://openreview.net/forum?id=fOXLhZIaUj", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2026/ICLR/Poster/5.5_Cancer-Myth_ Evaluating Large Language Models on Patient Questions with False Presuppositions_2026.pdf" }, { "title": "Color3D: Controllable and Consistent 3D Colorization with Personalized Colorizer", "authors": [ "Yecong Wan", "Mingwen Shao", "Renlong Wu", "Wangmeng Zuo" ], "year": 2026, "venue": "ICLR", "abstract": "In this work, we present Color3D, a highly adaptable framework for colorizing both static and dynamic 3D scenes from monochromatic inputs, delivering visually diverse and chromatically vibrant reconstructions with flexible user-guided control. In contrast to existing methods that focus solely on static scenarios and enforce multi-view consistency by averaging color variations which inevitably sacrifice both chromatic richness and controllability, our approach is able to preserve color diversity and steerability while ensuring cross-view and cross-time consistency. In particular, the core insight of our method is to colorize only a single key view and then fine-tune a personalized colorizer to propagate its color to novel views and time steps. Through personalization, the colorizer learns a scene-specific deterministic color mapping underlying the reference view, enabling it to consistently project corresponding colors to the content in novel views and video frames via its inherent inductive bias. Once trained, the personalized colorizer can be applied to infer consistent chrominance for all other images, enabling direct reconstruction of colorful 3D scenes with a dedicated Lab color space Gaussian splatting representation. The proposed framework ingeniously recasts complicated 3D colorization as a more tractable single image paradigm, allowing seamless integration of arbitrary image colorization models with enhanced flexibility and controllability. Extensive experiments across diverse static and dynamic 3D colorization benchmarks substantiate that our method can deliver more consistent and chromatically rich renderings with precise user control. Project Page: https://yecongwan.github.io/Color3D/.", "source": "openreview", "url": "https://openreview.net/forum?id=2aPK9PxPUq", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2026/ICLR/Poster/5.5_Color3D_ Controllable and Consistent 3D Colorization with Personalized Colorizer_2026.pdf" }, { "title": "Disrupting Hierarchical Reasoning: Adversarial Protection for Geographic Privacy in Multimodal Reasoning Models", "authors": [ "Jiaming Zhang", "CHE WANG", "Yang Cao", "Longtao Huang", "Wei Yang Bryan Lim" ], "year": 2026, "venue": "ICLR", "abstract": "Multi-modal large reasoning models (MLRMs) pose significant privacy risks by inferring precise geographic locations from personal images through hierarchical chain-of-thought reasoning. Existing privacy protection techniques, primarily designed for perception-based models, prove ineffective against MLRMs' sophisticated multi-step reasoning processes that analyze environmental cues. We introduce **ReasonBreak**, a novel adversarial framework specifically designed to disrupt hierarchical reasoning in MLRMs through concept-aware perturbations. Our approach is founded on the key insight that effective disruption of geographic reasoning requires perturbations aligned with conceptual hierarchies rather than uniform noise. ReasonBreak strategically targets critical conceptual dependencies within reasoning chains, generating perturbations that invalidate specific inference steps and cascade through subsequent reasoning stages. To facilitate this approach, we contribute **GeoPrivacy-6K**, a comprehensive dataset comprising 6,341 ultra-high-resolution images ($\\geq$2K) with hierarchical concept annotations. Extensive evaluation across seven state-of-the-art MLRMs (including GPT-o3, GPT-5, Gemini 2.5 Pro) demonstrates ReasonBreak's superior effectiveness, achieving a 14.4\\% improvement in tract-level protection (33.8\\% vs 19.4\\%) and nearly doubling block-level protection (33.5\\% vs 16.8\\%). This work establishes a new paradigm for privacy protection against reasoning-based threats.", "source": "openreview", "url": "https://openreview.net/forum?id=5S6YTG9dL0", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2026/ICLR/Poster/5.5_Disrupting Hierarchical Reasoning_ Adversarial Protection for Geographic Privacy in Multimodal _2026.pdf" }, { "title": "EnvSocial-Diff: A Diffusion-Based Crowd Simulation Model with Environmental Conditioning and Individual-Group Interaction", "authors": [ "Bingxue Zhao", "Qi Zhang", "Hui Huang" ], "year": 2026, "venue": "ICLR", "abstract": "Modeling realistic pedestrian trajectories requires accounting for both social interactions and environmental context, yet most existing approaches largely emphasize social dynamics. We propose EnvSocial-Diff: a diffusion-based crowd simulation model informed by social physics and augmented with environmental conditioning and individual-group interaction. Our structured environmental conditioning module explicitly encodes obstacles, objects of interest, and lighting levels, providing interpretable signals that capture scene constraints and attractors. In parallel, the individual-group interaction module goes beyond individual-level modeling by capturing both fine-grained interpersonal relations and group-level conformity through a graph-based design. Experiments on multiple benchmark datasets demonstrate that EnvSocial-Diff outperforms the latest state-of-the-art methods, underscoring the importance of explicit environmental conditioning and multi-level social interaction for realistic crowd simulation.", "source": "openreview", "url": "https://openreview.net/forum?id=2XBAm3Dbnt", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2026/ICLR/Poster/5.5_EnvSocial-Diff_ A Diffusion-Based Crowd Simulation Model with Environmental Conditioning and In_2026.pdf" }, { "title": "Federated Learning with Profile Mapping under Distribution Shifts and Drifts", "authors": [ "Mohan Li", "Dario Fenoglio", "Martin Gjoreski", "Marc Langheinrich" ], "year": 2026, "venue": "ICLR", "abstract": "Federated Learning (FL) enables decentralized model training across clients without sharing raw data, but its performance degrades under real-world data heterogeneity. Existing methods often fail to address distribution shift across clients and distribution drift over time, or they rely on unrealistic assumptions such as known number of client clusters and data heterogeneity types, which limits their generalizability. We introduce **Feroma**, a novel FL framework that explicitly handles both distribution shift and drift without relying on client or cluster identity. **Feroma** builds on client distribution profiles—compact, privacy-preserving representations of local data—that guide model aggregation and test-time model assignment through adaptive similarity-based weighting. This design allows **Feroma** to dynamically select aggregation strategies during training, ranging from clustered to personalized, and deploy suitable models to unseen, and unlabeled test clients without retraining, online adaptation, or prior knowledge on clients' data. Extensive experiments show that compared to 10 state-of-the-art methods, **Feroma** improves performance and stability under dynamic data heterogeneity conditions—an average accuracy gain of up to 12 percentage points over the best baselines across 6 benchmarks—while maintaining computational and communication overhead comparable to FedAvg. These results highlight that distribution-profile-based aggregation offers a practical path toward robust FL under both data distribution shifts and drifts.", "source": "openreview", "url": "https://openreview.net/forum?id=thoPskdIcE", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2026/ICLR/Poster/5.5_Federated Learning with Profile Mapping under Distribution Shifts and Drifts_2026.pdf" }, { "title": "FingerTip 20K: A Benchmark for Proactive and Personalized Mobile LLM Agents", "authors": [ "Qinglong Yang", "Haoming Li", "Haotian Zhao", "Xiaokai Yan", "Jingtao Ding", "Fengli Xu", "Yong Li" ], "year": 2026, "venue": "ICLR", "abstract": "Mobile GUI agents are becoming critical tools to improve user experience on smart devices, with multimodal large language models (MLLMs) emerging as the dominant paradigms in this domain. Current agents, however, rely on explicit human instructions, overlooking the potential to leverage the contextual information (like location, time, user profile) and historical data for proactive task suggestions. Besides, previous works focus on optimizing the success rate during task execution, but pay less attention to the personalized execution trajectory, thereby neglecting potentially vast differences in user preferences. To address these challenges, we introduce the FingerTip 20K benchmark. We collected 20K unique human demonstrations of multi-step Android device interactions across a variety of everyday apps. These demonstrations are not isolated but are continuously acquired from the users' long-term usage in their real lives, and encompass essential user-related contextual information. The benchmark contains two new tracks: proactive task suggestions by analyzing environment observation and users' previous intents, and personalized task execution by catering to users' action preferences. Our experiments reveal that the tracks we propose pose significant challenges for leveraging user-related information in GUI tasks. We also performed a human study to show that there exists a huge gap between existing agents and humans. The model fine-tuned with the data we collected effectively utilized user information and achieved good results, highlighting the potential of our approach in building more user-oriented mobile LLM agents. Our code is open-source at \\url{https://anonymous.4open.science/r/FingerTip-57B8} for reproducibility.", "source": "openreview", "url": "https://openreview.net/forum?id=n3iFV0gLMc", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2026/ICLR/Poster/5.5_FingerTip 20K_ A Benchmark for Proactive and Personalized Mobile LLM Agents_2026.pdf" }, { "title": "From Single to Multi-Granularity: Toward Long-Term Memory Association and Selection of Conversational Agents", "authors": [ "Derong Xu", "Yi Wen", "Pengyue Jia", "Yingyi Zhang", "Wenlin Zhang", "Yichao Wang", "Huifeng Guo", "Ruiming Tang", "Xiangyu Zhao", "Enhong Chen", "Tong Xu" ], "year": 2026, "venue": "ICLR", "abstract": "Large Language Models (LLMs) have recently been widely adopted in conversational agents. However, the increasingly long interactions between users and agents accumulate extensive dialogue records, making it difficult for LLMs with limited context windows to maintain a coherent long-term dialogue memory and deliver personalized responses. While retrieval-augmented memory systems have emerged to address this issue, existing methods often depend on single-granularity memory segmentation and retrieval. This approach falls short in capturing deep memory connections, leading to partial retrieval of useful information or substantial noise, resulting in suboptimal performance. To tackle these limits, we propose MemGAS, a framework that enhances memory consolidation by constructing multi-granularity association, adaptive selection, and retrieval. MemGAS is based on multi-granularity memory units and employs Gaussian Mixture Models to cluster and associate new memories with historical ones. An entropy-based router adaptively selects optimal granularity by evaluating query relevance distributions and balancing information completeness and noise. Retrieved memories are further refined via LLM-based filtering. Experiments on four long-term memory benchmarks demonstrate that MemGAS outperforms state-of-the-art methods on both question answer and retrieval tasks, achieving superior performance across different query types and top-K settings. \\footnote{https://anonymous.4open.science/r/MemGAS-626C/}", "source": "openreview", "url": "https://openreview.net/forum?id=i2yIvZARnG", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2026/ICLR/Poster/5.5_From Single to Multi-Granularity_ Toward Long-Term Memory Association and Selection of Conversa_2026.pdf" }, { "title": "Incentives in Federated Learning with Heterogeneous Agents", "authors": [ "Ariel D. Procaccia", "Han Shao", "Itai Shapira" ], "year": 2026, "venue": "ICLR", "abstract": "Federated learning promises significant sample-efficiency gains by pooling data across multiple agents, yet incentive misalignment is an obstacle: each update is costly to the contributor but boosts every participant. We introduce a game-theoretic framework that captures heterogeneous data: an agent’s utility depends on who supplies each sample, not just how many. Agents aim to meet a PAC-style accuracy threshold at minimal personal cost. We show that uncoordinated play yields pathologies: pure equilibria may not exist, and the best equilibrium can be arbitrarily more costly than cooperation. To steer collaboration, we analyze the cost-minimizing contribution vector, prove that computing it is NP-hard, and derive a polynomial-time linear program that achieves a logarithmic approximation. Finally, pairing the LP with a simple pay-what-you-contribute rule—each agent receives a payment equal to its sample cost—yields a mechanism that is strategy-proof and, within the class of contribution-based transfers, is unique.", "source": "openreview", "url": "https://openreview.net/forum?id=Nqjyrvh3pf", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2026/ICLR/Poster/5.5_Incentives in Federated Learning with Heterogeneous Agents_2026.pdf" }, { "title": "Learning Patient-Specific Disease Dynamics With Latent Flow Matching For Longitudinal Imaging Generation", "authors": [ "Hao Chen", "Rui Yin", "Yifan Chen", "Qi Chen", "Chao Li" ], "year": 2026, "venue": "ICLR", "abstract": "Understanding disease progression is a central clinical challenge with direct implications for early diagnosis and personalized treatment. While recent generative approaches have attempted to model progression, key mismatches remain: disease dynamics are inherently continuous and monotonic, yet latent representations are often scattered, lacking semantic structure, and diffusion-based models disrupt continuity through the random denoising process.\nIn this work, we propose treating disease dynamics as a velocity field and leveraging Flow Matching (FM) to align the temporal evolution of patient data. Unlike prior methods, our approach captures the intrinsic dynamics of disease, making progression more interpretable.\nHowever, a key challenge remains: in latent space, Autoencoders (AEs) do not guarantee alignment across patients or correlation with clinical severity (e.g., age and disease conditions). To address this, we propose learning patient-specific latent alignment, which enforces patient trajectories to lie along a specific axis, with magnitudes increasing monotonically with disease severity. This leads to a consistent and semantically meaningful latent space.\nTogether, we present ∆-LFM, a framework for modeling patient-specific latent progression with flow matching. Across three longitudinal MRI benchmarks, ∆-LFM demonstrates strong empirical performance and, more importantly, establishes a new framework for interpreting and visualizing disease dynamics.", "source": "openreview", "url": "https://openreview.net/forum?id=cuGnuOfQ4U", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2026/ICLR/Poster/5.5_Learning Patient-Specific Disease Dynamics With Latent Flow Matching For Longitudinal Imaging G_2026.pdf" }, { "title": "Low-pass Personalized Subgraph Federated Recommendation", "authors": [ "Wooseok Sim", "Hogun Park" ], "year": 2026, "venue": "ICLR", "abstract": "Federated Recommender Systems (FRS) preserve privacy by training decentralized models on client-specific user-item subgraphs without sharing raw data. However, FRS faces a unique challenge: subgraph structural imbalance, where drastic variations in subgraph scale (user/item counts) and connectivity (item degree) misalign client representations, making it challenging to train a robust model that respects each client’s unique structural characteristics. \n\nTo address this, we propose a Low-pass Personalized Subgraph Federated recommender system (LPSFed). LPSFed leverages graph Fourier transforms and low-pass spectral filtering to extract low-frequency structural signals that remain stable across subgraphs of varying size and degree, allowing robust personalized parameter updates guided by similarity to a neutral structural anchor. Additionally, we leverage a localized popularity bias-aware margin that captures item-degree imbalance within each subgraph and incorporates it into a personalized bias correction term to mitigate recommendation bias. Supported by theoretical analysis and validated on five real-world datasets, LPSFed achieves superior recommendation accuracy and enhances model robustness.", "source": "openreview", "url": "https://openreview.net/forum?id=SSd3GENRAU", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2026/ICLR/Poster/5.5_Low-pass Personalized Subgraph Federated Recommendation_2026.pdf" }, { "title": "MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workflow", "authors": [ "Ziyue Wang", "Junde Wu", "Linghan Cai", "Chang Han Low", "Xihong Yang", "Qiaxuan Li", "Yueming Jin" ], "year": 2026, "venue": "ICLR", "abstract": "Modern clinical diagnosis relies on the comprehensive analysis of multi-modal patient data, drawing on medical expertise to ensure systematic and rigorous reasoning. Recent advances in Vision–Language Models (VLMs) and agent-based methods are reshaping medical diagnosis by effectively integrating multi-modal information. However, they often output direct answers and empirical-driven conclusions without clinical evidence supported by quantitative analysis, which compromises their reliability and hinders clinical usability. \nHere we propose MedAgent-Pro, an agentic reasoning paradigm that mirrors modern diagnosis principles via a hierarchical diagnostic workflow, consisting of disease-level standardized plan generation and patient-level personalized step-by-step reasoning. To support disease-level planning, a retrieval-augmented generation agent is designed to access medical guidelines for alignment with clinical standards. For patient-level reasoning, MedAgent-Pro leverages professional tools such as visual models to take various actions to analyze multi-modal input, and performs evidence-based reflection to iteratively adjust memory, enforcing rigorous reasoning throughout the process. Extensive experiments across a wide range of anatomical regions, imaging modalities, and diseases demonstrate the superiority of MedAgent-Pro over mainstream VLMs, agentic systems and leading expert models. Ablation studies and expert evaluation further confirm its robustness and clinical relevance. Anonymized code link is available in the reproducibility statement.", "source": "openreview", "url": "https://openreview.net/forum?id=ZOuU0udyA4", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2026/ICLR/Poster/5.5_MedAgent-Pro_ Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workfl_2026.pdf" }, { "title": "OmniPortrait: Fine-Grained Personalized Portrait Synthesis via Pivotal Optimization", "authors": [ "Dongxu Yue", "Bo Lin", "Yao Tang", "Zhihai He", "Chun Yuan", "Jiajun Liang" ], "year": 2026, "venue": "ICLR", "abstract": "Image identity customization aims to synthesize realistic and diverse portraits of a specified identity, given a reference image and a text prompt. This task presents two key challenges: (1) generating realistic portraits that preserve fine-grained facial details of the reference identity, and (2) maintaining identity consistency while achieving strong alignment with the text prompt. Our findings suggest that existing single-stream methods fail to capture and guide fine-grained identity details.\nTo address these challenges, we introduce \\textit{OmniPortrait}, a novel diffusion-based framework for fine-grained identity fidelity and high editability in portrait synthesis. Our core idea is pivotal optimization, which leverages dual-stream identity guidance in a coarse-to-fine manner. First, a Pivot ID Encoder is proposed and trained with a face localization loss while avoiding the degradation of editability typically caused by fine-tuning the denoiser. Although this encoder primarily guides coarse-level identity synthesis, it provides a good initialization that serves as the identity pivot for optimization during inference.\nSecond, we propose Reference-Based Guidance, which performs on-the-fly feature matching and optimization over diffusion intermediate features conditioned on the identity pivot. In addition, our approach is able to generalize naturally to multi-identity customized image generation scenarios. Extensive experiments demonstrate significant improvements in both identity preservation and text alignment, establishing a new benchmark for image identity customization.", "source": "openreview", "url": "https://openreview.net/forum?id=DVmR3Ij0ap", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2026/ICLR/Poster/5.5_OmniPortrait_ Fine-Grained Personalized Portrait Synthesis via Pivotal Optimization_2026.pdf" }, { "title": "Operationalizing Data Minimization for Privacy-Preserving LLM Prompting", "authors": [ "Jijie Zhou", "Niloofar Mireshghallah", "Tianshi Li" ], "year": 2026, "venue": "ICLR", "abstract": "The rapid deployment of large language models (LLMs) in consumer applications has led to frequent exchanges of personal information. To obtain useful responses, users often share more than necessary, increasing privacy risks via memorization, context-based personalization, or security breaches. We present a framework to formally define and operationalize *data minimization*: for a given user prompt and response model, quantifying the least privacy-revealing disclosure that maintains utility, and propose a priority-queue tree search to locate this optimal point within a privacy-ordered transformation space. We evaluated the framework on four datasets spanning open-ended conversations (ShareGPT, WildChat) and knowledge-intensive tasks with single-ground-truth answers (CaseHOLD, MedQA), quantifying achievable data minimization with nine LLMs as the response model. Our results demonstrate that larger frontier LLMs can tolerate stronger data minimization while maintaining task quality than smaller open-source models (*85.7%* redaction for GPT-5 vs. *19.3%* for Qwen2.5-0.5B). By comparing with our search-derived benchmarks, we find that LLMs struggle to predict optimal data minimization directly, showing a bias toward abstraction that leads to oversharing. This suggests not just a privacy gap, but a capability gap: models may lack awareness of what information they actually need to solve a task.", "source": "openreview", "url": "https://openreview.net/forum?id=rpcnvW33EG", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2026/ICLR/Poster/5.5_Operationalizing Data Minimization for Privacy-Preserving LLM Prompting_2026.pdf" }, { "title": "Overlap-Adaptive Regularization for Conditional Average Treatment Effect Estimation", "authors": [ "Valentyn Melnychuk", "Dennis Frauen", "Jonas Schweisthal", "Stefan Feuerriegel" ], "year": 2026, "venue": "ICLR", "abstract": "The conditional average treatment effect (CATE) is widely used in personalized medicine to inform therapeutic decisions. However, state-of-the-art methods for CATE estimation (so-called meta-learners) often perform poorly in the presence of low overlap. In this work, we introduce a new approach to tackle this issue and improve the performance of existing meta-learners in the low-overlap regions. Specifically, we introduce Overlap-Adaptive Regularization (OAR) that regularizes target models proportionally to overlap weights so that, informally, the regularization is higher in regions with low overlap. To the best of our knowledge, our OAR is the first approach to leverage overlap weights in the regularization terms of the meta-learners. Our OAR approach is flexible and works with any existing CATE meta-learner: we demonstrate how OAR can be applied to both parametric and non-parametric second-stage models. Furthermore, we propose debiased versions of our OAR that preserve the Neyman-orthogonality of existing meta-learners and thus ensure more robust inference. Through a series of (semi-)synthetic experiments, we demonstrate that our OAR significantly improves CATE estimation in low-overlap settings in comparison to constant regularization.", "source": "openreview", "url": "https://openreview.net/forum?id=HMMSnGgYOy", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2026/ICLR/Poster/5.5_Overlap-Adaptive Regularization for Conditional Average Treatment Effect Estimation_2026.pdf" }, { "title": "Distilling and Adapting: A Topology-Aware Framework for Zero-Shot Interaction Prediction in Multiplex Biological Networks", "authors": [ "Alana Deng", "Sugitha Janarthanan", "Yan Sun", "Zihao Jing", "Pingzhao Hu" ], "year": 2026, "venue": "ICLR", "abstract": "Multiplex Biological Networks (MBNs), which represent multiple interaction types between entities, are crucial for understanding complex biological systems. Yet, existing methods often inadequately model multiplexity, struggle to integrate structural and sequence information, and face difficulties in zero-shot prediction for unseen entities with no prior neighbourhood information. To address these limitations, we propose a novel framework for zero-shot interaction prediction in MBNs by leveraging context-aware representation learning and knowledge distillation. Our approach leverages domain-specific foundation models to generate enriched embeddings, introduces a topology-aware graph tokenizer to capture multiplexity and higher-order connectivity, and employs contrastive learning to align embeddings across modalities. A teacher–student distillation strategy further enables robust zero-shot generalization. Experimental results demonstrate that our framework outperforms state-of-the-art methods in interaction prediction for MBNs, providing a powerful tool for exploring various biological interactions and advancing personalized therapeutics.", "source": "openreview", "url": "https://openreview.net/forum?id=GvK1y3xqmh", "decision_type": "Poster", "avg_rating": 5.3, "relative_path": "2026/ICLR/Poster/5.3_Distilling and Adapting_ A Topology-Aware Framework for Zero-Shot Interaction Prediction in Mul_2026.pdf" }, { "title": "Efficient and Sharp Off-Policy Learning under Unobserved Confounding", "authors": [ "Konstantin Hess", "Dennis Frauen", "Valentyn Melnychuk", "Stefan Feuerriegel" ], "year": 2026, "venue": "ICLR", "abstract": "We develop a novel method for personalized off-policy learning in scenarios with unobserved confounding. Thereby, we address a key limitation of standard policy learning: standard policy learning assumes unconfoundedness, meaning that no unobserved factors influence both treatment assignment and outcomes. However, this assumption is often violated, because of which standard policy learning produces biased estimates and thus leads to policies that can be harmful. To address this limitation, we employ causal sensitivity analysis and derive a semi-parametrically efficient estimator for a sharp bound on the value function under unobserved confounding. Our estimator has three advantages: (1) Unlike existing works, our estimator avoids unstable minimax optimization based on inverse propensity weighted outcomes. (2) Our estimator is semi-parametrically efficient. (3) We prove that our estimator leads to the optimal confounding-robust policy. Finally, we extend our theory to the related task of policy improvement under unobserved confounding, i.e., when a baseline policy such as the standard of care is available. We show in experiments with synthetic and real-world data that our method outperforms simple plug-in approaches and existing baselines. Our method is highly relevant for decision-making where unobserved confounding can be problematic, such as in healthcare and public policy.", "source": "openreview", "url": "https://openreview.net/forum?id=7nTKiJLkWS", "decision_type": "Poster", "avg_rating": 5.3, "relative_path": "2026/ICLR/Poster/5.3_Efficient and Sharp Off-Policy Learning under Unobserved Confounding_2026.pdf" }, { "title": "Embodied Agents Meet Personalization: Investigating Challenges and Solutions Through the Lens of Memory Utilization", "authors": [ "Taeyoon Kwon", "Dongwook Choi", "Hyojun Kim", "Sunghwan Kim", "Seungjun Moon", "Beong-woo Kwak", "Kuan-Hao Huang", "Jinyoung Yeo" ], "year": 2026, "venue": "ICLR", "abstract": "LLM-powered embodied agents have shown success on conventional object-rearrangement tasks, but providing personalized assistance that leverages user-specific knowledge from past interactions presents new challenges. We investigate these challenges through the lens of agents' memory utilization along two critical dimensions: object semantics (identifying objects based on personal meaning) and user patterns (recalling sequences from behavioral routines). To assess these capabilities, we construct Memento, an end-to-end two-stage evaluation framework comprising single-memory and joint-memory tasks. Our experiments reveal that current agents can recall simple object semantics but struggle to apply sequential user patterns to planning. Through in-depth analysis, we identify two critical bottlenecks: information overload and coordination failures when handling multiple memories. Based on these findings, we explore memory architectural approaches to address these challenges. Given our observation that episodic memory provides both personalized knowledge and in-context learning benefits, we design a hierarchical knowledge graph-based user-profile memory module that separately manages personalized knowledge, achieving substantial improvements on both single and joint-memory tasks.", "source": "openreview", "url": "https://openreview.net/forum?id=E5L43l5EIu", "decision_type": "Poster", "avg_rating": 5.3, "relative_path": "2026/ICLR/Poster/5.3_Embodied Agents Meet Personalization_ Investigating Challenges and Solutions Through the Lens o_2026.pdf" }, { "title": "FSPO: Few-Shot Optimization of Synthetic Preferences Effectively Personalizes to Real Users", "authors": [ "Anikait Singh", "Sheryl Hsu", "Kyle Hsu", "Eric Mitchell", "Stefano Ermon", "Tatsunori Hashimoto", "Archit Sharma", "Chelsea Finn" ], "year": 2026, "venue": "ICLR", "abstract": "Effective personalization of LLMs is critical for a broad range of user-interfacing applications such as virtual assistants and content curation. Inspired by the strong in-context capabilities of LLMs, we propose few-shot preference optimization (FSPO), an algorithm for LLM personalization that reframes reward modeling as a meta-learning problem. Under FSPO, an LLM learns to quickly infer a personalized reward function for a user via a few labeled preferences. FSPO also utilizes user description rationalization (RAT) to encourage better reward modeling and instruction following, recovering performance with the oracle user description. Since real-world preference data is challenging to collect at scale, we propose careful design choices to construct synthetic preference datasets for personalization, generating over 1M synthetic personalized preferences using publicly available LLMs. To successfully transfer from synthetic data to real users, we find it crucial for the data to exhibit both high diversity and coherent, self-consistent structure. We evaluate FSPO on personalized open-ended generation for up to 1,500 synthetic users across three domains: movie reviews, education, and open-ended question answering. We also run a controlled human study. Overall, FSPO achieves an 87% Alpaca Eval win-rate in generating responses that are personalized to synthetic users and a 70% win-rate with real human users in open-ended question answering.", "source": "openreview", "url": "https://openreview.net/forum?id=SzEc5fSBXv", "decision_type": "Poster", "avg_rating": 5.3, "relative_path": "2026/ICLR/Poster/5.3_FSPO_ Few-Shot Optimization of Synthetic Preferences Effectively Personalizes to Real Users_2026.pdf" }, { "title": "Preserve and Personalize: Personalized Text-to-Image Diffusion Models without Distributional Drift", "authors": [ "Gihoon Kim", "Hyungjin Park", "Taesup Kim" ], "year": 2026, "venue": "ICLR", "abstract": "Personalizing text-to-image diffusion models involves integrating novel visual concepts from a small set of reference images while retaining the model’s original generative capabilities. However, this process often leads to overfitting, where the model ignores the user’s prompt and merely replicates the reference images. We attribute this issue to a fundamental misalignment between the true goals of personalization, which are subject fidelity and text alignment, and the training objectives of existing methods that fail to enforce both objectives simultaneously. Specifically, prior approaches often overlook the need to explicitly preserve the pretrained model’s output distribution, resulting in distributional drift that undermines diversity and coherence. To resolve these challenges, we introduce a Lipschitz-based regularization objective that constrains parameter updates during personalization, ensuring bounded deviation from the original distribution. This promotes consistency with the pretrained model’s behavior while enabling accurate adaptation to new concepts. Furthermore, our method offers a computationally efficient alternative to commonly used, resource-intensive sampling techniques. Through extensive experiments across diverse diffusion model architectures, we demonstrate that our approach achieves superior performance in both quantitative metrics and qualitative evaluations, consistently excelling in visual fidelity and prompt adherence. We further support these findings with comprehensive analyses, including ablation studies and visualizations.", "source": "openreview", "url": "https://openreview.net/forum?id=2ge1Y6DWPw", "decision_type": "Poster", "avg_rating": 5.3, "relative_path": "2026/ICLR/Poster/5.3_Preserve and Personalize_ Personalized Text-to-Image Diffusion Models without Distributional Dr_2026.pdf" }, { "title": "Conformalized Survival Counterfactuals Prediction for General Right-Censored Data", "authors": [ "Sijie Ren", "Meng Yan", "Zhen Zhang", "Xu Yinghui", "Xinwei Sun" ], "year": 2026, "venue": "ICLR", "abstract": "This paper aims to develop a lower prediction bound (LPB) for survival time across different treatments in the general right-censored setting. Although previous methods have utilized conformal prediction to construct the LPB, their resulting prediction sets provide only probably approximately correct (PAC)–type miscoverage guarantees rather than exact ones. To address this problem, we propose a new calibration procedure under the potential outcome framework. Under the strong ignorability assumption, we propose a reweighting scheme that can transform the problem into a weighted conformal inference problem, allowing an LPB to be obtained via quantile regression with an exact miscoverage guarantee. Furthermore, our procedure is doubly robust against model misspecification. Empirical evaluations on synthetic and real-world clinical data demonstrate the validity and informativeness of our constructed LPBs, which indicate the potential of our analytical benchmark for comparing and selecting personalized treatments.", "source": "openreview", "url": "https://openreview.net/forum?id=1j0ormf8uI", "decision_type": "Poster", "avg_rating": 5.2, "relative_path": "2026/ICLR/Poster/5.2_Conformalized Survival Counterfactuals Prediction for General Right-Censored Data_2026.pdf" }, { "title": "Human Behavior Atlas: Benchmarking Unified Psychological And Social Behavior Understanding", "authors": [ "Keane Ong", "Wei Dai", "Carol Li", "Dewei Feng", "Hengzhi Li", "Jingyao Wu", "Jiaee Cheong", "Rui Mao", "Gianmarco Mengaldo", "Erik Cambria", "Paul Pu Liang" ], "year": 2026, "venue": "ICLR", "abstract": "Using intelligent systems to perceive psychological and social behaviors, that is, the underlying affective, cognitive, and pathological states that are manifested through observable behaviors and social interactions, remains a challenge due to their complex, multifaceted, and personalized nature. Existing work tackling these dimensions through specialized datasets and single-task systems often miss opportunities for scalability, cross-task transfer, and broader generalization. To address this gap, we curate Human Behavior Atlas, a unified benchmark of diverse behavioral tasks designed to support the development of foundation models for understanding psychological and social behaviors. Human Behavior Atlas comprises over 100,000 samples spanning text, audio, and visual modalities, covering tasks on *affective states*, *cognitive states*, *pathologies*, and *social processes*. Our unification efforts can reduce redundancy and cost, enable training to scale efficiently across tasks, and enhance generalization of behavioral features across domains. On Human Behavior Atlas, we train three models: Omnisapiens-7B SFT, Omnisapiens-7B BAM, and Omnisapiens-7B RL. We show that training on Human Behavior Atlas enables models to consistently outperform existing multimodal LLMs across diverse behavioral tasks. Pretraining on Human Behavior Atlas also improves transfer to novel behavioral datasets; with the targeted use of behavioral descriptors yielding meaningful performance gains. The benchmark, models, and codes can be found at: https://github.com/MIT-MI/human_behavior_atlas.", "source": "openreview", "url": "https://openreview.net/forum?id=ZKE23BBvlQ", "decision_type": "Poster", "avg_rating": 5.2, "relative_path": "2026/ICLR/Poster/5.2_Human Behavior Atlas_ Benchmarking Unified Psychological And Social Behavior Understanding_2026.pdf" }, { "title": "ACCORD: Alleviating Concept Coupling through Dependence Regularization for Text-to-Image Diffusion Personalization", "authors": [ "Shizhan Liu", "Hao ZHENG", "Hang Yu", "Jianguo Li" ], "year": 2026, "venue": "ICLR", "abstract": "Image personalization enables customizing Text-to-Image models with a few reference images but is plagued by \"concept coupling\"—the model creating spurious associations between a subject and its context. Existing methods tackle this indirectly, forcing a trade-off between personalization fidelity and text control. This paper is the first to formalize concept coupling as a statistical dependency problem, identifying two root causes: a Denoising Dependence Discrepancy that arises during the generative process, and a Prior Dependence Discrepancy within the learned concept itself. To address this, we introduce ACCORD, a framework with two targeted, plug-and-play regularization losses. The Denoising Decouple Loss minimizes dependency changes across denoising steps, while the Prior Decouple Loss aligns the concept’s relational priors with those of its superclass. Extensive experiments across subject, style, and face personalization demonstrate that ACCORD achieves a superior balance between fidelity and text control, consistently improving upon existing methods.", "source": "openreview", "url": "https://openreview.net/forum?id=CKYsYlRdCM", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2026/ICLR/Poster/5.0_ACCORD_ Alleviating Concept Coupling through Dependence Regularization for Text-to-Image Diffus_2026.pdf" }, { "title": "ALM-MTA: Front-Door Causal Multi-Touch Attribution Method for Creator-Ecosystem Optimization", "authors": [ "Yuguang Liu", "Luyao Xia", "Hu Liu", "yanzhangxi", "Jian Liang", "Han Li", "Kun Gai" ], "year": 2026, "venue": "ICLR", "abstract": "Consumption‑Drives‑Production (CDP) on social platforms aims to deliver interpretable incentive signals for creator‑ecosystem building and resource utilization improvement, which strongly relies on attributions. In large-scale and complex recommendation system, the absence of accurate labels together with unobserved confounding renders backdoor adjustments alone insufficient for reliable attribution. To address these problems, we propose Adversarial Learning Mediator based Multi‑Touch-Attribution (ALM-MTA), an extensible causal framework that leverages front-door identification with an adversarially learned mediator: a proxy trained to distillate outcome information to strengthen causal pathway from treatment to outcome and eliminate shortcut leakage. Then, we introduce contrastive learning that conditions front door marginalization on high match consumption upload pairs for ensuring positivity in large treatment spaces. To assess causality from non‑RCT logs, we also incorporate a non‑personalized bucketed protocol, estimating grouped uplift and computing AUUC over treatment clusters. Finally, we evaluate ALM-MTA performance using a real-world recommendation system with 400 million DAU (daily active users) and 30 billion samples. ALM-MTA has increased DAU with 0.04% and 0.6% of the daily active creators, with unit exposure efficiency increased by 670%. On causal utility, ALM-MTA achieves higher grouped AUUC than the SOTA in every propensity bucket, with a maximum gain of 0.070. In terms of accuracy, ALM-MTA improves upload AUC by 40% compared to SOTA. These results demonstrate that front -door deconfounding with adversarial mediator learning provides accurate, personalized and operationally efficient attribution for creator ecosystem optimization.", "source": "openreview", "url": "https://openreview.net/forum?id=3r68a6GOpg", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2026/ICLR/Poster/5.0_ALM-MTA_ Front-Door Causal Multi-Touch Attribution Method for Creator-Ecosystem Optimization_2026.pdf" }, { "title": "Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matching", "authors": [ "Ren Kishimoto", "Rikiya Takehi", "Koichi Tanaka", "Yoji Tomita", "Masahiro Nomura", "Riku Togashi", "Yuta Saito" ], "year": 2026, "venue": "ICLR", "abstract": "On two-sided matching platforms such as online dating and recruiting, recommendation algorithms often aim to maximize the total number of matches. However, this objective creates an imbalance, where some users receive far too many matches while many others receive very few and eventually abandon the platform. Retaining users is crucial for many platforms, such as those that depend heavily on subscriptions. Some may use fairness objectives to solve the problem of match maximization. However, fairness in itself is not the ultimate objective for many platforms, as users do not suddenly reward the platform simply because exposure is equalized. In practice, where user retention is often the ultimate goal, casually relying on fairness will leave the optimization of retention up to luck.\n\nIn this work, instead of maximizing matches or axiomatically defining fairness, we formally define the new problem setting of maximizing user retention in two-sided matching platforms. To this end, we introduce a dynamic learning-to-rank (LTR) algorithm called Matching for Retention (MRet). Unlike conventional algorithms for two-sided matching, our approach models user retention by learning personalized retention curves from each user’s profile and interaction history. Based on these curves, MRet dynamically adapts recommendations by jointly considering the retention gains of both the user receiving recommendations and those who are being recommended, so that limited matching opportunities can be allocated where they most improve overall retention. Naturally but importantly, empirical evaluations on synthetic and real-world datasets from a major online dating platform show that MRet achieves higher user retention, since conventional methods optimize matches or fairness rather than retention.", "source": "openreview", "url": "https://openreview.net/forum?id=g2cZaKmRrc", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2026/ICLR/Poster/5.0_Beyond Match Maximization and Fairness_ Retention-Optimized Two-Sided Matching_2026.pdf" }, { "title": "Black-Box Privacy Attacks on Shared Representations in Multitask Learning", "authors": [ "John Abascal", "Nicolás Berrios", "Alina Oprea", "Jonathan Ullman", "Adam Smith", "Matthew Jagielski" ], "year": 2026, "venue": "ICLR", "abstract": "The proliferation of diverse data across users and organizations has driven the development of machine learning methods that enable multiple entities to jointly train models while minimizing data sharing. Among these, *multitask learning* (MTL) is a powerful paradigm that leverages similarities among multiple tasks, each with insufficient samples to train a standalone model, to solve them simultaneously. MTL accomplishes this by learning a *shared representation* that captures common structure between tasks and generalizes well across them all. Despite being designed to be the smallest unit of shared information necessary to effectively learn patterns across multiple tasks, these shared representations can inadvertently leak sensitive information about the particular tasks they were trained~on.\n\nIn this work, we investigate privacy leakage in shared representations through the lens of inference attacks. Towards this, we propose a novel, *black-box task-inference* threat model where the adversary, given the embedding vectors produced by querying the shared representation on samples from a particular task, aims to determine whether the task was present in the multitask training dataset. Motivated by analysis of tracing attacks on mean estimation over mixtures of Gaussian distributions, we develop efficient, purely black-box attacks on machine learning models that exploit the dependencies between embeddings from the same task without requiring shadow models or labeled reference data. We evaluate our attacks across vision and language domains when MTL is used for personalization and for solving multiple distinct learning problems, and demonstrate that even with access only to fresh task samples rather than training data, a black-box adversary can successfully infer a task's inclusion in training.", "source": "openreview", "url": "https://openreview.net/forum?id=mTsWEVhcZM", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2026/ICLR/Poster/5.0_Black-Box Privacy Attacks on Shared Representations in Multitask Learning_2026.pdf" }, { "title": "Consis-GCPO: Consistency-Preserving Group Causal Preference Optimization for Vision Customization", "authors": [ "Qiaoqiao Jin", "Dong She", "Hualiang Wang", "Siming Fu", "Mu Liu", "Jidong Jiang" ], "year": 2026, "venue": "ICLR", "abstract": "Subject-driven generation faces a fundamental challenge: achieving high subject fidelity while maintaining semantic alignment with textual descriptions. While recent GRPO-based approaches have shown promise in aligning generative models with human preferences, they apply uniform optimization across all denoising timesteps, ignoring the temporal dynamics of how textual and visual conditions influence generation. We present Consis-GCPO, a causal reinforcement learning framework that reformulates multi-modal condition generation through discrete-time causal modeling. Our key insight is that different conditioning signals exert varying influence throughout the denoising process—text guides semantic structure in early steps while visual references anchor details in later stages. By introducing decoupled causal intervention trajectories, we quantify instantaneous causal effects at each timestep, transforming these measurements into temporally-weighted advantages for targeted optimization. This approach enables precise tracking of textual and visual contributions, ensuring accurate credit assignment for each conditioning modality. Extensive experiments demonstrate that Consis-GCPO significantly advances personalized generation, achieving superior subject consistency while preserving strong text-following capabilities, particularly excelling in complex multi-subject scenarios.", "source": "openreview", "url": "https://openreview.net/forum?id=OswqOlTYR2", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2026/ICLR/Poster/5.0_Consis-GCPO_ Consistency-Preserving Group Causal Preference Optimization for Vision Customizati_2026.pdf" }, { "title": "Fed-Duet: Dual Expert-Orchestrated Framework for Continual Federated Vision-Language Learning", "authors": [ "Tao GUO", "Junwei Chen", "Laizhong Cui" ], "year": 2026, "venue": "ICLR", "abstract": "Pretrained vision-language models (VLMs), such as CLIP, have shown promise in federated learning (FL) by bringing strong multimodal representations to edge devices. However, continual adaptation remains a core challenge in practical federated settings, where task distributions evolve over time and data remain non-IID across clients. In this emerging area, recent works adopt parameter-efficient fine-tuning (PEFT) as a lightweight way to reduce communication overhead, yet they fail to preserve satisfactory performance under continual learning conditions. Meanwhile, traditional federated continual learning (FCL) methods lack the capacity to maintain cross-modal alignment crucial to VLM performance. We introduce Fed-Duet, a novel Dual Expert-orchestrated framework for efficient federated continual learning in vision-language models. Fed-Duet features a dual-expert adaptation mechanism, combining server-coordinated semantic prompts with client-personalized modular adapters. These pathways are dynamically fused via a cross-attention mechanism, enabling effective knowledge transfer while preserving multimodal alignment and mitigating forgetting. We evaluate Fed-Duet across multiple challenging continual learning tasks in federated vision-language settings and demonstrate that it achieves superior performance and stability compared to existing approaches. Our work highlights the importance of coordinated expert composition in enabling scalable and robust multimodal continual learning. The code is available at https://anonymous.4open.science/r/FedDuet-0426/.", "source": "openreview", "url": "https://openreview.net/forum?id=Jk8g1OxyZY", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2026/ICLR/Poster/5.0_Fed-Duet_ Dual Expert-Orchestrated Framework for Continual Federated Vision-Language Learning_2026.pdf" }, { "title": "Learning Correlated Reward Models: Statistical Barriers and Opportunities", "authors": [ "Yeshwanth Cherapanamjeri", "Constantinos Costis Daskalakis", "Gabriele Farina", "Sobhan Mohammadpour" ], "year": 2026, "venue": "ICLR", "abstract": "Random Utility Models (RUMs) are a classical framework for modeling user preferences and play a key role in reward modeling for Reinforcement Learning from Human Feedback (RLHF). However, a crucial shortcoming of many of these techniques is the Independence of Irrelevant Alternatives (IIA) assumption, which collapses \\emph{all} human preferences to a universal underlying utility function, yielding a coarse approximation of the range of human preferences. On the other hand, statistical and computational guarantees for models avoiding this assumption are scarce. In this paper, we investigate the statistical and computational challenges of learning a \\emph{correlated} probit model, a fundamental RUM that avoids the IIA assumption. First, we establish that the classical data collection paradigm of pairwise preference data is \\emph{fundamentally insufficient} to learn correlational information, explaining the lack of statistical and computational guarantees in this setting. Next, we demonstrate that \\emph{best-of-three} preference data provably overcomes these shortcomings, and devise a statistically and computationally efficient estimator with near-optimal performance. These results highlight the benefits of higher-order preference data in learning correlated utilities, allowing for more fine-grained modeling of human preferences. Finally, we validate these theoretical guarantees on several real-world datasets, demonstrating improved personalization of human preferences.", "source": "openreview", "url": "https://openreview.net/forum?id=TbEyl6krsY", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2026/ICLR/Poster/5.0_Learning Correlated Reward Models_ Statistical Barriers and Opportunities_2026.pdf" }, { "title": "MobiEdit: Resource-efficient Knowledge Editing for Personalized On-device LLMs", "authors": [ "Zhenyan Lu", "Daliang Xu", "Dongqi Cai", "Zexi Li", "Wei Liu", "Jian Luan", "Fangming Liu", "Shangguang Wang", "Mengwei Xu" ], "year": 2026, "venue": "ICLR", "abstract": "Large language models (LLMs) are deployed on mobile devices to power killer applications such as intelligent assistants. LLMs pre-trained on general corpora often hallucinate when handling personalized or unseen queries, leading to incorrect or outdated responses. Knowledge editing addresses this by identifying and adjusting a small crucial portion of model weights, without compromising the general knowledge. However, prior knowledge editing methods are impractical to run on local devices due to the resource-heavy backpropagation (BP) needed for updates. We present MobiEdit, the first mobile knowledge editing framework that enables efficient LLM personalization on commercial off-the-shelf (COTS) mobile devices. MobiEdit replaces full-precision BP with quantized forward-only gradient estimation, thus compatible with the energy-efficient mobile neural processing units (NPUs). To further improve gradient estimation efficiency, we introduce two optimizations: an early stopping mechanism that adaptively terminates editing upon success and prefix activation reusing that reduce redundant computation across steps. Our approach enables real-time editing of 3B-parameter models (Qwen2.5-3B-Instruct and Llama3.2-3B-Instruct) on COTS mobile devices with 7.1$\\times$ less memory, 15.8 $\\times$ less energy and 3.4$\\times$ less latency compared to previous knowledge editing methods.", "source": "openreview", "url": "https://openreview.net/forum?id=fb7yTBOV3p", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2026/ICLR/Poster/5.0_MobiEdit_ Resource-efficient Knowledge Editing for Personalized On-device LLMs_2026.pdf" }, { "title": "Mod-Adapter: Tuning-Free and Versatile Multi-concept Personalization via Modulation Adapter", "authors": [ "Weizhi Zhong", "Huan Yang", "Zheng Liu", "Huiguo He", "Zijian He", "Xuesong Niu", "Di ZHANG", "Guanbin Li" ], "year": 2026, "venue": "ICLR", "abstract": "Personalized text-to-image generation aims to synthesize images of user-provided concepts in diverse contexts. Despite recent progress in multi-concept personalization, most are limited to object concepts and struggle to customize abstract concepts (e.g., pose, lighting). \nSome methods have begun exploring multi-concept personalization supporting abstract concepts, but they require test-time fine-tuning for each new concept, which is time-consuming and prone to overfitting on limited training images.\nIn this work, we propose a novel tuning-free method for multi-concept personalization that can effectively customize both object and abstract concepts without test-time fine-tuning. \nOur method builds upon the modulation mechanism in pre-trained Diffusion Transformers (DiTs) model, leveraging the localized and semantically meaningful properties of the modulation space. Specifically, we propose a novel module, Mod-Adapter, to predict concept-specific modulation direction for the modulation process of concept-related text tokens.\nIt introduces vision-language cross-attention for extracting concept visual features, and Mixture-of-Experts (MoE) layers that adaptively map the concept features into the modulation space.\nFurthermore, to mitigate the training difficulty caused by the large gap between the concept image space and the modulation space, we introduce a VLM-guided pre-training strategy that leverages the strong image understanding capabilities of vision-language models to provide semantic supervision signals.\nFor a comprehensive comparison, we extend a standard benchmark by incorporating abstract concepts. Our method achieves state-of-the-art performance in multi-concept personalization, supported by quantitative, qualitative, and human evaluations.", "source": "openreview", "url": "https://openreview.net/forum?id=6wZsaGILlN", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2026/ICLR/Poster/5.0_Mod-Adapter_ Tuning-Free and Versatile Multi-concept Personalization via Modulation Adapter_2026.pdf" }, { "title": "NextQuill: Causal Preference Modeling for Enhancing LLM Personalization", "authors": [ "Xiaoyan Zhao", "Juntao You", "Yang Zhang", "Wenjie Wang", "Hong Cheng", "Fuli Feng", "See-Kiong Ng", "Tat-Seng Chua" ], "year": 2026, "venue": "ICLR", "abstract": "Personalizing large language models (LLMs) is increasingly important as they are progressively integrated into real-world applications to support users’ daily lives. However, existing approaches often fail to distinguish which components of response predictions by model and ground-truth response in training data truly reflect user preferences, resulting in shallow personalization alignment. In this paper, we introduce NextQuill, a novel LLM personalization alignment framework grounded in causal preference modeling. We approach personalization from a causal perspective, recognizing that model-predicted responses (model side) and user-written ground-truth responses (data side) are both outcomes shape by user history (characteristics) and other context factors. To better capture user preferences, we define causal preference effects as the causal effect of the user history/characteristics on outcomes from the model/data side. Building on this foundation, NextQuill introduces two complementary alignment strategies: (1) aligning model-side causal preference effects (on predictions) with those of ground-truth data, rather than indiscriminately aligning all predictions, and (2) emphasizing learning the preference-driven ground-truth tokens, identified via data-side causal preference effects, rather than treating all tokens equally. As such, NextQuill shifts the alignment process toward learning from causal preference effects, facilitating more effective and personalized LLM adaptation. Experiments on multiple personalization benchmarks demonstrate that NextQuill substantially improves personalization quality. Code is available at \\url{https://anonymous.4open.science/r/NextQuill-1E4E}.", "source": "openreview", "url": "https://openreview.net/forum?id=xYpVlKMFqv", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2026/ICLR/Poster/5.0_NextQuill_ Causal Preference Modeling for Enhancing LLM Personalization_2026.pdf" }, { "title": "PERSONA: Dynamic and Compositional Inference-Time Personality Control via Activation Vector Algebra", "authors": [ "Xiachong Feng", "Liang Zhao", "Weihong Zhong", "Yichong Huang", "Yuxuan Gu", "Lingpeng Kong", "Xiaocheng Feng", "Bing Qin" ], "year": 2026, "venue": "ICLR", "abstract": "Current methods for personality control in Large Language Models rely on static prompting or expensive fine-tuning, failing to capture the dynamic and compositional nature of human traits. We introduce PERSONA, a training-free framework that achieves fine-tuning level performance through direct manipulation of personality vectors in activation space. Our key insight is that personality traits appear as extractable, approximately orthogonal directions in the model's representation space that support algebraic operations. The framework operates through three stages: Persona-Base extracts orthogonal trait vectors via contrastive activation analysis; Persona-Algebra enables precise control through vector arithmetic (scalar multiplication for intensity, addition for composition, subtraction for suppression); and Persona-Flow achieves context-aware adaptation by dynamically composing these vectors during inference. On PersonalityBench, our approach achieves a mean score of 9.60, nearly matching the supervised fine-tuning upper bound of 9.61 without any gradient updates. On our proposed Persona-Evolve benchmark for dynamic personality adaptation, we achieve up to 91% win rates across diverse model families. These results provide evidence that aspects of LLM personality are mathematically tractable, opening new directions for interpretable and efficient behavioral control.", "source": "openreview", "url": "https://openreview.net/forum?id=QZvGqaNBlU", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2026/ICLR/Poster/5.0_PERSONA_ Dynamic and Compositional Inference-Time Personality Control via Activation Vector Alg_2026.pdf" }, { "title": "Personalized Collaborative Learning with Affinity-Based Variance Reduction", "authors": [ "Chenyu Zhang", "Navid Azizan" ], "year": 2026, "venue": "ICLR", "abstract": "Multi-agent learning faces a fundamental tension: leveraging distributed collaboration without sacrificing the personalization needed for diverse agents. This tension intensifies when aiming for full personalization while adapting to unknown heterogeneity levels—gaining collaborative speedup when agents are similar, without performance degradation when they are different. Embracing the challenge, we propose personalized collaborative learning (PCL), a novel framework for heterogeneous agents to collaboratively learn personalized solutions with seamless adaptivity. Through carefully designed bias correction and importance correction mechanisms, our method AffPCL robustly handles both environment and objective heterogeneity. We prove that AffPCL reduces sample complexity over independent learning by a factor of $\\max\\\\{n^{-1}, \\delta\\\\}$, where $n$ is the number of agents and $\\delta\\in[0,1]$ measures their heterogeneity. This *affinity-based* acceleration automatically interpolates between the linear speedup of federated learning in homogeneous settings and the baseline of independent learning, without requiring prior knowledge of the system. Our analysis further reveals that an agent may obtain linear speedup even by collaborating with arbitrarily dissimilar agents, unveiling new insights into personalization and collaboration in the high heterogeneity regime.", "source": "openreview", "url": "https://openreview.net/forum?id=OvCyDyW9kU", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2026/ICLR/Poster/5.0_Personalized Collaborative Learning with Affinity-Based Variance Reduction_2026.pdf" }, { "title": "Privacy Beyond Pixels: Latent Anonymization for Privacy-Preserving Video Understanding", "authors": [ "Joseph Fioresi", "Ishan Rajendrakumar Dave", "Mubarak Shah" ], "year": 2026, "venue": "ICLR", "abstract": "We introduce a novel formulation of visual privacy preservation for video foundation models that operates entirely in the latent space. While spatio-temporal features learned by foundation models have deepened general understanding of video content, sharing or storing these extracted visual features for downstream tasks inadvertently reveals sensitive personal information like skin color, gender, or clothing. Current privacy preservation methods focus on input-pixel level anonymization, which requires retraining the entire utility video model and results in task-specific anonymization, making them unsuitable for recent video foundational models. To address these challenges, we introduce a lightweight Anonymizing Adapter Module (AAM) that removes private information from video features while retaining general task utility. AAM can be applied in a plug and play fashion to frozen video encoders, minimizing the computational burden of finetuning and re-extracting features. Our framework employs three newly designed training objectives: (1) a clip-level self-supervised privacy objective to reduce mutual information between static clips, (2) a co-training objective to retain utility across seen tasks, and (3) a latent consistency loss for generalization on unseen tasks. Our extensive evaluations demonstrate a significant 35% reduction in privacy leakage while maintaining near-baseline utility performance across various downstream tasks: Action Recognition (Kinetics400, UCF101, HMDB51), Temporal Action Detection (THUMOS14), and Anomaly Detection (UCF-Crime). We also provide an analysis on anonymization for sensitive temporal attribute recognition. Additionally, we propose new protocols for assessing gender bias in action recognition models, showing that our method effectively mitigates such biases and promotes more equitable video understanding.", "source": "openreview", "url": "https://openreview.net/forum?id=ncA3UUL0Ri", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2026/ICLR/Poster/5.0_Privacy Beyond Pixels_ Latent Anonymization for Privacy-Preserving Video Understanding_2026.pdf" }, { "title": "ReactID: Synchronizing Realistic Actions and Identity in Personalized Video Generation", "authors": [ "Wei Li", "Yiheng Zhang", "Fuchen Long", "Zhaofan Qiu", "Ting Yao", "Xiaoyan Sun", "Tao Mei" ], "year": 2026, "venue": "ICLR", "abstract": "Personalized video generation faces a fundamental trade-off between identity consistency and action realism: overly rigid identity preservation often leads to unnatural motion, while emphasis on action dynamics can compromise subject fidelity. This tension stems from three interrelated challenges: imprecise subject-video alignment, unstable training due to varying sample difficulties, and inadequate modeling of fine-grained actions. To address this, we propose ReactID, a comprehensive framework that harmonizes identity accuracy and motion naturalness through coordinated advances in data, training, and action modeling. First, we construct ReactID-Data, a large-scale dataset annotated with a high-precision pipeline combining vision-based entity label extraction, MLLM-based subject detection, and post-verification to ensure reliable subject-video correspondence. Second, we analyze learning difficulty along dimensions such as subject size, appearance similarity, and sampling strategy, and devise a progressive training curriculum that evolves from easy to hard samples, ensuring stable convergence while avoiding identity overfitting and copy-paste artifacts. Third, ReactID introduces a novel timeline-based conditioning mechanism that supplements monolithic text prompts with structured multi-action sequences. Each sub-action is annotated with precise timestamps and descriptions, and integrated into the diffusion model via two novel components: subject-aware cross-attention module to bind sub-action to the specific subject of interest and temporally-adaptive RoPE to embed the rescaled temporal coordinates invariant to action duration. Experiments show that ReactID achieves state-of-the-art performance in both identity preservation and action realism, effectively balancing the two objectives.", "source": "openreview", "url": "https://openreview.net/forum?id=yn0Wu7NsTa", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2026/ICLR/Poster/5.0_ReactID_ Synchronizing Realistic Actions and Identity in Personalized Video Generation_2026.pdf" }, { "title": "RedacBench: Can AI Erase Your Secrets?", "authors": [ "Hyunjun Jeon", "Kyuyoung Kim", "Jinwoo Shin" ], "year": 2026, "venue": "ICLR", "abstract": "The ability of modern language models to easily extract unstructured sensitive information has made redaction—the selective removal of such information—an essential task for data security. However, existing benchmarks and evaluation methods for redaction are often limited to predefined categories of data like personally identifiable information (PII), or particular techniques like masking. To bridge this gap, we introduce RedacBench, a novel benchmark for a comprehensive evaluation of redaction capabilities, independent of specific domains or redaction strategies. Constructed from 514 human-written texts from individuals, corporations, and governments, along with 187 security policies, RedacBench measures a model's ability to selectively remove policy-violating information while preserving the original text's utility. We robustly quantify this performance using metrics derived from 8,053 inferable propositions, assessing both security—through the redaction of sensitive propositions—and utility—through the preservation of non-sensitive ones. Our experiments on various redaction strategies using state-of-the-art language models reveal that while more advanced models and strategies can increase security, maintaining utility remains a significant challenge. To facilitate future research, we publicly release RedacBench along with a web-based playground for custom dataset creation and evaluation at https://hyunjunian.github.io/redaction-playground/.", "source": "openreview", "url": "https://openreview.net/forum?id=wf73W2xatC", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2026/ICLR/Poster/5.0_RedacBench_ Can AI Erase Your Secrets__2026.pdf" }, { "title": "Unified Multi-Modal Interactive and Reactive 3D Motion Generation via Rectified Flow", "authors": [ "Prerit Gupta", "Shourya Verma", "Ananth Grama", "Aniket Bera" ], "year": 2026, "venue": "ICLR", "abstract": "Generating realistic, context-aware two-person motion conditioned on diverse modalities remains a fundamental challenge for graphics, animation and embodied AI systems. Real-world applications such as VR/AR companions, social robotics and game agents require models capable of producing coordinated interpersonal behavior while flexibly switching between interactive and reactive generation. We introduce DualFlow, the first unified and efficient framework for multi-modal two-person motion generation. DualFlow conditions 3D motion generation on diverse inputs, including text, music, and prior motion sequences. Leveraging rectified flow, it achieves deterministic straight-line sampling paths between noise and data, reducing inference time and mitigating error accumulation common in diffusion-based models. To enhance semantic grounding, DualFlow employs a novel Retrieval-Augmented Generation (RAG) module for two-person motion that retrieves motion exemplars using music features and LLM-based text decompositions of spatial relations, body movements, and rhythmic patterns. We use contrastive rectified flow objective to further sharpen alignment with conditioning signals and add synchronization loss to improve inter-person temporal coordination. Extensive evaluations across interactive, reactive, and multi-modal benchmarks demonstrate that DualFlow consistently improves motion quality, responsiveness, and semantic fidelity. DualFlow achieves state-of-the-art performance in two-person multi-modal motion generation, producing coherent, expressive, and rhythmically synchronized motion. Code will be released upon acceptance.", "source": "openreview", "url": "https://openreview.net/forum?id=QaAgHKbJop", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2026/ICLR/Poster/5.0_Unified Multi-Modal Interactive and Reactive 3D Motion Generation via Rectified Flow_2026.pdf" }, { "title": "When Machine Learning Gets Personal: Evaluating Prediction and Explanation", "authors": [ "Louisa Cornelis", "Guillermo Bernardez", "Haewon Jeong", "Nina Miolane" ], "year": 2026, "venue": "ICLR", "abstract": "In high-stakes domains like healthcare, users often expect that sharing personal information with machine learning systems will yield tangible benefits, such as more accurate diagnoses and clearer explanations of contributing factors. However, the validity of this assumption remains largely unexplored. We propose a unified framework to fairly quantify if personalizing a model improves both prediction and explanation for every group who provide personal data. We show that its impacts on prediction and explanation can diverge: a model may become more or less explainable even when prediction is unchanged. For practical settings, we study a standard hypothesis test for detecting personalization effects on demographic groups. We derive a finite-sample lower bound on its probability of error as a function of group sizes, number of personal attributes, and desired benefit from personalization. This provides actionable insights, such as which dataset characteristics are necessary to test an effect, or the maximum effect that can be tested given a dataset. We apply our framework to real-world tabular datasets using feature-attribution methods, uncovering scenarios where effects are fundamentally untestable due to the dataset statistics. Our results highlight the need for joint evaluation of prediction and explanation in personalized models and the importance of designing models and datasets with sufficient information for such evaluation.", "source": "openreview", "url": "https://openreview.net/forum?id=fnfG8pI00B", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2026/ICLR/Poster/5.0_When Machine Learning Gets Personal_ Evaluating Prediction and Explanation_2026.pdf" }, { "title": "IGC-Net for conditional average potential outcome estimation over time", "authors": [ "Konstantin Hess", "Dennis Frauen", "Valentyn Melnychuk", "Stefan Feuerriegel" ], "year": 2026, "venue": "ICLR", "abstract": "Estimating potential outcomes for treatments over time based on observational data is important for personalized decision-making in medicine. However, many existing methods for this task fail to properly adjust for time-varying confounding and thus yield biased estimates. There are only a few neural methods with proper adjustments, but these have inherent limitations (e.g., division by propensity scores that are often close to zero), which result in poor performance. As a remedy, we introduce the iterative G-computation network (IGC-Net). Our IGC-Net is a novel, neural end-to-end model which adjusts for time-varying confounding in order to estimate conditional average potential outcomes (CAPOs) over time. Specifically, our IGC-Net is the first neural model to perform fully regression-based iterative G-computation for CAPOs in the time-varying setting. We evaluate the effectiveness of our IGC-Net across various experiments. In sum, this work represents a significant step towards personalized decision-making from electronic health records.", "source": "openreview", "url": "https://openreview.net/forum?id=ZmhpqpKzAT", "decision_type": "Poster", "avg_rating": 4.8, "relative_path": "2026/ICLR/Poster/4.8_IGC-Net for conditional average potential outcome estimation over time_2026.pdf" }, { "title": "Reducing Semantic Mismatch in Brain-to-Text Decoding Through Personalized Multimodal Masking", "authors": [ "Jiaxuan Chen", "Yu Qi", "Yueming Wang", "Gang Pan" ], "year": 2026, "venue": "ICLR", "abstract": "The rapid progress of large vision-language models (VLMs), such as CLIP, has spurred the development of a wide range of neural decoding frameworks. Nevertheless, most existing approaches still suffer from semantic mismatches during representational alignment. This challenge may stem from the fact that the human brain does not distribute attention uniformly across a visual scene, but rather selectively encodes salient or relevant regions. Moreover, such selectivity is closely related to individual interests and varies from person to person. To address this challenge, we propose Yo'Mind, a novel optimal transport (OT)-driven personalized multimodal semantic masking framework designed to bridge the semantic gap between brain and machines in interpreting visual scenes. Technically, Yo'Mind introduces a dynamic semantic pruning and allocation mechanism that adaptively masks redundant visual semantic components in stimulus images based on individual neural responses—without requiring extra human supervision or hyperparameter tuning. This strategy can be used to enhance semantic consensus between brain and machine representations during decoding. Furthermore, the inherent flexibility of OT theory enables Yo'Mind to perform brain-visual-linguistic alignment and cross-subject decoding within a unified end-to-end architecture. Extensive experiments demonstrate that our Yo'Mind offers several advantages, including state-of-the-art brain-to-text reconstruction performance and improved interpretability of the decoding process.", "source": "openreview", "url": "https://openreview.net/forum?id=ya00JrKTjp", "decision_type": "Poster", "avg_rating": 4.8, "relative_path": "2026/ICLR/Poster/4.8_Reducing Semantic Mismatch in Brain-to-Text Decoding Through Personalized Multimodal Masking_2026.pdf" }, { "title": "Personalized Feature Translation for Expression Recognition: An Efficient Source-Free Domain Adaptation Method", "authors": [ "Masoumeh Sharafi", "Soufiane Belharbi", "Muhammad Osama Zeeshan", "HOUSSEM Ben Salem", "Ali Etemad", "Alessandro Lameiras Koerich", "Marco Pedersoli", "Simon L Bacon", "Eric Granger" ], "year": 2026, "venue": "ICLR", "abstract": "Facial expression recognition (FER) models are employed in many video-based affective computing applications, such as human-computer interaction and healthcare monitoring. However, deep FER models often struggle with subtle expressions and high inter-subject variability, limiting their performance in real-world applications. To improve their performance, source-free domain adaptation (SFDA) methods have been proposed to personalize a pretrained source model using only unlabeled target domain data, thereby avoiding data privacy, storage, and transmission constraints. This paper addresses a challenging scenario, where source data is unavailable for adaptation, and only unlabeled target data consisting solely of neutral expressions is available. SFDA methods are not typically designed to adapt using target data from only a single class. Further, using models to generate facial images with non-neutral expressions can be unstable and computationally intensive. In this paper, personalized feature translation (PFT) is proposed for SFDA. Unlike current image translation methods for SFDA, our lightweight method operates in the latent space. We first pre-train the translator on the source domain data to transform the subject-specific style features from one source subject into another. Expression information is preserved by optimizing a combination of expression consistency and style-aware objectives. Then, the translator is adapted on neutral target data, without using source data or image synthesis. By translating in the latent space, PFT avoids the complexity and noise of face expression generation, producing discriminative embeddings optimized for classification. Using PFT eliminates the need for image synthesis, reduces computational overhead (using a lightweight translator), and only adapts part of the model, making the method efficient compared to image-based translation. Extensive experiments on four challenging video FER benchmark datasets, BioVid, StressID, BAH, and AffWild2, show that PFT consistently outperforms state-of-the-art SFDA methods, providing a cost-effective approach that is suitable for real-world, privacy-sensitive FER applications.", "source": "openreview", "url": "https://openreview.net/forum?id=0ucmlIQTlu", "decision_type": "Poster", "avg_rating": 4.7, "relative_path": "2026/ICLR/Poster/4.7_Personalized Feature Translation for Expression Recognition_ An Efficient Source-Free Domain Ad_2026.pdf" }, { "title": "Think-While-Generating: On-the-Fly Reasoning for Personalized Long-Form Generation", "authors": [ "Chengbing Wang", "Yang Zhang", "Wenjie Wang", "Xiaoyan Zhao", "Fuli Feng", "Xiangnan He", "Tat-Seng Chua" ], "year": 2026, "venue": "ICLR", "abstract": "Preference alignment has enabled large language models (LLMs) to better reflect human expectations, but current methods mostly optimize for population-level preferences, overlooking individual users. Personalization is essential, yet early approaches—such as prompt customization or fine-tuning—struggle to reason over implicit preferences, limiting real-world effectiveness. Recent “think-then-generate” methods address this by reasoning before response generation. However, they face challenges in long-form generation: their static one-shot reasoning must capture all relevant information for the full response generation, making learning difficult and limiting adaptability to evolving content. To address this issue, we propose **FlyThinker**, an efficient “think-while-generating” framework for personalized long-form generation. FlyThinker employs a separate reasoning model that generates latent token-level reasoning in parallel, which is fused into the generation model to dynamically guide response generation. This design enables reasoning and generation to run concurrently, ensuring inference efficiency. In addition, the reasoning model is designed to depend only on previous responses rather than its own prior outputs, which preserves training parallelism across different positions—allowing all reasoning tokens for training data to be produced in a single forward pass like standard LLM training, ensuring training efficiency. Extensive experiments on real-world benchmarks demonstrate that FlyThinker achieves better personalized generation while keeping training and inference efficiency.", "source": "openreview", "url": "https://openreview.net/forum?id=lle0aGQyQb", "decision_type": "Poster", "avg_rating": 4.7, "relative_path": "2026/ICLR/Poster/4.7_Think-While-Generating_ On-the-Fly Reasoning for Personalized Long-Form Generation_2026.pdf" }, { "title": "Traceable Black-Box Watermarks For Federated Learning", "authors": [ "Jiahao Xu", "Rui Hu", "Olivera Kotevska", "Zikai Zhang" ], "year": 2026, "venue": "ICLR", "abstract": "Due to the distributed nature of Federated Learning (FL) systems, each local client has access to the global model, posing a critical risk of model leakage. Existing works have explored injecting watermarks into local models to enable intellectual property protection. However, these methods either focus on non-traceable watermarks or traceable but white-box watermarks. We identify a gap in the literature regarding the formal definition of traceable black-box watermarking and the formulation of the problem of injecting such watermarks into FL systems. In this work, we first formalize the problem of injecting traceable black-box watermarks into FL. Based on the problem, we propose a novel server-side watermarking method, $\\mathbf{TraMark}$, which creates a traceable watermarked model for each client, enabling verification of model leakage in black-box settings. To achieve this, $\\mathbf{TraMark}$ partitions the model parameter space into two distinct regions: the main task region and the watermarking region. Subsequently, a personalized global model is constructed for each client by aggregating only the main task region while preserving the watermarking region. Each model then learns a unique watermark exclusively within the watermarking region using a distinct watermark dataset before being sent back to the local client. Extensive results across various FL systems demonstrate that $\\mathbf{TraMark}$ ensures the traceability of all watermarked models while preserving their main task performance. The code is available at https://anonymous.4open.science/r/TraMark.", "source": "openreview", "url": "https://openreview.net/forum?id=xHRuyXnJXd", "decision_type": "Poster", "avg_rating": 4.7, "relative_path": "2026/ICLR/Poster/4.7_Traceable Black-Box Watermarks For Federated Learning_2026.pdf" }, { "title": "Aligning Deep Implicit Preferences by Learning to Reason Defensively", "authors": [ "Peiming Li", "Zhiyuan Hu", "Yang Tang", "Shiyu Li", "Xi Chen" ], "year": 2026, "venue": "ICLR", "abstract": "Personalized alignment is crucial for enabling Large Language Models (LLMs) to engage effectively in user-centric interactions. However, current methods face a dual challenge: they fail to infer users' deep implicit preferences (including unstated goals, semantic context and risk tolerances), and they lack the defensive reasoning required to navigate real-world ambiguity. This cognitive gap leads to responses that are superficial, brittle and short-sighted. To address this, we propose Critique-Driven Reasoning Alignment (CDRA), which reframes alignment from a scalar reward-matching task into a structured reasoning process. First, to bridge the preference inference gap, we introduce the DeepPref benchmark. This dataset, comprising 3000 preference-query pairs across 20 topics, is curated by simulating a multi-faceted cognitive council that produces critique-annotated reasoning chains to deconstruct query semantics and reveal latent risks. Second, to instill defensive reasoning, we introduce the Personalized Generative Process Reward Model (Pers-GenPRM), which frames reward modeling as a personalized reasoning task. It generates a critique chain to evaluate a response's alignment with user preferences before outputting a final score based on this rationale. Ultimately, this interpretable, structured reward signal guides policy model through Critique-Driven Policy Alignment, a process-level online reinforcement learning algorithm integrating both numerical and natural language feedback. Experiments demonstrate that CDRA excels at discovering and aligning with users' true preferences while executing robust reasoning. Our code and dataset are available at https://anonymous.4open.science/r/Deep-pref-9DE9.", "source": "openreview", "url": "https://openreview.net/forum?id=ZA7i5Otjqd", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2026/ICLR/Poster/4.5_Aligning Deep Implicit Preferences by Learning to Reason Defensively_2026.pdf" }, { "title": "An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes", "authors": [ "Emil Javurek", "Valentyn Melnychuk", "Jonas Schweisthal", "Konstantin Hess", "Dennis Frauen", "Stefan Feuerriegel" ], "year": 2026, "venue": "ICLR", "abstract": "Predicting individualized potential outcomes in sequential decision-making is central\n for optimizing therapeutic decisions in personalized medicine (e.g., which\ndosing sequence to give to a cancer patient). However, predicting potential out-\ncomes over long horizons is notoriously difficult. Existing methods that break the\ncurse of the horizon typically lack strong theoretical guarantees such as orthogonality\n and quasi-oracle efficiency. In this paper, we revisit the problem of predicting\n individualized potential outcomes in sequential decision-making (i.e., estimating\n Q-functions in Markov decision processes with observational data) through a\ncausal inference lens. In particular, we develop a comprehensive theoretical foundation\n for meta-learners in this setting with a focus on beneficial theoretical properties.\n As a result, we yield a novel meta-learner called DRQ-learner and establish\nthat it is: (1) doubly robust (i.e., valid inference under model misspecification),\n(2) Neyman-orthogonal (i.e., insensitive to first-order estimation errors in the nuisance\n functions), and (3) achieves quasi-oracle efficiency (i.e., behaves asymptotically\n as if the ground-truth nuisance functions were known). Our DRQ-learner is\napplicable to settings with both discrete and continuous state spaces. Further, our\nDRQ-learner is flexible and can be used together with arbitrary machine learning\n models (e.g., neural networks). We validate our theoretical results through\nnumerical experiments, thereby showing that our meta-learner outperforms state-of-the-art baselines.", "source": "openreview", "url": "https://openreview.net/forum?id=PRu8Sybp1j", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2026/ICLR/Poster/4.5_An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes_2026.pdf" }, { "title": "Knowledge Externalization: Reversible Unlearning and Modular Retrieval in Multimodal Large Language Models", "authors": [ "Jiaqi Li", "Zihan You", "Ruoyan Shen", "Shenyu Zhang", "Songlin Zhai", "Yongrui Chen", "Chuanyi Zhang", "Jiahui Geng", "Fakhri Karray", "Sheng Bi", "Guilin Qi" ], "year": 2026, "venue": "ICLR", "abstract": "Multimodal Large Language Models (MLLMs) achieve remarkable cross-modal understanding by training on vast web-scale datasets, but inadvertently internalize sensitive personal and proprietary information. Existing machine unlearning methods address this by irreversibly altering model parameters to permanently erase knowledge. This destructive paradigm conflicts with modern privacy regulations that mandate auditable, reversible, and user-controllable data management. To address these challenges, we propose Knowledge Externalization, a novel framework for reversible and modular knowledge management in MLLMs. We first propose Dual-Stream Memory Tuning, a method that transfers targeted knowledge from a model's internal parameters into external memory tokens. To mitigate gradient interference when externalizing multiple concepts, we further introduce Soft Orthogonal Weighting, a technique that preserves the independence of each token. Our resulting framework demonstrates three key capabilities: (i) It achieves effective forgetting of target concepts within the base model, while enabling high-fidelity knowledge restoration using the corresponding memory token. (ii) It supports continuous knowledge editing, allowing the information stored within an external token to be dynamically updated post-externalization. (iii) It displays a remarkable emergent ability for compositionality, where multiple memory tokens (including edited ones) can be freely combined to simultaneously recover knowledge corresponding to each concept. Our source code will be released in the near future.", "source": "openreview", "url": "https://openreview.net/forum?id=ZHK6nBHRXw", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2026/ICLR/Poster/4.5_Knowledge Externalization_ Reversible Unlearning and Modular Retrieval in Multimodal Large Lang_2026.pdf" }, { "title": "Learning to Generate Stylized Handwritten Text via a Unified Representation of Style, Content, and Noise", "authors": [ "Honglie Wang", "Yan-Ming Zhang", "Wangzi Yao", "Fei Yin", "Cheng-Lin Liu" ], "year": 2026, "venue": "ICLR", "abstract": "Handwritten Text Generation (HTG) seeks to synthesize realistic and personalized handwriting by modeling stylistic and structural traits. While recent diffusion-based approaches have advanced generation fidelity, they typically rely on auxiliary style or content encoders with handcrafted objectives, leading to complex training pipelines and limited interaction across factors. In this work, we present InkSpire, a diffusion transformer based model that unifies style, content, and noise within a shared latent space. By eliminating explicit encoders, InkSpire streamlines optimization while enabling richer feature interaction and stronger in-context generation. To further enhance flexibility, we introduce a multi-line masked infilling strategy that allows training directly on raw text-line images, together with a revised positional encoding that supports arbitrary-length multi-line synthesis and fine-grained character editing. Moreover, InkSpire is trained on a bilingual Chinese–English corpus, enabling a single model to handle both Chinese and English handwriting generation with high fidelity and stylistic diversity, thereby overcoming the need for language-specific systems. Extensive experiments on IAM and ICDAR2013 demonstrate that InkSpire achieves superior structural accuracy and stylistic diversity compared to prior state-of-the-art methods.", "source": "openreview", "url": "https://openreview.net/forum?id=FBPuLChGNX", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2026/ICLR/Poster/4.5_Learning to Generate Stylized Handwritten Text via a Unified Representation of Style, Content, _2026.pdf" }, { "title": "MOSAIC: Multi-Subject Personalized Generation via Correspondence-Aware Alignment and Disentanglement", "authors": [ "Dong She", "Siming Fu", "Mushui Liu", "Qiaoqiao Jin", "Hualiang Wang", "Mu Liu", "Jidong Jiang" ], "year": 2026, "venue": "ICLR", "abstract": "Multi-subject personalized generation presents unique challenges in maintaining identity fidelity and semantic coherence when synthesizing images conditioned on multiple reference subjects. Existing methods often suffer from identity blending and attribute leakage due to inadequate modeling of how different subjects should interact within shared representation spaces. We present MOSAIC, a representation-centric framework that rethinks multi-subject generation through explicit semantic correspondence and orthogonal feature disentanglement. Our key insight is that multi-subject generation requires precise semantic alignment at the representation level—knowing exactly which regions in the generated image should attend to which parts of each reference. To enable this, we introduce SemAlign-MS, a meticulously annotated dataset providing fine-grained semantic correspondences between multiple reference subjects and target images, previously unavailable in this domain. Building on this foundation, we propose the semantic correspondence attention loss to enforce precise point-to-point semantic alignment, ensuring high consistency from each reference to its designated regions. Furthermore, we develop the multi-reference disentanglement loss to push different subjects into orthogonal attention subspaces, preventing feature interference while preserving individual identity characteristics. Extensive experiments demonstrate that MOSAIC achieves SOTA performance on multiple benchmarks. Notably, while existing methods typically degrade beyond 3 subjects, MOSAIC maintains high fidelity with 4+ reference subjects, opening new possibilities for complex multi-subject synthesis applications.", "source": "openreview", "url": "https://openreview.net/forum?id=7AH0y1OtnC", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2026/ICLR/Poster/4.5_MOSAIC_ Multi-Subject Personalized Generation via Correspondence-Aware Alignment and Disentangl_2026.pdf" }, { "title": "Mitigating Semantic Collapse in Generative Personalization with Test-Time Embedding Adjustment", "authors": [ "Anh Tuan Bui", "Thuy-Trang Vu", "Trung Le", "Junae Kim", "Tamas Abraham", "Rollin Omari", "Amardeep Kaur", "Dinh Phung" ], "year": 2026, "venue": "ICLR", "abstract": "In this paper, we investigate the semantic collapsing problem in generative personalization, an under-explored topic where the learned visual concept ($V$) gradually shifts from its original textual meaning and comes to dominate other concepts in multi-concept input prompts. This issue not only reduces the semantic richness of complex input prompts like \"a photo of $V$ wearing glasses and playing guitar\" into simpler, less contextually rich forms such as \"a photo of $V$\" but also leads to simplified output images that fail to capture the intended concept. We identify the root cause as unconstrained optimisation, which allows the learned embedding $V$ to drift arbitrarily in the embedding space, both in direction and magnitude. To address this, we propose a simple yet effective training-free method that adjusts the magnitude and direction of pre-trained embedding at inference time, effectively mitigating the semantic collapsing problem. Our method is broadly applicable across different personalization methods and demonstrates significant improvements in text-image alignment in diverse use cases. Our code is published at \\url{https://github.com/tuananhbui89/Embedding-Adjustment}.", "source": "openreview", "url": "https://openreview.net/forum?id=P7sPfvq7Ih", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2026/ICLR/Poster/4.5_Mitigating Semantic Collapse in Generative Personalization with Test-Time Embedding Adjustment_2026.pdf" }, { "title": "Personalized Reasoning: Just-in-time Personalization and Why LLMs Fail at It", "authors": [ "Shuyue Stella Li", "Avinandan Bose", "Faeze Brahman", "Simon Shaolei Du", "Pang Wei Koh", "Maryam Fazel", "Yulia Tsvetkov" ], "year": 2026, "venue": "ICLR", "abstract": "Current large language model (LLM) development treats task-solving and preference-alignment as separate challenges, optimizing first for objective correctness, then for alignment to aggregated human preferences. This paradigm fails in human-facing applications where solving a problem correctly is insufficient if the response mismatches the user’s needs. This challenge intensifies in just-in-time scenarios where no prior user interaction history exists due to cold-start conditions or privacy constraints. LLMs need to identify what they don’t know about user preferences, strategically elicit preference values through questioning, then adapt their reasoning processes and responses accordingly—a complicated chain of cognitive processes which we term personalized reasoning. We introduce PREFDISCO, an evaluation methodology that transforms static benchmarks into interactive personalization tasks using psychologically-grounded personas with sparse preferences. Our framework creates scenarios where identical questions require different reasoning chains depending on user context, as optimal explanation approaches vary by individual expertise and preferences while maintaining factual accuracy. Evaluation of 21 frontier models across 10 tasks reveals 29.0% of naive personalization attempts produce worse preference alignment than generic responses, yet generic responses also fail to serve individual user needs effectively. These findings suggest personalized reasoning requires dedicated development rather than emerging naturally. PREFDISCO establishes personalized reasoning as a measurable research frontier and reveals fundamental limitations in current LLMs’ interactive capabilities, providing a foundation for developing systems that can adapt to individual users in education, healthcare, and technical domains where personalization is critical.", "source": "openreview", "url": "https://openreview.net/forum?id=O1hfVE0UxG", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2026/ICLR/Poster/4.5_Personalized Reasoning_ Just-in-time Personalization and Why LLMs Fail at It_2026.pdf" }, { "title": "RPM: Reasoning-Level Personalization for Black-Box Large Language Models", "authors": [ "Jieyong Kim", "Tongyoung Kim", "SooJin Yoon", "Jaehyung Kim", "Dongha Lee" ], "year": 2026, "venue": "ICLR", "abstract": "While black-box large language models are widely deployed, they produce generic outputs that overlook individual user preferences.\nCurrent personalization methods are fundamentally limited to response-level personalization; they only match final outputs, failing to model the underlying reasoning that connects user behavior to responses. \nTo address this, this work introduces reasoning-level personalization as a new paradigm and proposes RPM, the first systematic framework that automatically discovers user-specific reasoning structures from raw behavioral data to guide the model's personalized inference.\nRPM constructs a structured model of user behavior—built from response-influential features and statistical factors—to create personalized reasoning paths and retrieve beneficial examples for guiding inference through a feature-based retrieval mechanism. \nExtensive experiments across four diverse tasks demonstrate that RPM consistently outperforms existing response-level methods while simultaneously enhancing both personalization performance and interpretability, providing a promising direction for black-box LLM personalization.", "source": "openreview", "url": "https://openreview.net/forum?id=oKKVLHFzZ8", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2026/ICLR/Poster/4.5_RPM_ Reasoning-Level Personalization for Black-Box Large Language Models_2026.pdf" }, { "title": "Towards Cognitively-Faithful Decision-Making Models to Improve AI Alignment", "authors": [ "Cyrus Cousins", "Vijay Keswani", "Vincent Conitzer", "Hoda Heidari", "Jana Schaich Borg", "Walter Sinnott-Armstrong" ], "year": 2026, "venue": "ICLR", "abstract": "Recent AI trends seek to align AI models to learned human-centric objectives, such as personal preferences, utility, or societal values. Using standard preference elicitation methods, researchers and practitioners build models of human decisions and judgments, to which AI models are aligned. However, standard elicitation methods often fail to capture the true cognitive processes behind human decision making, such as the use of heuristics or simplifying structured thought patterns. To address this limitation, we take an axiomatic approach to learning cognitively faithful decision processes from pairwise comparisons. Building on the vast literature characterizing cognitive processes that contribute to human decision-making and pairwise comparisons, we derive a class of models in which individual features are first processed with learned rules, then aggregated via a fixed rule, such as the Bradley-Terry rule, to produce a decision. This structured processing of information ensures that such models are realistic and feasible candidates to represent underlying human decision-making processes. We demonstrate the efficacy of this modeling approach by learning interpretable models of human decision making in a kidney allocation task, and show that our proposed models match or surpass the accuracy of prior models of human pairwise decision-making.", "source": "openreview", "url": "https://openreview.net/forum?id=ziP9zetlLp", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2026/ICLR/Poster/4.5_Towards Cognitively-Faithful Decision-Making Models to Improve AI Alignment_2026.pdf" }, { "title": "Your Language Model Secretly Contains Personality Subnetworks", "authors": [ "Ruimeng Ye", "Zihan Wang", "Zinan Ling", "Yang Xiao", "Manling Li", "Xiaolong Ma", "Bo Hui" ], "year": 2026, "venue": "ICLR", "abstract": "Humans shift between different personas depending on social context. Large Language Models (LLMs) demonstrate a similar flexibility in adopting different personas and behaviors. Existing approaches, however, typically adapt such behavior through external knowledge such as prompting, retrieval-augmented generation (RAG), or fine-tuning.\nWe ask: do LLMs really need external context or parameters to adapt to different behaviors, or do they already have such knowledge embedded in their parameters?\nIn this work, we show that LLMs already contain persona-specialized subnetworks in their parameter space. Using small calibration datasets, we identify distinct activation signatures associated with different personas. Guided by these statistics, we develop a masking strategy that isolates lightweight persona subnetworks. Building on the findings, we further discuss: how can we discover opposing subnetworks from the model that lead to binary-opposing personas, such as introvert-extrovert? \nTo further enhance separation in binary opposition scenarios, we introduce a contrastive pruning strategy that identifies parameters responsible for the statistical divergence between opposing personas. Our method is entirely training-free and relies solely on the language model's existing parameter space. Across diverse evaluation settings, the resulting subnetworks exhibit significantly stronger persona alignment than baselines that require external knowledge while being more efficient. Our findings suggest that diverse human-like behaviors are not merely induced in LLMs, but are already embedded in their parameter space—pointing toward a new perspective on controllable and interpretable personalization in large language models. Our code is available at https://github.com/Ruimeng-Ye/Persona.git.", "source": "openreview", "url": "https://openreview.net/forum?id=zzo3Sy3NSX", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2026/ICLR/Poster/4.5_Your Language Model Secretly Contains Personality Subnetworks_2026.pdf" }, { "title": "pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language Models", "authors": [ "Sajjad Ghiasvand", "Mahnoosh Alizadeh", "Ramtin Pedarsani" ], "year": 2026, "venue": "ICLR", "abstract": "Vision-Language Models (VLMs) like CLIP have demonstrated remarkable generalization in zero- and few-shot settings, but adapting them efficiently to decentralized, heterogeneous data remains a challenge. While prompt tuning has emerged as a popular parameter-efficient approach in personalized federated learning, existing methods often sacrifice generalization in favor of personalization, struggling particularly on unseen classes or domains. In this work, we propose pFedMMA, the first personalized federated learning framework that leverages multi-modal adapters for vision-language tasks. Each adapter contains modality-specific up- and down-projection layers alongside a globally shared projection that aligns cross-modal features. Our optimization strategy allows clients to locally adapt to personalized data distributions while collaboratively training the shared projection to improve global generalization. This design is also communication-efficient, as only the shared component is exchanged during communication rounds. Through extensive experiments across eleven datasets, including domain- and label-shift scenarios, we show that pFedMMA achieves state-of-the-art trade-offs between personalization and generalization, outperforming recent federated prompt tuning methods.", "source": "openreview", "url": "https://openreview.net/forum?id=aX3E6LirK5", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2026/ICLR/Poster/4.5_pFedMMA_ Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language Models_2026.pdf" }, { "title": "Inference-Time Personalized Safety Control via Paired Difference-in-Means Intervention", "authors": [ "Tran Huynh", "Ruoxi Jia" ], "year": 2026, "venue": "ICLR", "abstract": "Safety preferences are inherently subjective, yet current LLM safety alignment methods often impose universal standards that fail to account for individual sensitivities. In this work, we propose an efficient, training-free method for personalized safety control via inference-time activation intervention. Our approach steers internal representations to suppress user-specific undesired content while preserving model utility. We systematically evaluate three strategies for estimating intervention directions: Instance-Level Contrast Shift (ILCS), Unpaired Mean Shift (UMS), and our primary method, Paired Contrast Mean Shift (PCMS). We provide theoretical insights into each approach and highlight the advantages of PCMS. Empirical results across diverse open-weight models demonstrate that our method effectively reduces undesired content in line with individual preferences, with minimal impact on helpfulness—enabling more adaptive and user-aligned LLM behavior.", "source": "openreview", "url": "https://openreview.net/forum?id=VHiHVBNy1M", "decision_type": "Poster", "avg_rating": 4.4, "relative_path": "2026/ICLR/Poster/4.4_Inference-Time Personalized Safety Control via Paired Difference-in-Means Intervention_2026.pdf" }, { "title": "CAR-LoRA: Training Compression-Aware and Robust LoRA Adapters for Evolving LLMs", "authors": [ "Rana Shahroz", "Zhen Tan", "Ruichen Zhang", "Hua Wei", "Tianlong Chen", "Charles Fleming" ], "year": 2026, "venue": "ICLR", "abstract": "The deployment of large language models (LLMs) for specialized tasks on resource-constrained edge devices like smartphones and sensors presents a significant scalability problem. To run on such hardware, these massive models must be compressed using techniques like \\emph{quantization or pruning} to reduce their memory and computational footprint. Concurrently, foundational LLMs are periodically updated by their developers with new data, making their $\\textit{internal parameters shift over time}$. While parameter-efficient methods like Low-Rank Adaptation (LoRA) streamline personalization by fine-tuning only a small fraction of parameters, the resulting adapters are $\\textbf{brittle}$; a LoRA trained for one specific compression scheme is incompatible with another, and an adapter trained on an older base model performs poorly on an updated one. This forces a costly cycle of retraining for each unique device and every new model release. To address this, we introduce a novel framework that creates a single, universally portable adapter that is both $\\textbf{\\textit{(i)} compression-aware and \\textit{(ii)} temporally robust}$. We achieve this by augmenting the training process with a variety of simulated compression techniques during a single run, utilizing a quantized forward pass to build resilience while maintaining a full-precision backward pass for stable gradient optimization. $\\textit{This method yields a unified adapter robust to diverse compression artifacts and the subtle parameter shifts from model evolution}$. Extensive experiments on models such as $\\texttt{Llama-2, Llama-3.1, Gemma-2}$, and $\\texttt{Mistral}$ across reasoning benchmarks like $\\textit{SQA, MATH, and GSM8K}$ demonstrate that our single adapter achieves performance comparable to specialized adapters ($\\textit{e.g.}$, QLoRA) that are individually retrained for each compression scheme. Furthermore, we show this single adapter maintains its high performance when applied to future, evolved versions of the base model, eliminating the need for periodic retraining. Our work pioneers an efficient paradigm for edge AI, creating portable model patches that bridge the gap between cloud-based personalization, the diverse hardware ecosystem, and the lifecycle of evolving LLMs.", "source": "openreview", "url": "https://openreview.net/forum?id=5GimteSrgW", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2026/ICLR/Poster/4.0_CAR-LoRA_ Training Compression-Aware and Robust LoRA Adapters for Evolving LLMs_2026.pdf" }, { "title": "FeDaL: Federated Dataset Learning for General Time Series Foundation Models", "authors": [ "Shengchao Chen", "Guodong Long", "Michael Blumenstein", "Jing Jiang" ], "year": 2026, "venue": "ICLR", "abstract": "Dataset-level heterogeneity introduces significant domain biases that fundamentally degrade generalization on general Time Series Foundation Models (TSFMs), yet this challenge remains underexplored. This paper rethinks the from-scratch training of TSFMs using the paradigm of federated learning. We propose a novel Federated Dataset Learning (FeDaL) approach to tackle heterogeneous time series by learning dataset-agnostic temporal representations. Specifically, the distributed architecture of federated learning is a nature solution to decompose heterogeneous TS datasets into shared generalized knowledge and preserved personalized knowledge. Moreover, based on the TSFM architecture, FeDaL explicitly mitigates both local and global biases by adding two complementary mechanisms: Domain Bias Elimination (DBE) and Global Bias Elimination (GBE). FeDaL`s cross-dataset generalization has been extensively evaluated in real-world datasets spanning eight tasks (including various regression and classification), against 54 baselines. We further analyze federated scaling behavior, showing how data volume, client count, and join rate affect model performance under decentralization.", "source": "openreview", "url": "https://openreview.net/forum?id=HK6t5x5gJq", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2026/ICLR/Poster/4.0_FeDaL_ Federated Dataset Learning for General Time Series Foundation Models_2026.pdf" }, { "title": "Optimal Robust Subsidy Policies for Irrational Agent in Principal-Agent MDPs", "authors": [ "Bowen Hu", "Yixin Tao" ], "year": 2026, "venue": "ICLR", "abstract": "We investigate a principal-agent problem modeled within a Markov Decision Process, where the principal and the agent have their own rewards. The principal can provide subsidies to influence the agent’s action choices, and the agent’s resulting action policy determines the rewards accrued to the principal. Our focus is on designing a robust subsidy scheme that maximizes the principal’s cumulative expected return, even when the agent displays bounded rationality and may deviate from the optimal action policy after receiving subsidies.\n\nAs a baseline, we first analyze the case of a perfectly rational agent and show that the principal’s optimal subsidy coincides with the policy that maximizes social welfare, the sum of the utilities of both the principal and the agent. We then introduce a bounded-rationality model: the globally $\\epsilon$-incentive-compatible agent, who accepts any policy whose expected cumulative utility lies within $\\epsilon$ of the personal optimum. In this setting, we prove that the optimal robust subsidy scheme problem simplifies to a one-dimensional concave optimization. This reduction not only yields a clean analytical solution but also highlights a key structural insight: optimal subsidies are concentrated along the social-welfare-maximizing trajectories. We further characterize the loss in social welfare—the degradation under the robust subsidy scheme compared to the maximum achievable—and provide an upper bound on this loss. Finally, we investigate a finer-grained, state-wise $\\epsilon$-incentive-compatible model. In this setting, we show that under two natural definitions of state-wise incentive-compatibility, the problem becomes intractable: one definition results in non-Markovian agent action policy, while the other renders the search for an optimal subsidy scheme NP-hard.", "source": "openreview", "url": "https://openreview.net/forum?id=ZO6Iwd3BZ7", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2026/ICLR/Poster/4.0_Optimal Robust Subsidy Policies for Irrational Agent in Principal-Agent MDPs_2026.pdf" }, { "title": "Towards Personalized Deep Research: Benchmarks and Evaluations", "authors": [ "Yuan Liang", "Jiaxian Li", "Yuqing Wang", "WANG PIAOHONG", "Motong Tian", "Pai Liu", "Shuofei Qiao", "Runnan Fang", "He Zhu", "Ge Zhang", "Minghao Liu", "Yuchen Eleanor Jiang", "Ningyu Zhang", "Wangchunshu Zhou" ], "year": 2026, "venue": "ICLR", "abstract": "Deep Research Agents (DRAs) can autonomously conduct complex investigations and generate comprehensive reports, demonstrating strong real-world potential. However, existing evaluations mostly rely on close-ended benchmarks, while open-ended deep research benchmarks remain scarce and typically neglect personalized scenarios. To bridge this gap, we introduce Personalized Deep Research Bench, the first benchmark for evaluating personalization in DRAs. It pairs 50 diverse research tasks across 10 domains with 25 authentic user profiles that combine structured persona attributes with dynamic real-world contexts, yielding 250 realistic user-task queries. To assess system performance, we propose the PQR Evaluation Framework, which jointly measures (P) Personalization Alignment, (Q) Content Quality, and (R) Factual Reliability. Our experiments on a range of systems highlight current capabilities and limitations in handling personalized deep research. This work establishes a rigorous foundation for developing and evaluating the next generation of truly personalized AI research assistants.", "source": "openreview", "url": "https://openreview.net/forum?id=51LIRzF53v", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2026/ICLR/Poster/4.0_Towards Personalized Deep Research_ Benchmarks and Evaluations_2026.pdf" }, { "title": "Verification and Co-Alignment via Heterogeneous Consistency for Preference-Aligned LLM Annotations", "authors": [ "Cheng Chen", "Haiyan Yin", "Ivor Tsang" ], "year": 2026, "venue": "ICLR", "abstract": "Large Language Models (LLMs) are increasingly expected to be culturally customizable and personally aligned for natural language understanding (NLU). However, existing methods, from supervised fine-tuning (SFT) to personalized RLHF and prompting, either require costly large-scale annotations or remain constrained by pretraining distributions. Moreover, acquiring annotations that reflect subjective, diverse, and evolving user preferences is both expensive and labor-intensive. To address these limitations, we propose \\textit{\\textbf{H}eterogeneous-\\textbf{C}onsistency \\textbf{C}o-Alignment} (HCC) is a training-free annotation paradigm that leverages two heterogeneous models, which consists of an LLM, rich in knowledge yet often prone to overconfidence, is paired with a task-specialised lightweight model guided by a small user-preference set to verify and co-align misaligned outputs over unlabeled corpora. For verification, HCC introduces the reference-free \\textit{\\textbf{C}onsistent}-\\textit{\\textbf{A}nd}-\\textit{\\textbf{I}nconsistent} (\\textbf{CAI}) Ratio, an uncertainty signal derived from inter-model agreements (consistent samples) and disagreements (inconsistent samples) to determine when refinement is needed. For co-alignment, HCC employs a non-parametric, embedding-based preference assignment scheme to recalibrate inconsistent samples according to user preferences. Across eight NLU datasets and both open- and closed-source LLMs, HCC consistently improves annotation quality and, in several tasks, even enables \\textit{Llama-3-8B} to surpass \\textit{GPT-3.5/4o} after co-alignment. Moreover, CAI correlates strongly with accuracy and reliably tracks pre-/post-alignment gains, offering a reference-free signal for scaling preference-aligned annotation.", "source": "openreview", "url": "https://openreview.net/forum?id=jugY302BAh", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2026/ICLR/Poster/4.0_Verification and Co-Alignment via Heterogeneous Consistency for Preference-Aligned LLM Annotati_2026.pdf" }, { "title": "World2Minecraft: Occupancy-Driven Simulated Scenes Construction", "authors": [ "Lechao Zhang", "Haoran Xu", "Jingyu Gong", "Xuhong Wang", "Yuan Xie", "Xin Tan" ], "year": 2026, "venue": "ICLR", "abstract": "Embodied intelligence requires high-fidelity simulation environments to support perception and decision-making, yet existing platforms often suffer from data contamination and limited flexibility. To mitigate this, we propose World2Minecraft to convert real-world scenes into structured Minecraft environments based on 3D semantic occupancy prediction. In the reconstructed scenes, we can effortlessly perform downstream tasks such as Vision-Language Navigation(VLN). However, we observe that reconstruction quality heavily depends on accurate occupancy prediction, which remains limited by data scarcity and poor generalization in existing models. We introduce a low-cost, automated, and scalable data acquisition pipeline for creating customized occupancy datasets, and demonstrate its effectiveness through MinecraftOcc, a large-scale dataset featuring 100,165 images from 156 richly detailed indoor scenes. Extensive experiments show that our dataset provides a critical complement to existing datasets and poses a significant challenge to current SOTA methods. These findings contribute to improving occupancy prediction and highlight the value of World2Minecraft in providing a customizable and editable platform for personalized embodied AI research. We will publicly release the dataset and the complete generation framework to ensure reproducibility and encourage future work.", "source": "openreview", "url": "https://openreview.net/forum?id=dc90uPqxWF", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2026/ICLR/Poster/4.0_World2Minecraft_ Occupancy-Driven Simulated Scenes Construction_2026.pdf" }, { "title": "Tab-MIA: A Benchmark Dataset for Membership Inference Attacks on Tabular Data in LLMs", "authors": [ "Eyal German", "Sagiv Antebi", "Daniel Samira", "Asaf Shabtai", "Yuval Elovici" ], "year": 2026, "venue": "ICLR", "abstract": "Large language models (LLMs) are increasingly trained on tabular data, which, unlike unstructured text, often contains personally identifiable information (PII) in a highly structured and explicit format. As a result, privacy risks arise, since sensitive records can be inadvertently retained by the model and exposed through data extraction or membership inference attacks (MIAs). While existing MIA methods primarily target textual content, their efficacy and threat implications may differ when applied to structured data, due to its limited content, diverse data types, unique value distributions, and column-level semantics. In this paper, we present Tab-MIA, a benchmark dataset for evaluating MIAs on tabular data in LLMs and demonstrate how it can be used. Tab-MIA comprises five data collections, each represented in six different encoding formats. Using our Tab-MIA benchmark, we conduct the first evaluation of state-of-the-art MIA methods on LLMs fine-tuned with tabular data across multiple encoding formats. In the evaluation, we analyze the memorization behavior of pretrained LLMs on structured data derived from Wikipedia tables. Our findings show that LLMs memorize tabular data in ways that vary across encoding formats, making them susceptible to extraction via MIAs. Even when fine-tuned for as few as three epochs, models exhibit high vulnerability, with AUROC scores approaching 90% in most cases. Tab-MIA enables systematic evaluation of these risks and provides a foundation for developing privacy-preserving methods for tabular data in LLMs.", "source": "openreview", "url": "https://openreview.net/forum?id=ioYdy7aghG", "decision_type": "Poster", "avg_rating": 3.0, "relative_path": "2026/ICLR/Poster/3.0_Tab-MIA_ A Benchmark Dataset for Membership Inference Attacks on Tabular Data in LLMs_2026.pdf" }, { "title": "MAP: Multi-Human-Value Alignment Palette", "authors": [ "Xinran Wang", "Qi Le", "Ammar Ahmed", "Enmao Diao", "Yi Zhou", "Nathalie Baracaldo", "Jie Ding", "Ali Anwar" ], "year": 2025, "venue": "ICLR", "abstract": "Ensuring that generative AI systems align with human values is essential but challenging, especially when considering multiple human values and their potential trade-offs. Since human values can be personalized and dynamically change over time, the desirable levels of value alignment vary across different ethnic groups, industry sectors, and user cohorts. Within existing frameworks, it is hard to define human values and align AI systems accordingly across different directions simultaneously, such as harmlessness, helpfulness, and positiveness. To address this, we develop a novel, first-principle approach called Multi-Human-Value Alignment Palette (MAP), which navigates the alignment across multiple human values in a structured and reliable way. MAP formulates the alignment problem as an optimization task with user-defined constraints, which define human value targets. It can be efficiently solved via a primal-dual approach, which determines whether a user-defined alignment target is achievable and how to achieve it. We conduct a detailed theoretical analysis of MAP by quantifying the trade-offs between values, the sensitivity to constraints, the fundamental connection between multi-value alignment and sequential alignment, and proving that linear weighted rewards are sufficient for multi-value alignment. Extensive experiments demonstrate MAP's ability to align multiple values in a principled manner while delivering strong empirical performance across various tasks.", "source": "openreview", "url": "https://openreview.net/forum?id=NN6QHwgRrQ", "decision_type": "Oral", "avg_rating": 8.0, "relative_path": "2025/ICLR/Oral/8.0_MAP_ Multi-Human-Value Alignment Palette_2025.pdf" }, { "title": "RB-Modulation: Training-Free Stylization using Reference-Based Modulation", "authors": [ "Litu Rout", "Yujia Chen", "Nataniel Ruiz", "Abhishek Kumar", "Constantine Caramanis", "Sanjay Shakkottai", "Wen-Sheng Chu" ], "year": 2025, "venue": "ICLR", "abstract": "We propose Reference-Based Modulation (RB-Modulation), a new plug-and-play solution for training-free personalization of diffusion models.\nExisting training-free approaches exhibit difficulties in (a) style extraction from reference images in the absence of additional style or content text descriptions, (b) unwanted content leakage from reference style images, and (c) effective composition of style and content. \nRB-Modulation is built on a novel stochastic optimal controller where a style descriptor encodes the desired attributes through a terminal cost. \nThe resulting drift not only overcomes the difficulties above, but also ensures high fidelity to the reference style and adheres to the given text prompt. \nWe also introduce a cross-attention-based feature aggregation scheme that allows RB-Modulation to decouple content and style from the reference image.\nWith theoretical justification and empirical evidence, our test-time optimization framework demonstrates precise extraction and control of *content* and *style* in a training-free manner. \nFurther, our method allows a seamless composition of content and style, which marks a departure from the dependency on external adapters or ControlNets. See project page: https://rb-modulation.github.io/ for code and further details.", "source": "openreview", "url": "https://openreview.net/forum?id=bnINPG5A32", "decision_type": "Oral", "avg_rating": 8.0, "relative_path": "2025/ICLR/Oral/8.0_RB-Modulation_ Training-Free Stylization using Reference-Based Modulation_2025.pdf" }, { "title": "Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs", "authors": [ "Siyan Zhao", "Mingyi Hong", "Yang Liu", "Devamanyu Hazarika", "Kaixiang Lin" ], "year": 2025, "venue": "ICLR", "abstract": "Large Language Models (LLMs) are increasingly deployed as chatbots, yet their ability to personalize responses to user preferences remains limited. We introduce PrefEval, a benchmark for evaluating LLMs' ability to infer, memorize and adhere to user preferences in long-context conversational setting.\nPrefEval comprises 3,000 manually curated user preference and query pairs spanning 20 topics. PrefEval contains user personalization or preference information in both explicit and implicit preference forms, and evaluates LLM performance using a generation and a classification task. With PrefEval, we have evaluated 10 open-sourced and\nproprietary LLMs in multi-session conversations with varying context lengths up to 100k tokens. We benchmark with various prompting, iterative feedback, and retrieval-augmented generation methods. \nOur benchmarking effort reveals that state-of-the-art LLMs face significant challenges in following users' preference during conversations. In particular, in zero-shot settings, preference following accuracy falls below 10\\% at merely 10 turns (~3k tokens) across most evaluated models. Even with advanced prompting and retrieval methods, preference following still deteriorates in long-context conversations. Furthermore, we show that fine-tuning on PrefEval significantly improves performance. We believe PrefEval serves as a valuable resource for measuring, understanding, and enhancing LLMs' proactive preference following abilities, paving the way for personalized conversational agents.", "source": "openreview", "url": "https://openreview.net/forum?id=QWunLKbBGF", "decision_type": "Oral", "avg_rating": 7.5, "relative_path": "2025/ICLR/Oral/7.5_Do LLMs Recognize Your Preferences_ Evaluating Personalized Preference Following in LLMs_2025.pdf" }, { "title": "Model-agnostic meta-learners for estimating heterogeneous treatment effects over time", "authors": [ "Dennis Frauen", "Konstantin Hess", "Stefan Feuerriegel" ], "year": 2025, "venue": "ICLR", "abstract": "Estimating heterogeneous treatment effects (HTEs) over time is crucial in many disciplines such as personalized medicine. Existing works for this task have mostly focused on *model-based* learners that adapt specific machine-learning models and adjustment mechanisms. In contrast, model-agnostic learners - so-called *meta-learners* - are largely unexplored. In our paper, we propose several meta-learners that are model-agnostic and thus can be used in combination with arbitrary machine learning models (e.g., transformers) to estimate HTEs over time. We then provide a comprehensive theoretical analysis that characterizes the different learners and that allows us to offer insights into when specific learners are preferable. Furthermore, we propose a novel IVW-DR-learner that (i) uses a doubly robust (DR) and orthogonal loss; and (ii) leverages inverse-variance weights (IVWs) that we derive to stabilize the DR-loss over time. Our IVWs downweight extreme trajectories due to *products* of inverse-propensities in the DR-loss, resulting in a lower estimation variance. Our IVW-DR-learner achieves superior performance in our experiments, particularly in regimes with low overlap and long time horizons.", "source": "openreview", "url": "https://openreview.net/forum?id=QGGNvKaoIU", "decision_type": "Poster", "avg_rating": 7.0, "relative_path": "2025/ICLR/Poster/7.0_Model-agnostic meta-learners for estimating heterogeneous treatment effects over time_2025.pdf" }, { "title": "Proactive Privacy Amnesia for Large Language Models: Safeguarding PII with Negligible Impact on Model Utility", "authors": [ "Martin Kuo", "Jingyang Zhang", "Jianyi Zhang", "Minxue Tang", "Louis DiValentin", "Aolin Ding", "Jingwei Sun", "William Chen", "Amin Hass", "Tianlong Chen", "Yiran Chen", "Hai Li" ], "year": 2025, "venue": "ICLR", "abstract": "With the rise of large language models (LLMs), increasing research has recognized\ntheir risk of leaking personally identifiable information (PII) under malicious\nattacks. Although efforts have been made to protect PII in LLMs, existing methods\nstruggle to balance privacy protection with maintaining model utility. In this paper,\ninspired by studies of amnesia in cognitive science, we propose a novel approach,\nProactive Privacy Amnesia (PPA), to safeguard PII in LLMs while preserving their\nutility. This mechanism works by actively identifying and forgetting key memories\nmost closely associated with PII in sequences, followed by a memory implanting\nusing suitable substitute memories to maintain the LLM’s functionality. We conduct\nevaluations across multiple models to protect common PII, such as phone numbers\nand physical addresses, against prevalent PII-targeted attacks, demonstrating the\nsuperiority of our method compared with other existing defensive techniques. The\nresults show that our PPA method completely eliminates the risk of phone number\nexposure by 100% and significantly reduces the risk of physical address exposure\nby 9.8% – 87.6%, all while maintaining comparable model utility performance.", "source": "openreview", "url": "https://openreview.net/forum?id=io8uRPYktn", "decision_type": "Poster", "avg_rating": 7.0, "relative_path": "2025/ICLR/Poster/7.0_Proactive Privacy Amnesia for Large Language Models_ Safeguarding PII with Negligible Impact on_2025.pdf" }, { "title": "Revealing and Reducing Gender Biases in Vision and Language Assistants (VLAs)", "authors": [ "Leander Girrbach", "Stephan Alaniz", "Yiran Huang", "Trevor Darrell", "Zeynep Akata" ], "year": 2025, "venue": "ICLR", "abstract": "Pre-trained large language models (LLMs) have been reliably integrated with visual input for multimodal tasks. The widespread adoption of instruction-tuned image-to-text vision-language assistants (VLAs) like LLaVA and InternVL necessitates evaluating gender biases. We study gender bias in 22 popular open-source VLAs with respect to personality traits, skills, and occupations. Our results show that VLAs replicate human biases likely present in the data, such as real-world occupational imbalances. Similarly, they tend to attribute more skills and positive personality traits to women than to men, and we see a consistent tendency to associate negative personality traits with men. To eliminate the gender bias in these models, we find that fine-tuning-based debiasing methods achieve the best trade-off between debiasing and retaining performance on downstream tasks. We argue for pre-deploying gender bias assessment in VLAs and motivate further development of debiasing strategies to ensure equitable societal outcomes. Code is available at https://github.com/ExplainableML/vla-gender-bias.", "source": "openreview", "url": "https://openreview.net/forum?id=oStNAMWELS", "decision_type": "Poster", "avg_rating": 7.0, "relative_path": "2025/ICLR/Poster/7.0_Revealing and Reducing Gender Biases in Vision and Language Assistants (VLAs)_2025.pdf" }, { "title": "GANDALF: Generative AttentioN based Data Augmentation and predictive modeLing Framework for personalized cancer treatment", "authors": [ "Aishwarya Jayagopal", "Yanrong Zhang", "Robert John Walsh", "Tuan Zea Tan", "Anand D Jeyasekharan", "Vaibhav Rajan" ], "year": 2025, "venue": "ICLR", "abstract": "Effective treatment of cancer is a major challenge faced by healthcare providers, due to the highly individualized nature of patient responses to treatment. This is caused by the heterogeneity seen in cancer-causing alterations (mutations) across patient genomes. Limited availability of response data in patients makes it difficult to train personalized treatment recommendation models on mutations from clinical genomic sequencing reports. Prior methods tackle this by utilising larger, labelled pre-clinical laboratory datasets (‘cell lines’), via transfer learning. These methods augment patient data by learning a shared, domain-invariant representation, between the cell line and patient domains, which is then used to train a downstream drug response prediction (DRP) model. This approach augments data in the shared space but fails to model patient-specific characteristics, which have a strong influence on their drug response. We propose a novel generative attention-based data augmentation and predictive modeling framework, GANDALF, to tackle this crucial shortcoming of prior methods. GANDALF not only augments patient genomic data directly, but also accounts for its domain-specific characteristics. GANDALF outperforms state-of-the-art DRP models on publicly available patient datasets and emerges as the front-runner amongst SOTA cancer DRP models.", "source": "openreview", "url": "https://openreview.net/forum?id=WwmtcGr4lP", "decision_type": "Poster", "avg_rating": 6.8, "relative_path": "2025/ICLR/Poster/6.8_GANDALF_ Generative AttentioN based Data Augmentation and predictive modeLing Framework for per_2025.pdf" }, { "title": "Measuring And Improving Engagement of Text-to-Image Generation Models", "authors": [ "Varun Khurana", "Yaman Kumar Singla", "Jayakumar Subramanian", "Changyou Chen", "Rajiv Ratn Shah", "zhiqiang xu", "Balaji Krishnamurthy" ], "year": 2025, "venue": "ICLR", "abstract": "Recent advances in text-to-image generation have achieved impressive aesthetic quality, making these models usable for both personal and commercial purposes. However, in the fields of marketing and advertising, images are often created to be more engaging, as reflected in user behaviors such as increasing clicks, likes, and purchases, in addition to being aesthetically pleasing. To this end, we introduce the challenge of optimizing the image generation process for improved viewer engagement. In order to study image engagement and utility in real-world marketing scenarios, we collect *EngagingImageNet*, the first large-scale dataset of images, along with associated user engagement metrics. Further, we find that existing image evaluation metrics like aesthetics, CLIPScore, PickScore, ImageReward, *etc.* are unable to capture viewer engagement. To address the lack of reliable metrics for assessing image utility, we use the *EngagingImageNet* dataset to train *EngageNet*, an engagement-aware Vision Language Model (VLM) that predicts viewer engagement of images by leveraging contextual information about the tweet content, enterprise details, and posting time. We then explore methods to enhance the engagement of text-to-image models, making initial strides in this direction. These include conditioning image generation on improved prompts, supervised fine-tuning of stable diffusion on high-performing images, and reinforcement learning to align stable diffusion with *EngageNet*-based reward signals, all of which lead to the generation of images with higher viewer engagement. Finally, we propose the *Engagement Arena*, to benchmark text-to-image models based on their ability to generate engaging images, using *EngageNet* as the evaluator, thereby encouraging the research community to measure further advances in the engagement of text-to-image modeling. These contributions provide a new pathway for advancing utility-driven image generation, with significant implications for the commercial application of image generation. We have released our code and dataset on [behavior-in-the-wild.github.io/image-engagement](https://behavior-in-the-wild.github.io/image-engagement).", "source": "openreview", "url": "https://openreview.net/forum?id=TmCcNuo03f", "decision_type": "Poster", "avg_rating": 6.8, "relative_path": "2025/ICLR/Poster/6.8_Measuring And Improving Engagement of Text-to-Image Generation Models_2025.pdf" }, { "title": "Context Steering: Controllable Personalization at Inference Time", "authors": [ "Jerry Zhi-Yang He", "Sashrika Pandey", "Mariah L Schrum", "Anca Dragan" ], "year": 2025, "venue": "ICLR", "abstract": "To deliver high-quality, personalized responses, large language models (LLMs) must effectively incorporate context — personal, demographic, and cultural information specific to an end-user. For example, asking the model to explain Newton's second law with the context \"I am a toddler'' should produce a response different from when the context is \"I am a physics professor''. However, leveraging the context in practice is a nuanced and challenging task, and is often dependent on the specific situation or user base. The model must strike a balance between providing specific, personalized responses and maintaining general applicability. Current solutions, such as prompt-engineering and fine-tuning, require collection of contextually appropriate responses as examples, making them time-consuming and less flexible to use across different contexts. In this work, we introduce Context Steering (CoS) —a simple, training-free decoding approach that amplifies the influence of the context in next token predictions. CoS computes contextual influence by comparing the output probabilities from two LLM forward passes: one that includes the context and one that does not. By linearly scaling the contextual influence, CoS allows practitioners to flexibly control the degree of personalization for different use cases. We show that CoS can be applied to autoregressive LLMs, and demonstrates strong performance in personalized recommendations. Additionally, we show that CoS can function as a Bayesian Generative model to infer and quantify correlations between open-ended texts, broadening its potential applications.", "source": "openreview", "url": "https://openreview.net/forum?id=xQCXInDq0m", "decision_type": "Poster", "avg_rating": 6.7, "relative_path": "2025/ICLR/Poster/6.7_Context Steering_ Controllable Personalization at Inference Time_2025.pdf" }, { "title": "Domain Guidance: A Simple Transfer Approach for a Pre-trained Diffusion Model", "authors": [ "Jincheng Zhong", "XiangCheng Zhang", "Jianmin Wang", "Mingsheng Long" ], "year": 2025, "venue": "ICLR", "abstract": "Recent advancements in diffusion models have revolutionized generative modeling. However, the impressive and vivid outputs they produce often come at the cost of significant model scaling and increased computational demands. Consequently, building personalized diffusion models based on off-the-shelf models has emerged as an appealing alternative. In this paper, we introduce a novel perspective on conditional generation for transferring a pre-trained model. From this viewpoint, we propose *Domain Guidance*, a straightforward transfer approach that leverages pre-trained knowledge to guide the sampling process toward the target domain. Domain Guidance shares a formulation similar to advanced classifier-free guidance, facilitating better domain alignment and higher-quality generations. We provide both empirical and theoretical analyses of the mechanisms behind Domain Guidance. Our experimental results demonstrate its substantial effectiveness across various transfer benchmarks, achieving over a 19.6\\% improvement in FID and a 23.4\\% improvement in FD$_\\text{DINOv2}$ compared to standard fine-tuning. Notably, existing fine-tuned models can seamlessly integrate Domain Guidance to leverage these benefits, without additional training. Code is available at this repository: https://github.com/thuml/DomainGuidance.", "source": "openreview", "url": "https://openreview.net/forum?id=PplM2kDrl3", "decision_type": "Poster", "avg_rating": 6.7, "relative_path": "2025/ICLR/Poster/6.7_Domain Guidance_ A Simple Transfer Approach for a Pre-trained Diffusion Model_2025.pdf" }, { "title": "InstantPortrait: One-Step Portrait Editing via Diffusion Multi-Objective Distillation", "authors": [ "Zhixin Lai", "Keqiang Sun", "Fu-Yun Wang", "Dhritiman Sagar", "Erli Ding" ], "year": 2025, "venue": "ICLR", "abstract": "Real-time instruction-based portrait image editing is crucial in various applications, including filters, augmented reality, and video communications, etc. However, real-time portrait editing presents three significant challenges: identity preservation, fidelity to editing instructions, and fast model inference. Given that these aspects often present a trade-off, concurrently addressing them poses an even greater challenge. While diffusion-based image editing methods have shown promising capabilities in personalized image editing in recent years, they lack a dedicated focus on portrait editing and thus suffer from the aforementioned problems as well. To address the gap, this paper introduces an Instant-Portrait Network (IPNet), the first one-step diffusion-based model for portrait editing. We train the network in two stages. We first employ an annealing identity loss to train an Identity Enhancement Network (IDE-Net), to ensure robust identity preservation. We then train the IPNet using a novel diffusion Multi-Objective Distillation approach that integrates adversarial loss, identity distillation loss, and a novel Facial-Style Enhancing loss. The Diffusion Multi-Objective Distillation approach efficiently reduces inference steps, ensures identity consistency, and enhances the precision of instruction-based editing. Extensive comparison with prior models demonstrates IPNet as a superior model in terms of identity preservation, text fidelity, and inference speed.", "source": "openreview", "url": "https://openreview.net/forum?id=ZkFMe3OPfw", "decision_type": "Poster", "avg_rating": 6.7, "relative_path": "2025/ICLR/Poster/6.7_InstantPortrait_ One-Step Portrait Editing via Diffusion Multi-Objective Distillation_2025.pdf" }, { "title": "Neuron based Personality Trait Induction in Large Language Models", "authors": [ "Jia Deng", "Tianyi Tang", "Yanbin Yin", "Wenhao yang", "Xin Zhao", "Ji-Rong Wen" ], "year": 2025, "venue": "ICLR", "abstract": "Large language models (LLMs) have become increasingly proficient at simulating various personality traits, an important capability for supporting related applications (e.g., role-playing). To further improve this capacity, in this paper, we present a neuron based approach for personality trait induction in LLMs, with three major technical contributions. First, we construct PERSONALITYBENCH, a large-scale dataset for identifying and evaluating personality traits in LLMs. This dataset is grounded in the Big Five personality traits from psychology and designed to assess the generative capabilities of LLMs towards specific personality traits. Second, by leveraging PERSONALITYBENCH, we propose an efficient method for identifying personality-related neurons within LLMs by examining the opposite aspects of a given trait. Third, we develop a simple yet effective induction method that manipulates the values of these identified personality-related neurons, which enables fine-grained control over the traits exhibited by LLMs without training and modifying model parameters. Extensive experiments validates the efficacy of our neuron identification and trait induction methods. Notably, our approach achieves comparable performance as fine-tuned models, offering a more efficient and flexible solution for personality trait induction in LLMs.", "source": "openreview", "url": "https://openreview.net/forum?id=LYHEY783Np", "decision_type": "Poster", "avg_rating": 6.7, "relative_path": "2025/ICLR/Poster/6.7_Neuron based Personality Trait Induction in Large Language Models_2025.pdf" }, { "title": "On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations", "authors": [ "GUOJUN XIONG", "Shufan Wang", "Daniel Jiang", "Jian Li" ], "year": 2025, "venue": "ICLR", "abstract": "Federated reinforcement learning (FedRL) enables multiple agents to collaboratively learn a policy without needing to share the local trajectories collected during agent-environment interactions. However, in practice, the environments faced by different agents are often heterogeneous, but since existing FedRL algorithms learn a single policy across all agents, this may lead to poor performance. In this paper, we introduce a personalized FedRL framework (PFedRL) by taking advantage of possibly shared common structure among agents in heterogeneous environments. Specifically, we develop a class of PFedRL algorithms named PFedRL-Rep that learns (1) a shared feature representation collaboratively among all agents, and (2) an agent-specific weight vector personalized to its local environment. We analyze the convergence of PFedTD-Rep, a particular instance of the framework with temporal difference (TD) learning and linear representations. To the best of our knowledge, we are the first to prove a linear convergence speedup with respect to the number of agents in the PFedRL setting. To achieve this, we show that PFedTD-Rep is an example of federated two-timescale stochastic approximation with Markovian noise. Experimental results demonstrate that PFedTD-Rep, along with an extension to the control setting based on deep Q-networks (DQN), not only improve learning in heterogeneous settings, but also provide better generalization to new environments.", "source": "openreview", "url": "https://openreview.net/forum?id=BfUDZGqCAu", "decision_type": "Poster", "avg_rating": 6.7, "relative_path": "2025/ICLR/Poster/6.7_On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representati_2025.pdf" }, { "title": "PAL: Sample-Efficient Personalized Reward Modeling for Pluralistic Alignment", "authors": [ "Daiwei Chen", "Yi Chen", "Aniket Rege", "Zhi Wang", "Ramya Korlakai Vinayak" ], "year": 2025, "venue": "ICLR", "abstract": "Foundation models trained on internet-scale data benefit from extensive alignment to human preferences before deployment. However, existing methods typically assume a homogeneous preference shared by all individuals, overlooking the diversity inherent in human values. In this work, we propose a general reward modeling framework for pluralistic alignment (PAL), which incorporates diverse preferences from the ground up. PAL has a modular design that leverages commonalities across users while catering to individual personalization, enabling efficient few-shot localization of preferences for new users. Extensive empirical evaluation demonstrates that PAL matches or outperforms state-of-the-art methods on both text-to-text and text-to-image tasks: on Reddit TL;DR Summary, PAL is 1.7% more accurate for seen users and 36% more accurate for unseen users compared to the previous best method, with 100× less parameters. On Pick-a-Pic v2, PAL is 2.5% more accurate than the best method with 156× fewer learned parameters. Finally, we provide theoretical analysis for generalization of rewards learned via PAL framework showcasing the reduction in number of samples needed per user.", "source": "openreview", "url": "https://openreview.net/forum?id=1kFDrYCuSu", "decision_type": "Poster", "avg_rating": 6.7, "relative_path": "2025/ICLR/Poster/6.7_PAL_ Sample-Efficient Personalized Reward Modeling for Pluralistic Alignment_2025.pdf" }, { "title": "PvNeXt: Rethinking Network Design and Temporal Motion for Point Cloud Video Recognition", "authors": [ "Jie Wang", "Tingfa Xu", "Lihe Ding", "Xinjie Zhang", "Long Bai", "Jianan Li" ], "year": 2025, "venue": "ICLR", "abstract": "Point cloud video perception has become an essential task for the realm of 3D vision. Current 4D representation learning techniques typically engage in iterative processing coupled with dense query operations. Although effective in capturing temporal features, this approach leads to substantial computational redundancy. In this work, we propose a framework, named as PvNeXt, for effective yet efficient point cloud video recognition, via personalized one-shot query operation. Specially, PvNeXt consists of two key modules, the Motion Imitator and the Single-Step Motion Encoder. The former module, the Motion Imitator, is designed to capture the temporal dynamics inherent in sequences of point clouds, thus generating the virtual motion corresponding to each frame. The Single-Step Motion Encoder performs a one-step query operation, associating point cloud of each frame with its corresponding virtual motion frame, thereby extracting motion cues from point cloud sequences and capturing temporal dynamics across the entire sequence. Through the integration of these two modules, {PvNeXt} enables personalized one-shot queries for each frame, effectively eliminating the need for frame-specific looping and intensive query processes. Extensive experiments on multiple benchmarks demonstrate the effectiveness of our method.", "source": "openreview", "url": "https://openreview.net/forum?id=ZsU52Zkzjr", "decision_type": "Poster", "avg_rating": 6.7, "relative_path": "2025/ICLR/Poster/6.7_PvNeXt_ Rethinking Network Design and Temporal Motion for Point Cloud Video Recognition_2025.pdf" }, { "title": "TweedieMix: Improving Multi-Concept Fusion for Diffusion-based Image/Video Generation", "authors": [ "Gihyun Kwon", "Jong Chul Ye" ], "year": 2025, "venue": "ICLR", "abstract": "Despite significant advancements in customizing text-to-image and video generation models, generating images and videos that effectively integrate multiple personalized concepts remains challenging. To address this, we present TweedieMix, a novel method for composing customized diffusion models during the inference phase. By analyzing the properties of reverse diffusion sampling, our approach divides the sampling process into two stages. During the initial steps, we apply a multiple object-aware sampling technique to ensure the inclusion of the desired target objects. In the later steps, we blend the appearances of the custom concepts in the de-noised image space using Tweedie's formula. Our results demonstrate that TweedieMix can generate multiple personalized concepts with higher fidelity than existing methods. Moreover, our framework can be effortlessly extended to image-to-video diffusion models by extending the residual layer's features across frames}, enabling the generation of videos that feature multiple personalized concepts.", "source": "openreview", "url": "https://openreview.net/forum?id=ee2c4MEx9l", "decision_type": "Poster", "avg_rating": 6.7, "relative_path": "2025/ICLR/Poster/6.7_TweedieMix_ Improving Multi-Concept Fusion for Diffusion-based Image_Video Generation_2025.pdf" }, { "title": "EIA: ENVIRONMENTAL INJECTION ATTACK ON GENERALIST WEB AGENTS FOR PRIVACY LEAKAGE", "authors": [ "Zeyi Liao", "Lingbo Mo", "Chejian Xu", "Mintong Kang", "Jiawei Zhang", "Chaowei Xiao", "Yuan Tian", "Bo Li", "Huan Sun" ], "year": 2025, "venue": "ICLR", "abstract": "Recently, generalist web agents have demonstrated remarkable potential in autonomously completing a wide range of tasks on real websites, significantly boosting human productivity. However, web tasks, such as booking flights, usually involve users' personally identifiable information (PII), which may be exposed to potential privacy risks if web agents accidentally interact with compromised websites—a scenario that remains largely unexplored in the literature.\nIn this work, we narrow this gap by conducting the first study on the privacy risks of generalist web agents in adversarial environments. First, we present a realistic threat model for attacks on the website, where we consider two adversarial targets: stealing users' specific PII or the entire user request.\nThen, we propose a novel attack method, termed Environmental Injection Attack (EIA). EIA injects malicious content designed to adapt well to environments where the agents operate and our work instantiates EIA specifically for privacy scenarios in web environments.\nWe collect 177 action steps that involve diverse PII categories on realistic websites from the Mind2Web dataset, and conduct experiments using one of the most capable generalist web agent frameworks to date. The results demonstrate that EIA achieves up to 70\\% attack success rate (ASR) in stealing users' specific PII and 16\\% ASR in stealing a full user request at an action step. Additionally, by evaluating the detectability and testing defensive system prompts, we indicate that EIA is challenging to detect and mitigate.\nNotably, attacks that are not well adapted for a webpage can be detected through careful human inspection, leading to our discussion about the trade-off between security and autonomy. However, extra attackers' efforts can make EIA seamlessly adapted, rendering such human supervision ineffective. Thus, we further discuss the implications on defenses at the pre- and post-deployment stages of the websites without relying on human supervision and call for more advanced defense strategies.", "source": "openreview", "url": "https://openreview.net/forum?id=xMOLUzo2Lk", "decision_type": "Poster", "avg_rating": 6.6, "relative_path": "2025/ICLR/Poster/6.6_EIA_ ENVIRONMENTAL INJECTION ATTACK ON GENERALIST WEB AGENTS FOR PRIVACY LEAKAGE_2025.pdf" }, { "title": "Cross-Domain Off-Policy Evaluation and Learning for Contextual Bandits", "authors": [ "Yuta Natsubori", "Masataka Ushiku", "Yuta Saito" ], "year": 2025, "venue": "ICLR", "abstract": "Off-Policy Evaluation and Learning (OPE/L) in contextual bandits is rapidly gaining popularity in real systems because new policies can be evaluated and learned securely using only historical logged data. However, existing methods in OPE/L cannot handle many challenging but prevalent scenarios such as few-shot data, deterministic logging policies, and new actions. In many applications, such as personalized medicine, content recommendations, education, and advertising, we need to evaluate and learn new policies in the presence of these challenges. Existing methods cannot evaluate and optimize effectively in these situations due to the notorious variance issue or limited exploration in the logged data. To enable OPE/L even under these unsolved challenges, we propose a new problem setup of Cross-Domain OPE/L, where we have access not only to the logged data from the target domain in which the new policy will be implemented but also to logged datasets collected from other domains. This novel formulation is widely applicable because we can often use historical data not only from the target hospital, country, device, or user segment but also from other hospitals, countries, devices, or segments. We develop a new estimator and policy gradient method to solve OPE/L by leveraging both target and source datasets, resulting in substantially enhanced OPE/L in the previously unsolved situations in our empirical evaluations.", "source": "openreview", "url": "https://openreview.net/forum?id=Z8dr422vtr", "decision_type": "Poster", "avg_rating": 6.5, "relative_path": "2025/ICLR/Poster/6.5_Cross-Domain Off-Policy Evaluation and Learning for Contextual Bandits_2025.pdf" }, { "title": "DynFrs: An Efficient Framework for Machine Unlearning in Random Forest", "authors": [ "Shurong Wang", "Zhuoyang Shen", "Xinbao Qiao", "Tongning Zhang", "Meng Zhang" ], "year": 2025, "venue": "ICLR", "abstract": "Random Forests are widely recognized for establishing efficacy in classification and regression tasks, standing out in various domains such as medical diagnosis, finance, and personalized recommendations. These domains, however, are inherently sensitive to privacy concerns, as personal and confidential data are involved. With increasing demand for the right to be forgotten, particularly under regulations such as GDPR and CCPA, the ability to perform machine unlearning has become crucial for Random Forests. However, insufficient attention was paid to this topic, and existing approaches face difficulties in being applied to real-world scenarios. Addressing this gap, we propose the DynFrs framework designed to enable efficient machine unlearning in Random Forests while preserving predictive accuracy. Dynfrs leverages subsampling method Occ(q) and a lazy tag strategy Lzy, and is still adaptable to any Random Forest variant. In essence, Occ(q) ensures that each sample in the training set occurs only in a proportion of trees so that the impact of deleting samples is limited, and Lzy delays the reconstruction of a tree node until necessary, thereby avoiding unnecessary modifications on tree structures. In experiments, applying Dynfrs on Extremely Randomized Trees yields substantial improvements, achieving orders of magnitude faster unlearning performance and better predictive accuracy than existing machine unlearning methods for Random Forests.", "source": "openreview", "url": "https://openreview.net/forum?id=nsCOeCLR8e", "decision_type": "Poster", "avg_rating": 6.5, "relative_path": "2025/ICLR/Poster/6.5_DynFrs_ An Efficient Framework for Machine Unlearning in Random Forest_2025.pdf" }, { "title": "Generalized Behavior Learning from Diverse Demonstrations", "authors": [ "Varshith Sreeramdass", "Rohan R Paleja", "Letian Chen", "Sanne van Waveren", "Matthew Gombolay" ], "year": 2025, "venue": "ICLR", "abstract": "Diverse behavior policies are valuable in domains requiring quick test-time adaptation or personalized human-robot interaction. Human demonstrations provide rich information regarding task objectives and factors that govern individual behavior variations, which can be used to characterize \\textit{useful} diversity and learn diverse performant policies.\nHowever, we show that prior work that builds naive representations of demonstration heterogeneity fails in generating successful novel behaviors that generalize over behavior factors.\nWe propose Guided Strategy Discovery (GSD), which introduces a novel diversity formulation based on a learned task-relevance measure that prioritizes behaviors exploring modeled latent factors.\nWe empirically validate across three continuous control benchmarks for generalizing to in-distribution (interpolation) and out-of-distribution (extrapolation) factors that GSD outperforms baselines in novel behavior discovery by $\\sim$21\\%.\nFinally, we demonstrate that GSD can generalize striking behaviors for table tennis in a virtual testbed while leveraging human demonstrations collected in the real world.\nCode is available at https://github.com/CORE-Robotics-Lab/GSD.", "source": "openreview", "url": "https://openreview.net/forum?id=Q7EjHroO1w", "decision_type": "Poster", "avg_rating": 6.5, "relative_path": "2025/ICLR/Poster/6.5_Generalized Behavior Learning from Diverse Demonstrations_2025.pdf" }, { "title": "MMRole: A Comprehensive Framework for Developing and Evaluating Multimodal Role-Playing Agents", "authors": [ "Yanqi Dai", "Huanran Hu", "Lei Wang", "Shengjie Jin", "Xu Chen", "Zhiwu Lu" ], "year": 2025, "venue": "ICLR", "abstract": "Recently, Role-Playing Agents (RPAs) have garnered increasing attention for their potential to deliver emotional value and facilitate sociological research.\nHowever, existing studies are primarily confined to the textual modality, unable to simulate humans' multimodal perceptual capabilities.\nTo bridge this gap, we introduce the concept of Multimodal Role-Playing Agents (MRPAs), and propose a comprehensive framework, MMRole, for their development and evaluation, which comprises a personalized multimodal dataset and a robust evaluation approach.\nSpecifically, we construct a large-scale, high-quality dataset, MMRole-Data, consisting of 85 characters, 11K images, and 14K single or multi-turn dialogues.\nAdditionally, we present a robust evaluation approach, MMRole-Eval, encompassing eight metrics across three dimensions, where a reward model is designed to score MRPAs with the constructed ground-truth data for comparison.\nMoreover, we develop the first specialized MRPA, MMRole-Agent.\nExtensive evaluation results demonstrate the improved performance of MMRole-Agent and highlight the primary challenges in developing MRPAs, emphasizing the need for enhanced multimodal understanding and role-playing consistency.\nThe data, code, and models are all available at https://github.com/YanqiDai/MMRole.", "source": "openreview", "url": "https://openreview.net/forum?id=FGSgsefE0Y", "decision_type": "Poster", "avg_rating": 6.5, "relative_path": "2025/ICLR/Poster/6.5_MMRole_ A Comprehensive Framework for Developing and Evaluating Multimodal Role-Playing Agents_2025.pdf" }, { "title": "PAD: Personalized Alignment of LLMs at Decoding-time", "authors": [ "Ruizhe Chen", "Xiaotian Zhang", "Meng Luo", "Wenhao Chai", "Zuozhu Liu" ], "year": 2025, "venue": "ICLR", "abstract": "Aligning with personalized preferences, which vary significantly across cultural, educational, and political differences, poses a significant challenge due to the computational costs and data demands of traditional alignment methods. In response, this paper presents Personalized Alignment at Decoding-time (PAD), a novel framework designed to align LLM outputs with diverse personalized preferences during the inference phase, eliminating the need for additional training. By introducing a unique personalized reward modeling strategy, this framework decouples the text generation process from personalized preferences, facilitating the generation of generalizable token-level personalized rewards. The PAD algorithm leverages these rewards to guide the decoding process, dynamically tailoring the base model’s predictions to personalized preferences. Extensive experimental results demonstrate that PAD not only outperforms existing training-based alignment methods in terms of aligning with diverse preferences but also shows significant generalizability to preferences unseen during training and scalability across different base models. This work advances the capability of LLMs to meet user needs in real-time applications, presenting a substantial step forward in personalized LLM alignment.", "source": "openreview", "url": "https://openreview.net/forum?id=e7AUJpP8bV", "decision_type": "Poster", "avg_rating": 6.5, "relative_path": "2025/ICLR/Poster/6.5_PAD_ Personalized Alignment of LLMs at Decoding-time_2025.pdf" }, { "title": "SIM: Surface-based fMRI Analysis for Inter-Subject Multimodal Decoding from Movie-Watching Experiments", "authors": [ "Simon Dahan", "Gabriel Bénédict", "Logan Zane John Williams", "Yourong Guo", "Daniel Rueckert", "Robert Leech", "Emma Claire Robinson" ], "year": 2025, "venue": "ICLR", "abstract": "Current AI frameworks for brain decoding and encoding, typically train and test models within the same datasets. This limits their utility for cognitive training (neurofeedback) for which it would be useful to pool experiences across individuals to better simulate stimuli not sampled during training. A key obstacle to model generalisation is the degree of variability of inter-subject cortical organisation, which makes it difficult to align or compare cortical signals across participants. In this paper we address this through use of surface vision transformers, which build a generalisable model of cortical functional dynamics, through encoding the topography of cortical networks and their interactions as a moving image across a surface. This is then combined with tri-modal self-supervised contrastive (CLIP) alignment of audio, video, and fMRI modalities to enable the retrieval of visual and auditory stimuli from patterns of cortical activity (and vice-versa). We validate our approach on 7T task-fMRI data from 174 healthy participants engaged in the movie-watching experiment from the Human Connectome Project (HCP). Results show that it is possible to detect which movie clips an individual is watching purely from their brain activity, even for individuals and movies *not seen during training*. Further analysis of attention maps reveals that our model captures individual patterns of brain activity that reflect semantic and visual systems. This opens the door to future personalised simulations of brain function. Code \\& pre-trained models will be made available at https://github.com/metrics-lab/sim.", "source": "openreview", "url": "https://openreview.net/forum?id=OJsMGsO6yn", "decision_type": "Poster", "avg_rating": 6.5, "relative_path": "2025/ICLR/Poster/6.5_SIM_ Surface-based fMRI Analysis for Inter-Subject Multimodal Decoding from Movie-Watching Expe_2025.pdf" }, { "title": "SaLoRA: Safety-Alignment Preserved Low-Rank Adaptation", "authors": [ "Mingjie Li", "Wai Man Si", "Michael Backes", "Yang Zhang", "Yisen Wang" ], "year": 2025, "venue": "ICLR", "abstract": "As advancements in large language models (LLMs) continue and the demand for personalized models increases, parameter-efficient fine-tuning (PEFT) methods (e.g., LoRA) become essential due to their efficiency in reducing computation costs.\nHowever, recent studies have raised alarming concerns that LoRA fine-tuning could potentially compromise the safety alignment in LLMs, posing significant risks for the model owner.\nIn this paper, we first investigate the underlying mechanism by analyzing the changes in safety alignment related features before and after fine-tuning.\nThen, we propose a fixed safety module calculated by safety data and a task-specific initialization for trainable parameters in low-rank adaptations, termed Safety-alignment preserved Low-Rank Adaptation (SaLoRA). \nUnlike previous LoRA methods and their variants, SaLoRA enables targeted modifications to LLMs without disrupting their original alignments. \nOur experiments show that SaLoRA outperforms various adapters-based approaches across various evaluation metrics in different fine-tuning tasks.", "source": "openreview", "url": "https://openreview.net/forum?id=GOoVzE9nSj", "decision_type": "Poster", "avg_rating": 6.5, "relative_path": "2025/ICLR/Poster/6.5_SaLoRA_ Safety-Alignment Preserved Low-Rank Adaptation_2025.pdf" }, { "title": "AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents", "authors": [ "Ke Yang", "Yao Liu", "Sapana Chaudhary", "Rasool Fakoor", "Pratik Chaudhari", "George Karypis", "Huzefa Rangwala" ], "year": 2025, "venue": "ICLR", "abstract": "Autonomy via agents based on large language models (LLMs) that can carry out personalized yet standardized tasks presents a significant opportunity to drive human efficiency. There is an emerging need and interest in automating web tasks (e.g., booking a hotel for a given date within a budget). Being a practical use case itself, the web agent also serves as an important proof-of-concept example for various agent grounding scenarios, with its success promising advancements in many future applications. Meanwhile, much prior research focuses on handcrafting their web agent strategies (e.g., agent's prompting templates, reflective workflow, role-play and multi-agent systems, search or sampling methods, etc.) and the corresponding in-context examples. However, these custom strategies often struggle with generalizability across all potential real-world applications. On the other hand, there has been limited study on the misalignment between a web agent's observation and action representation, and the data on which the agent's underlying LLM has been pre-trained. This discrepancy is especially notable when LLMs are primarily trained for language completion rather than tasks involving embodied navigation actions and symbolic web elements. In our study, we enhance an LLM-based web agent by simply refining its observation and action space, aligning these more closely with the LLM's capabilities. This approach enables our base agent to significantly outperform previous methods on a wide variety of web tasks. Specifically, on WebArena, a benchmark featuring general-purpose web interaction tasks, our agent AgentOccam surpasses the previous state-of-the-art and concurrent work by 9.8 (+29.4%) and 5.9 (+15.8%) absolute points respectively, and boosts the success rate by 26.6 points (+161%) over similar plain web agents with its observation and action space alignment. Furthermore, on WebVoyager benchmark comprising tasks defined on real-world websites, AgentOccam exceeds the former best agent by 2.4 points (+4.6%) on tasks with deterministic answers. We achieve this without using in-context examples, new agent roles, online feedback or search strategies. AgentOccam's simple design highlights LLMs' impressive zero-shot performance on web tasks, and underlines the critical role of carefully tuning observation and action spaces for LLM-based agents.", "source": "openreview", "url": "https://openreview.net/forum?id=oWdzUpOlkX", "decision_type": "Poster", "avg_rating": 6.3, "relative_path": "2025/ICLR/Poster/6.3_AgentOccam_ A Simple Yet Strong Baseline for LLM-Based Web Agents_2025.pdf" }, { "title": "Encryption-Friendly LLM Architecture", "authors": [ "Donghwan Rho", "Taeseong Kim", "Minje Park", "Jung Woo Kim", "Hyunsik Chae", "Ernest K. Ryu", "Jung Hee Cheon" ], "year": 2025, "venue": "ICLR", "abstract": "Large language models (LLMs) offer personalized responses based on user interactions, but this use case raises serious privacy concerns. Homomorphic encryption (HE) is a cryptographic protocol supporting arithmetic computations in encrypted states and provides a potential solution for privacy-preserving machine learning (PPML). However, the computational intensity of transformers poses challenges for applying HE to LLMs. In this work, we propose a modified HE-friendly transformer architecture with an emphasis on inference following personalized (private) fine-tuning. Utilizing LoRA fine-tuning and Gaussian kernels, we achieve significant computational speedups---6.94$\\times$ for fine-tuning and 2.3$\\times$ for inference---while maintaining performance comparable to plaintext models. Our findings provide a viable proof of concept for offering privacy-preserving LLM services in areas where data protection is crucial. Our code is available on GitHub.", "source": "openreview", "url": "https://openreview.net/forum?id=pbre0HKsfE", "decision_type": "Poster", "avg_rating": 6.3, "relative_path": "2025/ICLR/Poster/6.3_Encryption-Friendly LLM Architecture_2025.pdf" }, { "title": "ClassDiffusion: More Aligned Personalization Tuning with Explicit Class Guidance", "authors": [ "Jiannan Huang", "Jun Hao Liew", "Hanshu Yan", "Yuyang Yin", "Yao Zhao", "Humphrey Shi", "Yunchao Wei" ], "year": 2025, "venue": "ICLR", "abstract": "Recent text-to-image customization works have proven successful in generating images of given concepts by fine-tuning diffusion models on a few examples. However, tuning-based methods inherently tend to overfit the concepts, resulting in failure to create the concept under multiple conditions (*e.g.*, headphone is missing when generating \"a `dog wearing a headphone\"). Interestingly, we notice that the base model before fine-tuning exhibits the capability to compose the base concept with other elements (*e.g.*, \"a dog wearing a headphone\"), implying that the compositional ability only disappears after personalization tuning. We observe a semantic shift in the customized concept after fine-tuning, indicating that the personalized concept is not aligned with the original concept, and further show through theoretical analyses that this semantic shift leads to increased difficulty in sampling the joint conditional probability distribution, resulting in the loss of the compositional ability. Inspired by this finding, we present **ClassDiffusion**, a technique that leverages a **semantic preservation loss** to explicitly regulate the concept space when learning a new concept. Although simple, this approach effectively prevents semantic drift during the fine-tuning process of the target concepts. Extensive qualitative and quantitative experiments demonstrate that the use of semantic preservation loss effectively improves the compositional abilities of fine-tuning models. Lastly, we also extend our ClassDiffusion to personalized video generation, demonstrating its flexibility.", "source": "openreview", "url": "https://openreview.net/forum?id=iTm4H6N4aG", "decision_type": "Poster", "avg_rating": 6.2, "relative_path": "2025/ICLR/Poster/6.2_ClassDiffusion_ More Aligned Personalization Tuning with Explicit Class Guidance_2025.pdf" }, { "title": "LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory", "authors": [ "Di Wu", "Hongwei Wang", "Wenhao Yu", "Yuwei Zhang", "Kai-Wei Chang", "Dong Yu" ], "year": 2025, "venue": "ICLR", "abstract": "Recent large language model (LLM)-driven chat assistant systems have integrated memory components to track user-assistant chat histories, enabling more accurate and personalized responses. However, their long-term memory capabilities in sustained interactions remain underexplored. We introduce LongMemEval, a comprehensive benchmark designed to evaluate five core long-term memory abilities of chat assistants: information extraction, multi-session reasoning, temporal reasoning, knowledge updates, and abstention. With 500 meticulously curated questions embedded within freely scalable user-assistant chat histories, LongMemEval presents a significant challenge to existing long-term memory systems, with commercial chat assistants and long-context LLMs showing a 30% accuracy drop on memorizing information across sustained interactions. We then present a unified framework that breaks down the long-term memory design into three stages: indexing, retrieval, and reading. Built upon key experimental insights, we propose several memory design optimizations including session decomposition for value granularity, fact-augmented key expansion for indexing, and time-aware query expansion for refining the search scope. Extensive experiments show that these optimizations greatly improve both memory recall and downstream question answering on LongMemEval. Overall, our study provides valuable resources and guidance for advancing the long-term memory capabilities of LLM-based chat assistants, paving the way toward more personalized and reliable conversational AI. Our benchmark and code are publicly available at https://github.com/xiaowu0162/LongMemEval.", "source": "openreview", "url": "https://openreview.net/forum?id=pZiyCaVuti", "decision_type": "Poster", "avg_rating": 6.2, "relative_path": "2025/ICLR/Poster/6.2_LongMemEval_ Benchmarking Chat Assistants on Long-Term Interactive Memory_2025.pdf" }, { "title": "Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold", "authors": [ "Lazar Atanackovic", "Xi Zhang", "Brandon Amos", "Mathieu Blanchette", "Leo J Lee", "Yoshua Bengio", "Alexander Tong", "Kirill Neklyudov" ], "year": 2025, "venue": "ICLR", "abstract": "Numerous biological and physical processes can be modeled as systems of interacting entities evolving continuously over time, e.g. the dynamics of communicating cells or physical particles. Learning the dynamics of such systems is essential for predicting the temporal evolution of populations across novel samples and unseen environments. Flow-based models allow for learning these dynamics at the population level - they model the evolution of the entire distribution of samples. However, current flow-based models are limited to a single initial population and a set of predefined conditions which describe different dynamics. We argue that multiple processes in natural sciences have to be represented as vector fields on the Wasserstein manifold of probability densities. That is, the change of the population at any moment in time depends on the population itself due to the interactions between samples. In particular, this is crucial for personalized medicine where the development of diseases and their respective treatment response depend on the microenvironment of cells specific to each patient. We propose *Meta Flow Matching* (MFM), a practical approach to integrate along these vector fields on the Wasserstein manifold by amortizing the flow model over the initial populations. Namely, we embed the population of samples using a Graph Neural Network (GNN) and use these embeddings to train a Flow Matching model. This gives MFM the ability to generalize over the initial distributions, unlike previously proposed methods. We demonstrate the ability of MFM to improve the prediction of individual treatment responses on a large-scale multi-patient single-cell drug screen dataset.", "source": "openreview", "url": "https://openreview.net/forum?id=9SYczU3Qgm", "decision_type": "Poster", "avg_rating": 6.2, "relative_path": "2025/ICLR/Poster/6.2_Meta Flow Matching_ Integrating Vector Fields on the Wasserstein Manifold_2025.pdf" }, { "title": "Personalized Visual Instruction Tuning", "authors": [ "Renjie Pi", "Jianshu Zhang", "Tianyang Han", "Jipeng Zhang", "Rui Pan", "Tong Zhang" ], "year": 2025, "venue": "ICLR", "abstract": "Recent advancements in multimodal large language models (MLLMs) have demonstrated significant progress; however, these models exhibit a notable limitation, which we refer to as \"face blindness.\" Specifically, they can engage in general conversations but fail to conduct personalized dialogues targeting at specific individuals. This deficiency hinders the application of MLLMs in personalized settings, such as tailored visual assistants on mobile devices, or domestic robots that need to recognize members of the family. In this paper, we introduce Personalized Visual Instruction Tuning (PVIT), a novel data curation and training framework designed to enable MLLMs to identify target individuals within an image and engage in personalized and coherent dialogues. Our approach involves the development of a sophisticated pipeline that autonomously generates training data containing personalized conversations. This pipeline leverages the capabilities of various visual experts, image generation models, and (multi-modal) large language models. To evaluate the personalized potential of MLLMs, we present a benchmark called P-Bench, which encompasses various question types with different levels of difficulty. The experiments demonstrate a substantial personalized performance enhancement after fine-tuning with our curated dataset.", "source": "openreview", "url": "https://openreview.net/forum?id=sAxdIJ4l6z", "decision_type": "Poster", "avg_rating": 6.2, "relative_path": "2025/ICLR/Poster/6.2_Personalized Visual Instruction Tuning_2025.pdf" }, { "title": "PortLLM: Personalizing Evolving Large Language Models with Training-Free and Portable Model Patches", "authors": [ "Rana Shahroz", "Pingzhi Li", "Sukwon Yun", "Zhenyu Wang", "Shahriar Nirjon", "Chau-Wai Wong", "Tianlong Chen" ], "year": 2025, "venue": "ICLR", "abstract": "As large language models (LLMs) increasingly shape the AI landscape, fine-tuning pretrained models has become more popular than in the pre-LLM era for achieving optimal performance in domain-specific tasks. However, pretrained LLMs such as ChatGPT are periodically evolved (i.e., model parameters are frequently updated), making it challenging for downstream users with limited resources to keep up with fine-tuning the newest LLMs for their domain application. Even though fine-tuning costs have nowadays been reduced thanks to the innovations of parameter-efficient fine-tuning such as LoRA, not all downstream users have adequate computing for frequent personalization. Moreover, access to fine-tuning datasets, particularly in sensitive domains such as healthcare, could be time-restrictive, making it crucial to retain the knowledge encoded in earlier fine-tuned rounds for future adaptation. In this paper, we present PORTLLM, a training-free framework that (i) creates an initial lightweight model update patch to capture domain-specific knowledge, and (ii) allows a subsequent seamless plugging for the continual personalization of evolved LLM at minimal cost. Our extensive experiments cover seven representative datasets, from easier question-answering tasks {BoolQ, SST2} to harder reasoning tasks {WinoGrande, GSM8K}, and models including {Mistral-7B,Llama2, Llama3.1, and Gemma2}, validating the portability of our designed model patches and showcasing the effectiveness of our proposed framework. For instance, PORTLLM achieves comparable performance to LoRA fine-tuning with reductions of up to 12.2× in GPU memory usage. Finally, we provide theoretical justifications to understand the portability of our model update patches, which offers new insights into the theoretical dimension of LLMs’ personalization.", "source": "openreview", "url": "https://openreview.net/forum?id=gyHoR6uFhU", "decision_type": "Poster", "avg_rating": 6.2, "relative_path": "2025/ICLR/Poster/6.2_PortLLM_ Personalizing Evolving Large Language Models with Training-Free and Portable Model Pat_2025.pdf" }, { "title": "Stabilized Neural Prediction of Potential Outcomes in Continuous Time", "authors": [ "Konstantin Hess", "Stefan Feuerriegel" ], "year": 2025, "venue": "ICLR", "abstract": "Patient trajectories from electronic health records are widely used to estimate conditional average potential outcomes (CAPOs) of treatments over time, which then allows to personalize care. Yet, existing neural methods for this purpose have a key limitation: while some adjust for time-varying confounding, these methods assume that the time series are recorded in discrete time. In other words, they are constrained to settings where measurements and treatments are conducted at fixed time steps, even though this is unrealistic in medical practice. In this work, we aim to estimate CAPOs in continuous time. The latter is of direct practical relevance because it allows for modeling patient trajectories where measurements and treatments take place at arbitrary, irregular timestamps. We thus propose a new method called stabilized continuous time inverse propensity network (SCIP-Net). For this, we further derive stabilized inverse propensity weights for robust estimation of the CAPOs. To the best of our knowledge, our SCIP-Net is the first neural method that performs proper adjustments for time-varying confounding in continuous time.", "source": "openreview", "url": "https://openreview.net/forum?id=aN57tSd5Us", "decision_type": "Poster", "avg_rating": 6.2, "relative_path": "2025/ICLR/Poster/6.2_Stabilized Neural Prediction of Potential Outcomes in Continuous Time_2025.pdf" }, { "title": "Uncovering Latent Memories in Large Language Models", "authors": [ "Sunny Duan", "Mikail Khona", "Abhiram Iyer", "Rylan Schaeffer", "Ila R Fiete" ], "year": 2025, "venue": "ICLR", "abstract": "Frontier AI systems are making transformative impacts across society, but such benefits are not without costs: models trained on web-scale datasets containing personal and private data raise profound concerns about data privacy and security. Language models are trained on extensive corpora including potentially sensitive or proprietary information, and the risk of data leakage, where the model response reveals pieces of such information, remains inadequately understood. Prior work has investigated that sequence complexity and the number of repetitions are the primary drivers of memorization. In this work, we examine the most vulnerable class of data: highly complex sequences that are presented only once during training. These sequences often contain the most sensitive information and pose considerable risk if memorized. By analyzing the progression of memorization for these sequences throughout training, we uncover a striking observation: many memorized sequences persist in the model's memory, exhibiting resistance to catastrophic forgetting even after just one encounter. Surprisingly, these sequences may not appear memorized immediately after their first exposure but can later be “uncovered” during training, even in the absence of subsequent exposures - a phenomenon we call \"latent memorization.\" Latent memorization presents a serious challenge for data privacy, as sequences that seem hidden at the final checkpoint of a model may still be easily recoverable. We demonstrate how these hidden sequences can be revealed through random weight perturbations, and we introduce a diagnostic test based on cross-entropy loss to accurately identify latent memorized sequences.", "source": "openreview", "url": "https://openreview.net/forum?id=KSBx6FBZpE", "decision_type": "Poster", "avg_rating": 6.2, "relative_path": "2025/ICLR/Poster/6.2_Uncovering Latent Memories in Large Language Models_2025.pdf" }, { "title": "VLAS: Vision-Language-Action Model with Speech Instructions for Customized Robot Manipulation", "authors": [ "Wei Zhao", "Pengxiang Ding", "Zhang Min", "Zhefei Gong", "Shuanghao Bai", "Han Zhao", "Donglin Wang" ], "year": 2025, "venue": "ICLR", "abstract": "Vision-language-action models (VLAs) have recently become highly prevalent in robot manipulation due to its end-to-end architecture and impressive performance. However, current VLAs are limited to processing human instructions in textual form, neglecting the more natural speech modality for human interaction. A typical approach of incorporating speech modality into VLA necessitates a separate speech recognition system to transcribe spoken instructions into text. Such a cascading pipeline raises two major concerns for robotic systems. First, the entire model grows in size and complexity, potentially resulting in redundant computations and increased memory consumption. Second, the transcription procedure would lose non-semantic information in the raw speech, such as voiceprint, which is crucial for a robot to successfully understand and complete customized tasks. To this end, we propose VLAS, the fisrt end-to-end policy model that seamlessly integrates speech modality for robot manipulation. We present a three-stage speech instruction tuning strategy leveraging multimodal datasets, including our manually curated SQA and CSI datasets. Furthermore, to facilitate personalized operations, we develop a voice retrieval-augmented generation (RAG) approach to enhance the robot's performance in tasks requiring individual-specific knowledge. Experimental results show that the proposed VLAS, following either textual or speech instructions, can achieve performance comparable to traditional VLAs on the CALVIN benchmark. In addition, we created a benchmark consisting of customization tasks, where our VLAS demonstrates absolute superiority by fully leveraging the auxiliary information in speech.", "source": "openreview", "url": "https://openreview.net/forum?id=K4FAFNRpko", "decision_type": "Poster", "avg_rating": 6.2, "relative_path": "2025/ICLR/Poster/6.2_VLAS_ Vision-Language-Action Model with Speech Instructions for Customized Robot Manipulation_2025.pdf" }, { "title": "Bad-PFL: Exploiting Backdoor Attacks against Personalized Federated Learning", "authors": [ "Mingyuan Fan", "Zhanyi Hu", "Fuyi Wang", "Cen Chen" ], "year": 2025, "venue": "ICLR", "abstract": "Data heterogeneity and backdoor attacks rank among the most significant challenges facing federated learning (FL). For data heterogeneity, personalized federated learning (PFL) enables each client to maintain a private personalized model to cater to client-specific knowledge. Meanwhile, vanilla FL has proven vulnerable to backdoor attacks. However, recent advancements in PFL community have demonstrated a potential immunity against such attacks. This paper explores this intersection further, revealing that existing federated backdoor attacks fail in PFL because backdoors about manually designed triggers struggle to survive in personalized models. To tackle this, we degisn Bad-PFL, which employs features from natural data as our trigger. As long as the model is trained on natural data, it inevitably embeds the backdoor associated with our trigger, ensuring its longevity in personalized models. Moreover, our trigger undergoes mutual reinforcement training with the model, further solidifying the backdoor's durability and enhancing attack effectiveness. The large-scale experiments across three benchmark datasets demonstrate the superior performance of Bad-PFL against various PFL methods, even when equipped with state-of-the-art defense mechanisms.", "source": "openreview", "url": "https://openreview.net/forum?id=79nO2DPjVX", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2025/ICLR/Poster/6.0_Bad-PFL_ Exploiting Backdoor Attacks against Personalized Federated Learning_2025.pdf" }, { "title": "Causally Motivated Sycophancy Mitigation for Large Language Models", "authors": [ "Haoxi Li", "Xueyang Tang", "Jie ZHANG", "Song Guo", "Sikai Bai", "Peiran Dong", "Yue Yu" ], "year": 2025, "venue": "ICLR", "abstract": "Incorporating user preferences into large language models (LLMs) can enhance the personalization and reliability of model outputs and facilitate the application of LLMs to real-world scenarios. However, leveraging user preferences can be a double-edged sword. Recent studies have found that improper utilization can incur sycophancy, where LLMs prioritize alignment with user preferences over the correctness of their outputs. To address sycophancy in LLMs, we analyze and model the problem through the lens of structured causal models (SCMs). We attribute sycophancy to LLMs' reliance on spurious correlations between user preferences and model outputs in this paper. Based on the proposed SCMs, we develop a novel framework, termed **CAUSM**, to mitigate sycophancy in LLMs by exploiting a significant causal signature. Specifically, we eliminate the spurious correlations embedded in the intermediate layers of LLMs through causally motivated head reweighting, and then calibrate the intra-head knowledge along the causal representation direction. Extensive experiments are conducted across diverse language tasks to demonstrate the superiority of our method over state-of-the-art competitors in mitigating sycophancy in LLMs.", "source": "openreview", "url": "https://openreview.net/forum?id=yRKelogz5i", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2025/ICLR/Poster/6.0_Causally Motivated Sycophancy Mitigation for Large Language Models_2025.pdf" }, { "title": "ChroKnowledge: Unveiling Chronological Knowledge of Language Models in Multiple Domains", "authors": [ "Yein Park", "Chanwoong Yoon", "Jungwoo Park", "Donghyeon Lee", "Minbyul Jeong", "Jaewoo Kang" ], "year": 2025, "venue": "ICLR", "abstract": "Large language models (LLMs) have brought significant changes to many aspects of our lives.\nHowever, assessing and ensuring their chronological knowledge remains challenging.\nExisting approaches fall short in addressing the temporal adaptability of knowledge, often relying on a fixed time-point view. \nTo overcome this, we introduce ChroKnowBench, a benchmark dataset designed to evaluate chronologically accumulated knowledge across three key aspects: multiple domains, time dependency, temporal state.\nOur benchmark distinguishes between knowledge that evolves (e.g., personal history, scientific discoveries, amended laws) and knowledge that remain constant (e.g., mathematical truths, commonsense facts). \nBuilding on this benchmark, we present ChroKnowledge (Chronological Categorization of Knowledge), a novel sampling-based framework for evaluating LLMs' non-parametric chronological knowledge.\nOur evaluation led to the following observations: \n(1) The ability of eliciting temporal knowledge varies depending on the data format that model was trained on.\n(2) LLMs partially recall knowledge or show a cut-off at temporal boundaries rather than recalling all aspects of knowledge correctly.\nThus, we apply our ChroKnowPrompt, an in-depth prompting to elicit chronological knowledge by traversing step-by-step through the surrounding time spans.\nWe observe that it successfully recalls objects across both open-source and proprietary LLMs, demonstrating versatility, though it faces challenges with dynamic datasets and unstructured formats.", "source": "openreview", "url": "https://openreview.net/forum?id=whaO3482bs", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2025/ICLR/Poster/6.0_ChroKnowledge_ Unveiling Chronological Knowledge of Language Models in Multiple Domains_2025.pdf" }, { "title": "DisEnvisioner: Disentangled and Enriched Visual Prompt for Customized Image Generation", "authors": [ "Jing He", "Haodong Li", "huyongzhe", "Guibao Shen", "Yingjie CAI", "Weichao Qiu", "Ying-Cong Chen" ], "year": 2025, "venue": "ICLR", "abstract": "In the realm of image generation, creating customized images from visual prompt with additional textual instruction emerges as a promising endeavor. However, existing methods, both tuning-based and tuning-free, struggle with interpreting the subject-essential attributes from the visual prompt. This leads to subject-irrelevant attributes infiltrating the generation process, ultimately compromising the personalization quality in both editability and ID preservation. In this paper, we present $\\textbf{DisEnvisioner}$, a novel approach for effectively extracting and enriching the subject-essential features while filtering out -irrelevant information, enabling exceptional customization performance, in a $\\textbf{tuning-free}$ manner and using only $\\textbf{a single image}$. Specifically, the feature of the subject and other irrelevant components are effectively separated into distinctive visual tokens, enabling a much more accurate customization. Aiming to further improving the ID consistency, we enrich the disentangled features, sculpting them into a more granular representation. Experiments demonstrate the superiority of our approach over existing methods in instruction response (editability), ID consistency, inference speed, and the overall image quality, highlighting the effectiveness and efficiency of DisEnvisioner.", "source": "openreview", "url": "https://openreview.net/forum?id=vQxqcVGrhR", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2025/ICLR/Poster/6.0_DisEnvisioner_ Disentangled and Enriched Visual Prompt for Customized Image Generation_2025.pdf" }, { "title": "DreamBench++: A Human-Aligned Benchmark for Personalized Image Generation", "authors": [ "Yuang Peng", "Yuxin Cui", "Haomiao Tang", "Zekun Qi", "Runpei Dong", "Jing Bai", "Chunrui Han", "Zheng Ge", "Xiangyu Zhang", "Shu-Tao Xia" ], "year": 2025, "venue": "ICLR", "abstract": "Personalized image generation holds great promise in assisting humans in everyday work and life due to its impressive function in creatively generating personalized content. However, current evaluations either are automated but misalign with humans or require human evaluations that are time-consuming and expensive. In this work, we present DreamBench++, a human-aligned benchmark that advanced multimodal GPT models automate. Specifically, we systematically design the prompts to let GPT be both human-aligned and self-aligned, empowered with task reinforcement. Further, we construct a comprehensive dataset comprising diverse images and prompts. By benchmarking 7 modern generative models, we demonstrate that \\dreambench results in significantly more human-aligned evaluation, helping boost the community with innovative findings.", "source": "openreview", "url": "https://openreview.net/forum?id=4GSOESJrk6", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2025/ICLR/Poster/6.0_DreamBench++_ A Human-Aligned Benchmark for Personalized Image Generation_2025.pdf" }, { "title": "HERO: Human-Feedback Efficient Reinforcement Learning for Online Diffusion Model Finetuning", "authors": [ "Ayano Hiranaka", "Shang-Fu Chen", "Chieh-Hsin Lai", "Dongjun Kim", "Naoki Murata", "Takashi Shibuya", "Wei-Hsiang Liao", "Shao-Hua Sun", "Yuki Mitsufuji" ], "year": 2025, "venue": "ICLR", "abstract": "Controllable generation through Stable Diffusion (SD) fine-tuning aims to improve fidelity, safety, and alignment with human guidance. Existing reinforcement learning from human feedback methods usually rely on predefined heuristic reward functions or pretrained reward models built on large-scale datasets, limiting their applicability to scenarios where collecting such data is costly or difficult. To effectively and efficiently utilize human feedback, we develop a framework, HERO, which leverages online human feedback collected on the fly during model learning. Specifically, HERO features two key mechanisms: (1) Feedback-Aligned Representation Learning, an online training method that captures human feedback and provides informative learning signals for fine-tuning, and (2) Feedback-Guided Image Generation, which involves generating images from SD's refined initialization samples, enabling faster convergence towards the evaluator's intent. We demonstrate that HERO is 4x more efficient in online feedback for body part anomaly correction compared to the best existing method. Additionally, experiments show that HERO can effectively handle tasks like reasoning, counting, personalization, and reducing NSFW content with only 0.5K online feedback. The code and project page are available at [https://hero-dm.github.io/](https://hero-dm.github.io/).", "source": "openreview", "url": "https://openreview.net/forum?id=yMHe9SRvxk", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2025/ICLR/Poster/6.0_HERO_ Human-Feedback Efficient Reinforcement Learning for Online Diffusion Model Finetuning_2025.pdf" }, { "title": "Hot-pluggable Federated Learning: Bridging General and Personalized FL via Dynamic Selection", "authors": [ "Lei Shen", "Zhenheng Tang", "Lijun Wu", "Yonggang Zhang", "Xiaowen Chu", "Tao Qin", "Bo Han" ], "year": 2025, "venue": "ICLR", "abstract": "Personalized federated learning (PFL) achieves high performance by assuming clients only meet test data locally, which does not meet many generic federated learning (GFL) scenarios. In this work, we theoretically show that PMs can be used to enhance GFL with a new learning problem named Selective FL (SFL), which involves optimizing PFL and model selection. However, storing and selecting whole models requires impractical computation and communication costs. To practically solve SFL, inspired by model components that attempt to edit a sub-model for specific purposes, we design an efficient and effective framework named Hot-Pluggable Federated Learning (HPFL). Specifically, clients individually train personalized plug-in modules based on a shared backbone, and upload them with a plug-in marker on the server modular store. In inference stage, an accurate selection algorithm allows clients to identify and retrieve suitable plug-in modules from the modular store to enhance their generalization performance on the target data distribution. Furthermore, we provide differential privacy protection during the selection with theoretical guarantee. Our comprehensive experiments and ablation studies demonstrate that HPFL significantly outperforms state-of-the-art GFL and PFL algorithms. Additionally, we empirically show HPFL's remarkable potential to resolve other practical FL problems such as continual federated learning and discuss its possible applications in one-shot FL, anarchic FL, and FL plug-in market. Our work is the first attempt towards improving GFL performance through a selecting mechanism with personalized plug-ins.", "source": "openreview", "url": "https://openreview.net/forum?id=B8akWa62Da", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2025/ICLR/Poster/6.0_Hot-pluggable Federated Learning_ Bridging General and Personalized FL via Dynamic Selection_2025.pdf" }, { "title": "Image-level Memorization Detection via Inversion-based Inference Perturbation", "authors": [ "Yue Jiang", "Haokun Lin", "Yang Bai", "Bo Peng", "Zhili Liu", "Yueming Lyu", "Yong Yang", "Xingzheng", "Jing Dong" ], "year": 2025, "venue": "ICLR", "abstract": "Recent studies have discovered that widely used text-to-image diffusion models can replicate training samples during image generation, a phenomenon known as memorization. Existing detection methods primarily focus on identifying memorized prompts. However, in real-world scenarios, image owners may need to verify whether their proprietary or personal images have been memorized by the model, even in the absence of paired prompts or related metadata. We refer to this challenge as image-level memorization detection, where current methods relying on original prompts fall short. In this work, we uncover two characteristics of memorized images after perturbing the inference procedure: lower similarity of the original images and larger magnitudes of TCNP.\nBuilding on these insights, we propose Inversion-based Inference Perturbation (IIP), a new framework for image-level memorization detection. Our approach uses unconditional DDIM inversion to derive latent codes that contain core semantic information of original images and optimizes random prompt embeddings to introduce effective perturbation. Memorized images exhibit distinct characteristics within the proposed pipeline, providing a robust basis for detection. To support this task, we construct a comprehensive setup for the image-level memorization detection, carefully curating datasets to simulate realistic memorization scenarios. Using this setup, we evaluate our IIP framework across three different memorization settings, demonstrating its state-of-the-art performance in identifying memorized images in various settings, even in the presence of data augmentation attacks.", "source": "openreview", "url": "https://openreview.net/forum?id=vwOq7twk7L", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2025/ICLR/Poster/6.0_Image-level Memorization Detection via Inversion-based Inference Perturbation_2025.pdf" }, { "title": "Language Models are Advanced Anonymizers", "authors": [ "Robin Staab", "Mark Vero", "Mislav Balunovic", "Martin Vechev" ], "year": 2025, "venue": "ICLR", "abstract": "Recent privacy research on large language models (LLMs) has shown that they achieve near-human-level performance at inferring personal data from online texts. With ever-increasing model capabilities, existing text anonymization methods are currently lacking behind regulatory requirements and adversarial threats. In this work, we take two steps to bridge this gap: First, we present a new setting for evaluating anonymization in the face of adversarial LLM inferences, allowing for a natural measurement of anonymization performance while remedying some of the shortcomings of previous metrics. Then, within this setting, we develop a novel LLM-based adversarial anonymization framework leveraging the strong inferential capabilities of LLMs to inform our anonymization procedure. We conduct a comprehensive experimental evaluation of adversarial anonymization across 13 LLMs on real-world and synthetic online texts, comparing it against multiple baselines and industry-grade anonymizers. Our evaluation shows that adversarial anonymization outperforms current commercial anonymizers both in terms of the resulting utility and privacy. We support our findings with a human study (n=50) highlighting a strong and consistent human preference for LLM-anonymized texts.", "source": "openreview", "url": "https://openreview.net/forum?id=82p8VHRsaK", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2025/ICLR/Poster/6.0_Language Models are Advanced Anonymizers_2025.pdf" }, { "title": "MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance", "authors": [ "Xierui Wang", "Siming Fu", "Qihan Huang", "Wanggui He", "Hao Jiang" ], "year": 2025, "venue": "ICLR", "abstract": "Recent advancements in text-to-image generation models have dramatically enhanced the generation of photorealistic images from textual prompts, leading to an increased interest in personalized text-to-image applications, particularly in multi-subject scenarios. However, these advances are hindered by two main challenges: firstly, the need to accurately maintain the details of each referenced subject in accordance with the textual descriptions; and secondly, the difficulty in achieving a cohesive representation of multiple subjects in a single image without introducing inconsistencies. To address these concerns, our research introduces the MS-Diffusion framework for layout-guided zero-shot image personalization with multi-subjects. This innovative approach integrates grounding tokens with the feature resampler to maintain detail fidelity among subjects. With the layout guidance, MS-Diffusion further improves the cross-attention to adapt to the multi-subject inputs, ensuring that each subject condition acts on specific areas. The proposed multi-subject cross-attention orchestrates harmonious inter-subject compositions while preserving the control of texts. Comprehensive quantitative and qualitative experiments affirm that this method surpasses existing models in both image and text fidelity, promoting the development of personalized text-to-image generation.", "source": "openreview", "url": "https://openreview.net/forum?id=PJqP0wyQek", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2025/ICLR/Poster/6.0_MS-Diffusion_ Multi-subject Zero-shot Image Personalization with Layout Guidance_2025.pdf" }, { "title": "Mining your own secrets: Diffusion Classifier Scores for Continual Personalization of Text-to-Image Diffusion Models", "authors": [ "Saurav Jha", "Shiqi Yang", "Masato Ishii", "Mengjie Zhao", "christian simon", "Muhammad Jehanzeb Mirza", "Dong Gong", "Lina Yao", "Shusuke Takahashi", "Yuki Mitsufuji" ], "year": 2025, "venue": "ICLR", "abstract": "Personalized text-to-image diffusion models have grown popular for their ability to efficiently acquire a new concept from user-defined text descriptions and a few images. However, in the real world, a user may wish to personalize a model on multiple concepts but one at a time, with no access to the data from previous concepts due to storage/privacy concerns. When faced with this continual learning (CL) setup, most personalization methods fail to find a balance between acquiring new concepts and retaining previous ones -- a challenge that *continual personalization* (CP) aims to solve. \nInspired by the successful CL methods that rely on class-specific information for regularization, we resort to the inherent class-conditioned density estimates, also known as *diffusion classifier* (DC) scores, for CP of text-to-image diffusion models. \nNamely, we propose using DC scores for regularizing the parameter-space and function-space of text-to-image diffusion models.\nUsing several diverse evaluation setups, datasets, and metrics, we show that our proposed regularization-based CP methods outperform the state-of-the-art C-LoRA, and other baselines. Finally, by operating in the replay-free CL setup and on low-rank adapters, our method incurs zero storage and parameter overhead, respectively, over the state-of-the-art.", "source": "openreview", "url": "https://openreview.net/forum?id=hUdLs6TqZL", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2025/ICLR/Poster/6.0_Mining your own secrets_ Diffusion Classifier Scores for Continual Personalization of Text-to-I_2025.pdf" }, { "title": "On-the-fly Preference Alignment via Principle-Guided Decoding", "authors": [ "Mingye Zhu", "Yi Liu", "Lei Zhang", "Junbo Guo", "Zhendong Mao" ], "year": 2025, "venue": "ICLR", "abstract": "With the rapidly expanding landscape of large language models, aligning model generations with human values and preferences is becoming increasingly important. Popular alignment methods, such as Reinforcement Learning from Human Feedback, have shown significant success in guiding models with greater control. However, these methods require considerable computational resources, which is inefficient, and substantial collection of training data to accommodate the diverse and pluralistic nature of human preferences, which is impractical. These limitations significantly constrain the scope and efficacy of both task-specific and general preference alignment methods. In this work, we introduce On-the-fly Preference Alignment via Principle-Guided Decoding (OPAD) to directly align\nmodel outputs with human preferences during inference, eliminating the need for fine-tuning. Our approach involves first curating a surrogate solution to an otherwise infeasible optimization problem and then designing a principle-guided reward function based on this surrogate. The final decoding policy is derived by maximizing this customized reward, which exploits the discrepancy between the\nconstrained policy and its unconstrained counterpart. OPAD directly modifies the model’s predictions during inference, ensuring principle adherence without incurring the computational overhead of retraining or fine-tuning. Experiments show that OPAD achieves competitive or superior performance in both general and personalized alignment tasks, demonstrating its efficiency and effectiveness compared to state-of-the-art baselines.", "source": "openreview", "url": "https://openreview.net/forum?id=cfn2O1qvxp", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2025/ICLR/Poster/6.0_On-the-fly Preference Alignment via Principle-Guided Decoding_2025.pdf" }, { "title": "PersonalLLM: Tailoring LLMs to Individual Preferences", "authors": [ "Thomas P Zollo", "Andrew Wei Tung Siah", "Naimeng Ye", "Ang Li", "Hongseok Namkoong" ], "year": 2025, "venue": "ICLR", "abstract": "As LLMs become capable of complex tasks, there is growing potential for personalized interactions tailored to the subtle and idiosyncratic preferences of the user. We present a public benchmark, PersonalLLM, focusing on adapting LLMs to provide maximal benefits for a particular user. Departing from existing alignment benchmarks that implicitly assume uniform preferences, we curate open-ended prompts paired with many high-quality answers over which users would be expected to display heterogeneous latent preferences. Instead of persona prompting LLMs based on high-level attributes (e.g., user race or response length), which yields homogeneous preferences relative to humans, we develop a method that can simulate a large user base with diverse preferences from a set of pre-trained reward models. Our dataset and generated personalities offer an innovative testbed for developing personalization algorithms that grapple with continual data sparsity---few relevant feedback from the particular user---by leveraging historical data from other (similar) users. We explore basic in-context learning and meta-learning baselines to illustrate the utility of PersonalLLM and highlight the need for future methodological development.", "source": "openreview", "url": "https://openreview.net/forum?id=2R7498e2Tx", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2025/ICLR/Poster/6.0_PersonalLLM_ Tailoring LLMs to Individual Preferences_2025.pdf" }, { "title": "Personality Alignment of Large Language Models", "authors": [ "Minjun Zhu", "Yixuan Weng", "Linyi Yang", "Yue Zhang" ], "year": 2025, "venue": "ICLR", "abstract": "Aligning large language models (LLMs) typically aim to reflect general human values and behaviors, but they often fail to capture the unique characteristics and preferences of individual users. To address this gap, we introduce the concept of Personality Alignment. This approach tailors LLMs' responses and decisions to match the specific preferences of individual users or closely related groups. Inspired by psychometrics, we created the Personality Alignment with Personality Inventories (PAPI) dataset, which includes data from over 320,000 real subjects across multiple personality assessments - including both the Big Five Personality Factors and Dark Triad traits. This comprehensive dataset enables quantitative evaluation of LLMs' alignment capabilities across both positive and potentially problematic personality dimensions. Recognizing the challenges of personality alignments—such as limited personal data, diverse preferences, and scalability requirements—we developed an activation intervention optimization method. This method enhances LLMs' ability to efficiently align with individual behavioral preferences using minimal data and computational resources. Remarkably, our method, PAS, achieves superior performance while requiring only 1/5 of the optimization time compared to DPO, offering practical value for personality alignment. Our work paves the way for future AI systems to make decisions and reason in truly personality ways, enhancing the relevance and meaning of AI interactions for each user and advancing human-centered artificial intelligence. The dataset and code are released at https://github.com/zhu-minjun/PAlign.", "source": "openreview", "url": "https://openreview.net/forum?id=0DZEs8NpUH", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2025/ICLR/Poster/6.0_Personality Alignment of Large Language Models_2025.pdf" }, { "title": "Simulating Human-like Daily Activities with Desire-driven Autonomy", "authors": [ "Yiding Wang", "Yuxuan Chen", "Fangwei Zhong", "Long Ma", "Yizhou Wang" ], "year": 2025, "venue": "ICLR", "abstract": "Desires motivate humans to interact autonomously with the complex world. In contrast, current AI agents require explicit task specifications, such as instructions or reward functions, which constrain their autonomy and behavioral diversity. In this paper, we introduce a Desire-driven Autonomous Agent (D2A) that can enable a large language model (LLM) to autonomously propose and select tasks, motivated by satisfying its multi-dimensional desires. Specifically, the motivational framework of D2A is mainly constructed by a dynamic $Value\\ System$, inspired by the Theory of Needs. It incorporates an understanding of human-like desires, such as the need for social interaction, personal fulfillment, and self-care. At each step, the agent evaluates the value of its current state, proposes a set of candidate activities, and selects the one that best aligns with its intrinsic motivations. We conduct experiments on Concordia, a text-based simulator, to demonstrate that our agent generates coherent, contextually relevant daily activities while exhibiting variability and adaptability similar to human behavior. A comparative analysis with other LLM-based agents demonstrates that our approach significantly enhances the rationality of the simulated activities.", "source": "openreview", "url": "https://openreview.net/forum?id=3ms8EQY7f8", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2025/ICLR/Poster/6.0_Simulating Human-like Daily Activities with Desire-driven Autonomy_2025.pdf" }, { "title": "Visually Guided Decoding: Gradient-Free Hard Prompt Inversion with Language Models", "authors": [ "Donghoon Kim", "Minji Bae", "Kyuhong Shim", "Byonghyo Shim" ], "year": 2025, "venue": "ICLR", "abstract": "Text-to-image generative models like DALL-E and Stable Diffusion have revolutionized visual content creation across various applications, including advertising, personalized media, and design prototyping.\nHowever, crafting effective textual prompts to guide these models remains challenging, often requiring extensive trial and error. \nExisting prompt inversion approaches, such as soft and hard prompt techniques, are not so effective due to the limited interpretability and incoherent prompt generation. \nTo address these issues, we propose Visually Guided Decoding (VGD), a gradient-free approach that leverages large language models (LLMs) and CLIP-based guidance to generate coherent and semantically aligned prompts. \nIn essence, VGD utilizes the robust text generation capabilities of LLMs to produce human-readable prompts. \nFurther, by employing CLIP scores to ensure alignment with user-specified visual concepts, VGD enhances the interpretability, generalization, and flexibility of prompt generation without the need for additional training. \nOur experiments demonstrate that VGD outperforms existing prompt inversion techniques in generating understandable and contextually relevant prompts, facilitating more intuitive and controllable interactions with text-to-image models.", "source": "openreview", "url": "https://openreview.net/forum?id=mQ55y4s5hj", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2025/ICLR/Poster/6.0_Visually Guided Decoding_ Gradient-Free Hard Prompt Inversion with Language Models_2025.pdf" }, { "title": "Adaptive Shrinkage Estimation for Personalized Deep Kernel Regression in Modeling Brain Trajectories", "authors": [ "Vasiliki Tassopoulou", "Haochang Shou", "Christos Davatzikos" ], "year": 2025, "venue": "ICLR", "abstract": "Longitudinal biomedical studies monitor individuals over time to capture dynamics in brain development, disease progression, and treatment effects. However, estimating trajectories of brain biomarkers is challenging due to biological variability, inconsistencies in measurement protocols (e.g., differences in MRI scanners) as well as scarcity and irregularity in longitudinal measurements. Herein,\nwe introduce a novel personalized deep kernel regression framework for forecasting brain biomarkers, with application to regional volumetric measurements. Our approach integrates two key components: a population model that captures brain trajectories from a large and diverse cohort, and a subject-specific model that captures individual trajectories. To optimally combine these, we propose Adaptive Shrinkage Estimation, which effectively balances population and subject-specific models. We assess our model’s performance through predictive accuracy metrics, uncertainty quantification, and validation against external clinical studies. Benchmarking against state-of-the-art statistical and machine learning models—including linear mixed effects models, generalized additive models, and deep learning methods—demonstrates the superior predictive performance of our approach. Additionally, we apply our method to predict trajectories of composite neuroimaging biomarkers, which highlights the versatility of our approach in modeling the progression of longitudinal neuroimaging biomarkers. Furthermore, validation on three external neuroimaging studies confirms the robustness of our method across different clinical contexts. We make the code available at https://github.com/vatass/AdaptiveShrinkageDKGP.", "source": "openreview", "url": "https://openreview.net/forum?id=peX9zpWgg4", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2025/ICLR/Poster/5.8_Adaptive Shrinkage Estimation for Personalized Deep Kernel Regression in Modeling Brain Traject_2025.pdf" }, { "title": "Amulet: ReAlignment During Test Time for Personalized Preference Adaptation of LLMs", "authors": [ "Zhaowei Zhang", "Fengshuo Bai", "Qizhi Chen", "Chengdong Ma", "Mingzhi Wang", "Haoran Sun", "Zilong Zheng", "Yaodong Yang" ], "year": 2025, "venue": "ICLR", "abstract": "How to align large language models (LLMs) with user preferences from a static general dataset has been frequently studied. However, user preferences are usually personalized, changing, and diverse. This leads to the problem that the actual user preferences often do not coincide with those trained by the model developers in the practical use of LLMs. Since we cannot collect enough data and retrain for every demand, researching efficient real-time preference adaptation methods based on the backbone LLMs during test time is important. To this end, we introduce **Amulet**, a novel, training-free framework that formulates the decoding process of every token as a separate online learning problem with the guidance of simple user-provided prompts, thus enabling real-time optimization to satisfy users' personalized preferences. To reduce the computational cost brought by this optimization process for each token, we additionally provide a closed-form solution for each iteration step of the optimization process, thereby reducing the computational time cost to a negligible level. The detailed experimental results demonstrate that Amulet can achieve significant performance improvements in rich settings with combinations of different LLMs, datasets, and user preferences, while maintaining acceptable computational efficiency.", "source": "openreview", "url": "https://openreview.net/forum?id=f9w89OY2cp", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2025/ICLR/Poster/5.8_Amulet_ ReAlignment During Test Time for Personalized Preference Adaptation of LLMs_2025.pdf" }, { "title": "Do LLMs estimate uncertainty well in instruction-following?", "authors": [ "Juyeon Heo", "Miao Xiong", "Christina Heinze-Deml", "Jaya Narain" ], "year": 2025, "venue": "ICLR", "abstract": "Large language models (LLMs) could be valuable personal AI agents across various domains, provided they can precisely follow user instructions. However, recent studies have shown significant limitations in LLMs' instruction-following capabilities, raising concerns about their reliability in high-stakes applications. \nAccurately estimating LLMs' uncertainty in adhering to instructions is critical to mitigating deployment risks. We present, to our knowledge, the first systematic evaluation of the uncertainty estimation abilities of LLMs in the context of instruction-following. \nOur study identifies key challenges with existing instruction-following benchmarks, where multiple factors are entangled with uncertainty stems from instruction-following, complicating the isolation and comparison across methods and models.\nTo address these issues, we introduce a controlled evaluation setup with two benchmark versions of data, enabling a comprehensive comparison of uncertainty estimation methods under various conditions.\nOur findings show that existing uncertainty methods struggle, particularly when models make subtle errors in instruction following. While internal model states provide some improvement, they remain inadequate in more complex scenarios. \nThe insights from our controlled evaluation setups provide a crucial understanding of LLMs' limitations and potential for uncertainty estimation in instruction-following tasks, paving the way for more trustworthy AI agents.", "source": "openreview", "url": "https://openreview.net/forum?id=IHp3vOVQO2", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2025/ICLR/Poster/5.8_Do LLMs estimate uncertainty well in instruction-following__2025.pdf" }, { "title": "DreamDistribution: Learning Prompt Distribution for Diverse In-distribution Generation", "authors": [ "Brian Nlong Zhao", "Yuhang Xiao", "Jiashu Xu", "XINYANG JIANG", "Yifan Yang", "Dongsheng Li", "Laurent Itti", "Vibhav Vineet", "Yunhao Ge" ], "year": 2025, "venue": "ICLR", "abstract": "The popularization of Text-to-Image (T2I) diffusion models enables the generation of high-quality images from text descriptions. However, generating diverse customized images with reference visual attributes remains challenging. This work focuses on personalizing T2I diffusion models at a more abstract concept or category level, adapting commonalities from a set of reference images while creating new instances with sufficient variations. We introduce a solution that allows a pretrained T2I diffusion model to learn a set of soft prompts, enabling the generation of novel images by sampling prompts from the learned distribution. These prompts offer text-guided editing capabilities and additional flexibility in controlling variation and mixing between multiple distributions. We also show the adaptability of the learned prompt distribution to other tasks, such as text-to-3D. Finally we demonstrate effectiveness of our approach through quantitative analysis including automatic evaluation and human assessment.", "source": "openreview", "url": "https://openreview.net/forum?id=oQoQ4u6MQC", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2025/ICLR/Poster/5.8_DreamDistribution_ Learning Prompt Distribution for Diverse In-distribution Generation_2025.pdf" }, { "title": "Generative Adapter: Contextualizing Language Models in Parameters with A Single Forward Pass", "authors": [ "Tong Chen", "Hao Fang", "Patrick Xia", "Xiaodong Liu", "Benjamin Van Durme", "Luke Zettlemoyer", "Jianfeng Gao", "Hao Cheng" ], "year": 2025, "venue": "ICLR", "abstract": "Large language models (LLMs) acquire substantial knowledge during pretraining but often need adaptation to new contexts, tasks, or domains, typically achieved through fine-tuning or prompting. However, fine-tuning incurs significant training costs, while prompting increases inference overhead. Inspired by fast weight memory, we introduce GenerativeAdapter, an effective and efficient adaptation method that encode test-time context into language model parameters with a single forward pass.\nGenerativeAdapter augments a frozen pretrained LM with a lightweight adapter generator, trained via self-supervised learning, to produce parameter-efficient adapters.\nNotably, our generator is general-purpose, i.e., one generator can adapt the corresponding base model for all langauge processing scenarios.\nWe apply GenerativeAdapter to two pretrained LMs (Mistral-7B-Instruct and Llama2-7B-Chat) and evaluate the adapted models across knowledge acquisition from documents, learning from demonstrations, and personalization for users.\nIn StreamingQA, our approach is effective in injecting knowledge into the LM's parameters, achieving a 63.5\\% improvement in F1 score over the model with supervised fine-tuning (from $19.5$ to $31.5$) for contexts as long as 32K tokens.\nIn the MetaICL in-context learning evaluation, our method achieves an average accuracy of $44.9$ across 26 tasks, outperforming the base model. \nOn MSC, our method proves to be highly competitive in memorizing user information from conversations with a 4x reduction in computation and memory costs compared to \nprompting with full conversation history.\nOverall, GenerativeAdapter provides a viable solution for adapting large LMs to evolving information and providing tailored user experience, while reducing training and inference costs relative to traditional fine-tuning and prompting techniques.", "source": "openreview", "url": "https://openreview.net/forum?id=bc3sUsS6ck", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2025/ICLR/Poster/5.8_Generative Adapter_ Contextualizing Language Models in Parameters with A Single Forward Pass_2025.pdf" }, { "title": "LocoVR: Multiuser Indoor Locomotion Dataset in Virtual Reality", "authors": [ "Kojiro Takeyama", "Yimeng Liu", "Misha Sra" ], "year": 2025, "venue": "ICLR", "abstract": "Understanding human locomotion is crucial for AI agents such as robots, particularly in complex indoor home environments. Modeling human trajectories in these spaces requires insight into how individuals maneuver around physical obstacles and manage social navigation dynamics. These dynamics include subtle behaviors influenced by proxemics - the social use of space, such as stepping aside to allow others to pass or choosing longer routes to avoid collisions. Previous research has developed datasets of human motion in indoor scenes, but these are often limited in scale and lack the nuanced social navigation dynamics common in home environments. \nTo address this, we present LocoVR, a dataset of 7000+ two-person trajectories captured in virtual reality from over 130 different indoor home environments. LocoVR provides accurate trajectory and precise spatial information, along with rich examples of socially-motivated movement behaviors. \nFor example, the dataset captures instances of individuals navigating around each other in narrow spaces, adjusting paths to respect personal boundaries in living areas, and coordinating movements in high-traffic zones like entryways and kitchens. Our evaluation shows that LocoVR significantly enhances model performance in three practical indoor tasks utilizing human trajectories, and demonstrates predicting socially-aware navigation patterns in home environments.", "source": "openreview", "url": "https://openreview.net/forum?id=9mBodivRIo", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2025/ICLR/Poster/5.8_LocoVR_ Multiuser Indoor Locomotion Dataset in Virtual Reality_2025.pdf" }, { "title": "Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models", "authors": [ "Jun Luo", "Chen Chen", "Shandong Wu" ], "year": 2025, "venue": "ICLR", "abstract": "Federated prompt learning benefits federated learning with CLIP-like Vision-Language Model's (VLM's) robust representation learning ability through prompt learning. However, current federated prompt learning methods are habitually restricted to the traditional FL paradigm, where the participating clients are generally only allowed to download a single globally aggregated model from the server. While justifiable for training full-sized models under federated settings, in this work, we argue that this paradigm is ill-suited for lightweight prompts. By facilitating the clients to download multiple pre-aggregated prompts as fixed non-local experts, we propose Personalized Federated Mixture of Adaptive Prompts (pFedMoAP), a novel FL framework that personalizes the prompt learning process through the lens of Mixture of Experts (MoE). pFedMoAP implements a local attention-based gating network that learns to generate enhanced text features for better alignment with local image data, benefiting from both local and downloaded non-local adaptive prompt experts. Extensive experiments on 9 datasets under various federated settings demonstrate the efficacy of the proposed pFedMoAP algorithm. The code is available at https://github.com/ljaiverson/pFedMoAP.", "source": "openreview", "url": "https://openreview.net/forum?id=xiDJaTim3P", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2025/ICLR/Poster/5.8_Mixture of Experts Made Personalized_ Federated Prompt Learning for Vision-Language Models_2025.pdf" }, { "title": "Personalized Representation from Personalized Generation", "authors": [ "Shobhita Sundaram", "Julia Chae", "Yonglong Tian", "Sara Beery", "Phillip Isola" ], "year": 2025, "venue": "ICLR", "abstract": "Modern vision models excel at general purpose downstream tasks. It is unclear, however, how they may be used for personalized vision tasks, which are both fine-grained and data-scarce. Recent works have successfully applied synthetic data to general-purpose representation learning, while advances in T2I diffusion models have enabled the generation of personalized images from just a few real examples. Here, we explore a potential connection between these ideas, and formalize the challenge of using personalized synthetic data to learn personalized representations, which encode knowledge about an object of interest and may be flexibly applied to any downstream task relating to the target object. We introduce an evaluation suite for this challenge, including reformulations of two existing datasets and a novel dataset explicitly constructed for this purpose, and propose a contrastive learning approach that makes creative use of image generators. We show that our method improves personalized representation learning for diverse downstream tasks, from recognition to segmentation, and analyze characteristics of image generation approaches that are key to this gain.", "source": "openreview", "url": "https://openreview.net/forum?id=jw7P4MHLWw", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2025/ICLR/Poster/5.8_Personalized Representation from Personalized Generation_2025.pdf" }, { "title": "Preference Diffusion for Recommendation", "authors": [ "Shuo Liu", "An Zhang", "Guoqing Hu", "Hong Qian", "Tat-Seng Chua" ], "year": 2025, "venue": "ICLR", "abstract": "Recommender systems aim to predict personalized item rankings by modeling user preference distributions derived from historical behavior data. While diffusion models (DMs) have recently gained attention for their ability to model complex distributions, current DM-based recommenders typically rely on traditional objectives such as mean squared error (MSE) or standard recommendation objectives. These approaches are either suboptimal for personalized ranking tasks or fail to exploit the full generative potential of DMs. To address these limitations, we propose \\textbf{PreferDiff}, an optimization objective tailored for DM-based recommenders. PreferDiff reformulates the traditional Bayesian Personalized Ranking (BPR) objective into a log-likelihood generative framework, enabling it to effectively capture user preferences by integrating multiple negative samples. To handle the intractability, we employ variational inference, minimizing the variational upper bound. Furthermore, we replace MSE with cosine error to improve alignment with recommendation tasks, and we balance generative learning and preference modeling to enhance the training stability of DMs. PreferDiff devises three appealing properties. First, it is the first personalized ranking loss designed specifically for DM-based recommenders. Second, it improves ranking performance and accelerates convergence by effectively addressing hard negatives. Third, we establish its theoretical connection to Direct Preference Optimization (DPO), demonstrating its potential to align user preferences within a generative modeling framework. Extensive experiments across six benchmarks validate PreferDiff's superior recommendation performance. \nOur codes are available at \\url{https://github.com/lswhim/PreferDiff}.", "source": "openreview", "url": "https://openreview.net/forum?id=6GATHdOi1x", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2025/ICLR/Poster/5.8_Preference Diffusion for Recommendation_2025.pdf" }, { "title": "Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models", "authors": [ "Linh Tran", "Wei Sun", "Stacy Patterson", "Ana Milanova" ], "year": 2025, "venue": "ICLR", "abstract": "Multimodal Large Language Models (LLMs) are pivotal in revolutionizing customer support and operations by integrating multiple modalities such as text, images, and audio. Federated Prompt Learning (FPL) is a recently proposed approach that combines pre-trained multimodal LLMs such as vision-language models with federated learning to create personalized, privacy-preserving AI systems. However, balancing the competing goals of personalization, generalization, and privacy remains a significant challenge. Over-personalization can lead to overfitting, reducing generalizability, while stringent privacy measures, such as differential privacy, can hinder both personalization and generalization. In this paper, we propose a Differentially Private Federated Prompt Learning (DP-FPL) approach to tackle this challenge by leveraging a low-rank factorization scheme to capture generalization while maintaining a residual term that preserves expressiveness for personalization. To ensure privacy, we introduce a novel method where we apply local differential privacy to the two low-rank components of the local prompt, and global differential privacy to the global prompt. Our approach mitigates the impact of privacy noise on the model performance while balancing the tradeoff between personalization and generalization. Extensive experiments demonstrate the effectiveness of our approach over other benchmarks.", "source": "openreview", "url": "https://openreview.net/forum?id=Equ277PBN0", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2025/ICLR/Poster/5.8_Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models_2025.pdf" }, { "title": "Refine-by-Align: Reference-Guided Artifacts Refinement through Semantic Alignment", "authors": [ "Yizhi Song", "Liu He", "Zhifei Zhang", "Soo Ye Kim", "He Zhang", "Wei Xiong", "Zhe Lin", "Brian L. Price", "Scott Cohen", "Jianming Zhang", "Daniel Aliaga" ], "year": 2025, "venue": "ICLR", "abstract": "Personalized image generation has emerged from the recent advancements in generative models. However, these generated personalized images often suffer from localized artifacts such as incorrect logos, reducing fidelity and fine-grained identity details of the generated results. Furthermore, there is little prior work tackling this problem. To help improve these identity details in the personalized image generation, we introduce a new task: reference-guided artifacts refinement. We present Refine-by-Align, a first-of-its-kind model that employs a diffusion-based framework to address this challenge. Our model consists of two stages: Alignment Stage and Refinement Stage, which share weights of a unified neural network model. Given a generated image, a masked artifact region, and a reference image, the alignment stage identifies and extracts the corresponding regional features in the reference, which are then used by the refinement stage to fix the artifacts. Our model-agnostic pipeline requires no test-time tuning or optimization. It automatically enhances image fidelity and reference identity in the generated image, generalizing well to existing models on various tasks including but not limited to customization, generative compositing, view synthesis, and virtual try-on. Extensive experiments and comparisons demonstrate that our pipeline greatly pushes the boundary of fine details in the image synthesis models.", "source": "openreview", "url": "https://openreview.net/forum?id=D9CRb1KZQc", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2025/ICLR/Poster/5.8_Refine-by-Align_ Reference-Guided Artifacts Refinement through Semantic Alignment_2025.pdf" }, { "title": "Understanding the Stability-based Generalization of Personalized Federated Learning", "authors": [ "Yingqi Liu", "Qinglun Li", "Jie Tan", "Yifan Shi", "Li Shen", "Xiaochun Cao" ], "year": 2025, "venue": "ICLR", "abstract": "Despite great achievements in algorithm design for Personalized Federated Learning (PFL), research on the theoretical analysis of generalization is still in its early stages. Some theoretical results have investigated the generalization performance of personalized models under the problem setting and hypothesis in convex conditions, which can not reflect the real iteration performance during non-convex training. To further understand the real performance from a generalization perspective, we propose the first algorithm-dependent generalization analysis with uniform stability for the typical PFL method, Partial Model Personalization, on smooth and non-convex objectives. Specifically, we decompose the generalization errors into aggregation errors and fine-tuning errors, then creatively establish a generalization analysis framework corresponding to the gradient estimation process of the personalized training. This framework builds up the bridge among PFL, FL and Pure Local Training for personalized aims in heterogeneous scenarios, which clearly demonstrates the effectiveness of PFL from the generalization perspective. Moreover, we demonstrate the impact of trivial factors like learning steps, stepsizes and communication topologies and obtain the excess risk analysis with optimization errors for PFL. Promising experiments on CIFAR datasets also corroborate our theoretical insights. Our code can be seen in https://github.com/YingqiLiu1999/Understanding-the-Stability-based-Generalization-of-Personalized-Federated-Learning.", "source": "openreview", "url": "https://openreview.net/forum?id=znhZbonEoe", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2025/ICLR/Poster/5.8_Understanding the Stability-based Generalization of Personalized Federated Learning_2025.pdf" }, { "title": "A Benchmark for Semantic Sensitive Information in LLMs Outputs", "authors": [ "Qingjie Zhang", "Han Qiu", "Di Wang", "Yiming Li", "Tianwei Zhang", "Wenyu Zhu", "Haiqin Weng", "Liu Yan", "Chao Zhang" ], "year": 2025, "venue": "ICLR", "abstract": "Large language models (LLMs) can output sensitive information, which has emerged as a novel safety concern. Previous works focus on structured sensitive information (e.g. personal identifiable information). \nHowever, we notice that sensitive information can also be at semantic level, i.e. semantic sensitive information (SemSI). \nParticularly, *simple natural questions* can let state-of-the-art (SOTA) LLMs output SemSI. \n%which is hard to be detected compared with structured ones. \nCompared to previous work of structured sensitive information in LLM's outputs, SemSI are hard to define and are rarely studied. \nTherefore, we propose a novel and large-scale investigation on the existence of SemSI in SOTA LLMs induced by simple natural questions. \nFirst, we construct a comprehensive and labeled dataset of semantic sensitive information, SemSI-Set, by including three typical categories of SemSI. \nThen, we propose a large-scale benchmark, SemSI-Bench, to systematically evaluate semantic sensitive information in 25 SOTA LLMs. \nOur finding reveals that SemSI widely exists in SOTA LLMs' outputs by querying with simple natural questions.\nWe open-source our project at https://semsi-project.github.io/.", "source": "openreview", "url": "https://openreview.net/forum?id=p3mxzKmuZy", "decision_type": "Poster", "avg_rating": 5.3, "relative_path": "2025/ICLR/Poster/5.3_A Benchmark for Semantic Sensitive Information in LLMs Outputs_2025.pdf" }, { "title": "SeCom: On Memory Construction and Retrieval for Personalized Conversational Agents", "authors": [ "Zhuoshi Pan", "Qianhui Wu", "Huiqiang Jiang", "Xufang Luo", "Hao Cheng", "Dongsheng Li", "Yuqing Yang", "Chin-Yew Lin", "H. Vicky Zhao", "Lili Qiu", "Jianfeng Gao" ], "year": 2025, "venue": "ICLR", "abstract": "To deliver coherent and personalized experiences in long-term conversations, existing approaches typically perform retrieval augmented response generation by constructing memory banks from conversation history at either the turn-level, session-level, or through summarization techniques.\nIn this paper, we explore the impact of different memory granularities and present two key findings: (1) Both turn-level and session-level memory units are suboptimal, affecting not only the quality of final responses, but also the accuracy of the retrieval process.\n(2) The redundancy in natural language introduces noise, hindering precise retrieval. We demonstrate that *LLMLingua-2*, originally designed for prompt compression to accelerate LLM inference, can serve as an effective denoising method to enhance memory retrieval accuracy.\n\nBuilding on these insights, we propose **SeCom**, a method that constructs a memory bank with topical segments by introducing a conversation **Se**gmentation model, while performing memory retrieval based on **Com**pressed memory units.\nExperimental results show that **SeCom** outperforms turn-level, session-level, and several summarization-based methods on long-term conversation benchmarks such as *LOCOMO* and *Long-MT-Bench+*. Additionally, the proposed conversation segmentation method demonstrates superior performance on dialogue segmentation datasets such as *DialSeg711*, *TIAGE*, and *SuperDialSeg*.", "source": "openreview", "url": "https://openreview.net/forum?id=xKDZAW0He3", "decision_type": "Poster", "avg_rating": 5.2, "relative_path": "2025/ICLR/Poster/5.2_SeCom_ On Memory Construction and Retrieval for Personalized Conversational Agents_2025.pdf" }, { "title": "PaRa: Personalizing Text-to-Image Diffusion via Parameter Rank Reduction", "authors": [ "Shangyu Chen", "Zizheng Pan", "Jianfei Cai", "Dinh Phung" ], "year": 2025, "venue": "ICLR", "abstract": "Personalizing a large-scale pretrained Text-to-Image (T2I) diffusion model is chal-\nlenging as it typically struggles to make an appropriate trade-off between its training\ndata distribution and the target distribution, i.e., learning a novel concept with only a\nfew target images to achieve personalization (aligning with the personalized target)\nwhile preserving text editability (aligning with diverse text prompts). In this paper,\nwe propose PaRa, an effective and efficient Parameter Rank Reduction approach\nfor T2I model personalization by explicitly controlling the rank of the diffusion\nmodel parameters to restrict its initial diverse generation space into a small and\nwell-balanced target space. Our design is motivated by the fact that taming a T2I\nmodel toward a novel concept such as a specific art style implies a small generation\nspace. To this end, by reducing the rank of model parameters during finetuning, we\ncan effectively constrain the space of the denoising sampling trajectories towards\nthe target. With comprehensive experiments, we show that PaRa achieves great\nadvantages over existing finetuning approaches on single/multi-subject generation\nas well as single-image editing. Notably, compared to the prevailing fine-tuning\ntechnique LoRA, PaRa achieves better parameter efficiency (2× fewer learnable\nparameters) and much better target image alignment.", "source": "openreview", "url": "https://openreview.net/forum?id=KZgo2YQbhc", "decision_type": "Spotlight", "avg_rating": 7.5, "relative_path": "2025/ICLR/Spotlight/7.5_PaRa_ Personalizing Text-to-Image Diffusion via Parameter Rank Reduction_2025.pdf" }, { "title": "Temporal Heterogeneous Graph Generation with Privacy, Utility, and Efficiency", "authors": [ "Xinyu He", "Dongqi Fu", "Hanghang Tong", "Ross Maciejewski", "Jingrui He" ], "year": 2025, "venue": "ICLR", "abstract": "Nowadays, temporal heterogeneous graphs attract much research and industrial attention for building the next-generation Relational Deep Learning models and applications, due to their informative structures and features. While providing timely and precise services like personalized recommendations and question answering, this rich information also introduces extra exposure risk for each node in the graph. The distinctive local topology, the abundant heterogeneous features, and the time dimension of the graph data are more prone to expose sensitive information and narrow down the scope of victim candidates, which calls for well-defined protection techniques on graphs. To this end, we propose a Temporal Heterogeneous Graph Generator balancing Privacy, Utility, and Efficiency, named THePUff. More specifically, we first propose a differential privacy algorithm to perturb the input temporal heterogeneous graph for protecting privacy, and then utilize both the perturbed graph and the original one in a generative adversarial setting for THePUff to learn and generate privacy-guaranteed and utility-preserved graph data in an efficient manner. We further propose 6 new metrics in the temporal setting to measure heterogeneous graph utility and privacy. Finally, based on temporal heterogeneous graph datasets with up to 1 million nodes and 20 million edges, the experiments show that THePUff generates utilizable temporal heterogeneous graphs with privacy protected, compared with state-of-the-art baselines.", "source": "openreview", "url": "https://openreview.net/forum?id=tj5xJInWty", "decision_type": "Spotlight", "avg_rating": 7.3, "relative_path": "2025/ICLR/Spotlight/7.3_Temporal Heterogeneous Graph Generation with Privacy, Utility, and Efficiency_2025.pdf" }, { "title": "DailyDilemmas: Revealing Value Preferences of LLMs with Quandaries of Daily Life", "authors": [ "Yu Ying Chiu", "Liwei Jiang", "Yejin Choi" ], "year": 2025, "venue": "ICLR", "abstract": "As users increasingly seek guidance from LLMs for decision-making in daily life, many of these decisions are not clear-cut and depend significantly on the personal values and ethical standards of people. We present DailyDilemmas, a dataset of 1,360 moral dilemmas encountered in everyday life. Each dilemma presents two possible actions, along with affected parties and relevant human values for each action. Based on these dilemmas, we gather a repository of human values covering diverse everyday topics, such as interpersonal relationships, workplace, and environmental issues. With DailyDilemmas, we evaluate LLMs on these dilemmas to determine what action they will choose and the values represented by these action choices. Then, we analyze values through the lens of five theoretical frameworks inspired by sociology, psychology, and philosophy, including the World Values Survey, Moral Foundations Theory, Maslow's Hierarchy of Needs, Aristotle's Virtues, and Plutchik's Wheel of Emotions. For instance, we find LLMs are most aligned with self-expression over survival in World Values Survey and care over loyalty in Moral Foundations Theory. Interestingly, we find substantial preference differences in models for some core values. For example, for truthfulness, Mixtral-8x7B neglects it by 9.7% while GPT-4-turbo selects it by 9.4%. We also study the recent guidance released by OpenAI (ModelSpec), and Anthropic (Constitutional AI) to understand how their designated principles reflect their models' actual value prioritization when facing nuanced moral reasoning in daily-life settings. Finally, we find that end users cannot effectively steer such prioritization using system prompts.", "source": "openreview", "url": "https://openreview.net/forum?id=PGhiPGBf47", "decision_type": "Spotlight", "avg_rating": 7.2, "relative_path": "2025/ICLR/Spotlight/7.2_DailyDilemmas_ Revealing Value Preferences of LLMs with Quandaries of Daily Life_2025.pdf" }, { "title": "Going Deeper into Locally Differentially Private Graph Neural Networks", "authors": [ "Longzhu He", "Chaozhuo Li", "Peng Tang", "Sen Su" ], "year": 2025, "venue": "ICML", "abstract": "Graph Neural Networks (GNNs) have demonstrated superior performance in a variety of graph mining and learning tasks. However, when node representations involve sensitive personal information or variables related to individuals, learning from graph data can raise significant privacy concerns. Although recent studies have explored local differential privacy (LDP) to address these concerns, they often introduce significant distortions to graph data, severely degrading private learning utility (e.g., node classification accuracy). In this paper, we present UPGNET, an LDP-based privacy-preserving graph learning framework that enhances utility while protecting user data privacy. Specifically, we propose a three-stage pipeline that generalizes the LDP protocols for node features, targeting privacy-sensitive scenarios. Our analysis identifies two key factors that affect the utility of privacy-preserving graph learning: *feature dimension* and *neighborhood size*. Based on the above analysis, UPGNET enhances utility by introducing two core layers: High-Order Aggregator (HOA) layer and the Node Feature Regularization (NFR) layer. Extensive experiments on real-world datasets indicate that UPGNET significantly outperforms existing methods in terms of both privacy protection and learning utility.", "source": "openreview", "url": "https://openreview.net/forum?id=2aKHuXdr7Q", "decision_type": "Oral", "avg_rating": null, "relative_path": "2025/ICML/Oral/x_Going Deeper into Locally Differentially Private Graph Neural Networks_2025.pdf" }, { "title": "A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings", "authors": [ "Shib Sankar Dasgupta", "Michael Boratko", "Andrew McCallum" ], "year": 2025, "venue": "ICML", "abstract": "Personalized item recommendation typically suffers from data sparsity, which is most often addressed by learning vector representations of users and items via low-rank matrix factorization. While this effectively densifies the matrix by assuming users and movies can be represented by linearly dependent latent features, it does not capture more complicated interactions. For example, vector representations struggle with set-theoretic relationships, such as negation and intersection, e.g. recommending a movie that is “comedy and action, but not romance”. In this work, we formulate the problem of personalized item recommendation as matrix completion where rows are set-theoretically dependent. To capture this set-theoretic dependence we represent each user and attribute by a hyperrectangle or box (i.e. a Cartesian product of intervals). Box embeddings can intuitively be understood as trainable Venn diagrams, and thus not only inherently represent similarity (via the Jaccard index), but also naturally and faithfully support arbitrary set-theoretic relationships. Queries involving set-theoretic constraints can be efficiently computed directly on the embedding space by performing geometric operations on the representations. We empirically demonstrate the superiority of box embeddings over vector-based neural methods on both simple and complex item recommendation queries by up to 30% overall.", "source": "openreview", "url": "https://openreview.net/forum?id=27tMzmzDjO", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Em_2025.pdf" }, { "title": "Advancing Personalized Learning with Neural Collapse for Long-Tail Challenge", "authors": [ "Hanglei Hu", "Yingying Guo", "Zhikang Chen", "Sen Cui", "Fei Wu", "Kun Kuang", "Min Zhang", "Bo Jiang" ], "year": 2025, "venue": "ICML", "abstract": "Personalized learning, especially data-based methods, has garnered widespread attention in recent years, aiming to meet individual student needs. However, many works rely on the implicit assumption that benchmarks are high-quality and well-annotated, which limits their practical applicability. In real-world scenarios, these benchmarks often exhibit long-tail distributions, significantly impacting model performance. To address this challenge, we propose a novel method called **N**eural-**C**ollapse-**A**dvanced personalized **L**earning (NCAL), designed to learn features that conform to the same simplex equiangular tight frame (ETF) structure. NCAL introduces Text-modality Collapse (TC) regularization to optimize the distribution of text embeddings within the large language model (LLM) representation space. Notably, NCAL is model-agnostic, making it compatible with various architectures and approaches, thereby ensuring broad applicability. Extensive experiments demonstrate that NCAL effectively enhances existing works, achieving new state-of-the-art performance. Additionally, NCAL mitigates class imbalance, significantly improving the model’s generalization ability.", "source": "openreview", "url": "https://openreview.net/forum?id=W7phL2sNif", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Advancing Personalized Learning with Neural Collapse for Long-Tail Challenge_2025.pdf" }, { "title": "Aligning LLMs by Predicting Preferences from User Writing Samples", "authors": [ "Stéphane Aroca-Ouellette", "Natalie Mackraz", "Barry-John Theobald", "Katherine Metcalf" ], "year": 2025, "venue": "ICML", "abstract": "Accommodating human preferences is essential for creating aligned LLM agents that deliver personalized and effective interactions. Recent work has shown the potential for LLMs acting as writing agents to infer a description of user preferences. Agent alignment then comes from conditioning on the inferred preference description. However, existing methods often produce generic preference descriptions that fail to capture the unique and individualized nature of human preferences. This paper introduces PROSE, a method designed to enhance the precision of preference descriptions inferred from user writing samples. PROSE incorporates two key elements: (1) iterative refinement of inferred preferences, and (2) verification of inferred preferences across multiple user writing samples. We evaluate PROSE with several LLMs (i.e., Qwen2.5 7B and 72B Instruct, GPT-mini, and GPT-4o) on a summarization and an email writing task. We find that PROSE more accurately infers nuanced human preferences, improving the quality of the writing agent's generations over CIPHER (a state-of-the-art method for inferring preferences) by 33\\%. Lastly, we demonstrate that ICL and PROSE are complementary methods, and combining them provides up to a 9\\% improvement over ICL alone. Code: https://github.com/apple/ml-predict", "source": "openreview", "url": "https://openreview.net/forum?id=eUMGCipgtE", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Aligning LLMs by Predicting Preferences from User Writing Samples_2025.pdf" }, { "title": "EVOLvE: Evaluating and Optimizing LLMs For In-Context Exploration", "authors": [ "Allen Nie", "Yi Su", "Bo Chang", "Jonathan Lee", "Ed H. Chi", "Quoc V Le", "Minmin Chen" ], "year": 2025, "venue": "ICML", "abstract": "Despite their success in many domains, large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty. This is crucial as many real-world applications, ranging from personalized recommendations to healthcare interventions, demand that LLMs not only predict but also actively learn to make optimal decisions through exploration. In this work, we measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications. We develop a comprehensive suite of environments, including both context-free and contextual bandits with varying task difficulties, to benchmark LLMs' performance. Motivated by the existence of optimal exploration algorithms, we propose efficient ways to integrate this algorithmic knowledge into LLMs: by providing explicit algorithm-guided support during inference; and through algorithm distillation via in-context demonstrations and fine-tuning, using synthetic data generated from these algorithms. Impressively, these techniques allow us to achieve superior exploration performance with smaller models, surpassing larger models on various tasks. We conducted an extensive ablation study to shed light on various factors, such as task difficulty and data representation, that influence the efficiency of LLM exploration. Additionally, we conduct a rigorous analysis of the LLM's exploration efficiency using the concept of regret, linking its ability to explore to the model size and underlying algorithm.", "source": "openreview", "url": "https://openreview.net/forum?id=ck7dvZFbRW", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_EVOLvE_ Evaluating and Optimizing LLMs For In-Context Exploration_2025.pdf" }, { "title": "EasyRef: Omni-Generalized Group Image Reference for Diffusion Models via Multimodal LLM", "authors": [ "Zhuofan Zong", "Dongzhi Jiang", "Bingqi Ma", "Guanglu Song", "Hao Shao", "Dazhong Shen", "Yu Liu", "Hongsheng Li" ], "year": 2025, "venue": "ICML", "abstract": "Significant achievements in personalization of diffusion models have been witnessed. Conventional tuning-free methods mostly encode multiple reference images by averaging or concatenating their image embeddings as the injection condition, but such an image-independent operation cannot perform interaction among images to capture consistent visual elements within multiple references. Although tuning-based approaches can effectively extract consistent elements within multiple images through the training process, it necessitates test-time finetuning for each distinct image group. This paper introduces EasyRef, a plug-and-play adaption method that empowers diffusion models to condition consistent visual elements (e.g., style and human facial identity, etc.) across multiple reference images under instruction controls. To effectively exploit consistent visual elements within multiple images, we leverage the multi-image comprehension and instruction-following capabilities of the multimodal large language model (MLLM), prompting it to capture consistent visual elements based on the instruction. Besides, injecting the MLLM's representations into the diffusion process through adapters can easily generalize to unseen domains. To mitigate computational costs and enhance fine-grained detail preservation, we introduce an efficient reference aggregation strategy and a progressive training scheme. Finally, we introduce MRBench, a new multi-reference image generation benchmark. Experimental results demonstrate EasyRef surpasses both tuning-free and tuning-based methods, achieving superior aesthetic quality and robust zero-shot generalization across diverse domains.", "source": "openreview", "url": "https://openreview.net/forum?id=GNTmqRTpzr", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_EasyRef_ Omni-Generalized Group Image Reference for Diffusion Models via Multimodal LLM_2025.pdf" }, { "title": "EduLLM: Leveraging Large Language Models and Framelet-Based Signed Hypergraph Neural Networks for Student Performance Prediction", "authors": [ "Ming Li", "Yukang Cheng", "Lu Bai", "Feilong Cao", "Ke Lv", "Jiye Liang", "Pietro Lio" ], "year": 2025, "venue": "ICML", "abstract": "The growing demand for personalized learning underscores the importance of accurately predicting students' future performance to support tailored education and optimize instructional strategies. Traditional approaches predominantly focus on temporal modeling using historical response records and learning trajectories. While effective, these methods often fall short in capturing the intricate interactions between students and learning content, as well as the subtle semantics of these interactions. To address these gaps, we present EduLLM, the first framework to leverage large language models in combination with hypergraph learning for student performance prediction. The framework incorporates FraS-HNN ($\\underline{\\mbox{Fra}}$melet-based $\\underline{\\mbox{S}}$igned $\\underline{\\mbox{H}}$ypergraph $\\underline{\\mbox{N}}$eural $\\underline{\\mbox{N}}$etworks), a novel spectral-based model for signed hypergraph learning, designed to model interactions between students and multiple-choice questions. In this setup, students and questions are represented as nodes, while response records are encoded as positive and negative signed hyperedges, effectively capturing both structural and semantic intricacies of personalized learning behaviors. FraS-HNN employs framelet-based low-pass and high-pass filters to extract multi-frequency features. EduLLM integrates fine-grained semantic features derived from LLMs, synergizing with signed hypergraph representations to enhance prediction accuracy. Extensive experiments conducted on multiple educational datasets demonstrate that EduLLM significantly outperforms state-of-the-art baselines, validating the novel integration of LLMs with FraS-HNN for signed hypergraph learning.", "source": "openreview", "url": "https://openreview.net/forum?id=60q5yK7yjW", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_EduLLM_ Leveraging Large Language Models and Framelet-Based Signed Hypergraph Neural Networks f_2025.pdf" }, { "title": "Efficient Personalized Adaptation for Physiological Signal Foundation Model", "authors": [ "Chenrui Wu", "Haishuai Wang", "Xiang Zhang", "Chengqi Zhang", "Jiajun Bu" ], "year": 2025, "venue": "ICML", "abstract": "Time series analysis is crucial across various fields like energy, environment, transportation, finance and health. Deep learning has significantly advanced this field, particularly, the Time Series Foundation Model (TSFM) excels in multiple domains due to extensive pre-training. In this work, we focus on TSFM's challenges in medical practice: limited computing resources and medical data privacy. TSFM variants include fine-tuned models and those pre-trained for rapid deployment on diverse data. There may not be enough computing resources to train physiological signals locally in hospitals, and generalized TSFM is still inferior to task-specific methods on private, imbalanced local data. To address this, we propose PhysioPFM, a framework for efficiently personalizing TSFM. Our approach involves low-rank pre-training on public datasets, generator training by trained LoRA weights, and efficient weight generation via local data. Experimental results demonstrate that integrating generated models with TSFM enhances performance, and transferability, and reduces the need for additional sensitive data training.", "source": "openreview", "url": "https://openreview.net/forum?id=55ysNwbOTI", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Efficient Personalized Adaptation for Physiological Signal Foundation Model_2025.pdf" }, { "title": "FedPHA: Federated Prompt Learning for Heterogeneous Client Adaptation", "authors": [ "Chengying Fang", "Wenke Huang", "Guancheng Wan", "Yihao Yang", "Mang Ye" ], "year": 2025, "venue": "ICML", "abstract": "Federated Prompt Learning (FPL) adapts pre-trained Vision-Language Models (VLMs) to federated learning through prompt tuning, leveraging their transferable representations and strong generalization capabilities. Traditional methods often require uniform prompt lengths for federated aggregation, limiting adaptability to clients with diverse prompt lengths and distribution biases. In this paper, we propose **Fed**erated **P**rompt Learning for **H**eterogeneous Client **A**daptation (FedPHA), a novel framework that combines a fixed-length global prompt for efficient aggregation with local prompts of varying lengths to capture client-specific data characteristics. Additionally, FedPHA designs Singular Value Decomposition (SVD) based projection and bidirectional alignment to disentangle global conflicts arising from client heterogeneity, ensuring that personalized client tasks effectively utilize non-harmful global knowledge. This approach ensures that global knowledge improves model generalization while local knowledge preserves local optimization. Experimental results validate the effectiveness of FedPHA in achieving a balance between global and personalized knowledge in federated learning scenarios.", "source": "openreview", "url": "https://openreview.net/forum?id=y7pDvbi9xz", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_FedPHA_ Federated Prompt Learning for Heterogeneous Client Adaptation_2025.pdf" }, { "title": "Federated Disentangled Tuning with Textual Prior Decoupling and Visual Dynamic Adaptation", "authors": [ "Yihao Yang", "Wenke Huang", "Guancheng Wan", "Bin Yang", "Mang Ye" ], "year": 2025, "venue": "ICML", "abstract": "Federated Parameter-Efficient Fine-Tuning aims to adapt Vision-Language Models for downstream tasks in distributed environments. However, data heterogeneity across participants hinders collaborative effectiveness, necessitating personalized adaptation to cover distinct data distributions. Current personalized methods suffer from two limitations. 1) Textual Property Loss: Existing methods facilitate the collaboration between decoupled prompts at the feature level, which potentially undermines the textual properties of the prompts. 2) Visual Feature Diversity: The diversity of visual features makes it challenging to leverage naive image features directly for image-text alignment in downstream tasks. In this work, we propose Federated Disentangled Tuning with Textual Prior Decoupling and Visual Dynamic Adaptation (FedDDA) to overcome the above limitations. Specifically, we encourage decoupling prompts in a way that maximizes the efficacy of prior knowledge, which is essential for maintaining a coherent linguistic context. Furthermore, we design a visual adaption model to reshape visual space to optimally align with the textual space. Extensive experiments on various image classification tasks show the effectiveness of our work in addressing data heterogeneity. The codes are released at https://github.com/MoratalYang/FedDDA.", "source": "openreview", "url": "https://openreview.net/forum?id=0p86Mhg014", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Federated Disentangled Tuning with Textual Prior Decoupling and Visual Dynamic Adaptation_2025.pdf" }, { "title": "Federated Oriented Learning: A Practical One-Shot Personalized Federated Learning Framework", "authors": [ "Guan Huang", "Tao Shu" ], "year": 2025, "venue": "ICML", "abstract": "Personalized Federated Learning (PFL) has become a promising learning paradigm, enabling the training of high-quality personalized models through multiple communication rounds between clients and a central server. However, directly applying traditional PFL in real-world environments where communication is expensive, limited, or infeasible is challenging, as seen in Low Earth Orbit (LEO) satellite constellations, which face severe communication constraints due to their high mobility, limited contact windows. To address these issues, we introduce Federated Oriented Learning (FOL), a novel four-stage one-shot PFL algorithm designed to enhance local model performance by leveraging neighboring models within stringent communication constraints. FOL comprises model pretraining, model collection, model alignment (via fine-tuning, pruning, post fine-tuning, and ensemble refinement), and knowledge distillation stages. We establish two theoretical guarantees on empirical risk discrepancy between student and teacher models and the convergence of the distillation process. Extensive experiments on datasets Wildfire, Hurricane, CIFAR-10, CIFAR-100, and SVHN demonstrate that FOL consistently outperforms state-of-the-art one-shot Federated Learning (OFL) methods; for example, it achieves accuracy improvements of up to 39.24\\% over the baselines on the Wildfire dataset.", "source": "openreview", "url": "https://openreview.net/forum?id=jwjvkWsePB", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Federated Oriented Learning_ A Practical One-Shot Personalized Federated Learning Framework_2025.pdf" }, { "title": "FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks", "authors": [ "Zhaoxuan Kan", "Husheng Han", "shangyi shi", "Tenghui Hua", "Hang Lu", "Xiaowei Li", "Jianan Mu", "Xing Hu" ], "year": 2025, "venue": "ICML", "abstract": "Graph Convolutional Neural Networks (GCNs) have gained widespread popularity in various fields like personal healthcare and financial systems, due to their remarkable performance. Despite the growing demand for cloud-based GCN services, privacy concerns over sensitive graph data remain significant. Homomorphic Encryption (HE) facilitates Privacy-Preserving Machine Learning (PPML) by allowing computations to be performed on encrypted data. However, HE introduces substantial computational overhead, particularly for GCN operations that require rotations and multiplications in matrix products. The sparsity of GCNs offers significant performance potential, but their irregularity introduces additional operations that reduce practical gains. In this paper, we propose FicGCN, a HE-based framework specifically designed to harness the sparse characteristics of GCNs and strike a globally optimal balance between aggregation and combination operations. FicGCN employs a latency-aware packing scheme, a Sparse Intra-Ciphertext Aggregation (SpIntra-CA) method to minimize rotation overhead, and a region-based data reordering driven by local adjacency structure. \nWe evaluated FicGCN on several popular datasets, and the results show that FicGCN achieved the best performance across all tested datasets, with up to a $4.10\\times$ improvement over the latest design.", "source": "openreview", "url": "https://openreview.net/forum?id=m74x7brnd6", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_FicGCN_ Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Netw_2025.pdf" }, { "title": "From Individual Experience to Collective Evidence: A Reporting-Based Framework for Identifying Systemic Harms", "authors": [ "Jessica Dai", "Paula Gradu", "Inioluwa Deborah Raji", "Benjamin Recht" ], "year": 2025, "venue": "ICML", "abstract": "When an individual reports a negative interaction with some system, how can their personal experience be contextualized within broader patterns of system behavior? We study the *reporting database* problem, where individual reports of adverse events arrive sequentially, and are aggregated over time. In this work, our goal is to identify whether there are subgroups—defined by any combination of relevant\nfeatures—that are disproportionately likely to experience harmful interactions with the system. We formalize this problem as a sequential hypothesis test, and identify conditions on reporting behavior that are sufficient for making inferences about disparities in true rates of harm across subgroups. We show that algorithms for sequential hypothesis tests can be applied to this problem with a standard multiple testing\ncorrection. We then demonstrate our method on real-world datasets, including mortgage decisions and vaccine side effects; on each, our method (re-)identifies subgroups known to experience disproportionate harm using only a fraction of the data that was initially used to discover them.", "source": "openreview", "url": "https://openreview.net/forum?id=ywmoIu5t9i", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_From Individual Experience to Collective Evidence_ A Reporting-Based Framework for Identifying _2025.pdf" }, { "title": "From RAG to Memory: Non-Parametric Continual Learning for Large Language Models", "authors": [ "Bernal Jiménez Gutiérrez", "Yiheng Shu", "Weijian Qi", "Sizhe Zhou", "Yu Su" ], "year": 2025, "venue": "ICML", "abstract": "Our ability to continuously acquire, organize, and leverage knowledge is a key feature of human intelligence that AI systems must approximate to unlock their full potential. Given the challenges in continual learning with large language models (LLMs), retrieval-augmented generation (RAG) has become the dominant way to introduce new information. However, its reliance on vector retrieval hinders its ability to mimic the dynamic and interconnected nature of human long-term memory. Recent RAG approaches augment vector\nembeddings with various structures like knowledge graphs to address some of these gaps, namely sense-making and associativity. However, their performance on more basic factual memory tasks drops considerably below standard RAG. We address this unintended deterioration and propose HippoRAG 2, a framework that outperforms standard RAG comprehensively on factual, sense-making, and associative memory tasks. HippoRAG 2 builds upon the Personalized PageRank algorithm used in HippoRAG and enhances it with deeper passage integration and more effective online use of an LLM. This combination pushes this RAG system closer to the effectiveness of human long-term memory, achieving a 7% improvement in associative memory tasks over the state-of-the-art embedding model while also exhibiting superior factual knowledge and sense-making memory capabilities. This work paves the way for non-parametric continual learning for LLMs. Code and data are available at https://github.com/OSU-NLP-Group/HippoRAG.", "source": "openreview", "url": "https://openreview.net/forum?id=LWH8yn4HS2", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_From RAG to Memory_ Non-Parametric Continual Learning for Large Language Models_2025.pdf" }, { "title": "GHOST: Generalizable One-Shot Federated Graph Learning with Proxy-Based Topology Knowledge Retention", "authors": [ "Jiaru Qian", "Guancheng Wan", "Wenke Huang", "Guibin Zhang", "Yuxin Wu", "Bo Du", "Mang Ye" ], "year": 2025, "venue": "ICML", "abstract": "Federated Graph Learning (FGL) proposes an effective approach to collaboratively training Graph Neural Networks (GNNs) while maintaining privacy. Nevertheless, communication efficiency becomes a critical bottleneck in environments with limited resources. In this context, one-shot FGL emerges as a promising solution by restricting communication to a single round. However, prevailing FGL methods face two key challenges in the one-shot setting: 1) They heavily rely on gradual personalized optimization over multiple rounds, undermining the capability of the global model to efficiently generalize across diverse graph structures. 2) They are prone to overfitting to local data distributions due to extreme structural bias, leading to catastrophic forgetting. To address these issues, we introduce **GHOST**, an innovative one-shot FGL framework. In GHOST, we establish a proxy model for each client to leverage diverse local knowledge and integrate it to train the global model. During training, we identify and consolidate parameters essential for capturing topological knowledge, thereby mitigating catastrophic forgetting. Extensive experiments on real-world tasks demonstrate the superiority and generalization capability of GHOST. The code is available at https://github.com/JiaruQian/GHOST.", "source": "openreview", "url": "https://openreview.net/forum?id=nAk0ENu8LS", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_GHOST_ Generalizable One-Shot Federated Graph Learning with Proxy-Based Topology Knowledge Rete_2025.pdf" }, { "title": "Gradient-based Explanations for Deep Learning Survival Models", "authors": [ "Sophie Hanna Langbein", "Niklas Koenen", "Marvin N. Wright" ], "year": 2025, "venue": "ICML", "abstract": "Deep learning survival models often outperform classical methods in time-to-event predictions, particularly in personalized medicine, but their \"black box\" nature hinders broader adoption. We propose a framework for gradient-based explanation methods tailored to survival neural networks, extending their use beyond regression and classification. We analyze the implications of their theoretical assumptions for time-dependent explanations in the survival setting and propose effective visualizations incorporating the temporal dimension. Experiments on synthetic data show that gradient-based methods capture the magnitude and direction of local and global feature effects, including time dependencies. We introduce GradSHAP(t), a gradient-based counterpart to SurvSHAP(t), which outperforms SurvSHAP(t) and SurvLIME in a computational speed vs. accuracy trade-off. Finally, we apply these methods to medical data with multi-modal inputs, revealing relevant tabular features and visual patterns, as well as their temporal dynamics.", "source": "openreview", "url": "https://openreview.net/forum?id=P0wSGDoip1", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Gradient-based Explanations for Deep Learning Survival Models_2025.pdf" }, { "title": "Harnessing Heterogeneous Statistical Strength for Personalized Federated Learning via Hierarchical Bayesian Inference", "authors": [ "Mahendra Singh Thapa", "Rui Li" ], "year": 2025, "venue": "ICML", "abstract": "Personalized federated learning (PFL) based on Bayesian approach tackle the challenges from statistical heterogeneity of client data by computing a personalized posterior distribution over the parameters of each client's local model and constructing a global distribution by aggregating the parameters of these personalized posteriors. However, the heuristic aggregation methods introduce strong biases and result in global models with poor generalization. We thus propose a novel hierarchical Bayesian inference framework for PFL by specifying a conjugate hyper-prior over the parameters of the personalized posteriors. This allows us to jointly compute a global posterior distribution for aggregation and the personalized ones at local level. This hierarchical Bayesian inference framework achieves elegant balance between local personalization and global model robustness. Extensive empirical study shows that by effectively sharing the heterogeneous statistical strength across the local models while retaining their distinctive characteristics, our framework yields state-of-the-art performance. We also show that existing Bayesian PFLs are special cases of our framework.", "source": "openreview", "url": "https://openreview.net/forum?id=Zn6hmmBnAa", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Harnessing Heterogeneous Statistical Strength for Personalized Federated Learning via Hierarchi_2025.pdf" }, { "title": "Interaction-Aware Gaussian Weighting for Clustered Federated Learning", "authors": [ "Alessandro Licciardi", "Davide Leo", "Eros Fanì", "Barbara Caputo", "Marco Ciccone" ], "year": 2025, "venue": "ICML", "abstract": "Federated Learning (FL) emerged as a decentralized paradigm to train models while preserving privacy. However, conventional FL struggles with data heterogeneity and class imbalance, which degrade model performance.\nClustered FL balances personalization and decentralized training by grouping clients with analogous data distributions, enabling improved accuracy while adhering to privacy constraints. This approach effectively mitigates the adverse impact of heterogeneity in FL.\nIn this work, we propose a novel clustering method for FL, **FedGWC** (Federated Gaussian Weighting Clustering), which groups clients based on their data distribution, allowing training of a more robust and personalized model on the identified clusters. **FedGWC** identifies homogeneous clusters by transforming individual empirical losses to model client interactions with a Gaussian reward mechanism. Additionally, we introduce the *Wasserstein Adjusted Score*, a new clustering metric for FL to evaluate cluster cohesion with respect to the individual class distribution. Our experiments on benchmark datasets show that **FedGWC** outperforms existing FL algorithms in cluster quality and classification accuracy, validating the efficacy of our approach.", "source": "openreview", "url": "https://openreview.net/forum?id=dZAQxNFKGg", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Interaction-Aware Gaussian Weighting for Clustered Federated Learning_2025.pdf" }, { "title": "Learning Policy Committees for Effective Personalization in MDPs with Diverse Tasks", "authors": [ "Luise Ge", "Michael Lanier", "Anindya Sarkar", "Bengisu Guresti", "Chongjie Zhang", "Yevgeniy Vorobeychik" ], "year": 2025, "venue": "ICML", "abstract": "Many dynamic decision problems, such as robotic control, involve a series of tasks, many of which are unknown at training time.\nTypical approaches for these problems, such as multi-task and meta reinforcement learning, do not generalize well when the tasks are diverse. On the other hand, approaches that aim to tackle task diversity, such as using task embedding as policy context and task clustering, typically lack performance guarantees and require a large number of training tasks. To address these challenges, we propose a novel approach for learning a policy committee that includes at least one near-optimal policy with high probability for tasks encountered during execution. While we show that this problem is in general inapproximable, we present two practical algorithmic solutions.\nThe first yields provable approximation and task sample complexity guarantees when tasks are low-dimensional (the best we can do due to inapproximability), whereas the second is a general and practical gradient-based approach. In addition, we provide a provable sample complexity bound for few-shot learning. Our experiments on MuJoCo and Meta-World show that the proposed approach outperforms state-of-the-art multi-task, meta-, and task clustering baselines in training, generalization, and few-shot learning, often by a large margin. Our code is available at https://github.com/CERL-WUSTL/PACMAN.", "source": "openreview", "url": "https://openreview.net/forum?id=cBukWQKWvQ", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Learning Policy Committees for Effective Personalization in MDPs with Diverse Tasks_2025.pdf" }, { "title": "Leveraging Model Guidance to Extract Training Data from Personalized Diffusion Models", "authors": [ "Xiaoyu Wu", "Jiaru Zhang", "Steven Wu" ], "year": 2025, "venue": "ICML", "abstract": "Diffusion Models (DMs) have evolved into advanced image generation tools, especially for few-shot fine-tuning where a pretrained DM is fine-tuned on a small set of images to capture specific styles or objects. Many people upload these personalized checkpoints online, fostering communities such as Civitai and HuggingFace. However, model owners may overlook the potential risks of data leakage by releasing their fine-tuned checkpoints. Moreover, concerns regarding copyright violations arise when unauthorized data is used during fine-tuning. In this paper, we ask: \"Can training data be extracted from these fine-tuned DMs shared online?\" A successful extraction would present not only data leakage threats but also offer tangible evidence of copyright infringement. To answer this, we propose FineXtract, a framework for extracting fine-tuning data. Our method approximates fine-tuning as a gradual shift in the model's learned distribution---from the original pretrained DM toward the fine-tuning data. By extrapolating the models before and after fine-tuning, we guide the generation toward high-probability regions within the fine-tuned data distribution. We then apply a clustering algorithm to extract the most probable images from those generated using this extrapolated guidance. Experiments on DMs fine-tuned with datasets such as WikiArt, DreamBooth, and real-world checkpoints posted online validate the effectiveness of our method, extracting approximately 20% of fine-tuning data in most cases, significantly surpassing baseline performance. The code is available.", "source": "openreview", "url": "https://openreview.net/forum?id=HGnMNUTdUz", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Leveraging Model Guidance to Extract Training Data from Personalized Diffusion Models_2025.pdf" }, { "title": "MTL-UE: Learning to Learn Nothing for Multi-Task Learning", "authors": [ "Yi Yu", "Song Xia", "SIYUAN YANG", "Chenqi Kong", "Wenhan Yang", "Shijian Lu", "Yap-Peng Tan", "Alex Kot" ], "year": 2025, "venue": "ICML", "abstract": "Most existing unlearnable strategies focus on preventing unauthorized users from training single-task learning (STL) models with personal data. Nevertheless, the paradigm has recently shifted towards multi-task data and multi-task learning (MTL), targeting generalist and foundation models that can handle multiple tasks simultaneously. Despite their growing importance, MTL data and models have been largely neglected while pursuing unlearnable strategies. This paper presents MTL-UE, the first unified framework for generating unlearnable examples for multi-task data and MTL models. Instead of optimizing perturbations for each sample, we design a generator-based structure that introduces label priors and class-wise feature embeddings which leads to much better attacking performance. In addition, MTL-UE incorporates intra-task and inter-task embedding regularization to increase inter-class separation and suppress intra-class variance which enhances the attack robustness greatly. Furthermore, MTL-UE is versatile with good supports for dense prediction tasks in MTL. It is also plug-and-play allowing integrating existing surrogate-dependent unlearnable methods with little adaptation. Extensive experiments show that MTL-UE achieves superior attacking performance consistently across 4 MTL datasets, 3 base UE methods, 5 model backbones, and 5 MTL task-weighting strategies. Code is available at https://github.com/yuyi-sd/MTL-UE.", "source": "openreview", "url": "https://openreview.net/forum?id=w16xG4A7W4", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_MTL-UE_ Learning to Learn Nothing for Multi-Task Learning_2025.pdf" }, { "title": "MTSTRec: Multimodal Time-Aligned Shared Token Recommender", "authors": [ "Ming-Yi Hong", "Yen-Jung Hsu", "Miao-Chen Chiang", "Che Lin" ], "year": 2025, "venue": "ICML", "abstract": "Sequential recommendation in e-commerce utilizes users' anonymous browsing histories to personalize product suggestions without relying on private information. Existing item ID-based methods and multimodal models often overlook the temporal alignment of modalities like textual descriptions, visual content, and prices in user browsing sequences. To address this limitation, this paper proposes the Multimodal Time-aligned Shared Token Recommender (MTSTRec), a transformer-based framework with a single time-aligned shared token per product for efficient cross-modality fusion. MTSTRec preserves the distinct contributions of each modality while aligning them temporally to better capture user preferences. Extensive experiments demonstrate that MTSTRec achieves state-of-the-art performance across multiple sequential recommendation benchmarks, significantly improving upon existing multimodal fusion. Our code is available at https://github.com/idssplab/MTSTRec.", "source": "openreview", "url": "https://openreview.net/forum?id=yWDvVl9Wtp", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_MTSTRec_ Multimodal Time-Aligned Shared Token Recommender_2025.pdf" }, { "title": "On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains", "authors": [ "Xun Xian", "Ganghua Wang", "Xuan Bi", "Rui Zhang", "Jayanth Srinivasa", "Ashish Kundu", "Charles Fleming", "Mingyi Hong", "Jie Ding" ], "year": 2025, "venue": "ICML", "abstract": "Retrieval-Augmented Generation (RAG) has been empirically shown to enhance the performance of large language models (LLMs) in knowledge-intensive domains such as healthcare, finance, and legal contexts. Given a query, RAG retrieves relevant documents from a corpus and integrates them into the LLMs’ generation process. In this study, we investigate the adversarial robustness of RAG, focusing specifically on examining the retrieval system. First, across 225 different setup combinations of corpus, retriever, query, and targeted information, we show that retrieval systems are vulnerable to universal poisoning attacks in medical Q&A. In such attacks, adversaries generate poisoned documents containing a broad spectrum of targeted information, such as personally identifiable information. When these poisoned documents are inserted into a corpus, they can be accurately retrieved by any users, as long as attacker-specified queries are used. To understand this vulnerability, we discovered that the deviation from the query’s embedding to that of the poisoned document tends to follow a pattern in which the high similarity between the poisoned document and the query is retained, thereby enabling precise retrieval. Based on these findings, we develop a new detection-based defense to ensure the safe use of RAG. Through extensive experiments spanning various Q&A domains, we observed that our proposed method consistently achieves excellent detection rates in nearly all cases.", "source": "openreview", "url": "https://openreview.net/forum?id=SOMDiaGoil", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Appl_2025.pdf" }, { "title": "On-Device Collaborative Language Modeling via a Mixture of Generalists and Specialists", "authors": [ "Dongyang Fan", "Bettina Messmer", "Nikita Doikov", "Martin Jaggi" ], "year": 2025, "venue": "ICML", "abstract": "On-device LLMs have gained increasing attention for their ability to enhance privacy and provide a personalized user experience. To facilitate private learning with scarce data, Federated Learning has become a standard approach. However, it faces challenges such as computational resource heterogeneity and data heterogeneity among end users. We propose CoMiGS ($\\textbf{Co}$llaborative learning with a $\\textbf{Mi}$xture of $\\textbf{G}$eneralists and $\\textbf{S}$pecialists), the first approach to address both challenges. A key innovation of our method is the bi-level optimization formulation of the Mixture-of-Experts learning objective, where the router is optimized using a separate validation set to ensure alignment with the target distribution. We solve our objective with alternating minimization, for which we provide a theoretical analysis. Our method shares generalist experts across users while localizing a varying number of specialist experts, thereby adapting to users’ computational resources and preserving privacy. Through extensive experiments, we show CoMiGS effectively balances general and personalized knowledge for each token generation. We demonstrate that CoMiGS remains robust against overfitting—due to the generalists' regularizing effect—while adapting to local data through specialist expertise. We open source our codebase for collaborative LLMs.", "source": "openreview", "url": "https://openreview.net/forum?id=Eog0kXX7hW", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_On-Device Collaborative Language Modeling via a Mixture of Generalists and Specialists_2025.pdf" }, { "title": "Permutation-based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Data", "authors": [ "Xinshuai Dong", "Ignavier Ng", "Boyang Sun", "Haoyue Dai", "Guang-Yuan Hao", "Shunxing Fan", "Peter Spirtes", "Yumou Qiu", "Kun Zhang" ], "year": 2025, "venue": "ICML", "abstract": "Recent advances have shown that statistical tests for the rank of cross-covariance matrices play an important role in causal discovery. These rank tests include partial correlation tests as special cases and provide further graphical information about latent variables. Existing rank tests typically assume that all the continuous variables can be perfectly measured, and yet, in practice many variables can only be measured after discretization. For example, in psychometric studies, the continuous level of certain personality dimensions of a person can only be measured after being discretized into order-preserving options such as disagree, neutral, and agree. Motivated by this, we propose Mixed data Permutation-based Rank Test (MPRT), which properly controls the statistical errors even when some or all variables are discretized. Theoretically, we establish the exchangeability and estimate the asymptotic null distribution by permutations; as a consequence, MPRT can effectively control the Type I error in the presence of discretization while previous methods cannot. Empirically, our method is validated by extensive experiments on synthetic data and real-world data to demonstrate its effectiveness as well as applicability in causal discovery (code will be available at https://github.com/dongxinshuai/scm-identify).", "source": "openreview", "url": "https://openreview.net/forum?id=VBTHduhm4K", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Permutation-based Rank Test in the Presence of Discretization and Application in Causal Discove_2025.pdf" }, { "title": "Private Model Personalization Revisited", "authors": [ "Conor Snedeker", "Xinyu Zhou", "Raef Bassily" ], "year": 2025, "venue": "ICML", "abstract": "We study model personalization under user-level differential privacy (DP) in the shared representation framework. In this problem, there are $n$ users whose data is statistically heterogeneous, and their optimal parameters share an unknown embedding $U^* \\in\\mathbb{R}^{d\\times k}$ that maps the user parameters in $\\mathbb{R}^d$ to low-dimensional representations in $\\mathbb{R}^k$, where $k\\ll d$. Our goal is to privately recover the shared embedding and the local low-dimensional representations with small excess risk in the federated setting. We propose a private, efficient federated learning algorithm to learn the shared embedding based on the FedRep algorithm in (Collins et al.,\n2021). Unlike (Collins et al., 2021), our algorithm satisfies differential privacy, and our results hold for the case of noisy labels. In contrast to prior work on private model personalization (Jain et al., 2021), our utility guarantees hold under a larger class of users' distributions (sub-Gaussian instead of Gaussian distributions). Additionally, in natural parameter regimes, we improve the privacy error term in (Jain\net al., 2021) by a factor of $\\widetilde{O}(dk)$. Next, we consider the binary classification setting. We present an information-theoretic construction to privately learn the shared embedding and derive a margin-based accuracy guarantee that is independent of $d$. Our method utilizes the Johnson-Lindenstrauss transform to reduce the effective dimensions of the shared embedding and the users' data. This result shows that dimension-independent risk bounds are possible in this setting under a margin loss.", "source": "openreview", "url": "https://openreview.net/forum?id=hw1kGPcSZ5", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Private Model Personalization Revisited_2025.pdf" }, { "title": "Prompt-to-Leaderboard: Prompt-Adaptive LLM Evaluations", "authors": [ "Evan Frick", "Connor Chen", "Joseph Tennyson", "Tianle Li", "Wei-Lin Chiang", "Anastasios Nikolas Angelopoulos", "Ion Stoica" ], "year": 2025, "venue": "ICML", "abstract": "Large language model (LLM) evaluations typically rely on aggregated metrics like accuracy or human preference, averaging across users and prompts. This averaging obscures user- and prompt-specific variations in model performance. To address this, we propose Prompt-to-Leaderboard (P2L), a method that produces leaderboards specific to a prompt or set of prompts. The core idea is to train an LLM taking natural language prompts as input to output a vector of Bradley-Terry coefficients which are then used to predict the human preference vote. The resulting prompt-dependent leaderboards allow for unsupervised task-specific evaluation, optimal routing of queries to models, personalization, and automated evaluation of model strengths and weaknesses. Data from Chatbot Arena suggest that P2L better captures the nuanced landscape of language model performance than the averaged leaderboard. Furthermore, our findings suggest that P2L's ability to produce prompt-specific evaluations follows a power law scaling similar to that observed in LLMs themselves. In January 2025, the router we trained based on this methodology achieved the \\#1 spot on the Chatbot Arena leaderboard. Our code is available at this GitHub link: https://github.com/lmarena/p2l.", "source": "openreview", "url": "https://openreview.net/forum?id=7VPRrzFEN8", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Prompt-to-Leaderboard_ Prompt-Adaptive LLM Evaluations_2025.pdf" }, { "title": "Quamba2: A Robust and Scalable Post-training Quantization Framework for Selective State Space Models", "authors": [ "Hung-Yueh Chiang", "Chi-Chih Chang", "Natalia Frumkin", "Kai-Chiang Wu", "Mohamed S. Abdelfattah", "Diana Marculescu" ], "year": 2025, "venue": "ICML", "abstract": "State Space Models (SSMs) are gaining attention as an efficient alternative to Transformers due to their constant memory complexity and comparable performance. Yet, deploying large-scale SSMs on cloud-based services or resource-constrained devices faces challenges. To address this, quantizing SSMs using low bit-width data types is proposed to reduce model size and leverage hardware acceleration. Given that SSMs are sensitive to quantization errors, recent advancements focus on quantizing a specific model or bit-width to improve their efficiency while maintaining performance. However, different bit-width configurations, such as W4A8 for cloud service throughput and W4A16 for improving question-answering on personal devices, are necessary for specific scenarios.\nTo this end, we present Quamba2, compatible with \\textbf{W8A8}, \\textbf{W4A8}, and \\textbf{W4A16} for both \\textbf{Mamba} and \\textbf{Mamba2}, addressing the rising demand for SSM deployment across various platforms. We propose an offline approach to quantize inputs of a linear recurrence in 8-bit by sorting and clustering for $x$, combined with a per-state-group quantization for $B$ and $C$. To ensure compute-invariance in the SSM output, we offline rearrange weights according to the clustering sequence. The experiments show Quamba2-8B outperforms several state-of-the-art SSMs quantization methods and delivers 1.3$\\times$ and 3$\\times$ speedup in the pre-filling and generation stages and 4$\\times$ memory reduction with only a $1.6$% accuracy drop on average. The code and quantized models will be released at:", "source": "openreview", "url": "https://openreview.net/forum?id=Zm0Kper4yx", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Quamba2_ A Robust and Scalable Post-training Quantization Framework for Selective State Space M_2025.pdf" }, { "title": "Recommendations with Sparse Comparison Data: Provably Fast Convergence for Nonconvex Matrix Factorization", "authors": [ "Suryanarayana Sankagiri", "Jalal Etesami", "Matthias Grossglauser" ], "year": 2025, "venue": "ICML", "abstract": "In this paper, we consider a recommender system that elicits user feedback through pairwise comparisons instead of ratings. We study the problem of learning personalised preferences from such comparison data via collaborative filtering. Similar to the classical matrix completion setting, we assume that users and items are endowed with low-dimensional latent features. These features give rise to user-item utilities, and the comparison outcomes are governed by a discrete choice model over these utilities. The task of learning these features is then formulated as a maximum likelihood problem over the comparison dataset. Despite the resulting optimization problem being nonconvex, we show that gradient-based methods converge exponentially to the latent features, given a warm start. Importantly, this result holds in a sparse data regime, where each user compares only a few pairs of items. Our main technical contribution is to extend key concentration results commonly used in matrix completion to our model. Simulations reveal that the empirical performance of the method exceeds theoretical predictions, even when some assumptions are relaxed. Our work demonstrates that learning personalised recommendations from comparison data is both computationally and statistically efficient.", "source": "openreview", "url": "https://openreview.net/forum?id=vcNJgiEGdz", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Recommendations with Sparse Comparison Data_ Provably Fast Convergence for Nonconvex Matrix Fac_2025.pdf" }, { "title": "Reinforcement Learning Control of a Physical Robot Device for Assisted Human Walking without a Simulator", "authors": [ "Junmin Zhong", "Emiliano Quinones Yumbla", "Seyed Yousef Soltanian", "Ruofan Wu", "Wenlong Zhang", "Jennie Si" ], "year": 2025, "venue": "ICML", "abstract": "This study presents an innovative reinforcement learning (RL) control approach to facilitate soft exosuit-assisted human walking. Our goal is to address the ongoing challenges in developing reliable RL-based methods for controlling physical devices. To overcome key obstacles—such as limited data, the absence of a simulator for human-robot interaction during walking, the need for low computational overhead in real-time deployment, and the demand for rapid adaptation to achieve personalized control while ensuring human safety—we propose an online Adaptation from an offline Imitating Expert Policy (AIP) approach. Our offline learning mimics human expert actions through real human walking demonstrations without robot assistance. The resulted policy is then used to initialize online actor-critic learning, the goal of which is to optimally personalize robot assistance. In addition to being fast and robust, our online RL method also posses important properties such as learning convergence, dynamic stability, and solution optimality. We have successfully demonstrated our simple\nand robust framework for safe robot control on all five tested human participants, without selectively presenting results. The qualitative performance guarantees provided by our online RL, along with the consistent experimental validation of AIP control, represent the first demonstration of online adaptation for softsuit control personalization and serve as important evidence for the use of online RL in controlling a physical device to solve a real-life problem.", "source": "openreview", "url": "https://openreview.net/forum?id=yAdcCADXqH", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Reinforcement Learning Control of a Physical Robot Device for Assisted Human Walking without a _2025.pdf" }, { "title": "Rethinking the Temperature for Federated Heterogeneous Distillation", "authors": [ "Fan Qi", "Daxu Shi", "Chuokun Xu", "Shuai Li", "Changsheng Xu" ], "year": 2025, "venue": "ICML", "abstract": "Federated Distillation (FedKD) relies on lightweight knowledge carriers like logits for efficient client-server communication. \nAlthough logit-based methods have demonstrated promise in addressing statistical and architectural heterogeneity in federated learning (FL), current approaches remain constrained by suboptimal temperature calibration during knowledge fusion.\nTo address these limitations, we propose ReT-FHD, a framework featuring: 1) Multi-level Elastic Temperature, which dynamically adjusts distillation intensities across model layers, achieving optimized knowledge transfer between heterogeneous local models; 2) Category-Aware Global Temperature Scaling that implements class-specific temperature calibration based on confidence distributions in global logits, enabling personalized distillation policies; 3) Z-Score Guard, a blockchain-verified validation mechanism mitigating 44\\% of label-flipping and model poisoning attacks. Evaluations across diverse benchmarks with varying model/data heterogeneity demonstrate that the ReT-FHD achieves significant accuracy improvements over baseline methods while substantially reducing communication costs compared to existing approaches. Our work establishes that properly calibrated logits can serve as self-sufficient carriers for building scalable and secure heterogeneous FL systems.", "source": "openreview", "url": "https://openreview.net/forum?id=f9xsNQ8oSd", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Rethinking the Temperature for Federated Heterogeneous Distillation_2025.pdf" }, { "title": "SAFER: A Calibrated Risk-Aware Multimodal Recommendation Model for Dynamic Treatment Regimes", "authors": [ "Yishan Shen", "Yuyang Ye", "Hui Xiong", "Yong Chen" ], "year": 2025, "venue": "ICML", "abstract": "Dynamic treatment regimes (DTRs) are critical to precision medicine, optimizing long-term outcomes through personalized, real-time decision-making in evolving clinical contexts, but require careful supervision for unsafe treatment risks. Existing efforts rely primarily on clinician-prescribed gold standards despite the absence of a known optimal strategy, and predominantly using structured EHR data without extracting valuable insights from clinical notes, limiting their reliability for treatment recommendations. In this work, we introduce SAFER, a calibrated risk-aware tabular-language recommendation framework for DTR that integrates both structured EHR and clinical notes, enabling them to learn from each other, and addresses inherent label uncertainty by assuming ambiguous optimal treatment solution for deceased patients. Moreover, SAFER employs conformal prediction to provide statistical guarantees, ensuring safe treatment recommendations while filtering out uncertain predictions. Experiments on two publicly available sepsis datasets demonstrate that SAFER outperforms state-of-the-art baselines across multiple recommendation metrics and counterfactual mortality rate, while offering robust formal assurances. These findings underscore SAFER’s potential as a trustworthy and theoretically grounded solution for high-stakes DTR applications.", "source": "openreview", "url": "https://openreview.net/forum?id=7UqNM85dD6", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_SAFER_ A Calibrated Risk-Aware Multimodal Recommendation Model for Dynamic Treatment Regimes_2025.pdf" }, { "title": "SIMPLEMIX: Frustratingly Simple Mixing of Off- and On-policy Data in Language Model Preference Learning", "authors": [ "Tianjian Li", "Daniel Khashabi" ], "year": 2025, "venue": "ICML", "abstract": "Aligning language models with human preferences relies on pairwise preference datasets. While some studies suggest that on-policy data consistently outperforms off-policy data for preference learning, others indicate that the advantages of on-policy data are task-dependent, highlighting the need for a systematic exploration of their interplay.\n\nIn this work, we show that on-policy and off-policy data offer complementary strengths: on-policy data is particularly effective for reasoning tasks like math and coding, while off-policy data performs better on subjective tasks such as creative writing and making personal recommendations. Guided by these findings, we introduce SimpleMix, an approach to combine the complementary strengths of on-policy and off-policy preference learning by simply mixing these two data sources. Our empirical results across diverse tasks and benchmarks demonstrate that SimpleMix substantially improves language model alignment. Specifically, SimpleMix improves upon on-policy DPO and off-policy DPO by an average of 6.03 on Alpaca Eval 2.0. Moreover, it surpasses prior approaches that are much more complex in combining on- and off-policy data, such as HyPO and DPO-Mix-P, by an average of 3.05. These findings validate the effectiveness and efficiency of SimpleMix for enhancing preference-based alignment.", "source": "openreview", "url": "https://openreview.net/forum?id=ucU1o3PNB0", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_SIMPLEMIX_ Frustratingly Simple Mixing of Off- and On-policy Data in Language Model Preference _2025.pdf" }, { "title": "Skip the Equations: Learning Behavior of Personalized Dynamical Systems Directly From Data", "authors": [ "Krzysztof Kacprzyk", "Julianna Piskorz", "Mihaela van der Schaar" ], "year": 2025, "venue": "ICML", "abstract": "While black-box approaches are commonly used for data-driven modeling of dynamical systems, they often obscure a system's underlying behavior and properties, limiting adoption in areas such as medicine and pharmacology. A two-step process of discovering ordinary differential equations (ODEs) and their subsequent mathematical analysis can yield insights into the system's dynamics. However, this analysis may be infeasible for complex equations, and refining the ODE to meet certain behavioral requirements can be challenging. Direct semantic modeling has recently been proposed to address these issues by predicting the system's behavior, such as the trajectory's shape, directly from data, bypassing post-hoc mathematical analysis. In this work, we extend the original instantiation, limited to one-dimensional trajectories and inputs, to accommodate multi-dimensional trajectories with additional personalization, allowing evolution to depend on auxiliary static features (e.g., patient covariates). In a series of experiments, we show how our approach enables practitioners to integrate prior knowledge, understand the dynamics, ensure desired behaviors, and revise the model when necessary.", "source": "openreview", "url": "https://openreview.net/forum?id=2gpjvMEAMm", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Skip the Equations_ Learning Behavior of Personalized Dynamical Systems Directly From Data_2025.pdf" }, { "title": "Synthesizing Images on Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability", "authors": [ "Chen Wei", "Chi Zhang", "Jiachen Zou", "Haotian Deng", "Dietmar Heinke", "Quanying Liu" ], "year": 2025, "venue": "ICML", "abstract": "Human decision-making in cognitive tasks and daily life exhibits considerable variability, shaped by factors such as task difficulty, individual preferences, and personal experiences. \nUnderstanding this variability across individuals is essential for uncovering the perceptual and decision-making mechanisms that humans rely on when faced with uncertainty and ambiguity. \nWe propose a systematic Boundary Alignment Manipulation (BAM) framework for studying human perceptual variability through image generation. BAM combines perceptual boundary sampling in ANNs and human behavioral experiments to systematically investigate this phenomenon. \nOur perceptual boundary sampling algorithm generates stimuli along ANN perceptual boundaries that intrinsically induce significant perceptual variability. \nThe efficacy of these stimuli is empirically validated through large-scale behavioral experiments involving 246 participants across 116,715 trials, culminating in the variMNIST dataset containing 19,943 systematically annotated images.\nThrough personalized model alignment and adversarial generation, we establish a reliable method for simultaneously predicting and manipulating the divergent perceptual decisions of pairs of participants.\nThis work bridges the gap between computational models and human individual difference research, providing new tools for personalized perception analysis. Code and data for this work are publicly available.", "source": "openreview", "url": "https://openreview.net/forum?id=SibkcjNnsC", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Synthesizing Images on Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perc_2025.pdf" }, { "title": "TRACE Back from the Future: A Probabilistic Reasoning Approach to Controllable Language Generation", "authors": [ "Gwen Yidou Weng", "Benjie Wang", "Guy Van den Broeck" ], "year": 2025, "venue": "ICML", "abstract": "As large language models (LMs) advance, there is an increasing need to control their outputs to align with human values (e.g., detoxification) or desired attributes (e.g., personalization, topic). However, autoregressive models focus on next-token predictions and struggle with global properties that require looking ahead. Existing solutions either post-train LMs for each new attribute—expensive and inflexible—or approximate the Expected Attribute Probability (EAP) of future sequences by sampling or training, which is slow and unreliable for rare attributes. We introduce **TRACE** (Tractable Probabilistic Reasoning for Adaptable Controllable gEneration), a novel framework that efficiently computes EAP and adapts to new attributes through tractable *probabilistic* reasoning and lightweight *control*. TRACE distills a Hidden Markov Model (HMM) from an LM and pairs it with a small classifier to estimate attribute probabilities, enabling exact EAP computation over the HMM’s predicted futures. This EAP is then used to reweigh the LM’s next-token probabilities for globally compliant continuations. Empirically, TRACE achieves state-of-the-art detoxification results with only 20% decoding overhead, yields 76 low-resource personalized LMs within seconds, and seamlessly extends to composite attributes.", "source": "openreview", "url": "https://openreview.net/forum?id=LhkSfpfRXW", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_TRACE Back from the Future_ A Probabilistic Reasoning Approach to Controllable Language Generat_2025.pdf" }, { "title": "Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning", "authors": [ "Rongzhe Wei", "Mufei Li", "Mohsen Ghassemi", "Eleonora Kreacic", "Yifan Li", "Xiang Yue", "Bo Li", "Vamsi K. Potluru", "Pan Li", "Eli Chien" ], "year": 2025, "venue": "ICML", "abstract": "Large Language Models (LLMs) embed sensitive, human-generated data, prompting the need for unlearning methods. Although certified unlearning offers strong privacy guarantees, its restrictive assumptions make it unsuitable for LLMs, giving rise to various heuristic approaches typically assessed through empirical evaluations. These standard evaluations randomly select data for removal, apply unlearning techniques, and use membership inference attacks (MIAs) to compare unlearned models against models retrained without the removed data. However, to ensure robust privacy protections for every data point, it is essential to account for scenarios in which certain data subsets face elevated risks. Prior research suggests that outliers, particularly including data tied to minority groups, often exhibit higher memorization propensity which indicates they may be more difficult to unlearn. Building on these insights, we introduce a complementary, minority-aware evaluation framework to highlight blind spots in existing frameworks. We substantiate our findings with carefully designed experiments, using canaries with personally identifiable information (PII) to represent these minority subsets and demonstrate that they suffer at least 20\\% higher privacy leakage across various unlearning methods, MIAs, datasets, and LLM scales. Our proposed minority-aware evaluation framework marks an essential step toward more equitable and comprehensive assessments of LLM unlearning efficacy.", "source": "openreview", "url": "https://openreview.net/forum?id=NsU6MKwbis", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning_2025.pdf" }, { "title": "Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees", "authors": [ "Xin Yu", "Zelin He", "Ying Sun", "Lingzhou Xue", "Runze Li" ], "year": 2025, "venue": "ICML", "abstract": "Personalized federated learning (PFL) offers a flexible framework for aggregating information across distributed clients with heterogeneous data. This work considers a personalized federated learning setting that simultaneously learns global and local models. While purely local training has no communication cost, collaborative learning among the clients can leverage shared knowledge to improve statistical accuracy, presenting an accuracy-communication trade-off in personalized federated learning. However, the theoretical analysis of how personalization quantitatively influences sample and algorithmic efficiency and their inherent trade-off is largely unexplored. This paper makes a contribution towards filling this gap, by providing a quantitative characterization of the personalization degree on the tradeoff. The results further offer theoretical insights for choosing the personalization degree. As a side contribution, we establish the minimax optimality in terms of statistical accuracy for a widely studied PFL formulation. The theoretical result is validated on both synthetic and real-world datasets and its generalizability is verified in a non-convex setting.", "source": "openreview", "url": "https://openreview.net/forum?id=MM6ZWF7gl9", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learni_2025.pdf" }, { "title": "Variational Counterfactual Intervention Planning to Achieve Target Outcomes", "authors": [ "Xin Wang", "Shengfei Lyu", "Chi Luo", "Xiren Zhou", "Huanhuan Chen" ], "year": 2025, "venue": "ICML", "abstract": "A key challenge in personalized healthcare is identifying optimal intervention sequences to guide temporal systems toward target outcomes, a novel problem we formalize as counterfactual target achievement. In addressing this problem, directly adopting counterfactual estimation methods face compounding errors due to the unobservability of counterfactuals. To overcome this, we propose Variational Counterfactual Intervention Planning (VCIP), which reformulates the problem by modeling the conditional likelihood of achieving target outcomes, implemented through variational inference. By leveraging the g-formula to bridge the gap between interventional and observational log-likelihoods, VCIP enables reliable training from observational data. Experiments on both synthetic and real-world datasets show that VCIP significantly outperforms existing methods in target achievement accuracy.", "source": "openreview", "url": "https://openreview.net/forum?id=ggyHPOXLGH", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Variational Counterfactual Intervention Planning to Achieve Target Outcomes_2025.pdf" }, { "title": "SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation", "authors": [ "Jiayue Liu", "Zhongchao Yi", "Zhengyang Zhou", "Qihe Huang", "Kuo Yang", "Xu Wang", "Yang Wang" ], "year": 2025, "venue": "ICML", "abstract": "Discovering regularities from spatiotemporal systems can benefit various scientific and social planning. Current spatiotemporal learners usually train an independent model from a specific source data that leads to limited transferability among sources, where even correlated tasks requires new design and training. The key towards increasing cross-domain knowledge is to enable collective intelligence and model evolution. In this paper, inspired by neuroscience theories, we theoretically derive the increased information boundary via learning cross-domain collective intelligence and propose a Synaptic EVOlutional spatiotemporal network, SynEVO, where SynEVO breaks the model independence and enables cross-domain knowledge to be shared and aggregated. Specifically, we first re-order the sample groups to imitate the human curriculum learning, and devise two complementary learners, elastic common container and task-independent extractor to allow model growth and task-wise commonality and personality disentanglement. Then an adaptive dynamic coupler with a new difference metric determines whether the new sample group should be incorporated into common container to achieve model evolution under various domains. Experiments show that SynEVO improves the generalization capacity by at most 42\\% under cross-domain scenarios and SynEVO provides a paradigm of NeuroAI for knowledge transfer and adaptation.\nCode available at [https://github.com/Rodger-Lau/SynEVO](https://github.com/Rodger-Lau/SynEVO).", "source": "openreview", "url": "https://openreview.net/forum?id=Q3rGQUGgWo", "decision_type": "Spotlight", "avg_rating": null, "relative_path": "2025/ICML/Spotlight/x_SynEVO_ A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation_2025.pdf" }, { "title": "HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned Guidance", "authors": [ "Jiazi Bu", "Pengyang Ling", "Yujie Zhou", "Pan Zhang", "Tong Wu", "Xiaoyi Dong", "Yuhang Zang", "Yuhang Cao", "Dahua Lin", "Jiaqi Wang" ], "year": 2025, "venue": "NeurIPS", "abstract": "Text-to-image (T2I) diffusion/flow models have drawn considerable attention recently due to their remarkable ability to deliver flexible visual creations. Still, high-resolution image synthesis presents formidable challenges due to the scarcity and complexity of high-resolution content. Recent approaches have investigated training-free strategies to enable high-resolution image synthesis with pre-trained models. However, these techniques often struggle with generating high-quality visuals and tend to exhibit artifacts or low-fidelity details, as they typically rely solely on the endpoint of the low-resolution sampling trajectory while neglecting intermediate states that are critical for preserving structure and synthesizing finer detail. To this end, we present HiFlow, a training-free and model-agnostic framework to unlock the resolution potential of pre-trained flow models. Specifically, HiFlow establishes a virtual reference flow within the high-resolution space that effectively captures the characteristics of low-resolution flow information, offering guidance for high-resolution generation through three key aspects: initialization alignment for low-frequency consistency, direction alignment for structure preservation, and acceleration alignment for detail fidelity. By leveraging such flow-aligned guidance, HiFlow substantially elevates the quality of high-resolution image synthesis of T2I models and demonstrates versatility across their personalized variants. Extensive experiments validate HiFlow's capability in achieving superior high-resolution image quality over state-of-the-art methods.", "source": "openreview", "url": "https://openreview.net/forum?id=koG76YqOwo", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2025/NeurIPS/Poster/5.0_HiFlow_ Training-free High-Resolution Image Generation with Flow-Aligned Guidance_2025.pdf" }, { "title": "Principled Fine-tuning of LLMs from User-Edits: A Medley of Preference, Supervision, and Reward", "authors": [ "Dipendra Misra", "Aldo Pacchiano", "Ta-Chung Chi", "Ge Gao" ], "year": 2025, "venue": "NeurIPS", "abstract": "We study how to fine-tune LLMs using user-edit deployment data consisting of a set of context, an agent's response, and user edits. This deployment data is naturally generated by users in applications such as LLMs-based writing assistants and coding agents. The _natural_ origin of user edits makes it a desired source for adapting and personalizing of LLMs. In this setup, there emerges a unification of various feedback types namely preferences, supervised labels, and cost that are typically studied separately in the literature. In this paper, we initiate the theoretical investigation of learning from user edits. We first derive bounds for learning algorithms that learn from each of these feedback types. We prove that these algorithms have different trade-offs depending upon the user, data distribution, and model class. We then propose a simple ensembling procedure to jointly learn from these feedback types. On two domains from Gao et al. 2024, we show our ensembling procedure outperforms these methods that learn from individual feedback. Further, we show that our proposed procedure can robustly adapt to different user-edit distributions at test time.", "source": "openreview", "url": "https://openreview.net/forum?id=Em9QmNobh0", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2025/NeurIPS/Poster/5.0_Principled Fine-tuning of LLMs from User-Edits_ A Medley of Preference, Supervision, and Reward_2025.pdf" }, { "title": "Towards Resilient Safety-driven Unlearning for Diffusion Models against Downstream Fine-tuning", "authors": [ "Boheng Li", "Renjie Gu", "Junjie Wang", "Leyi Qi", "Yiming Li", "Run Wang", "Zhan Qin", "Tianwei Zhang" ], "year": 2025, "venue": "NeurIPS", "abstract": "Text-to-image (T2I) diffusion models have achieved impressive image generation quality and are increasingly fine-tuned for personalized applications. However, these models often inherit unsafe behaviors from toxic pretraining data, raising growing safety concerns. While recent safety-driven unlearning methods have made promising progress in suppressing model toxicity, they are found to be fragile to downstream fine-tuning, as we reveal that state-of-the-art methods largely fail to retain their effectiveness even when fine-tuned on entirely benign datasets. To mitigate this problem, in this paper, we propose ResAlign, a safety-driven unlearning framework with enhanced resilience against downstream fine-tuning. By modeling downstream fine-tuning as an implicit optimization problem with a Moreau envelope-based reformulation, ResAlign enables efficient gradient estimation to minimize the recovery of harmful behaviors. Additionally, a meta-learning strategy is proposed to simulate a diverse distribution of fine-tuning scenarios to improve generalization. Extensive experiments across a wide range of datasets, fine-tuning methods, and configurations demonstrate that ResAlign consistently outperforms prior unlearning approaches in retaining safety, while effectively preserving benign generation capability. Our code and pretrained models are publicly available at https://github.com/AntigoneRandy/ResAlign.", "source": "openreview", "url": "https://openreview.net/forum?id=iEtCCt6FjP", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2025/NeurIPS/Poster/5.0_Towards Resilient Safety-driven Unlearning for Diffusion Models against Downstream Fine-tuning_2025.pdf" }, { "title": "Vid-SME: Membership Inference Attacks against Large Video Understanding Models", "authors": [ "Qi Li", "Runpeng Yu", "Xinchao Wang" ], "year": 2025, "venue": "NeurIPS", "abstract": "Multimodal large language models (MLLMs) demonstrates remarkable capabilities in handling complex multimodal tasks and are increasingly adopted in video understanding applications. However, their rapid advancement raises serious data privacy concerns, particularly given the potential inclusion of sensitive video content, such as personal recordings and surveillance footage, in their training datasets. Determining improperly used videos during training remains a critical and unresolved challenge. Despite considerable progress on membership inference attacks (MIAs) for text and image data in MLLMs, existing methods fail to generalize effectively to the video domain. These methods suffer from poor scalability as more frames are sampled and generally achieve negligible true positive rates at low false positive rates (TPR@Low FPR), mainly due to their failure to capture the inherent temporal variations of video frames and to account for model behavior differences as the number of frames varies. To address these challenges, we introduce Vid-SME (**Vid**eo **S**harma–**M**ittal **E**ntropy), the first membership inference method tailored for video data used in video understanding LLMs (VULLMs). Vid-SME leverages the confidence of model output and integrates adaptive parameterization to compute Sharma–Mittal entropy (SME) for video inputs. By leveraging the SME difference between natural and temporally-reversed video frames, Vid-SME derives robust membership scores to determine whether a given video is part of the model's training set. Experiments on various self-trained and open-sourced VULLMs demonstrate the strong effectiveness of Vid-SME.", "source": "openreview", "url": "https://openreview.net/forum?id=icoV59tH6D", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2025/NeurIPS/Poster/5.0_Vid-SME_ Membership Inference Attacks against Large Video Understanding Models_2025.pdf" }, { "title": "BNMusic: Blending Environmental Noises into Personalized Music", "authors": [ "Chi Zuo", "Martin B. Møller", "Pablo Martínez-Nuevo", "Huayang Huang", "Yu Wu", "Ye Zhu" ], "year": 2025, "venue": "NeurIPS", "abstract": "While being disturbed by environmental noises, the acoustic masking technique is a conventional way to reduce the annoyance in audio engineering that seeks to cover up the noises with other dominant yet less intrusive sounds. However, misalignment between the dominant sound and the noise—such as mismatched downbeats—often requires an excessive volume increase to achieve effective masking. Motivated by recent advances in cross-modal generation, in this work, we introduce an alternative method to acoustic masking, aiming to reduce the noticeability of environmental noises by blending them into personalized music generated based on user-provided text prompts. Following the paradigm of music generation using mel-spectrogram representations, we propose a Blending Noises into Personalized Music (BNMusic) framework with two key stages. The first stage synthesizes a complete piece of music in a mel-spectrogram representation that encapsulates the musical essence of the noise. In the second stage, we adaptively amplifying the generated music segment to further reduce noise perception and enhance the blending effectiveness, while preserving auditory quality. Our experiments with comprehensive evaluations on MusicBench, EPIC-SOUNDS, and ESC-50 demonstrate the effectiveness of our framework, highlighting the ability to blend environmental noise with rhythmically aligned, adaptively amplified, and enjoyable music segments, minimizing the noticeability of the noise, thereby improving overall acoustic experiences. Project page: https://d-fas.github.io/BNMusic_page/.", "source": "openreview", "url": "https://openreview.net/forum?id=IIgVYnadfR", "decision_type": "Poster", "avg_rating": 4.8, "relative_path": "2025/NeurIPS/Poster/4.8_BNMusic_ Blending Environmental Noises into Personalized Music_2025.pdf" }, { "title": "DEFT: Decompositional Efficient Fine-Tuning for Text-to-Image Models", "authors": [ "Komal Kumar", "Rao Muhammad Anwer", "Fahad Shahbaz Khan", "Salman Khan", "Ivan Laptev", "Hisham Cholakkal" ], "year": 2025, "venue": "NeurIPS", "abstract": "Efficient fine-tuning of pre-trained Text-to-Image (T2I) models involves adjusting the model to suit a particular task or dataset while minimizing computational resources and limiting the number of trainable parameters. However, it often faces challenges in striking a trade-off between aligning with the target distribution: learning a novel concept from a limited image for personalization and retaining the instruction ability needed for unifying multiple tasks, all while maintaining editability (aligning with a variety of prompts or in-context generation). In this work, we introduce DEFT, Decompositional Efficient Fine-Tuning, an efficient fine-tuning framework that adapts a pre-trained weight matrix by decomposing its update into two components with two trainable matrices: (1) a projection onto the complement of a low-rank subspace spanned by a low-rank matrix, and (2) a low-rank update. The single trainable low-rank matrix defines the subspace, while the other trainable low-rank matrix enables parameter adaptation within that subspace. We conducted extensive experiments on the Dreambooth and Dreambench Plus datasets for personalization, the InsDet dataset for object and scene adaptation, and the VisualCloze dataset for a universal image generation framework through visual in-context learning with both Stable Diffusion and a unified model. Our results demonstrated state-of-the-art performance, highlighting the emergent properties of efficient fine-tuning. Our code is available on \\href{https://github.com/MAXNORM8650/DEFT}{DEFT}.", "source": "openreview", "url": "https://openreview.net/forum?id=R9xJSk5SQ2", "decision_type": "Poster", "avg_rating": 4.8, "relative_path": "2025/NeurIPS/Poster/4.8_DEFT_ Decompositional Efficient Fine-Tuning for Text-to-Image Models_2025.pdf" }, { "title": "Overcoming Sparsity Artifacts in Crosscoders to Interpret Chat-Tuning", "authors": [ "Julian Minder", "Clément Dumas", "Caden Juang", "Bilal Chughtai", "Neel Nanda" ], "year": 2025, "venue": "NeurIPS", "abstract": "Model diffing is the study of how fine-tuning changes a model's representations and internal algorithms. \nMany behaviors of interest are introduced during fine-tuning, and model diffing offers a promising lens to interpret such behaviors. \nCrosscoders are a recent model diffing method that learns a shared dictionary of interpretable concepts represented as latent directions in both the base and fine-tuned models, allowing us to track how concepts shift or emerge during fine-tuning. \nNotably, prior work has observed concepts with no direction in the base model, and it was hypothesized that these model-specific latents were concepts introduced during fine-tuning.\nHowever, we identify two issues which stem from the crosscoders L1 training loss that can misattribute concepts as unique to the fine-tuned model, when they really exist in both models. \nWe develop Latent Scaling to flag these issues by more accurately measuring each latent's presence across models.\nIn experiments comparing Gemma 2 2B base and chat models, we observe that the standard crosscoder suffers heavily from these issues. Building on these insights, we train a crosscoder with BatchTopK loss and show that it substantially mitigates these issues, finding more genuinely chat-specific and highly interpretable concepts. We recommend practitioners adopt similar techniques.\nUsing the BatchTopK crosscoder, we successfully identify a set of chat-specific latents that are both interpretable and causally effective, representing concepts such as false information and personal question, along with multiple refusal-related latents that show nuanced preferences for different refusal triggers. \nOverall, our work advances best practices for the crosscoder-based methodology for model diffing and demonstrates that it can provide concrete insights into how chat-tuning modifies model behavior.", "source": "openreview", "url": "https://openreview.net/forum?id=yFdNygEryH", "decision_type": "Poster", "avg_rating": 4.8, "relative_path": "2025/NeurIPS/Poster/4.8_Overcoming Sparsity Artifacts in Crosscoders to Interpret Chat-Tuning_2025.pdf" }, { "title": "Personalized Exercise Recommendation with Semantically-Grounded Knowledge Tracing", "authors": [ "Yilmazcan Ozyurt", "Tunaberk Almaci", "Stefan Feuerriegel", "Mrinmaya Sachan" ], "year": 2025, "venue": "NeurIPS", "abstract": "We introduce ExRec, a general framework for personalized exercise recommendation with semantically-grounded knowledge tracing. Our method builds on the observation that existing exercise recommendation approaches simulate student performance via knowledge tracing (KT) but they often overlook two key aspects: (a) the semantic content of questions and (b) the sequential, structured progression of student learning. To address this, our ExRec presents an end-to-end pipeline, from annotating the KCs of questions and learning their semantic representations to training KT models and optimizing several reinforcement learning (RL) methods. Moreover, we improve standard Q-learning-based continuous RL methods via a tailored model-based value estimation (MVE) approach that directly leverages the components of KT model in estimating cumulative knowledge improvement. We validate the effectiveness of our ExRec using various RL methods across four real-world tasks with different educational goals in online math learning. We further show that ExRec generalizes robustly to new, unseen questions and that it produces interpretable student learning trajectories. Together, our findings highlight the promise of KT-guided RL for effective personalization in education.", "source": "openreview", "url": "https://openreview.net/forum?id=ILZ7ZPEHD5", "decision_type": "Poster", "avg_rating": 4.8, "relative_path": "2025/NeurIPS/Poster/4.8_Personalized Exercise Recommendation with Semantically-Grounded Knowledge Tracing_2025.pdf" }, { "title": "Preventing Shortcuts in Adapter Training via Providing the Shortcuts", "authors": [ "Anujraaj Goyal", "Guocheng Qian", "Huseyin Coskun", "Aarush Gupta", "Himmy Tam", "Daniil Ostashev", "Ju Hu", "Dhritiman Sagar", "Sergey Tulyakov", "Kfir Aberman", "Kuan-Chieh Wang" ], "year": 2025, "venue": "NeurIPS", "abstract": "Adapter-based training has emerged as a key mechanism for extending the capabilities of powerful foundation image generators, enabling personalized and stylized text-to-image synthesis. These adapters are typically trained to capture a specific target attribute, such as subject identity, using single-image reconstruction objectives. However, because the input image inevitably contains a mixture of visual factors, adapters are prone to entangle the target attribute with incidental ones, such as pose, expression, and lighting. This spurious correlation problem limits generalization and obstructs the model's ability to adhere to the input text prompt. In this work, we uncover a simple yet effective solution: provide the very shortcuts we wish to eliminate during adapter training. In Shortcut-Rerouted Adapter Training, confounding factors are routed through auxiliary modules, such as ControlNet or LoRA, eliminating the incentive for the adapter to internalize them. The auxiliary modules are then removed during inference. When applied to tasks like facial and full-body identity injection, our approach improves generation quality, diversity, and prompt adherence. These results point to a general design principle in the era of large models: when seeking disentangled representations, the most effective path may be to establish shortcuts for what should NOT be learned.", "source": "openreview", "url": "https://openreview.net/forum?id=cZMno8E3yp", "decision_type": "Poster", "avg_rating": 4.8, "relative_path": "2025/NeurIPS/Poster/4.8_Preventing Shortcuts in Adapter Training via Providing the Shortcuts_2025.pdf" }, { "title": "Probabilistic Reasoning with LLMs for Privacy Risk Estimation", "authors": [ "Jonathan Zheng", "Alan Ritter", "Sauvik Das", "Wei Xu" ], "year": 2025, "venue": "NeurIPS", "abstract": "Probabilistic reasoning is a key aspect of both human and artificial intelligence that allows for handling uncertainty and ambiguity in decision-making. In this paper, we introduce a new numerical reasoning task under uncertainty for large language models, focusing on estimating the privacy risk of user-generated documents containing privacy-sensitive information. We propose BRANCH, a new LLM methodology that estimates the $k$-privacy value of a text—the size of the population matching the given information. BRANCH factorizes a joint probability distribution of personal information as random variables. The probability of each factor in a population is estimated separately using a Bayesian network and combined to compute the final $k$-value. Our experiments show that this method successfully estimates the $k$-value 73% of the time, a 13% increase compared to o3-mini with chain-of-thought reasoning. We also find that LLM uncertainty is a good indicator for accuracy, as high variance predictions are 37.47% less accurate on average.", "source": "openreview", "url": "https://openreview.net/forum?id=HMVQ00vabY", "decision_type": "Poster", "avg_rating": 4.8, "relative_path": "2025/NeurIPS/Poster/4.8_Probabilistic Reasoning with LLMs for Privacy Risk Estimation_2025.pdf" }, { "title": "Salient Concept-Aware Generative Data Augmentation", "authors": [ "Tianchen Zhao", "Xuanbai Chen", "Zhihua Li", "Jun Fang", "DONGSHENG An", "Xiang Xu", "Zhuowen Tu", "Yifan Xing" ], "year": 2025, "venue": "NeurIPS", "abstract": "Recent generative data augmentation methods conditioned on both image and text prompts struggle to balance between fidelity and diversity, as it is challenging to preserve essential image details while aligning with varied text prompts. \nThis challenge arises because representations in the synthesis process often become entangled with non-essential input image attributes such as environmental contexts, creating conflicts with text prompts intended to modify these elements.\nTo address this, we propose a personalized image generation framework that uses a salient concept-aware image embedding model to reduce the influence of irrelevant visual details during the synthesis process, thereby maintaining intuitive alignment between image and text inputs.\nBy generating images that better preserve class-discriminative features with additional controlled variations, our framework effectively enhances the diversity of training datasets and thereby improves the robustness of downstream models.\nOur approach demonstrates superior performance across eight fine-grained vision datasets, outperforming state-of-the-art augmentation methods with averaged classification accuracy improvements by 0.73\\% and 6.5\\% under conventional and long-tail settings, respectively.", "source": "openreview", "url": "https://openreview.net/forum?id=wH3F1ZoK70", "decision_type": "Poster", "avg_rating": 4.8, "relative_path": "2025/NeurIPS/Poster/4.8_Salient Concept-Aware Generative Data Augmentation_2025.pdf" }, { "title": "Unleashing Foundation Vision Models: Adaptive Transfer for Diverse Data-Limited Scientific Domains", "authors": [ "Qiankun Li", "Feng He", "Huabao Chen", "Xin Ning", "Kun Wang", "Zengfu Wang" ], "year": 2025, "venue": "NeurIPS", "abstract": "In the big data era, the computer vision field benefits from large-scale datasets such as LAION-2B, LAION-400M, and ImageNet-21K, Kinetics, on which popular models like the ViT and ConvNeXt series have been pre-trained, acquiring substantial knowledge. \nHowever, numerous downstream tasks in specialized and data-limited scientific domains continue to pose significant challenges. \nIn this paper, we propose a novel Cluster Attention Adapter (CLAdapter), which refines and adapts the rich representations learned from large-scale data to various data-limited downstream tasks. Specifically, CLAdapter introduces attention mechanisms and cluster centers to personalize the enhancement of transformed features through distribution correlation and transformation matrices. This enables models fine-tuned with CLAdapter to learn distinct representations tailored to different feature sets, facilitating the models' adaptation from rich pre-trained features to various downstream scenarios effectively. In addition, CLAdapter's unified interface design allows for seamless integration with multiple model architectures, including CNNs and Transformers, in both 2D and 3D contexts.\nThrough extensive experiments on 10 datasets spanning domains such as generic, multimedia, biological, medical, industrial, agricultural, environmental, geographical, materials science, out-of-distribution (OOD), and 3D analysis, CLAdapter achieves state-of-the-art performance across diverse data-limited scientific domains, demonstrating its effectiveness in unleashing the potential of foundation vision models via adaptive transfer.\nCode is available at https://github.com/qklee-lz/CLAdapter.", "source": "openreview", "url": "https://openreview.net/forum?id=Wlw8jkGscY", "decision_type": "Poster", "avg_rating": 4.8, "relative_path": "2025/NeurIPS/Poster/4.8_Unleashing Foundation Vision Models_ Adaptive Transfer for Diverse Data-Limited Scientific Doma_2025.pdf" }, { "title": "Graph-Theoretic Insights into Bayesian Personalized Ranking for Recommendation", "authors": [ "Kai Zheng", "Jianxin Wang", "Jinhui Xu" ], "year": 2025, "venue": "NeurIPS", "abstract": "Graph self-supervised learning (GSL) is essential for processing graph-structured data, reducing the need for manual labeling. Traditionally, this paradigm has extensively utilized Bayesian Personalized Ranking (BPR) as its primary loss function. Despite its widespread application, the theoretical analysis of its node relations evaluation have remained largely unexplored. This paper employs recent advancements in latent hyperbolic geometry to deepen our understanding of node relationships from a graph-theoretical perspective. We analyze BPR’s limitations, particularly its reliance on local connectivity through 2-hop paths, which overlooks global connectivity and the broader topological structure. To address these shortcomings, we purpose a novel loss function, BPR+, designed to encompass even-hop paths and better capture global connectivity and topological nuances. This approach facilitates a more detailed measurement of user-item relationships and improves the granularity of relationship assessments. We validate BPR+ through extensive empirical testing across five real-world datasets and demonstrate its efficacy in refining graph self-supervised learning frameworks. Additionally, we explore the application of BPR+ in drug repositioning, highlighting its potential to support pharmaceutical research and development. Our findings not only illuminate the success factors of previous methodologies but also offer new theoretical insights into this learning paradigm.", "source": "openreview", "url": "https://openreview.net/forum?id=tmtUA2X57D", "decision_type": "Poster", "avg_rating": 4.7, "relative_path": "2025/NeurIPS/Poster/4.7_Graph-Theoretic Insights into Bayesian Personalized Ranking for Recommendation_2025.pdf" }, { "title": "Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death", "authors": [ "Sihyung Park", "Wenbin Lu", "Shu Yang" ], "year": 2025, "venue": "NeurIPS", "abstract": "Truncation by death, a prevalent challenge in critical care, renders traditional dynamic treatment regime (DTR) evaluation inapplicable due to ill-defined potential outcomes. We introduce a principal stratification-based method, focusing on the always-survivor value function. We derive a semiparametrically efficient, multiply robust estimator for multi-stage DTRs, demonstrating its robustness and efficiency. Empirical validation and an application to electronic health records showcase its utility for personalized treatment optimization.", "source": "openreview", "url": "https://openreview.net/forum?id=feLdTALuq3", "decision_type": "Poster", "avg_rating": 4.6, "relative_path": "2025/NeurIPS/Poster/4.6_Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death_2025.pdf" }, { "title": "A Reliable Cryptographic Framework for Empirical Machine Unlearning Evaluation", "authors": [ "Yiwen Tu", "Pingbang Hu", "Jiaqi W. Ma" ], "year": 2025, "venue": "NeurIPS", "abstract": "Machine unlearning updates machine learning models to remove information from specific training data samples, complying with data protection regulations that allow individuals to request the removal of their personal data. Despite the recent development of numerous unlearning algorithms, reliable evaluation of these algorithms remains an open research question. In this work, we focus on membership inference attack (MIA) based evaluation, one of the most common approaches for evaluating unlearning algorithms, and address various pitfalls of existing evaluation metrics that lack theoretical understanding and reliability. Specifically, by modeling the proposed evaluation process as a cryptographic game between unlearning algorithms and MIA adversaries, the naturally-induced evaluation metric measures the data removal efficacy of unlearning algorithms and enjoys provable guarantees that existing evaluation metrics fail to satisfy. Furthermore, we propose a practical and efficient approximation of the induced evaluation metric and demonstrate its effectiveness through both theoretical analysis and empirical experiments. Overall, this work presents a novel and reliable approach to empirically evaluating unlearning algorithms, paving the way for the development of more effective unlearning techniques.", "source": "openreview", "url": "https://openreview.net/forum?id=TYoYJStuN9", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2025/NeurIPS/Poster/4.5_A Reliable Cryptographic Framework for Empirical Machine Unlearning Evaluation_2025.pdf" }, { "title": "Accelerated Evolving Set Processes for Local PageRank Computation", "authors": [ "BinbinHuang", "Luo Luo", "Yanghua Xiao", "Deqing Yang", "Baojian Zhou" ], "year": 2025, "venue": "NeurIPS", "abstract": "This work proposes a novel framework based on nested evolving set processes to accelerate Personalized PageRank (PPR) computation. At each stage of the process, we employ a localized inexact proximal point iteration to solve a simplified linear system. We show that the time complexity of such localized methods is upper bounded by $\\min\\{\\tilde{\\mathcal{O}}(R^2/\\epsilon^2), \\tilde{\\mathcal{O}}(m)\\}$ to obtain an $\\epsilon$-approximation of the PPR vector, where $m$ denotes the number of edges in the graph and $R$ is a constant defined via nested evolving set processes. Furthermore, the algorithms induced by our framework require solving only $\\tilde{\\mathcal{O}}(1/\\sqrt{\\alpha})$ such linear systems, where $\\alpha$ is the damping factor. When $1/\\epsilon^2\\ll m$, this implies the existence of an algorithm that computes an $\\epsilon$-approximation of the PPR vector with an overall time complexity of $\\tilde{\\mathcal{O}}(R^2 / (\\sqrt{\\alpha}\\epsilon^2))$, independent of the underlying graph size. Our result resolves an open conjecture from existing literature. Experimental results on real-world graphs validate the efficiency of our methods, demonstrating significant convergence in the early stages.", "source": "openreview", "url": "https://openreview.net/forum?id=zDOo34mbpl", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2025/NeurIPS/Poster/4.5_Accelerated Evolving Set Processes for Local PageRank Computation_2025.pdf" }, { "title": "Agnostic Continuous-Time Online Learning", "authors": [ "Pramith Devulapalli", "Changlong Wu", "Ananth Grama", "Wojciech Szpankowski" ], "year": 2025, "venue": "NeurIPS", "abstract": "We study agnostic online learning from continuous-time data streams, a setting that naturally arises in applications such as environmental monitoring, personalized recommendation, and high-frequency trading. Unlike classical discrete-time models, learners in this setting must interact with a continually evolving data stream while making queries and updating models only at sparse, strategically selected times. We develop a general theoretical framework for learning from both *oblivious* and *adaptive* data streams, which may be noisy and non-stationary. For oblivious streams, we present a black-box reduction to classical online learning that yields a regret bound of $T \\cdot R(S)/S$ for any class with discrete-time regret $R(S)$, where $T$ is the time horizon and $S$ is the *query budget*. For adaptive streams, which can evolve in response to learner actions, we design a dynamic query strategy in conjunction with a novel importance weighting scheme that enables unbiased loss estimation. In particular, for hypothesis class $\\mathcal{H}$ with a finite Littlestone dimension, we establish a tight regret bound of $\\tilde{\\Theta}(T \\cdot \\sqrt{\\mathsf{Ldim}(\\mathcal{H})/S})$ that holds in both settings. Our results provide the first *quantitative* characterization of agnostic learning in continuous-time online environments with limited interaction.", "source": "openreview", "url": "https://openreview.net/forum?id=9LoVCfMLDl", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2025/NeurIPS/Poster/4.5_Agnostic Continuous-Time Online Learning_2025.pdf" }, { "title": "Capturing Individual Human Preferences with Reward Features", "authors": [ "Andre Barreto", "Vincent Dumoulin", "Yiran Mao", "Mark Rowland", "Nicolas Perez-Nieves", "Bobak Shahriari", "Yann Dauphin", "Doina Precup", "Hugo Larochelle" ], "year": 2025, "venue": "NeurIPS", "abstract": "Reinforcement learning from human feedback usually models preferences using a reward function that does not distinguish between people. We argue that this is unlikely to be a good design choice in contexts with high potential for disagreement, like in the training of large language models. We formalise and analyse the problem of learning a reward model that can be specialised to a user. Using the principle of empirical risk minimisation, we derive a probably approximately correct (PAC) bound showing the dependency of the approximation error on the number of training examples, as usual, and also on the number of human raters who provided feedback on them. Based on our theoretical findings, we discuss how to best collect pairwise preference data and argue that adaptive reward models should be beneficial when there is considerable disagreement among users. We also propose a concrete architecture for an adaptive reward model. Our approach leverages the observation that individual preferences can be captured as a linear combination of a set of general reward features. We show how to learn such features and subsequently use them to quickly adapt the reward model to a specific individual, even if their preferences are not reflected in the training data. We present experiments with large language models illustrating our theoretical results and comparing the proposed architecture with a non-adaptive baseline. Consistent with our analysis, the benefits provided by our model increase with the number of raters and the heterogeneity of their preferences. We also show that our model compares favourably to adaptive counterparts, including those performing in-context personalisation.", "source": "openreview", "url": "https://openreview.net/forum?id=TgCkj4uEPl", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2025/NeurIPS/Poster/4.5_Capturing Individual Human Preferences with Reward Features_2025.pdf" }, { "title": "Democratizing Clinical Risk Prediction with Cross-Cohort Cross-Modal Knowledge Transfer", "authors": [ "Qiannan Zhang", "Manqi Zhou", "Zilong Bai", "Chang Su", "Fei Wang" ], "year": 2025, "venue": "NeurIPS", "abstract": "Clinical risk prediction plays a crucial role in early disease detection and personalized intervention. While recent models increasingly incorporate multimodal data, their development typically assumes access to large-scale, multimodal datasets and substantial computational resources. In practice, however, most clinical sites operate under resource constraints, with access limited to EHR data alone and insufficient capacity to train complicated models. This gap highlights the urgent need to democratize clinical risk prediction by enabling effective deployment in data- and resource-limited local clinical settings. In this work, we propose a cross-cohort cross-modal knowledge transfer framework that leverages the multimodal model trained on a nationwide cohort and adapts it to local cohorts with only EHR data. We focus on EHR and genetic data as representative multimodal inputs and address two key challenges. First, to mitigate the influence of noisy or less informative biological signals, we propose a novel mixture-of-aggregations design to enhance the modeling of informative and relevant genetic features. Second, to support rapid model adaptation in low-resource sites, we develop a lightweight graph-guided fine-tuning method that adapts pretrained phenotypical EHR representations to target cohorts using limited patient data. \nExtensive experiments on real-world clinical data validate the effectiveness of our proposed model.", "source": "openreview", "url": "https://openreview.net/forum?id=7dJfwHG3GN", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2025/NeurIPS/Poster/4.5_Democratizing Clinical Risk Prediction with Cross-Cohort Cross-Modal Knowledge Transfer_2025.pdf" }, { "title": "EMLoC: Emulator-based Memory-efficient Fine-tuning with LoRA Correction", "authors": [ "Hsi-Che Lin", "Yu-Chu Yu", "Kai-Po Chang", "Yu-Chiang Frank Wang" ], "year": 2025, "venue": "NeurIPS", "abstract": "Open-source foundation models have seen rapid adoption and development, enabling powerful general-purpose capabilities across diverse domains. However, fine-tuning large foundation models for domain-specific or personalized tasks remains prohibitively expensive for most users due to the significant memory overhead beyond that of inference. We introduce EMLoC, an Emulator-based Memory-efficient fine-tuning framework with LoRA Correction, which enables model fine-tuning within the same memory budget required for inference. EMLoC constructs a task-specific light-weight emulator using activation-aware singular value decomposition (SVD) on a small downstream calibration set. Fine-tuning then is performed on this lightweight emulator via LoRA. To tackle the misalignment between the original model and the compressed emulator, we propose a novel compensation algorithm to correct the fine-tuned LoRA module, which thus can be merged into the original model for inference. EMLoC supports flexible compression ratios and standard training pipelines, making it adaptable to a wide range of applications. Extensive experiments demonstrate that EMLoC outperforms other baselines across multiple datasets and modalities. Moreover, without quantization, EMLoC enables fine-tuning of a 38B model, which originally required 95GB of memory, on a single 24GB consumer GPU—bringing efficient and practical model adaptation to individual users.", "source": "openreview", "url": "https://openreview.net/forum?id=vqaWAmuzRt", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2025/NeurIPS/Poster/4.5_EMLoC_ Emulator-based Memory-efficient Fine-tuning with LoRA Correction_2025.pdf" }, { "title": "EchoShot: Multi-Shot Portrait Video Generation", "authors": [ "Jiahao Wang", "Hualian Sheng", "Sijia Cai", "Weizhan Zhang", "Caixia Yan", "Yachuang Feng", "Bing Deng", "Jieping Ye" ], "year": 2025, "venue": "NeurIPS", "abstract": "Video diffusion models substantially boost the productivity of artistic workflows with high-quality portrait video generative capacity. However, prevailing pipelines are primarily constrained to single-shot creation, while real-world applications urge multiple shots with identity consistency and flexible content controllability. In this work, we propose EchoShot, a native and scalable multi-shot framework for portrait customization built upon a foundation video diffusion model. To start with, we propose shot-aware position embedding mechanisms within the video diffusion transformer architecture to model inter-shot variations and establish intricate correspondence between multi-shot visual content and their textual descriptions. This simple yet effective design enables direct training on multi-shot video data without introducing additional computational overhead. To facilitate model training within multi-shot scenarios, we construct PortraitGala, a large-scale and high-fidelity human-centric video dataset featuring cross-shot identity consistency and fine-grained captions such as facial attributes, outfits, and dynamic motions. To further enhance applicability, we extend EchoShot to perform reference image-based personalized multi-shot generation and long video synthesis with infinite shot counts. Extensive evaluations demonstrate that EchoShot achieves superior identity consistency as well as attribute-level controllability in multi-shot portrait video generation. Notably, the proposed framework demonstrates potential as a foundational paradigm for general multi-shot video modeling. Project page: https://johnneywang.github.io/EchoShot-webpage.", "source": "openreview", "url": "https://openreview.net/forum?id=BpbJc1Jfbv", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2025/NeurIPS/Poster/4.5_EchoShot_ Multi-Shot Portrait Video Generation_2025.pdf" }, { "title": "Enhancing Personalized Multi-Turn Dialogue with Curiosity Reward", "authors": [ "Yanming Wan", "Jiaxing Wu", "Marwa Abdulhai", "Lior Shani", "Natasha Jaques" ], "year": 2025, "venue": "NeurIPS", "abstract": "Effective conversational agents must personalize their interactions to adapt to user preferences, personalities, and attributes across diverse domains like education and healthcare. Current methods like Reinforcement Learning from Human Feedback (RLHF), often prioritize helpfulness and safety but fall short in fostering truly empathetic, adaptive, and personalized dialogues. Existing personalization approaches typically rely on extensive user history, limiting their effectiveness for new or context-limited users. To address these limitations, we propose leveraging a user model to incorporate a curiosity-based intrinsic reward into multi-turn RLHF. This novel reward mechanism encourages the agent to actively infer user traits by optimizing conversations to improve its user model's accuracy. Consequently, the agent delivers more personalized interactions by learning more about the user. We demonstrate our method's effectiveness in two distinct domains: significantly improving personalization performance in a conversational recommendation task, and personalizing conversations for different learning styles in an educational setting with improved generalization capabilities compared to traditional multi-turn RLHF, all while maintaining conversation quality. Our method offers a promising solution for creating more personalized, adaptive, and engaging conversational agents.", "source": "openreview", "url": "https://openreview.net/forum?id=tVRtDIwDmQ", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2025/NeurIPS/Poster/4.5_Enhancing Personalized Multi-Turn Dialogue with Curiosity Reward_2025.pdf" }, { "title": "FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning", "authors": [ "Li Zhang", "Zhongxuan Han", "XiaoHua Feng", "Jiaming Zhang", "Yuyuan Li", "Chaochao Chen" ], "year": 2025, "venue": "NeurIPS", "abstract": "With emerging application of Federated Learning (FL) in decision-making scenarios, it is imperative to regulate model fairness to prevent disparities across sensitive groups (e.g., female, male).\nCurrent research predominantly focuses on two concepts of group fairness within FL: *Global Fairness* (overall model disparity across all clients) and *Local Fairness* (the disparity within each client).\nHowever, the non-decomposable, non-differentiable nature of fairness criteria pose two fundamental, unresolved challenges for fair FL: (i) *Harmonizing global and local fairness, especially in multi-class classification*; (ii) *Enabling a controllable, optimal accuracy-fairness trade-off*.\nTo tackle the aforementioned challenges, we propose a novel controllable federated group-fairness calibration framework, named FedFACT.\nFedFACT identifies the Bayes-optimal classifiers under both global and local fairness constraints in multi-class case, yielding models with minimal performance decline while guaranteeing fairness.\nTo effectively realize an adjustable, optimal accuracy-fairness balance, we derive specific characterizations of the Bayes-optimal fair classifiers for reformulating fair FL as personalized cost-sensitive learning problem for in-processing, and bi-level optimization for post-processing.\nTheoretically, we provide convergence and generalization guarantees for FedFACT to approach the near-optimal accuracy under given fairness levels.\nExtensive experiments on multiple datasets across various data heterogeneity demonstrate that FedFACT consistently outperforms baselines in balancing accuracy and global-local fairness.", "source": "openreview", "url": "https://openreview.net/forum?id=6lCY5bLW8E", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2025/NeurIPS/Poster/4.5_FedFACT_ A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning_2025.pdf" }, { "title": "JarvisArt: Liberating Human Artistic Creativity via an Intelligent Photo Retouching Agent", "authors": [ "Yunlong Lin", "ZiXu Lin", "Kunjie Lin", "Jinbin Bai", "Panwang Pan", "Chenxin Li", "Haoyu Chen", "Zhongdao Wang", "Xinghao Ding", "Wenbo Li", "Shuicheng YAN" ], "year": 2025, "venue": "NeurIPS", "abstract": "Photo retouching has become integral to contemporary visual storytelling, enabling users to capture aesthetics and express creativity. While professional tools such as Adobe Lightroom offer powerful capabilities, they demand substantial expertise and manual effort. In contrast, existing AI-based solutions provide automation but often suffer from limited adjustability and poor generalization, failing to meet diverse and personalized editing needs. To bridge this gap, we introduce JarvisArt, a multi-modal large language model (MLLM)-driven agent that understands user intent, mimics the reasoning process of professional artists, and intelligently coordinates over 200 retouching tools within Lightroom. JarvisArt undergoes a two-stage training process: an initial Chain-of-Thought supervised fine-tuning to establish basic reasoning and tool-use skills, followed by Group Relative Policy Optimization for Retouching (GRPO-R) to further enhance its decision-making and tool proficiency. We also propose the Agent-to-Lightroom Protocol to facilitate seamless integration with Lightroom. To evaluate performance, we develop MMArt-Bench, a novel benchmark constructed from real-world user edits. JarvisArt demonstrates user-friendly interaction, superior generalization, and fine-grained control over both global and local adjustments, paving a new avenue for intelligent photo retouching. Notably, it outperforms GPT-4o with a 60\\% improvement in average pixel-level metrics on MMArt-Bench for content fidelity, while maintaining comparable instruction-following capabilities.", "source": "openreview", "url": "https://openreview.net/forum?id=XPLf9H27aO", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2025/NeurIPS/Poster/4.5_JarvisArt_ Liberating Human Artistic Creativity via an Intelligent Photo Retouching Agent_2025.pdf" }, { "title": "Learning to Route: Per-Sample Adaptive Routing for Multimodal Multitask Prediction", "authors": [ "Marzieh Ajirak", "Oded Bein", "Ellen Rose Bowen", "Dora Kanellopoulos", "Avital Falk", "FAITH M. GUNNING", "Nili Solomonov", "Logan Grosenick" ], "year": 2025, "venue": "NeurIPS", "abstract": "We propose a unified framework for adaptive routing in multitask, multimodal prediction settings where data heterogeneity and task interactions vary across samples. We introduce a routing-based architecture that dynamically selects modality processing pathways and task-sharing strategies on a per-sample basis. Our model defines multiple modality paths, including raw and fused representations of text and numeric features, and learns to route each input through the most informative modality-task expert combination. Task-specific predictions are produced by shared or independent heads depending on the routing decision, and the entire system is trained end-to-end. We evaluate the model on both synthetic data and real-world psychotherapy notes, predicting depression and anxiety outcomes. Our experiments show that our method consistently outperforms fixed multitask or single-task baselines, and that the learned routing policy provides interpretable insights into modality relevance and task structure. This addresses critical challenges in personalized healthcare by providing per-subject adaptive information processing that accounts for data and task correlation heterogeneity.", "source": "openreview", "url": "https://openreview.net/forum?id=peYBx7AiKw", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2025/NeurIPS/Poster/4.5_Learning to Route_ Per-Sample Adaptive Routing for Multimodal Multitask Prediction_2025.pdf" }, { "title": "Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data", "authors": [ "Dennis Frauen", "Maresa Schröder", "Konstantin Hess", "Stefan Feuerriegel" ], "year": 2025, "venue": "NeurIPS", "abstract": "Estimating heterogeneous treatment effects (HTEs) is crucial for personalized decision-making. However, this task is challenging in survival analysis, which includes time-to-event data with censored outcomes (e.g., due to study dropout). In this paper, we propose a toolbox of orthogonal survival learners to estimate HTEs from time-to-event data under censoring. Our learners have three main advantages: (i) we show that learners from our toolbox are guaranteed to be orthogonal and thus robust with respect to nuisance estimation errors; (ii) our toolbox allows for incorporating a custom weighting function, which can lead to robustness against different types of low overlap, and (iii) our learners are \\emph{model-agnostic} (i.e., they can be combined with arbitrary machine learning models). We instantiate the learners from our toolbox using several weighting functions and, as a result, propose various neural orthogonal survival learners. Some of these coincide with existing survival learners (including survival versions of the DR- and R-learner), while others are novel and further robust w.r.t. low overlap regimes specific to the survival setting (i.e., survival overlap and censoring overlap). We then empirically verify the effectiveness of our learners for HTE estimation in different low-overlap regimes through numerical experiments. In sum, we provide practitioners with a large toolbox of learners that can be used for randomized and observational studies with censored time-to-event data.", "source": "openreview", "url": "https://openreview.net/forum?id=EdP45Yxdc3", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2025/NeurIPS/Poster/4.5_Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event _2025.pdf" }, { "title": "Personalized Image Editing in Text-to-Image Diffusion Models via Collaborative Direct Preference Optimization", "authors": [ "Connor Dunlop", "Matthew Zheng", "Kavana Venkatesh", "Pinar Yanardag" ], "year": 2025, "venue": "NeurIPS", "abstract": "Text-to-image (T2I) diffusion models have made remarkable strides in generating and editing high-fidelity images from text. Yet, these models remain fundamentally generic, failing to adapt to the nuanced aesthetic preferences of individual users. In this work, we present the first framework for personalized image editing in diffusion models, introducing Collaborative Direct Preference Optimization (C-DPO), a novel method that aligns image edits with user-specific preferences while leveraging collaborative signals from like-minded individuals. Our approach encodes each user as a node in a dynamic preference graph and learns embeddings via a lightweight graph neural network, enabling information sharing across users with overlapping visual tastes. We enhance a diffusion model's editing capabilities by integrating these personalized embeddings into a novel DPO objective, which jointly optimizes for individual alignment and neighborhood coherence. Comprehensive experiments, including user studies and quantitative benchmarks, demonstrate that our method consistently outperforms baselines in generating edits that are aligned with user preferences.", "source": "openreview", "url": "https://openreview.net/forum?id=BBZEcVu1nA", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2025/NeurIPS/Poster/4.5_Personalized Image Editing in Text-to-Image Diffusion Models via Collaborative Direct Preferenc_2025.pdf" }, { "title": "SketchMind: A Multi-Agent Cognitive Framework for Assessing Student-Drawn Scientific Sketches", "authors": [ "Ehsan Latif", "Zirak Khan", "Xiaoming Zhai" ], "year": 2025, "venue": "NeurIPS", "abstract": "Scientific sketches (e.g., models) offer a powerful lens into students' conceptual understanding, yet AI-powered automated assessment of such free-form, visually diverse artifacts remains a critical challenge. Existing solutions often treat sketch evaluation as either an image classification task or monolithic vision-language models, which lack interpretability, pedagogical alignment, and adaptability across cognitive levels. To address these limitations, we present SketchMind, a cognitively grounded, multi-agent framework for evaluating and improving student-drawn scientific sketches. SketchMind introduces Sketch Reasoning Graphs (SRGs), semantic graph representations that embed domain concepts and Bloom's taxonomy-based cognitive labels. The system comprises modular agents responsible for rubric parsing, sketch perception, cognitive alignment, and iterative feedback with sketch modification, enabling personalized and transparent evaluation. We evaluate SketchMind on a curated dataset of 3,575 student-generated sketches across six science assessment items with different highest order of Bloom's level that require students to draw models to explain phenomena. Compared to baseline GPT-4o performance without SRG (average accuracy: 55.6%), the model with SRG integration achieves 77.1% average accuracy (+21.4% average absolute gain). We also demonstrate that multi-agent orchestration with SRG enhances SketchMind performance, for example, SketchMind with GPT-4.1 gains an average 8.9% increase in sketch prediction accuracy, outperforming single-agent pipelines across all items. Human evaluators rated the feedback and co-created sketches generated by SketchMind with GPT-4.1, which achieved an average of 4.1 out of 5, significantly higher than those of baseline models (e.g., 2.3 for GPT-4o). Experts noted the system’s potential to meaningfully support conceptual growth through guided revision. Our code and (pending approval) dataset will be released to support reproducibility and future research in AI-driven education.", "source": "openreview", "url": "https://openreview.net/forum?id=qS3WgmGs9s", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2025/NeurIPS/Poster/4.5_SketchMind_ A Multi-Agent Cognitive Framework for Assessing Student-Drawn Scientific Sketches_2025.pdf" }, { "title": "ViDAR: Video Diffusion-Aware 4D Reconstruction From Monocular Inputs", "authors": [ "Michal Nazarczuk", "Sibi Catley-Chandar", "Thomas Tanay", "Zhensong Zhang", "Gregory Slabaugh", "Eduardo Pérez-Pellitero" ], "year": 2025, "venue": "NeurIPS", "abstract": "Dynamic Novel View Synthesis aims to generate photorealistic views of moving subjects from arbitrary viewpoints. This task is particularly challenging when relying on monocular video, where disentangling structure from motion is ill-posed and supervision is scarce. We introduce Video Diffusion-Aware Reconstruction (ViDAR), a novel 4D reconstruction framework that leverages personalised diffusion models to synthesise a pseudo multi-view supervision signal for training a Gaussian splatting representation. By conditioning on scene-specific features, ViDAR recovers fine-grained appearance details while mitigating artefacts introduced by monocular ambiguity. To address the spatio-temporal inconsistency of diffusion-based supervision, we propose a diffusion-aware loss function and a camera pose optimisation strategy that aligns synthetic views with the underlying scene geometry. Experiments on DyCheck, a challenging benchmark with extreme viewpoint variation, show that ViDAR outperforms all state-of-the-art baselines in visual quality and geometric consistency. We further highlight ViDAR’s strong improvement over baselines on dynamic regions and provide a new benchmark to compare performance in reconstructing motion-rich parts of the scene.", "source": "openreview", "url": "https://openreview.net/forum?id=mLVqiNH0aA", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2025/NeurIPS/Poster/4.5_ViDAR_ Video Diffusion-Aware 4D Reconstruction From Monocular Inputs_2025.pdf" }, { "title": "XVerse: Consistent Multi-Subject Control of Identity and Semantic Attributes via DiT Modulation", "authors": [ "Bowen Chen", "Brynn zhao", "Haomiao Sun", "Li Chen", "Xu Wang", "Daniel Kang Du", "Xinglong Wu" ], "year": 2025, "venue": "NeurIPS", "abstract": "Achieving fine-grained control over subject identity and semantic attributes (pose, style, lighting) in text-to-image generation, particularly for multiple subjects, often undermines the editability and coherence of Diffusion Transformers (DiTs). Many approaches introduce artifacts or suffer from attribute entanglement. To overcome these challenges, we propose a novel multi-subject controlled generation model XVerse. By transforming reference images into offsets for token-specific text-stream modulation, XVerse allows for precise and independent control for specific subject without disrupting image latents or features. Consequently, XVerse offers high-fidelity, editable multi-subject image synthesis with robust control over individual subject characteristics and semantic attributes. This advancement significantly improves personalized and complex scene generation capabilities.", "source": "openreview", "url": "https://openreview.net/forum?id=49ueGcxA8W", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2025/NeurIPS/Poster/4.5_XVerse_ Consistent Multi-Subject Control of Identity and Semantic Attributes via DiT Modulation_2025.pdf" }, { "title": "FACE: A General Framework for Mapping Collaborative Filtering Embeddings into LLM Tokens", "authors": [ "Chao Wang", "Yixin Song", "Jinhui Ye", "Chuan Qin", "Dazhong Shen", "Lingfeng Liu", "Xiang Wang", "Yanyong Zhang" ], "year": 2025, "venue": "NeurIPS", "abstract": "Recently, large language models (LLMs) have been explored for integration with collaborative filtering (CF)-based recommendation systems, which are crucial for personalizing user experiences. However, a key challenge is that LLMs struggle to interpret the latent, non-semantic embeddings produced by CF approaches, limiting recommendation effectiveness and further applications. To address this, we propose FACE, a general interpretable framework that maps CF embeddings into pre-trained LLM tokens. Specifically, we introduce a disentangled projection module to decompose CF embeddings into concept-specific vectors, followed by a quantized autoencoder to convert continuous embeddings into LLM tokens (descriptors). Then, we design a contrastive alignment objective to ensure that the tokens align with corresponding textual signals. Hence, the model-agnostic FACE framework achieves semantic alignment without fine-tuning LLMs and enhances recommendation performance by leveraging their pre-trained capabilities. Empirical results on three real-world recommendation datasets demonstrate performance improvements in benchmark models, with interpretability studies confirming the interpretability of the descriptors. Code is available in \\url{https://github.com/YixinRoll/FACE}.", "source": "openreview", "url": "https://openreview.net/forum?id=loznSxLomv", "decision_type": "Poster", "avg_rating": 4.4, "relative_path": "2025/NeurIPS/Poster/4.4_FACE_ A General Framework for Mapping Collaborative Filtering Embeddings into LLM Tokens_2025.pdf" }, { "title": "Generator-Mediated Bandits: Thompson Sampling for GenAI-Powered Adaptive Interventions", "authors": [ "Marc Brooks", "Gabriel Durham", "Kihyuk Hong", "Ambuj Tewari" ], "year": 2025, "venue": "NeurIPS", "abstract": "Recent advances in generative artificial intelligence (GenAI) models have enabled the generation of personalized content that adapts to up-to-date user context. While personalized decision systems are often modeled using bandit formulations, the integration of GenAI introduces new structure into otherwise classical sequential learning problems. In GenAI-powered interventions, the agent selects a query, but the environment experiences a stochastic response drawn from the generative model. Standard bandit methods do not explicitly account for this structure, where actions influence rewards only through stochastic, observed treatments. We introduce generator-mediated bandit-Thompson sampling (GAMBITTS), a bandit approach designed for this action/treatment split, using mobile health interventions with large language model-generated text as a motivating case study. GAMBITTS explicitly models both the treatment and reward generation processes, using information in the delivered treatment to accelerate policy learning relative to standard methods. We establish regret bounds for GAMBITTS by decomposing sources of uncertainty in treatment and reward, identifying conditions where it achieves stronger guarantees than standard bandit approaches. In simulation studies, GAMBITTS consistently outperforms conventional algorithms by leveraging observed treatments to more accurately estimate expected rewards.", "source": "openreview", "url": "https://openreview.net/forum?id=soMxYQaMnF", "decision_type": "Poster", "avg_rating": 4.4, "relative_path": "2025/NeurIPS/Poster/4.4_Generator-Mediated Bandits_ Thompson Sampling for GenAI-Powered Adaptive Interventions_2025.pdf" }, { "title": "UniCTokens: Boosting Personalized Understanding and Generation via Unified Concept Tokens", "authors": [ "Ruichuan An", "Sihan Yang", "Renrui Zhang", "Zijun Shen", "Ming Lu", "Gaole Dai", "Hao Liang", "Ziyu Guo", "Shilin Yan", "Yulin Luo", "Bocheng Zou", "Chaoqun Yang", "Wentao Zhang" ], "year": 2025, "venue": "NeurIPS", "abstract": "Personalized models have demonstrated remarkable success in understanding and generating concepts provided by users. However, existing methods use separate concept tokens for understanding and generation, treating these tasks in isolation. This may result in limitations for generating images with complex prompts. For example, given the concept $\\langle bo\\rangle$, generating \"$\\langle bo\\rangle$ wearing its hat\" without additional textual descriptions of its hat. We call this kind of generation \\textit{\\textbf{personalized attribute-reasoning generation}}. To address the limitation, we present UniCTokens, a novel framework that effectively integrates personalized information into a unified vision language model (VLM) for understanding and generation. UniCTokens trains a set of unified concept tokens to leverage complementary semantics, boosting two personalized tasks. Moreover, we propose a progressive training strategy with three stages: understanding warm-up, bootstrapping generation from understanding, and deepening understanding from generation to enhance mutual benefits between both tasks. To quantitatively evaluate the unified VLM personalization, we present UnifyBench, the first benchmark for assessing concept understanding, concept generation, and attribute-reasoning generation. Experimental results on UnifyBench indicate that UniCTokens shows competitive performance compared to leading methods in concept understanding, concept generation, and achieving state-of-the-art results in personalized attribute-reasoning generation. Our research demonstrates that enhanced understanding improves generation, and the generation process can yield valuable insights into understanding. Our code and dataset will be released at: \\href{https://github.com/arctanxarc/UniCTokens}{https://github.com/arctanxarc/UniCTokens}.", "source": "openreview", "url": "https://openreview.net/forum?id=gNwJTjxmBe", "decision_type": "Poster", "avg_rating": 4.3, "relative_path": "2025/NeurIPS/Poster/4.3_UniCTokens_ Boosting Personalized Understanding and Generation via Unified Concept Tokens_2025.pdf" }, { "title": "BridgePure: Limited Protection Leakage Can Break Black-Box Data Protection", "authors": [ "Yihan Wang", "Yiwei Lu", "Xiao-Shan Gao", "Gautam Kamath", "Yaoliang Yu" ], "year": 2025, "venue": "NeurIPS", "abstract": "Availability attacks, or unlearnable examples, are defensive techniques that allow data owners to modify their datasets in ways that prevent unauthorized machine learning models from learning effectively while maintaining the data's intended functionality. It has led to the release of popular black-box tools (e.g., APIs) for users to upload personal data and receive protected counterparts. In this work, we show that such black-box protections can be substantially compromised if a small set of unprotected in-distribution data is available. Specifically, we propose a novel threat model of protection leakage, where an adversary can (1) easily acquire (unprotected, protected) pairs by querying the black-box protections with a small unprotected dataset; and (2) train a diffusion bridge model to build a mapping between unprotected and protected data. This mapping, termed BridgePure, can effectively remove the protection from any previously unseen data within the same distribution. \nBridgePure demonstrates superior purification performance on classification and style mimicry tasks, exposing critical vulnerabilities in black-box data protection. \nWe suggest that practitioners implement multi-level countermeasures to mitigate such risks.", "source": "openreview", "url": "https://openreview.net/forum?id=JebheQvpIb", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_BridgePure_ Limited Protection Leakage Can Break Black-Box Data Protection_2025.pdf" }, { "title": "DoseSurv: Predicting Personalized Survival Outcomes under Continuous-Valued Treatments", "authors": [ "Moritz Gögl", "Yu Liu", "Christopher Yau", "Peter Watkinson", "Tingting Zhu" ], "year": 2025, "venue": "NeurIPS", "abstract": "Estimating heterogeneous treatment effects (HTEs) of continuous-valued interventions on survival, that is, time-to-event (TTE) outcomes, is crucial in various fields, notably in clinical decision-making and in driving the advancement of next-generation clinical trials. However, while HTE estimation for continuous-valued (i.e., dosage-dependent) interventions and for TTE outcomes have been separately explored, their combined application remains largely overlooked in the machine learning literature. We propose DoseSurv, a varying-coefficient network designed to estimate HTEs for different dosage-dependent and non-dosage treatment options from TTE data. DoseSurv uses radial basis functions to model continuity in dose-response relationships and learns balanced representations to address covariate shifts arising in HTE estimation from observational TTE data. We present experiments across various treatment scenarios on both simulated and real-world data, demonstrating DoseSurv's superior performance over existing baseline models.", "source": "openreview", "url": "https://openreview.net/forum?id=5wdssRcI2Z", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_DoseSurv_ Predicting Personalized Survival Outcomes under Continuous-Valued Treatments_2025.pdf" }, { "title": "Foundation Cures Personalization: Improving Personalized Models’ Prompt Consistency via Hidden Foundation Knowledge", "authors": [ "Yiyang Cai", "Zhengkai Jiang", "Yulong Liu", "Chunyang Jiang", "Wei Xue", "Yike Guo", "Wenhan Luo" ], "year": 2025, "venue": "NeurIPS", "abstract": "Facial personalization faces challenges to maintain identity fidelity without disrupting the foundation model's prompt consistency. The mainstream personalization models employ identity embedding to integrate identity information within the attention mechanisms. However, our preliminary findings reveal that identity embeddings compromise the effectiveness of other tokens in the prompt, thereby limiting high prompt consistency and attribute-level controllability. Moreover, by deactivating identity embedding, personalization models still demonstrate the underlying foundation models' ability to control facial attributes precisely. It suggests that such foundation models' knowledge can be leveraged to cure the ill-aligned prompt consistency of personalization models. Building upon these insights, we propose FreeCure, a framework that improves the prompt consistency of personalization models with their latent foundation models' knowledge. First, by setting a dual inference paradigm with/without identity embedding, we identify attributes (e.g., hair, accessories, etc.) for enhancements. Second, we introduce a novel foundation-aware self-attention module, coupled with an inversion-based process to bring well-aligned attribute information to the personalization process. Our approach is training-free, and can effectively enhance a wide array of facial attributes; and it can be seamlessly integrated into existing popular personalization models based on both Stable Diffusion and FLUX. FreeCure has consistently shown significant improvements in prompt consistency across these facial personalization models while maintaining the integrity of their original identity fidelity.", "source": "openreview", "url": "https://openreview.net/forum?id=KQ9KCDS4zp", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_Foundation Cures Personalization_ Improving Personalized Models’ Prompt Consistency via Hidden _2025.pdf" }, { "title": "IGD: Token Decisiveness Modeling via Information Gain in LLMs for Personalized Recommendation", "authors": [ "Zijie Lin", "Yang Zhang", "Xiaoyan Zhao", "Fengbin ZHU", "Fuli Feng", "Tat-Seng Chua" ], "year": 2025, "venue": "NeurIPS", "abstract": "Large Language Models (LLMs) have shown strong potential for recommendation by framing item prediction as a token-by-token language generation task. However, existing methods treat all item tokens equally, simply pursuing likelihood maximization during both optimization and decoding. This overlooks crucial token-level differences in decisiveness—many tokens contribute little to item discrimination yet can dominate optimization or decoding.\nTo quantify token decisiveness, we propose a novel perspective that models item generation as a decision process, measuring token decisiveness by the Information Gain (IG) each token provides in reducing uncertainty about the generated item. Our empirical analysis reveals that most tokens have low IG but often correspond to high logits, disproportionately influencing training loss and decoding, which may impair model performance.\nBuilding on these insights, we introduce an Information Gain-based Decisiveness-aware Token handling (IGD) strategy that integrates token decisiveness into both tuning and decoding. Specifically, IGD downweights low-IG tokens during tuning and rebalances decoding to emphasize tokens with high IG. In this way, IGD moves beyond pure likelihood maximization, effectively prioritizing high-decisiveness tokens. Extensive experiments on four benchmark datasets with two LLM backbones demonstrate that IGD consistently improves recommendation accuracy, achieving significant gains on widely used ranking metrics compared to strong baselines. Our codes are available at \\url{https://github.com/ZJLin2oo1/IGD}.", "source": "openreview", "url": "https://openreview.net/forum?id=ygNaCTGUwJ", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_IGD_ Token Decisiveness Modeling via Information Gain in LLMs for Personalized Recommendation_2025.pdf" }, { "title": "Imagine360: Immersive 360 Video Generation from Perspective Anchor", "authors": [ "Jing Tan", "Shuai Yang", "Tong Wu", "Jingwen He", "Yuwei Guo", "Ziwei Liu", "Dahua Lin" ], "year": 2025, "venue": "NeurIPS", "abstract": "$360^\\circ$ videos offer a hyper-immersive experience that allows the viewers to explore a dynamic scene from full 360 degrees. \nTo achieve more accessible and personalized content creation in $360^\\circ$ video format, we seek to lift standard perspective videos into $360^\\circ$ equirectangular videos. To this end, we introduce **Imagine360**, the first perspective-to-$360^\\circ$ video generation framework that creates high-quality $360^\\circ$ videos with rich and diverse motion patterns from video anchors.\nImagine360 learns fine-grained spherical visual and motion patterns from limited $360^\\circ$ video data with several key designs. \n**1)** Firstly we adopt the dual-branch design, including a perspective and a panorama video denoising branch to provide local and global constraints for $360^\\circ$ video generation, with motion module and spatial LoRA layers fine-tuned on $360^\\circ$ videos.\n**2)** Additionally, an antipodal mask is devised to capture long-range motion dependencies, enhancing the reversed camera motion between antipodal pixels across hemispheres.\n**3)** To handle diverse perspective video inputs, we propose rotation-aware designs that adapt to varying video masking due to changing camera poses across frames.\n**4)** Lastly, we introduce a new 360 video dataset featuring 10K high-quality, trimmed 360 video clips with structured motion to facilitate training.\nExtensive experiments show Imagine360 achieves superior graphics quality and motion coherence with our curated dataset among state-of-the-art $360^\\circ$ video generation methods. We believe Imagine360 holds promise for advancing personalized, immersive $360^\\circ$ video creation.", "source": "openreview", "url": "https://openreview.net/forum?id=BcYfsMMpV1", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_Imagine360_ Immersive 360 Video Generation from Perspective Anchor_2025.pdf" }, { "title": "Inference-Time Personalized Alignment with a Few User Preference Queries", "authors": [ "Victor-Alexandru Pădurean", "Parameswaran Kamalaruban", "Nachiket Kotalwar", "Alkis Gotovos", "Adish Singla" ], "year": 2025, "venue": "NeurIPS", "abstract": "We study the problem of aligning a generative model's response with a user's preferences. Recent works have proposed several different formulations for personalized alignment; however, they either require a large amount of user preference queries or require that the preference be explicitly specified as a text input. In this paper, we propose a novel inference-time personalized alignment method, UserAlign, that elicits the user's preferences with a few queries as pairwise response comparisons. In particular, UserAlign builds on the theoretical framework of best-arm identification in logistic bandits and selects a personalized response from a fixed pool of the model's generated responses. The key idea is to consider the user's feedback consistent and noise-free, and incorporate it into the theoretical framework to identify the best response quickly. Experimental results across several tasks, involving personalized text and image generation, showcase the effectiveness of UserAlign in achieving personalized alignment.", "source": "openreview", "url": "https://openreview.net/forum?id=irYb8GGDyh", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_Inference-Time Personalized Alignment with a Few User Preference Queries_2025.pdf" }, { "title": "Integrating Drug Substructures and Longitudinal Electronic Health Records for Personalized Drug Recommendation", "authors": [ "Wenjie Du", "Xuqiang Li", "Jinke Feng", "Shuai Zhang", "Wen Zhang", "Yang Wang" ], "year": 2025, "venue": "NeurIPS", "abstract": "Drug recommendation systems aim to identify optimal drug combinations for patient care, balancing therapeutic efficacy and safety. Advances in large-scale longitudinal EHRs have enabled learning-based approaches that leverage patient histories such as diagnoses, procedures, and previously prescribed drugs, to model complex patient-drug relationships. Yet, many existing solutions overlook standard clinical practices that favor certain drugs for specific conditions and fail to fully integrate the influence of molecular substructures on drug efficacy and safety. In response, we propose \\textbf{SubRec}, a unified framework that integrates representation learning across both patient and drug spaces. Specifically, SubRec introduces a conditional information bottleneck to extract core drug substructures most relevant to patient conditions, thereby enhancing interpretability and clinical alignment. Meanwhile, an adaptive vector quantization mechanism is designed to generate patient–drug interaction patterns into a condition-aware codebook which reuses clinically meaningful patterns, reduces training overhead, and provides a controllable latent space for recommendation. Crucially, the synergy between condition-specific substructure learning and discrete patient prototypes allows SubRec to make accurate and personalized drug recommendations. Experimental results on the real-world MIMIC III and IV demonstrate our model's advantages. \nThe source code is available at \\href{https://anonymous.4open.science/r/DrugRecommendation-5173}{https://anonymous.4open.science/}.", "source": "openreview", "url": "https://openreview.net/forum?id=ml2TynfZI0", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_Integrating Drug Substructures and Longitudinal Electronic Health Records for Personalized Drug_2025.pdf" }, { "title": "LLM-Driven Treatment Effect Estimation Under Inference Time Text Confounding", "authors": [ "Yuchen Ma", "Dennis Frauen", "Jonas Schweisthal", "Stefan Feuerriegel" ], "year": 2025, "venue": "NeurIPS", "abstract": "Estimating treatment effects is crucial for personalized decision-making in medicine, but this task faces unique challenges in clinical practice. At training time, models for estimating treatment effects are typically trained on well-structured medical datasets that contain detailed patient information. However, at inference time, predictions are often made using textual descriptions (e.g., descriptions with self-reported symptoms), which are incomplete representations of the original patient information. In this work, we make three contributions. (1) We show that the discrepancy between the data available during training time and inference time can lead to biased estimates of treatment effects. We formalize this issue as an \\emph{inference time text confounding} problem, where confounders are fully observed during training time but only partially available through text at inference time. (2) To address this problem, we propose a novel framework for estimating treatment effects that explicitly accounts for inference time text confounding. Our framework leverages large language models (LLMs) together with a custom doubly robust learner to mitigate biases caused by the inference time text confounding. (3) Through a series of experiments, we demonstrate the effectiveness of our framework in real-world applications.", "source": "openreview", "url": "https://openreview.net/forum?id=sv41aaGTit", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_LLM-Driven Treatment Effect Estimation Under Inference Time Text Confounding_2025.pdf" }, { "title": "Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings", "authors": [ "Yehya Farhat", "Hamza ElMokhtar Shili", "Fangshuo Liao", "Chen Dun", "Mirian Del Carmen Hipolito Garcia", "Guoqing Zheng", "Ahmed Hassan Awadallah", "Robert Sim", "Dimitrios Dimitriadis", "Anastasios Kyrillidis" ], "year": 2025, "venue": "NeurIPS", "abstract": "Mixture-of-Experts (MoEs) achieve scalability by dynamically activating subsets of their components.\nYet, understanding how expertise emerges through joint training of gating mechanisms and experts remains incomplete, especially in scenarios without clear task partitions. Motivated by inference costs and data heterogeneity, we study how joint training of gating functions and experts can dynamically allocate domain-specific expertise across multiple underlying data distributions.\nAs an outcome of our framework, we develop an instance tailored specifically to decentralized training scenarios, introducing *Dynamically Decentralized Orchestration of MoEs* or *DDOME*. *DDOME* leverages heterogeneity emerging from distributional shifts across decentralized data sources to specialize experts dynamically. By integrating a pretrained common expert to inform a gating function, *DDOME* achieves personalized expert subset selection on-the-fly, facilitating just-in-time personalization. \nWe empirically validate *DDOME* within a Federated Learning (FL) context: *DDOME* attains from 4\\% up to an 24\\% accuracy improvement over state-of-the-art FL baselines in image and text classification tasks, while maintaining competitive zero-shot generalization capabilities. Furthermore, we provide theoretical insights confirming that the joint gating-experts training is critical for achieving meaningful expert specialization.", "source": "openreview", "url": "https://openreview.net/forum?id=3FBByWp6GL", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_Learning to Specialize_ Joint Gating-Expert Training for Adaptive MoEs in Decentralized Setting_2025.pdf" }, { "title": "Listwise Preference Diffusion Optimization for User Behavior Trajectories Prediction", "authors": [ "Hongtao Huang", "Chengkai Huang", "Junda Wu", "Tong Yu", "Julian McAuley", "Lina Yao" ], "year": 2025, "venue": "NeurIPS", "abstract": "Forecasting multi-step user behavior trajectories requires reasoning over structured preferences across future actions, a challenge overlooked by traditional sequential recommendation. This problem is critical for applications such as personalized commerce and adaptive content delivery, where anticipating a user’s complete action sequence enhances both satisfaction and business outcomes. We identify an essential limitation of existing paradigms: their inability to capture global, listwise dependencies among sequence items. To address this, we formulate User Behavior Trajectory Prediction (UBTP) as a new task setting that explicitly models longterm user preferences. We introduce Listwise Preference Diffusion Optimization (LPDO), a diffusion-based training framework that directly optimizes structured preferences over entire item sequences. LPDO incorporates a Plackett–Luce supervision signal and derives a tight variational lower bound aligned with listwise ranking likelihoods, enabling coherent preference generation across denoising steps and overcoming the independent-token assumption of prior diffusion methods. To rigorously evaluate multi-step prediction quality, we propose the task-specific metric: Sequential Match (SeqMatch), which measures exact trajectory agreement, and adopt Perplexity (PPL), which assesses probabilistic fidelity. Extensive experiments on real-world user behavior benchmarks demonstrate that LPDO consistently outperforms state-of-the-art baselines, establishing a new benchmark for structured preference learning with diffusion models.", "source": "openreview", "url": "https://openreview.net/forum?id=x5KUOlYKQr", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_Listwise Preference Diffusion Optimization for User Behavior Trajectories Prediction_2025.pdf" }, { "title": "Miss-ReID: Delivering Robust Multi-Modality Object Re-Identification Despite Missing Modalities", "authors": [ "Ruida Xi" ], "year": 2025, "venue": "NeurIPS", "abstract": "Multi-modality object Re-IDentification (ReID) targets to retrieve special objects by integrating complementary information from diverse visual sources.\nHowever, existing models that are trained on modality-complete datasets typically exhibit significantly degraded discrimination\nduring inference with modality-incomplete inputs.\nThis disparity highlights the necessity of developing a robust multi-modality ReID model that remains effective in real-world applications. For that, this paper delivers a flexible framework tailored for more realistic multi-modality retrieval scenario, dubbed as Miss-ReID, which is the first work to friendly support both the modality-missing training and inference conditions. The core of Miss-ReID lies in compensating for missing visual cues via vision-text knowledge transfer driven by Vision-Language foundation Models (VLMs), effectively mitigating performance degradation. In brief, we capture diverse visual features from accessible modalities first, and then build memory banks to store heterogeneous prototypes for each identity, preserving multi-modality characteristics. Afterwards, we employ structure-aware query interactions to dynamically distill modality-invariant object structures from existing localized visual patches, which are further reversed into pseudo-word tokens that encapsulate the identity-relevant structural semantics.\nIn tandem, the inverted tokens, integrated with learnable modality prompts, are embedded into crafted textual template to form the personalized linguistic descriptions tailored for diverse modalities.\nUltimately, harnessing VLMs' inherent vision-text alignment capability, the resulting textual features effectively function as compensatory semantic representations for missing visual modalities, after being optimized with some memory-based alignment constraints.\nExtensive experiments demonstrate our model's efficacy and superiority over state-of-the-art methods in various modality-missing scenarios, and our endeavors further propel multi-modality ReID into real-world applications.", "source": "openreview", "url": "https://openreview.net/forum?id=7x5X6gTCUH", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_Miss-ReID_ Delivering Robust Multi-Modality Object Re-Identification Despite Missing Modalities_2025.pdf" }, { "title": "Personalized Safety in LLMs: A Benchmark and A Planning-Based Agent Approach", "authors": [ "Yuchen Wu", "Edward Sun", "Kaijie Zhu", "Jianxun Lian", "Jose Hernandez-Orallo", "Aylin Caliskan", "Jindong Wang" ], "year": 2025, "venue": "NeurIPS", "abstract": "Large language models (LLMs) typically generate identical or similar responses for all users given the same prompt, posing serious safety risks in high-stakes applications where user vulnerabilities differ widely.\nExisting safety evaluations primarily rely on context-independent metrics—such as factuality, bias, or toxicity—overlooking the fact that the same response may carry divergent risks depending on the user's background or condition.\nWe introduce ``personalized safety'' to fill this gap and present PENGUIN—a benchmark comprising 14,000 scenarios across seven sensitive domains with both context-rich and context-free variants. Evaluating six leading LLMs, we demonstrate that personalized user information significantly improves safety scores by 43.2%, confirming the effectiveness of personalization in safety alignment. However, not all context attributes contribute equally to safety enhancement. To address this, we develop RAISE—a training-free, two-stage agent framework that strategically acquires user-specific background. RAISE improves safety scores by up to 31.6% over six vanilla LLMs, while maintaining a low interaction cost of just 2.7 user queries on average. Our findings highlight the importance of selective information gathering in safety-critical domains and offer a practical solution for personalizing LLM responses without model retraining. This work establishes a foundation for safety research that adapts to individual user contexts rather than assuming a universal harm standard.", "source": "openreview", "url": "https://openreview.net/forum?id=Gsi42ohBoM", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_Personalized Safety in LLMs_ A Benchmark and A Planning-Based Agent Approach_2025.pdf" }, { "title": "Personalized Visual Content Generation in Conversational Systems", "authors": [ "Xianquan Wang", "Zhaocheng Du", "Huibo Xu", "Shukang Yin", "Yupeng Han", "Jieming Zhu", "Kai Zhang", "Qi Liu" ], "year": 2025, "venue": "NeurIPS", "abstract": "With the rapid progress of large language models (LLMs) and diffusion models, there has been growing interest in personalized content generation. However, current conversational systems often present the same recommended content to all users, falling into the dilemma of \"one-size-fits-all.\" To break this limitation and boost user engagement, in this paper, we introduce PCG (**P**ersonalized Visual **C**ontent **G**eneration), a unified framework for personalizing item images within conversational systems. We tackle two key bottlenecks: the depth of personalization and the fidelity of generated images. Specifically, an LLM-powered Inclinations Analyzer is adopted to capture user likes and dislikes from context to construct personalized prompts. Moreover, we design a dual-stage LoRA mechanism—Global LoRA for understanding task-specific visual style, and Local LoRA for capturing preferred visual elements from conversation history. During training, we introduce the visual content condition method to ensure LoRA learns both historical visual context and maintains fidelity to the original item images. Extensive experiments on benchmark conversational datasets—including objective metrics and GPT-based evaluations—demonstrate that our framework outperforms strong baselines, which highlight its potential to redefine personalization in visual content generation for conversational scenarios like e-commerce and real-world recommendation.", "source": "openreview", "url": "https://openreview.net/forum?id=6MUgQXkxIC", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_Personalized Visual Content Generation in Conversational Systems_2025.pdf" }, { "title": "Perturb a Model, Not an Image: Towards Robust Privacy Protection via Anti-Personalized Diffusion Models", "authors": [ "Tae-Young Lee", "Juwon Seo", "Jong Hwan Ko", "Gyeong-Moon Park" ], "year": 2025, "venue": "NeurIPS", "abstract": "Recent advances in diffusion models have enabled high-quality synthesis of specific subjects, such as identities or objects. This capability, while unlocking new possibilities in content creation, also introduces significant privacy risks, as personalization techniques can be misused by malicious users to generate unauthorized images. Although several studies have attempted to counter this by generating adversarially perturbed samples designed to disrupt personalization, they rely on unrealistic assumptions and become ineffective in the presence of even a few clean images or under simple image transformations. To address these challenges, we shift the protection target from the images to the diffusion model itself to hinder the personalization of specific subjects, through our novel framework called $\\textbf{A}$nti-$\\textbf{P}$ersonalized $\\textbf{D}$iffusion $\\textbf{M}$odels ($\\textbf{APDM}$). We first provide a theoretical analysis demonstrating that a naive approach of existing loss functions to diffusion models is inherently incapable of ensuring convergence for robust anti-personalization. Motivated by this finding, we introduce Direct Protective Optimization (DPO), a novel loss function that effectively disrupts subject personalization in the target model without compromising generative quality. Moreover, we propose a new dual-path optimization strategy, coined Learning to Protect (L2P). By alternating between personalization and protection paths, L2P simulates future personalization trajectories and adaptively reinforces protection at each step.\nExperimental results demonstrate that our framework outperforms existing methods, achieving state-of-the-art performance in preventing unauthorized personalization.\nThe code is available at https://github.com/KU-VGI/APDM.", "source": "openreview", "url": "https://openreview.net/forum?id=5XoqKCmkS7", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_Perturb a Model, Not an Image_ Towards Robust Privacy Protection via Anti-Personalized Diffusio_2025.pdf" }, { "title": "SMARTraj$^2$: A Stable Multi-City Adaptive Method for Multi-View Spatio-Temporal Trajectory Representation Learning", "authors": [ "Tangwen Qian", "Junhe Li", "Yile Chen", "Gao Cong", "Zezhi Shao", "Jun Zhang", "Tao Sun", "Fei Wang", "Yongjun Xu" ], "year": 2025, "venue": "NeurIPS", "abstract": "Spatio-temporal trajectory representation learning plays a crucial role in various urban applications such as transportation systems, urban planning, and environmental monitoring. Existing methods can be divided into single-view and multi-view approaches, with the latter offering richer representations by integrating multiple sources of spatio-temporal data. However, these methods often struggle to generalize across diverse urban scenes due to multi-city structural heterogeneity, which arises from the disparities in road networks, grid layouts, and traffic regulations across cities, and the amplified seesaw phenomenon, where optimizing for one city, view, or task can degrade performance in others. These challenges hinder the deployment of trajectory learning models across multiple cities, limiting their real-world applicability. In this work, we propose SMARTraj$^2$, a novel stable multi-city adaptive method for multi-view spatio-temporal trajectory representation learning. Specifically, we introduce a feature disentanglement module to separate domain-invariant and domain-specific features, and a personalized gating mechanism to dynamically stabilize the contributions of different views and tasks. Our approach achieves superior generalization across heterogeneous urban scenes while maintaining robust performance across multiple downstream tasks. Extensive experiments on benchmark datasets demonstrate the effectiveness of SMARTraj$^2$ in enhancing cross-city generalization and outperforming state-of-the-art methods. See our project website at \\url{https://github.com/GestaltCogTeam/SMARTraj}.", "source": "openreview", "url": "https://openreview.net/forum?id=JkVQmaE5pK", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_SMARTraj$^2$_ A Stable Multi-City Adaptive Method for Multi-View Spatio-Temporal Trajectory Rep_2025.pdf" }, { "title": "Self-Refining Language Model Anonymizers via Adversarial Distillation", "authors": [ "Kyuyoung Kim", "Hyunjun Jeon", "Jinwoo Shin" ], "year": 2025, "venue": "NeurIPS", "abstract": "Large language models (LLMs) are increasingly used in sensitive domains, where their ability to infer personal data from seemingly benign text introduces emerging privacy risks. While recent LLM-based anonymization methods help mitigate such risks, they often rely on proprietary models (e.g., GPT-4), raising concerns about cost and the potential exposure of sensitive data to untrusted external systems. To address this, we introduce $\\textit{SElf-refining Anonymization with Language model}$ (SEAL), a novel distillation framework for training small language models (SLMs) to perform effective anonymization without relying on external models at inference time. SEAL leverages adversarial interactions between an LLM anonymizer and an inference model to collect trajectories of anonymized texts and inferred attributes, which are then used to distill anonymization and critique capabilities into SLMs through supervised fine-tuning and preference learning. The resulting models learn both to anonymize text and to evaluate their outputs, enabling iterative improvement of anonymization quality via self-refinement. Experiments on SynthPAI, a dataset of synthetic personal profiles and text comments, demonstrate that SLMs trained with SEAL achieve substantial improvements in anonymization capabilities. Notably, 8B models attain a privacy-utility trade-off comparable to that of the GPT-4 anonymizer and, with self-refinement, even surpass it in terms of privacy protection. These results highlight the effectiveness of our adversarial distillation framework for training SLMs as efficient anonymizers.", "source": "openreview", "url": "https://openreview.net/forum?id=S1F2qhendd", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_Self-Refining Language Model Anonymizers via Adversarial Distillation_2025.pdf" }, { "title": "Sum Estimation under Personalized Local Differential Privacy", "authors": [ "Dajun Sun", "Wei Dong", "Yuan Qiu", "Ke Yi", "Graham Cormode" ], "year": 2025, "venue": "NeurIPS", "abstract": "People have diverse privacy requirements. This is best modeled using a personalized local differential privacy model where each user privatizes their data using a possibly different privacy parameter. While the model of personalized local differential privacy is a natural and important one, prior work has failed to give meaningful error bounds. In this paper, we study the foundational sum/mean estimation problem under this model. We present two novel protocols that achieve strong error guarantees. The first gives a guarantee based on the radius of the data, suiting inputs that are centered around zero. The second extends the guarantee to the diameter of the data, capturing the case when the points are situated arbitrarily. Experimental results on both synthetic and real data show that our protocols significantly outperform existing methods in terms of accuracy while providing a strong level of privacy.", "source": "openreview", "url": "https://openreview.net/forum?id=AXlquRUO0S", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_Sum Estimation under Personalized Local Differential Privacy_2025.pdf" }, { "title": "Teaching Language Models to Evolve with Users: Dynamic Profile Modeling for Personalized Alignment", "authors": [ "Weixiang Zhao", "Xingyu Sui", "Yulin Hu", "Jiahe Guo", "Haixiao Liu", "Biye Li", "Yanyan Zhao", "Bing Qin", "Ting Liu" ], "year": 2025, "venue": "NeurIPS", "abstract": "Personalized alignment is essential for enabling large language models (LLMs) to engage effectively in user-centric dialogue. While recent prompt-based and offline optimization methods offer preliminary solutions, they fall short in cold-start scenarios and long-term personalization due to their inherently static and shallow designs. In this work, we introduce the Reinforcement Learning for Personalized Alignment (RLPA) framework, in which an LLM interacts with a simulated user model to iteratively infer and refine user profiles through dialogue. The training process is guided by a dual-level reward structure: the Profile Reward encourages accurate construction of user representations, while the Response Reward incentivizes generation of responses consistent with the inferred profile. We instantiate RLPA by fine-tuning Qwen-2.5-3B-Instruct, resulting in Qwen-RLPA, which achieves state-of-the-art performance in personalized dialogue. Empirical evaluations demonstrate that Qwen-RLPA consistently outperforms prompting and offline fine-tuning baselines, and even surpasses advanced commercial models such as Claude-3.5 and GPT-4o. Further analysis highlights Qwen-RLPA's robustness in reconciling conflicting user preferences, sustaining long-term personalization and delivering more efficient inference compared to recent reasoning-focused LLMs. These results emphasize the potential of dynamic profile inference as a more effective paradigm for building personalized dialogue systems.", "source": "openreview", "url": "https://openreview.net/forum?id=1V3Toke6XP", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_Teaching Language Models to Evolve with Users_ Dynamic Profile Modeling for Personalized Alignm_2025.pdf" }, { "title": "VaMP: Variational Multi-Modal Prompt Learning for Vision-Language Models", "authors": [ "Silin Cheng", "Kai Han" ], "year": 2025, "venue": "NeurIPS", "abstract": "Vision-language models (VLMs), such as CLIP, have shown strong generalization under zero-shot settings, yet adapting them to downstream tasks with limited supervision remains a significant challenge. Existing multi-modal prompt learning methods typically rely on fixed, shared prompts and deterministic parameters, which limits their ability to capture instance-level variation or model uncertainty across diverse tasks and domains. To tackle this issue, we propose a novel Variational Multi-Modal Prompt Learning (VaMP) framework that enables sample-specific, uncertainty-aware prompt tuning in multi-modal representation learning. VaMP generates instance-conditioned prompts by sampling from a learned posterior distribution, allowing the model to personalize its behavior based on input content.\nTo further enhance the integration of local and global semantics, we introduce a class-aware prior derived from the instance representation and class prototype. Building upon these, we formulate prompt tuning as variational inference over latent prompt representations and train the entire framework end-to-end through reparameterized sampling. Experiments on few-shot and domain generalization benchmarks show that VaMP achieves state-of-the-art performance, highlighting the benefits of modeling both uncertainty and task structure in our method. Project page: https://visual-ai.github.io/vamp", "source": "openreview", "url": "https://openreview.net/forum?id=8an1xVyKxS", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_VaMP_ Variational Multi-Modal Prompt Learning for Vision-Language Models_2025.pdf" }, { "title": "VisualLens: Personalization through Task-Agnostic Visual History", "authors": [ "Wang Bill Zhu", "Deqing Fu", "Kai Sun", "Yi Lu", "Zhaojiang Lin", "Seungwhan Moon", "Kanika Narang", "MUSTAFA CANIM", "Yue Liu", "Anuj Kumar", "Xin Luna Dong" ], "year": 2025, "venue": "NeurIPS", "abstract": "Existing recommendation systems either rely on user interaction logs, such as online shopping history for shopping recommendations, or focus on text signals. \nHowever, item-based histories are not always accessible and generalizable for multimodal recommendation.\nWe hypothesize that a user's visual history --- comprising images from daily life --- can offer rich, task-agnostic insights into their interests and preferences, and thus be leveraged for effective personalization.\nTo this end, we propose VisualLens, a novel framework that leverages multimodal large language models (MLLMs) to enable personalization using task-agnostic visual history.\nVisualLens extracts, filters, and refines a spectrum user profile from the visual history to support personalized recommendation.\nWe created two new benchmarks, Google-Review-V and Yelp-V, with task-agnostic visual histories, and show that VisualLens improves over state-of-the-art item-based multimodal recommendations by 5-10\\% on Hit@3, and outperforms GPT-4o by 2-5\\%.\nFurther analysis shows that VisualLens is robust across varying history lengths and excels at adapting to both longer histories and unseen content categories.", "source": "openreview", "url": "https://openreview.net/forum?id=FsgwcrJWp8", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_VisualLens_ Personalization through Task-Agnostic Visual History_2025.pdf" }, { "title": "Adaptive Latent-Space Constraints in Personalized Federated Learning", "authors": [ "Sana Ayromlou", "D. B. Emerson" ], "year": 2025, "venue": "NeurIPS", "abstract": "Federated learning (FL) is an effective and widely used approach to training deep learning models on decentralized datasets held by distinct clients. FL also strengthens both security and privacy protections for training data. Common challenges associated with statistical heterogeneity between distributed datasets have spurred significant interest in personalized FL (pFL) methods, where models combine aspects of global learning with local modeling specific to each client’s unique characteristics. This work investigates the efficacy of theoretically supported, adaptive MMD measures in pFL, primarily focusing on the Ditto framework, a state-of-the-art technique for distributed data heterogeneity. The use of such measures significantly improves model performance across a variety of tasks, especially those with pronounced feature heterogeneity. Additional experiments demonstrate that such measures are directly applicable to other pFL techniques and yield similar improvements across a number of datasets. Finally, the results motivate the use of constraints tailored to the various kinds of heterogeneity expected in FL systems.", "source": "openreview", "url": "https://openreview.net/forum?id=nsv3ogqRIU", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_Adaptive Latent-Space Constraints in Personalized Federated Learning_2025.pdf" }, { "title": "Decentralized Dynamic Cooperation of Personalized Models for Federated Continual Learning", "authors": [ "Danni Yang", "Zhikang Chen", "Sen Cui", "Mengyue Yang", "Ding Li", "Abudukelimu Wuerkaixi", "Haoxuan Li", "Jinke Ren", "Mingming Gong" ], "year": 2025, "venue": "NeurIPS", "abstract": "Federated continual learning (FCL) has garnered increasing attention for its ability to support distributed computation in environments with evolving data distributions. However, the emergence of new tasks introduces both temporal and cross-client shifts, making catastrophic forgetting a critical challenge. Most existing works aggregate knowledge from clients into a global model, which may not enhance client performance since irrelevant knowledge could introduce interference, especially in heterogeneous scenarios. Additionally, directly applying decentralized approaches to FCL suffers from ineffective group formation caused by task changes. To address these challenges, we propose a decentralized dynamic cooperation framework for FCL, where clients establish dynamic cooperative learning coalitions to balance the acquisition of new knowledge and the retention of prior learning, thereby obtaining personalized models. To maximize model performance, each client engages in selective cooperation, dynamically allying with others who offer meaningful performance gains. This results in non-overlapping, variable coalitions at each stage of the task. Moreover, we use coalitional affinity game to simulate coalition relationships between clients. By assessing both client gradient coherence and model similarity, we quantify the client benefits derived from cooperation. We also propose a merge-blocking algorithm and a dynamic cooperative evolution algorithm to achieve cooperative and dynamic equilibrium. Comprehensive experiments demonstrate the superiority of our method compared to various baselines. Code is available at: https://github.com/ydn3229/DCFCL.", "source": "openreview", "url": "https://openreview.net/forum?id=16BGOheRzm", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_Decentralized Dynamic Cooperation of Personalized Models for Federated Continual Learning_2025.pdf" }, { "title": "GeneMAN: Generalizable Single-Image 3D Human Reconstruction from Multi-Source Human Data", "authors": [ "Wentao Wang", "Hang Ye", "Fangzhou Hong", "Xue Yang", "Jianfu Zhang", "Yizhou Wang", "Ziwei Liu", "Liang Pan" ], "year": 2025, "venue": "NeurIPS", "abstract": "Given a single in-the-wild human photo, it remains a challenging task to reconstruct a high-fidelity 3D human model. Existing methods face difficulties including a) the varying body proportions captured by in-the-wild human images; b) diverse personal belongings within the shot; and c) ambiguities in human postures and inconsistency in human textures. In addition, the scarcity of high-quality human data intensifies the challenge. To address these problems, we propose a Generalizable image-to-3D huMAN reconstruction framework, dubbed GeneMAN, building upon a comprehensive multi-source collection of high-quality human data, including 3D scans, multi-view videos, single photos, and our generated synthetic human data. GeneMAN encompasses three key modules. 1) Without relying on parametric human models (e.g., SMPL), GeneMAN first trains a human-specific text-to-image diffusion model and a view-conditioned diffusion model, serving as GeneMAN 2D human prior and 3D human prior for reconstruction, respectively. 2) With the help of the pretrained human prior models, the Geometry Initialization-&-Sculpting pipeline is leveraged to recover high-quality 3D human geometry given a single image. 3) To achieve high-fidelity 3D human textures, GeneMAN employs the Multi-Space Texture Refinement pipeline, consecutively refining textures in the latent and the pixel spaces. Extensive experimental results demonstrate that GeneMAN could generate high-quality 3D human models from a single image input, outperforming prior state-of-the-art methods. Notably, GeneMAN could reveal much better generalizability in dealing with in-the-wild images, often yielding high-quality 3D human models in natural poses with common items, regardless of the body proportions in the input images.", "source": "openreview", "url": "https://openreview.net/forum?id=NNHgm6VJkC", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_GeneMAN_ Generalizable Single-Image 3D Human Reconstruction from Multi-Source Human Data_2025.pdf" }, { "title": "Guard Me If You Know Me: Protecting Specific Face-Identity from Deepfakes", "authors": [ "Kaiqing Lin", "Zhiyuan Yan", "Ke-Yue Zhang", "Li Hao", "Yue Zhou", "Yuzhen Lin", "Weixiang Li", "Taiping Yao", "Shouhong Ding", "Bin Li" ], "year": 2025, "venue": "NeurIPS", "abstract": "Securing personal identity against deepfake attacks is increasingly critical in the digital age, especially for celebrities and political figures whose faces are easily accessible and frequently targeted.\nMost existing deepfake detection methods focus on general-purpose scenarios and often ignore the valuable prior knowledge of known facial identities, e.g., \"VIP individuals\" whose authentic facial data are already available. \nIn this paper, we propose **VIPGuard**, a unified multimodal framework designed to capture fine-grained and comprehensive facial representations of a given identity, compare them against potentially fake or similar-looking faces, and reason over these comparisons to make accurate and explainable predictions.\nSpecifically, our framework consists of three main stages. First, we fine-tune a multimodal large language model (MLLM) to learn detailed and structural facial attributes. \nSecond, we perform identity-level discriminative learning to enable the model to distinguish subtle differences between highly similar faces, including real and fake variations. Finally, we introduce user-specific customization, where we model the unique characteristics of the target face identity and perform semantic reasoning via MLLM to enable personalized and explainable deepfake detection.\nOur framework shows clear advantages over previous detection works, where traditional detectors mainly rely on low-level visual cues and provide no human-understandable explanations, while other MLLM-based models often lack a detailed understanding of specific face identities.\nTo facilitate the evaluation of our method, we build a comprehensive identity-aware benchmark called **VIPBench** for personalized deepfake detection, involving the latest 7 face-swapping and 7 entire face synthesis techniques for generation. \nExtensive experiments show that our model outperforms existing methods in both detection and explanation.\nThe code is available at https://github.com/KQL11/VIPGuard .", "source": "openreview", "url": "https://openreview.net/forum?id=7nTWoceJGK", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_Guard Me If You Know Me_ Protecting Specific Face-Identity from Deepfakes_2025.pdf" }, { "title": "Homogeneous Algorithms Can Reduce Competition in Personalized Pricing", "authors": [ "Nathanael Jo", "Ashia C. Wilson", "Kathleen Creel", "Manish Raghavan" ], "year": 2025, "venue": "NeurIPS", "abstract": "Firms' algorithm development practices are often homogeneous Whether firms train algorithms on similar data or rely on similar pre-trained models, the result is correlated predictions. In the context of personalized pricing, correlated algorithms can be viewed as a means to collude among competing firms, but whether or not this conduct is legal depends on the mechanisms of achieving collusion. We investigate the precise mechanisms through a formal game-theoretic model. Indeed, we find that (1) higher correlation diminishes consumer welfare and (2) as consumers become more price sensitive, firms are increasingly incentivized to compromise on the accuracy of their predictions in exchange for coordination. We demonstrate our theoretical results in a stylized empirical study where two firms compete using personalized pricing algorithms. Our results demonstrate a new mechanism for achieving collusion through correlation, which allows us to analyze its legal implications. Correlation through algorithms is a new frontier of anti-competitive behavior that is largely unconsidered by US antitrust law.", "source": "openreview", "url": "https://openreview.net/forum?id=aYd4wSCle4", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_Homogeneous Algorithms Can Reduce Competition in Personalized Pricing_2025.pdf" }, { "title": "Improving Video Generation with Human Feedback", "authors": [ "Jie Liu", "Gongye Liu", "Jiajun Liang", "Ziyang Yuan", "Xiaokun Liu", "Mingwu Zheng", "Xiele Wu", "Qiulin Wang", "Menghan Xia", "Xintao Wang", "Xiaohong Liu", "Fei Yang", "Pengfei Wan", "Di ZHANG", "Kun Gai", "Yujiu Yang", "Wanli Ouyang" ], "year": 2025, "venue": "NeurIPS", "abstract": "Video generation has achieved significant advances through rectified flow techniques, but issues like unsmooth motion and misalignment between videos and prompts persist. In this work, we develop a systematic pipeline that harnesses human feedback to mitigate these problems and refine the video generation model. Specifically, we begin by constructing a large-scale human preference dataset focused on modern video generation models, incorporating pairwise annotations across multi-dimensions. We then introduce VideoReward, a multi-dimensional video reward model, and examine how annotations and various design choices impact its rewarding efficacy. From a unified reinforcement learning perspective aimed at maximizing reward with KL regularization, we introduce three alignment algorithms for flow-based models. These include two training-time strategies: direct preference optimization for flow (Flow-DPO) and reward weighted regression for flow (Flow-RWR), and an inference-time technique, Flow-NRG, which applies reward guidance directly to noisy videos. Experimental results indicate that VideoReward significantly outperforms existing reward models, and Flow-DPO demonstrates superior performance compared to both Flow-RWR and supervised fine-tuning methods. Additionally, Flow-NRG lets users assign custom weights to multiple objectives during inference, meeting personalized video quality needs.", "source": "openreview", "url": "https://openreview.net/forum?id=nHkg4yc7SP", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_Improving Video Generation with Human Feedback_2025.pdf" }, { "title": "Knowledge Starts with Practice: Knowledge-Aware Exercise Generative Recommendation with Adaptive Multi-Agent Cooperation", "authors": [ "Yangtao Zhou", "Hua Chu", "Yongxiang Chen", "Ziwen Wang", "Jiacheng Liu", "Jianan Li", "Yueying Feng", "Xiangming Li", "Zihan Han", "Qingshan Li" ], "year": 2025, "venue": "NeurIPS", "abstract": "Adaptive learning, which requires the in-depth understanding of students' learning processes and rational planning of learning resources, plays a crucial role in intelligent education. However, how to effectively model these two processes and seamlessly integrate them poses significant implementation challenges for adaptive learning. As core learning resources, exercises have the potential to diagnose students' knowledge states during the learning processes and provide personalized learning recommendations to strengthen students' knowledge, thereby serving as a bridge to boost student-oriented adaptive learning. Therefore, we introduce a novel task called Knowledge-aware Exercise Generative Recommendation (KEGR). It aims to dynamically infer students' knowledge states from their past exercise responses and customizably generate new exercises. To achieve KEGR, we propose an adaptive multi-agent cooperation framework, called ExeGen, inspired by the excellent reasoning and generative capabilities of LLM-based AI agents. Specifically, ExeGen coordinates four specialized agents for supervision, knowledge state perception, exercise generation, and quality refinement through an adaptive loop workflow pipeline. More importantly, we devise two enhancement mechanisms in ExeGen: 1) A human-simulated knowledge perception mechanism mimics students' cognitive processes and generates interpretable knowledge state descriptions via demonstration-based In-Context Learning (ICL). In this mechanism, a dual-matching strategy is further designed to retrieve highly relevant demonstrations for reliable ICL reasoning. 2) An exercise generation-adversarial mechanism collaboratively refines exercise generation leveraging a group of quality evaluation expert agents via iterative adversarial feedback. Finally, a comprehensive evaluation protocol is carefully designed to assess ExeGen. Extensive experiments on real-world educational datasets and a practical deployment in college education demonstrate the effectiveness and superiority of ExeGen. The code is available at https://github.com/dsz532/exeGen.", "source": "openreview", "url": "https://openreview.net/forum?id=vqLoLqUUNB", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_Knowledge Starts with Practice_ Knowledge-Aware Exercise Generative Recommendation with Adaptiv_2025.pdf" }, { "title": "LOMIA: Label-Only Membership Inference Attacks against Pre-trained Large Vision-Language Models", "authors": [ "Yihao LIU", "Xinqi LYU", "Dong Wang", "Yanjie Li", "Bin Xiao" ], "year": 2025, "venue": "NeurIPS", "abstract": "Large vision-language models (VLLMs) have driven significant progress in multi-modal systems, enabling a wide range of applications across domains such as healthcare, education, and content generation. Despite the success, the large-scale datasets used to train these models often contain sensitive or personally identifiable information, raising serious privacy concerns. To audit and better understand such risks, membership inference attacks (MIAs) have become a key tool. However, existing MIAs against VLLMs predominantly assume access to full-model logits, which are typically unavailable in many practical deployments. To facilitate MIAs in a more realistic and restrictive setting, we propose a novel framework: label-only membership inference attacks (LOMIA) targeting pre-trained VLLMs where only the model’s top-1 prediction is available. Within this framework, we propose three effective attack methods, all of which exploit the intuition that training samples are more likely to be memorized by the VLLMs, resulting in outputs that exhibit higher semantic alignment and lower perplexity. Our experiments show that our framework surpasses existing label-only attack adaptations for different VLLMs and competes with state-of-the-art logits-based attacks across all metrics on three widely used open-source VLLMs and GPT-4o.", "source": "openreview", "url": "https://openreview.net/forum?id=7JjS2cdBYN", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_LOMIA_ Label-Only Membership Inference Attacks against Pre-trained Large Vision-Language Models_2025.pdf" }, { "title": "Learning to Generate Human-Human-Object Interactions from Textual Descriptions", "authors": [ "Jeonghyeon Na", "Sangwon Baik", "Inhee Lee", "Junyoung Lee", "Hanbyul Joo" ], "year": 2025, "venue": "NeurIPS", "abstract": "The way humans interact with each other, including interpersonal distances, spatial configuration, and motion, varies significantly across different situations. To enable machines to understand such complex, context-dependent behaviors, it is essential to model multiple people in relation to the surrounding scene context.\nIn this paper, we present a novel research problem to model the correlations between two people engaged in a shared interaction involving an object. We refer to this formulation as Human-Human-Object Interactions (HHOIs).\nTo overcome the lack of dedicated datasets for HHOIs, we present a newly captured HHOIs dataset and a method to synthesize HHOI data by leveraging image generative models. As an intermediary, we obtain individual human-object interaction (HOIs) and human-human interaction (HHIs) from the HHOIs, and with these data, we train an text-to-HOI and text-to-HHI model using score-based diffusion model. Finally, we present a unified generative framework that integrates the two individual model, capable of synthesizing complete HHOIs in a single advanced sampling process. Our method extends HHOI generation to multi-human settings, enabling interactions involving more than two individuals.\nExperimental results show that our method generates realistic HHOIs conditioned on textual descriptions, outperforming previous approaches that focus only on single-human HOIs. Furthermore, we introduce multi-human motion generation involving objects as an application of our framework.", "source": "openreview", "url": "https://openreview.net/forum?id=FmUa6bKscB", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_Learning to Generate Human-Human-Object Interactions from Textual Descriptions_2025.pdf" }, { "title": "Localizing Knowledge in Diffusion Transformers", "authors": [ "Arman Zarei", "Samyadeep Basu", "Keivan Rezaei", "Zihao Lin", "Sayan Nag", "Soheil Feizi" ], "year": 2025, "venue": "NeurIPS", "abstract": "Understanding how knowledge is distributed across the layers of generative models is crucial for improving interpretability, controllability, and adaptation. While prior work has explored knowledge localization in UNet-based architectures, Diffusion Transformer (DiT)-based models remain underexplored in this context. In this paper, we propose a model- and knowledge-agnostic method to localize where specific types of knowledge are encoded within the DiT blocks. We evaluate our method on state-of-the-art DiT-based models, including PixArt-$\\alpha$, FLUX, and SANA, across six diverse knowledge categories. We show that the identified blocks are both interpretable and causally linked to the expression of knowledge in generated outputs.\nBuilding on these insights, we apply our localization framework to two key applications: *model personalization* and *knowledge unlearning*. In both settings, our localized fine-tuning approach enables efficient and targeted updates, reducing computational cost, improving task-specific performance, and better preserving general model behavior with minimal interference to unrelated or surrounding content.\nOverall, our findings offer new insights into the internal structure of DiTs and introduce a practical pathway for more interpretable, efficient, and controllable model editing.", "source": "openreview", "url": "https://openreview.net/forum?id=SiBVbL7rsX", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_Localizing Knowledge in Diffusion Transformers_2025.pdf" }, { "title": "Low-Rank Head Avatar Personalization with Registers", "authors": [ "Sai Tanmay Reddy Chakkera", "Aggelina Chatziagapi", "Md Moniruzzaman", "Chen-ping Yu", "Yi-Hsuan Tsai", "Dimitris Samaras" ], "year": 2025, "venue": "NeurIPS", "abstract": "We introduce a novel method for low-rank personalization of a generic model for head avatar generation. Prior work proposes generic models that achieve high-quality face animation by leveraging large-scale datasets of multiple identities. However, such generic models usually fail to synthesize unique identity-specific details, since they learn a general domain prior. To adapt to specific subjects, we find that it is still challenging to capture high-frequency facial details via popular solutions like low-rank adaptation (LoRA). This motivates us to propose a specific architecture, a Register Module, that enhances the performance of LoRA, while requiring only a small number of parameters to adapt to an unseen identity. Our module is applied to intermediate features of a pre-trained model, storing and re-purposing information in a learnable 3D feature space. To demonstrate the efficacy of our personalization method, we collect a dataset of talking videos of individuals with distinctive facial details, such as wrinkles and tattoos. Our approach faithfully captures unseen faces, outperforming existing methods quantitatively and qualitatively.", "source": "openreview", "url": "https://openreview.net/forum?id=mhARf5VzCn", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_Low-Rank Head Avatar Personalization with Registers_2025.pdf" }, { "title": "Matryoshka Pilot: Learning to Drive Black-Box LLMs with LLMs", "authors": [ "ChangHao Li", "Yuchen Zhuang", "Rushi Qiang", "Haotian Sun", "Hanjun Dai", "Chao Zhang", "Bo Dai" ], "year": 2025, "venue": "NeurIPS", "abstract": "Despite the impressive generative abilities of black-box large language models (LLMs), their inherent opacity hinders further advancements in capabilities such as reasoning, planning, and personalization. \nExisting works aim to enhance LLM capabilities via domain-specific adaptation, which require additional training on accessible model parameters, an infeasible option for black-box LLMs. \nTo address this challenge, we introduce Matryoshka Pilot (M-Pilot), a lightweight white-box LLM controller that guides a large-scale black-box LLM generator by decomposing complex tasks into a series of intermediate outputs.\nSpecifically, we consider the black-box LLM as an environment, with M-Pilot serving as a policy to provide intermediate guidance through prompts for driving the black-box LLM. \nM-Pilot is trained to pivot the outputs of the black-box LLM aligning with preferences during iterative interaction, which enables controllable multi-turn generation and self-improvement in optimizing intermediate guidance. \nEmpirical evaluations on diverse tasks demonstrate that our method effectively enhances the capabilities of black-box LLMs in complex, long-horizon tasks.", "source": "openreview", "url": "https://openreview.net/forum?id=KfZm1bkS8C", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_Matryoshka Pilot_ Learning to Drive Black-Box LLMs with LLMs_2025.pdf" }, { "title": "MemSim: A Bayesian Simulator for Evaluating Memory of LLM-based Personal Assistants", "authors": [ "Zeyu Zhang", "Quanyu Dai", "Luyu Chen", "Zeren Jiang", "Rui Li", "Jieming Zhu", "Xu Chen", "Yi Xie", "Zhenhua Dong", "Ji-Rong Wen" ], "year": 2025, "venue": "NeurIPS", "abstract": "LLM-based agents have been widely applied as personal assistants, capable of memorizing information from user messages and responding to personal queries. However, there still lacks an objective and automatic evaluation on their memory capability, largely due to the challenges in constructing reliable questions and answers (QAs) according to user messages. In this paper, we propose MemSim, a Bayesian simulator designed to automatically construct reliable QAs from generated user messages, simultaneously keeping their diversity and scalability. Specifically, we introduce the Bayesian Relation Network (BRNet) and a causal generation mechanism to mitigate the impact of LLM hallucinations on factual information, facilitating the automatic creation of an evaluation dataset. Based on MemSim, we generate a dataset in the daily-life scenario, named MemDaily, and conduct extensive experiments to assess the effectiveness of our approach. We also provide a benchmark for evaluating different memory mechanisms in LLM-based agents with the MemDaily dataset.", "source": "openreview", "url": "https://openreview.net/forum?id=vAT2xlaWJY", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_MemSim_ A Bayesian Simulator for Evaluating Memory of LLM-based Personal Assistants_2025.pdf" }, { "title": "PANTHER: Generative Pretraining Beyond Language for Sequential User Behavior Modeling", "authors": [ "Guilin Li", "Yun Zhang", "Xiuyuan Chen", "Chengqi Li", "Bo Wang", "Linghe Kong", "Wenjia Wang", "Weiran Huang", "Matthias Hwai Yong Tan" ], "year": 2025, "venue": "NeurIPS", "abstract": "Large language models (LLMs) have shown that generative pretraining can distill vast world knowledge into compact token representations. While LLMs encapsulate extensive world knowledge, they remain limited in modeling the behavioral knowledge contained within user interaction histories. User behavior forms a distinct modality, where each action—defined by multi-dimensional attributes such as time, context, and transaction type—constitutes a behavioral token. Modeling these high-cardinality, sparse, and irregular sequences is challenging, and discriminative models often falter under limited supervision. To bridge this gap, we extend generative pretraining to user behavior, learning transferable representations from unlabeled behavioral data analogous to how LLMs learn from text. We present PANTHER, a hybrid generative–discriminative framework that unifies user behavior pretraining and downstream adaptation, enabling large-scale sequential user representation learning and real-time inference. PANTHER introduces: (1) Structured Tokenization to compress multi-dimensional transaction attributes into an interpretable vocabulary; (2) Sequence Pattern Recognition Module (SPRM) for modeling periodic transaction motifs; (3) a Unified User-Profile Embedding that fuses static demographics with dynamic transaction histories, enabling both personalized predictions and population-level knowledge transfer; and (4) Real-time scalability enabled by offline caching of pre-trained embeddings for millisecond-level inference.Fully deployed and operational online at WeChat Pay, PANTHER delivers a 25.6\\% boost in next-transaction prediction HitRate@1 and a 38.6\\% relative improvement in fraud detection recall over baselines. Cross-domain evaluations on public benchmarks (CCT, MBD, MovieLens-1M, Yelp) show strong generalization, achieving up to 21\\% HitRate@1 gains over transformer baselines, establishing PANTHER as a scalable, high-performance framework for industrial user sequential behavior modeling.", "source": "openreview", "url": "https://openreview.net/forum?id=4FUdUFvvmp", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_PANTHER_ Generative Pretraining Beyond Language for Sequential User Behavior Modeling_2025.pdf" }, { "title": "Part-Aware Bottom-Up Group Reasoning for Fine-Grained Social Interaction Detection", "authors": [ "Dongkeun Kim", "Minsu Cho", "Suha Kwak" ], "year": 2025, "venue": "NeurIPS", "abstract": "Social interactions often emerge from subtle, fine-grained cues such as facial expressions, gaze, and gestures. However, existing methods for social interaction detection overlook such nuanced cues and primarily rely on holistic representations of individuals. Moreover, they directly detect social groups without explicitly modeling the underlying interactions between individuals. These drawbacks limit their ability to capture localized social signals and introduce ambiguity when group configurations should be inferred from social interactions grounded in nuanced cues. In this work, we propose a part-aware bottom-up group reasoning framework for fine-grained social interaction detection. The proposed method infers social groups and their interactions using body part cues and their interpersonal relations. Our model first detects individuals and enhances their features using part-aware cues, and then infers group configuration by associating individuals via similarity-based reasoning, which considers not only spatial relations but also subtle social cues that signal interactions, leading to more accurate group inference. Experiments on the NVI dataset demonstrate that our method outperforms prior methods, achieving the new state of the art, while additional results on the Café dataset further validate its generalizability to group activity understanding.", "source": "openreview", "url": "https://openreview.net/forum?id=uiuA0ixKLd", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_Part-Aware Bottom-Up Group Reasoning for Fine-Grained Social Interaction Detection_2025.pdf" }, { "title": "Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation", "authors": [ "Ting Wei", "Biao Mei", "Junliang Lyu", "Renquan Zhang", "Feng Zhou", "Yifan Sun" ], "year": 2025, "venue": "NeurIPS", "abstract": "Personalized Bayesian federated learning (PBFL) handles non-i.i.d. client data and quantifies uncertainty by combining personalization with Bayesian inference. However, current PBFL methods face two main limitations: posterior inference on clients often assumes restrictive parametric forms, and server-side posterior aggregation typically relies on naive parameter averaging. To overcome these issues, we propose FedWBA, a novel PBFL method that enhances both local inference and global aggregation. At the client level, we use particle-based variational inference for nonparametric posterior representation. At the server level, we introduce particle-based Wasserstein barycenter aggregation, offering a more geometrically meaningful approach. Theoretically, we provide local and global convergence guarantees for FedWBA. Locally, we prove a KL divergence decrease lower bound per iteration for variational inference convergence. Globally, we show that the Wasserstein barycenter converges to the true parameter as the client data size increases. Empirically, experiments show that FedWBA outperforms baselines in prediction accuracy, uncertainty calibration, and convergence rate, with ablation studies confirming its robustness.", "source": "openreview", "url": "https://openreview.net/forum?id=V75MK7uh67", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation_2025.pdf" }, { "title": "Personalized Federated Conformal Prediction with Localization", "authors": [ "Yinjie Min", "Chuchen Zhang", "Liuhua Peng", "Changliang Zou" ], "year": 2025, "venue": "NeurIPS", "abstract": "Personalized federated learning addresses data heterogeneity across distributed agents but lacks uncertainty quantification that is both agent-specific and instance-specific, which is a critical requirement for risk-sensitive applications. We propose personalized federated conformal prediction (PFCP), a novel framework that combines personalized federated learning with conformal prediction to provide statistically valid agent-personalized prediction sets with instance-localization. By leveraging privacy-preserving knowledge transfer from other source agents, PFCP ensures marginal coverage guarantees for target agents while significantly improving conditional coverage performance on individual test instances, which has been validated by extensive experiments.", "source": "openreview", "url": "https://openreview.net/forum?id=QQUhGPST45", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_Personalized Federated Conformal Prediction with Localization_2025.pdf" }, { "title": "RePIC: Reinforced Post-Training for Personalizing Multi-Modal Language Models", "authors": [ "Yeongtak Oh", "Dohyun Chung", "Juhyeon Shin", "Sangha Park", "Johan Barthelemy", "Jisoo Mok", "Sungroh Yoon" ], "year": 2025, "venue": "NeurIPS", "abstract": "Recent multi-modal large language models (MLLMs) often struggle to generate personalized image captions, even when trained on high-quality captions. In this work, we observe that such limitations persist in existing post-training-based MLLM personalization methods. Specifically, despite being post-tuned with large-scale caption data through supervised fine-tuning (SFT), these models frequently fail to produce faithful descriptions in real-world scenarios, such as multi-concept image captioning. However, acquiring large-scale, high-quality captions for such complex settings is both costly and difficult. To address the data-centric nature of SFT, we propose a reinforcement learning (RL)-based post-training framework. To the best of our knowledge, this is the first RL-based approach to post-train MLLMs for personalized image captioning. Our method significantly enhances both visual recognition and personalized generation capabilities of MLLMs, and consistently outperforms existing SFT-based baselines, especially in the challenging multi-concept image captioning task. Project page: https://github.com/oyt9306/RePIC", "source": "openreview", "url": "https://openreview.net/forum?id=DG0F1cdjN7", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_RePIC_ Reinforced Post-Training for Personalizing Multi-Modal Language Models_2025.pdf" }, { "title": "SPOT-Trip: Dual-Preference Driven Out-of-Town Trip Recommendation", "authors": [ "Yinghui Liu", "Hao Miao", "Guojiang Shen", "Yan Zhao", "Xiangjie Kong", "Ivan Lee" ], "year": 2025, "venue": "NeurIPS", "abstract": "Out-of-town trip recommendation aims to generate a sequence of Points of Interest (POIs) for users traveling from their hometowns to previously unvisited regions based on personalized itineraries, e.g., origin, destination, and trip duration. Modeling the complex user preferences--which often exhibit a two-fold nature of static and dynamic interests--is critical for effective recommendations. However, the sparsity of out-of-town check-in data presents significant challenges in capturing such user preferences. Meanwhile, existing methods often conflate the static and dynamic preferences, resulting in suboptimal performance. In this paper, we for the first time systematically study the problem of out-of-town trip recommendation. A novel framework SPOT-Trip is proposed to explicitly learns the dual static-dynamic user preferences. Specifically, to handle scarce data, we construct a POI attribute knowledge graph to enrich the semantic modeling of users’ hometown and out-of-town check-ins, enabling the static preference modeling through attribute relation-aware aggregation. Then, we employ neural ordinary differential equations (ODEs) to capture the continuous evolution of latent dynamic user preferences and innovatively combine a temporal point process to describe the instantaneous probability of each preference behavior. Further, a static-dynamic fusion module is proposed to merge the learned static and dynamic user preferences. Extensive experiments on real data offer insight into the effectiveness of the proposed solutions, showing that SPOT-Trip achieves performance improvement by up to 17.01%.", "source": "openreview", "url": "https://openreview.net/forum?id=UG1723eoKq", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_SPOT-Trip_ Dual-Preference Driven Out-of-Town Trip Recommendation_2025.pdf" }, { "title": "StyleGuard: Preventing Text-to-Image-Model-based Style Mimicry Attacks by Style Perturbations", "authors": [ "Yanjie Li", "Wenxuan Zhang", "Xinqi LYU", "Yihao LIU", "Bin Xiao" ], "year": 2025, "venue": "NeurIPS", "abstract": "Recently, text-to-image diffusion models have been widely used for style mimicry and personalized customization through methods such as DreamBooth and Textual Inversion. This has raised concerns about intellectual property protection and the generation of deceptive content.\nRecent studies, such as Glaze and Anti-DreamBooth, have proposed using adversarial noise to protect images from these attacks. However, recent purification-based methods, such as DiffPure and Noise Upscaling, have successfully attacked these latest defenses, showing the vulnerabilities of these methods. \nMoreover, present methods show limited transferability across models, making them less effective against unknown text-to-image models.\nTo address these issues, we propose a novel anti-mimicry method, StyleGuard. We propose a novel style loss that optimizes the style-related features in the latent space to make it deviate from the original image, which improves model-agnostic transferability.\nAdditionally, to enhance the perturbation's ability to bypass diffusion-based purification, we designed a novel upscale loss that involves ensemble purifiers and upscalers during training.\nExtensive experiments on the WikiArt and CelebA datasets demonstrate that StyleGuard outperforms existing methods in robustness against various transformations and purifications, effectively countering style mimicry in various models. Moreover, StyleGuard is effective on different style mimicry methods, including DreamBooth and Textual Inversion. The code is available at \\url{https://github.com/PolyLiYJ/StyleGuard}.", "source": "openreview", "url": "https://openreview.net/forum?id=SptbUlfhJg", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_StyleGuard_ Preventing Text-to-Image-Model-based Style Mimicry Attacks by Style Perturbations_2025.pdf" }, { "title": "TP-MDDN: Task-Preferenced Multi-Demand-Driven Navigation with Autonomous Decision-Making", "authors": [ "Shanshan Li", "Da Huang", "Yu He", "Yanwei Fu", "Yu-Gang Jiang", "Xiangyang Xue" ], "year": 2025, "venue": "NeurIPS", "abstract": "In daily life, people often move through spaces to find objects that meet their needs, posing a key challenge in embodied AI. Traditional Demand-Driven Navigation (DDN) handles one need at a time but does not reflect the complexity of real-world tasks involving multiple needs and personal choices. To bridge this gap, we introduce Task-Preferenced Multi-Demand-Driven Navigation (TP-MDDN), a new benchmark for long-horizon navigation involving multiple sub-demands with explicit task preferences. To solve TP-MDDN, we propose AWMSystem, an autonomous decision-making system composed of three key modules: BreakLLM (instruction decomposition), LocateLLM (goal selection), and StatusMLLM (task monitoring). For spatial memory, we design MASMap, which combines 3D point cloud accumulation with 2D semantic mapping for accurate and efficient environmental understanding. Our Dual-Tempo action generation framework integrates zero-shot planning with policy-based fine control, and is further supported by an Adaptive Error Corrector that handles failure cases in real time. Experiments demonstrate that our approach outperforms state-of-the-art baselines in both perception accuracy and navigation robustness.", "source": "openreview", "url": "https://openreview.net/forum?id=xrAqVVk2qe", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_TP-MDDN_ Task-Preferenced Multi-Demand-Driven Navigation with Autonomous Decision-Making_2025.pdf" }, { "title": "Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation", "authors": [ "Xinghao Wu", "Xuefeng Liu", "Jianwei Niu", "Guogang Zhu", "Mingjia Shi", "Shaojie Tang", "Jing Yuan" ], "year": 2025, "venue": "NeurIPS", "abstract": "Federated Learning (FL) faces challenges due to data heterogeneity, which limits the global model’s performance across diverse client distributions. Personalized Federated Learning (PFL) addresses this by enabling each client to process an individual model adapted to its local distribution. Many existing methods assume that certain global model parameters are difficult to train effectively in a collaborative manner under heterogeneous data. Consequently, they localize or fine-tune these parameters to obtain personalized models. In this paper, we reveal that both the feature extractor and classifier of the global model are inherently strong, and the primary cause of its suboptimal performance is the mismatch between local features and the global classifier. Although existing methods alleviate this mismatch to some extent and improve performance, we find that they either (1) fail to fully resolve the mismatch while degrading the feature extractor, or (2) address the mismatch only post-training, allowing it to persist during training. This increases inter-client gradient divergence, hinders model aggregation, and ultimately leaves the feature extractor suboptimal for client data. To address this issue, we propose FedPFT, a novel framework that resolves the mismatch during training using personalized prompts. These prompts, along with local features, are processed by a shared self-attention-based transformation module, ensuring alignment with the global classifier. Additionally, this prompt-driven approach offers strong flexibility, enabling task-specific prompts to incorporate additional training objectives (\\eg, contrastive learning) to further enhance the feature extractor. Extensive experiments show that FedPFT outperforms state-of-the-art methods by up to 5.07%, with further gains of up to 7.08% when collaborative contrastive learning is incorporated.", "source": "openreview", "url": "https://openreview.net/forum?id=vTJFQu5YXz", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transforma_2025.pdf" }, { "title": "Track, Inpaint, Resplat: Subject-driven 3D and 4D Generation with Progressive Texture Infilling", "authors": [ "Shuhong Zheng", "Ashkan Mirzaei", "Igor Gilitschenski" ], "year": 2025, "venue": "NeurIPS", "abstract": "Current 3D/4D generation methods are usually optimized for photorealism, efficiency, and aesthetics. However, they often fail to preserve the semantic identity of the subject across different viewpoints. Adapting generation methods with one or few images of a specific subject (also known as Personalization or Subject-driven generation) allows generating visual content that align with the identity of the subject. However, personalized 3D/4D generation is still largely underexplored. In this work, we introduce TIRE (Track, Inpaint, REsplat), a novel method for subject-driven 3D/4D generation. It takes an initial 3D asset produced by an existing 3D generative model as input and uses video tracking to identify the regions that need to be modified. Then, we adopt a subject-driven 2D inpainting model for progressively infilling the identified regions. Finally, we resplat the modified 2D multi-view observations back to 3D while still maintaining consistency. Extensive experiments demonstrate that our approach significantly improves identity preservation in 3D/4D generation compared to state-of-the-art methods. Our project website is available at https://zsh2000.github.io/track-inpaint-resplat.github.io/.", "source": "openreview", "url": "https://openreview.net/forum?id=kZahfVKYbl", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_Track, Inpaint, Resplat_ Subject-driven 3D and 4D Generation with Progressive Texture Infilling_2025.pdf" }, { "title": "Unlearned but Not Forgotten: Data Extraction after Exact Unlearning in LLM", "authors": [ "Xiaoyu Wu", "Yifei Pang", "Terrance Liu", "Steven Wu" ], "year": 2025, "venue": "NeurIPS", "abstract": "Large Language Models are typically trained on datasets collected from the web, which may inadvertently contain harmful or sensitive personal information. To address growing privacy concerns, unlearning methods have been proposed to remove the influence of specific data from trained models. Of these, exact unlearning---which retrains the model from scratch without the target data---is widely regarded as the gold standard for mitigating privacy risks in deployment. In this paper, we revisit this assumption in a practical deployment setting where both the pre- and post-unlearning logits API are exposed, such as in open-weight scenarios. Targeting this setting, we introduce a novel data extraction attack that leverages signals from the pre-unlearning model to guide the post-unlearning model, uncovering patterns that reflect the removed data distribution. Combining model guidance with a token filtering strategy, our attack significantly improves extraction success rates---doubling performance in some cases---across common benchmarks such as MUSE, TOFU, and WMDP. Furthermore, we demonstrate our attack's effectiveness on a simulated medical diagnosis dataset to highlight real-world privacy risks associated with exact unlearning. In light of our findings, which suggest that unlearning may, in a contradictory way, \\textit{increase} the risk of privacy leakage during real-world deployments, we advocate for evaluation of unlearning methods to consider broader threat models that account not only for post-unlearning models but also for adversarial access to prior checkpoints. Code is publicly available at: https://github.com/Nicholas0228/unlearned_data_extraction_llm.", "source": "openreview", "url": "https://openreview.net/forum?id=BpAx3OuNOr", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_Unlearned but Not Forgotten_ Data Extraction after Exact Unlearning in LLM_2025.pdf" }, { "title": "Conformal Prediction for Causal Effects of Continuous Treatments", "authors": [ "Maresa Schröder", "Dennis Frauen", "Jonas Schweisthal", "Konstantin Hess", "Valentyn Melnychuk", "Stefan Feuerriegel" ], "year": 2025, "venue": "NeurIPS", "abstract": "Uncertainty quantification of causal effects is crucial for safety-critical applications such as personalized medicine. A powerful approach for this is conformal prediction, which has several practical benefits due to model-agnostic finite-sample guarantees. Yet, existing methods for conformal prediction of causal effects are limited to binary/discrete treatments and make highly restrictive assumptions, such as known propensity scores. In this work, we provide a novel conformal prediction method for potential outcomes of continuous treatments. We account for the additional uncertainty introduced through propensity estimation so that our conformal prediction intervals are valid even if the propensity score is unknown. Our contributions are three-fold: (1) We derive finite-sample validity guarantees for prediction intervals of potential outcomes of continuous treatments. (2) We provide an algorithm for calculating the derived intervals. (3) We demonstrate the effectiveness of the conformal prediction intervals in experiments on synthetic and real-world datasets. To the best of our knowledge, we are the first to propose conformal prediction for continuous treatments when the propensity score is unknown and must be estimated from data.", "source": "openreview", "url": "https://openreview.net/forum?id=1nL84tQNnK", "decision_type": "Poster", "avg_rating": 3.8, "relative_path": "2025/NeurIPS/Poster/3.8_Conformal Prediction for Causal Effects of Continuous Treatments_2025.pdf" }, { "title": "FedMGP: Personalized Federated Learning with Multi-Group Text-Visual Prompts", "authors": [ "Weihao Bo", "Yanpeng Sun", "Yu Wang", "Xinyu Zhang", "Zechao Li" ], "year": 2025, "venue": "NeurIPS", "abstract": "In this paper, we introduce FedMGP, a new paradigm for personalized federated prompt learning in vision-language models (VLMs). Existing federated prompt learning (FPL) methods often rely on a single, text-only prompt representation, which leads to client-specific overfitting and unstable aggregation under heterogeneous data distributions. Toward this end, FedMGP equips each client with multiple groups of paired textual and visual prompts, enabling the model to capture diverse, fine-grained semantic and instance-level cues. A diversity loss is introduced to drive each prompt group to specialize in distinct and complementary semantic aspects, ensuring that the groups collectively cover a broader range of local characteristics.During communication, FedMGP employs a dynamic prompt aggregation strategy based on similarity-guided probabilistic sampling: each client computes the cosine similarity between its prompt groups and the global prompts from the previous round, then samples s groups via a softmax-weighted distribution. This soft selection mechanism preferentially aggregates semantically aligned knowledge while still enabling exploration of underrepresented patterns—effectively balancing the preservation of common knowledge with client-specific features. Notably, FedMGP maintains parameter efficiency by redistributing a fixed prompt capacity across multiple groups, achieving state-of-the-art performance with the lowest communication parameters (5.1k) among all federated prompt learning methods. Theoretical analysis shows that our dynamic aggregation strategy promotes robust global representation learning by reinforcing shared semantics while suppressing client-specific noise. Extensive experiments demonstrate that FedMGP consistently outperforms prior approaches in both personalization and domain generalization across diverse federated vision-language benchmarks.The code will be released on https://github.com/weihao-bo/FedMGP.git.", "source": "openreview", "url": "https://openreview.net/forum?id=E6SFbnPiVP", "decision_type": "Poster", "avg_rating": 3.8, "relative_path": "2025/NeurIPS/Poster/3.8_FedMGP_ Personalized Federated Learning with Multi-Group Text-Visual Prompts_2025.pdf" }, { "title": "Learning Personalized Ad Impact via Contextual Reinforcement Learning under Delayed Rewards", "authors": [ "Yuwei Cheng", "Zifeng Zhao", "Haifeng Xu" ], "year": 2025, "venue": "NeurIPS", "abstract": "Online advertising platforms use automated auctions to connect advertisers with potential customers, requiring effective bidding strategies to maximize profits. Accurate ad impact estimation requires considering three key factors: delayed and long-term effects, cumulative ad impacts such as reinforcement or fatigue, and customer heterogeneity. However, these effects are often not jointly addressed in previous studies. To capture these factors, we model ad bidding as a Contextual Markov Decision Process (CMDP) with delayed Poisson rewards. For efficient estimation, we propose a two-stage maximum likelihood estimator combined with data-splitting strategies, ensuring controlled estimation error based on the first-stage estimator's (in)accuracy. Building on this, we design a reinforcement learning algorithm to derive efficient personalized bidding strategies. This approach achieves a near-optimal regret bound of $\\tilde{\\mathcal{O}}(dH^2\\sqrt{T})$, where $d$ is the contextual dimension, $H$ is the number of rounds, and $T$ is the number of customers. Our theoretical findings are validated by simulation experiments.", "source": "openreview", "url": "https://openreview.net/forum?id=LVPq1j357N", "decision_type": "Poster", "avg_rating": 3.8, "relative_path": "2025/NeurIPS/Poster/3.8_Learning Personalized Ad Impact via Contextual Reinforcement Learning under Delayed Rewards_2025.pdf" }, { "title": "Many LLMs Are More Utilitarian Than One", "authors": [ "Anita Keshmirian", "Razan Baltaji", "Babak Hemmatian", "Hadi Asghari", "Lav R. Varshney" ], "year": 2025, "venue": "NeurIPS", "abstract": "Moral judgment is integral to large language models' (LLMs) social reasoning. As multi-agent systems gain prominence, it becomes crucial to understand how LLMs function when collaborating compared to operating as individual agents. In human moral judgment, group deliberation leads to a Utilitarian Boost: a tendency to endorse norm violations that inflict harm but maximize benefits for the greatest number of people. We study whether a similar dynamic emerges in multi-agent LLM systems. We test six models on well-established sets of moral dilemmas across two conditions: (1) Solo, where models reason independently, and (2) Group, where they engage in multi-turn discussions in pairs or triads. In personal dilemmas, where agents decide whether to directly harm an individual for the benefit of others, all models rated moral violations as more acceptable when part of a group, demonstrating a Utilitarian Boost similar to that observed in humans. However, the mechanism for the boost in LLMs differed: While humans in groups become more utilitarian due to heightened sensitivity to decision outcomes, LLM groups showed diverse profiles, for example, reduced sensitivity to norms or enhanced impartiality. We report model differences in when and how strongly the boost manifests. We also discuss prompt and agent compositions that enhance or mitigate the effect. We end with a discussion of the implications for AI alignment, multi-agent design, and artificial moral reasoning. Code available at: https://github.com/baltaci-r/MoralAgents", "source": "openreview", "url": "https://openreview.net/forum?id=wn3VBRz5GK", "decision_type": "Poster", "avg_rating": 3.8, "relative_path": "2025/NeurIPS/Poster/3.8_Many LLMs Are More Utilitarian Than One_2025.pdf" }, { "title": "Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections", "authors": [ "Wei Zhuo", "Zhaohuan Zhan", "Han Yu" ], "year": 2025, "venue": "NeurIPS", "abstract": "Federated Learning (FL) on graph-structured data typically faces non-IID challenges, particularly in scenarios where each client holds a distinct subgraph sampled from a global graph. In this paper, we introduce **Fed**erated learning with **Aux**iliary projections (FedAux), a personalized subgraph FL framework that learns to align, compare, and aggregate heterogeneously distributed local models without sharing raw data or node embeddings. In FedAux, each client jointly trains (i) a local GNN and (ii) a learnable auxiliary projection vector (APV) that differentiably projects node embeddings onto a 1D space. A soft-sorting operation followed by a lightweight 1D convolution refines these embeddings in the ordered space, enabling the APV to effectively capture client-specific information. After local training, these APVs serve as compact signatures that the server uses to compute inter‑client similarities and perform similarity‑weighted parameter mixing, yielding personalized models while preserving cross‑client knowledge transfer. Moreover, we provide rigorous theoretical analysis to establish the convergence and rationality of our design. Empirical evaluations across diverse graph benchmarks demonstrate that FedAux substantially outperforms existing baselines in both accuracy and personalization performance. The code is available at [https://github.com/JhuoW/FedAux](https://github.com/JhuoW/FedAux).", "source": "openreview", "url": "https://openreview.net/forum?id=yrNw1R8o2W", "decision_type": "Poster", "avg_rating": 3.8, "relative_path": "2025/NeurIPS/Poster/3.8_Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections_2025.pdf" }, { "title": "Q-Palette: Fractional-Bit Quantizers Toward Optimal Bit Allocation for Efficient LLM Deployment", "authors": [ "Deokjae Lee", "Hyun Oh Song" ], "year": 2025, "venue": "NeurIPS", "abstract": "We study weight-only post-training quantization (PTQ), which quantizes the weights of a large language model (LLM) without retraining, using little or no calibration data. Weight-only PTQ is crucial for reducing the memory footprint and latency of LLM inference, especially in memory-bound, small-batch inference scenarios, such as personalized inference on edge devices. Despite its importance, irregular weight distributions with heavy-tailed outliers in LLMs complicate quantization, recently motivating rotation-based methods that transform weights into near-Gaussian distributions, which are more regular with fewer outliers, thereby reducing quantization error. In this work, we first derive the information-theoretically optimal bit allocation for Gaussianized weights under given bit budgets, revealing that fine-grained fractional-bit quantizers approaching the Gaussian distortion-rate bound are essential to achieve near-optimal quantization performance. To bridge this theoretical insight and practical implementation, we introduce Q-Palette, a versatile collection of fractional-bit quantizers that range from trellis-coded quantizers offering near-optimal distortion to simpler vector and scalar quantizers optimized for faster inference, all efficiently implemented with optimized CUDA kernels across various bitwidths. Furthermore, leveraging Q-Palette as a foundational component, we propose a novel mixed-scheme quantization framework, jointly optimizing quantizer choices and layer fusion decisions given resource constraints. The code is available at https://github.com/snu-mllab/Q-Palette.", "source": "openreview", "url": "https://openreview.net/forum?id=l4F50jpiVH", "decision_type": "Poster", "avg_rating": 3.8, "relative_path": "2025/NeurIPS/Poster/3.8_Q-Palette_ Fractional-Bit Quantizers Toward Optimal Bit Allocation for Efficient LLM Deployment_2025.pdf" }, { "title": "SAEMark: Steering Personalized Multilingual LLM Watermarks with Sparse Autoencoders", "authors": [ "Zhuohao Yu", "Xingru Jiang", "Weizheng Gu", "Yidong Wang", "Qingsong Wen", "Shikun Zhang", "Wei Ye" ], "year": 2025, "venue": "NeurIPS", "abstract": "Watermarking LLM-generated text is critical for content attribution and misinformation prevention, yet existing methods compromise text quality and require white-box model access with logit manipulation or training, which exclude API-based models and multilingual scenarios. We propose SAEMark, an **inference-time framework** for *multi-bit* watermarking that embeds personalized information through *feature-based rejection sampling*, fundamentally different from logit-based or rewriting-based approaches: we **do not modify model outputs directly** and require only **black-box access**, while naturally supporting multi-bit message embedding and generalizing across diverse languages and domains. We instantiate the framework using *Sparse Autoencoders* as deterministic feature extractors and provide theoretical worst-case analysis relating watermark accuracy to computational budget. Experiments across 4 datasets demonstrate strong watermarking performance on English, Chinese, and code while preserving text quality. SAEMark establishes a new paradigm for **scalable, quality-preserving watermarks** that work seamlessly with closed-source LLMs across languages and domains.", "source": "openreview", "url": "https://openreview.net/forum?id=tXnyVPNOfa", "decision_type": "Poster", "avg_rating": 3.8, "relative_path": "2025/NeurIPS/Poster/3.8_SAEMark_ Steering Personalized Multilingual LLM Watermarks with Sparse Autoencoders_2025.pdf" }, { "title": "Adaptive Preference Arithmetic: A Personalized Agent with Adaptive Preference Arithmetic for Dynamic Preference Modeling", "authors": [ "Hongyi Nie", "Yaqing Wang", "Mingyang Zhou", "Feiyang Pan", "Quanming Yao", "Zhen Wang" ], "year": 2025, "venue": "NeurIPS", "abstract": "As large language models (LLMs) are increasingly used as personalized user assistants, effectively adapting to users' evolving preferences is critical for delivering high-quality personalized responses. While user preferences are often stable in content, their relative strengths shift over time due to changing goals and contexts. Therefore, modeling these dynamic preference strengths can enable finer-grained personalization. However, current methods face two major challenges: (i) limited user feedback makes it difficult to estimate preference strengths accurately, and (ii) natural language ambiguity limits the controllability of preference-guided generation. To address these issues, we propose AdaPA-Agent, a LLM-agent personalization framework that models dynamic preference strengths via Adaptive Preference Arithmetic. First, instead of requiring additional user feedback, AdaPA-Agent employs an alignment-based strength estimation module to estimate the strength of user preferences from the existing user-agent interaction. Then, it guides controllable personalized generation by linearly combining next-token distributions, weighted by the estimated strengths of individual preferences. Experiments on two personalization tasks-conversational recommendation and personalized web interaction-demonstrate that AdaPA-Agent better aligning with users' changing intents, and has achieved over 18.9\\% and 14.2\\% improvements compared to ReAct, the widely-used agent framework.", "source": "openreview", "url": "https://openreview.net/forum?id=gkG8JOOUF4", "decision_type": "Poster", "avg_rating": 3.2, "relative_path": "2025/NeurIPS/Poster/3.2_Adaptive Preference Arithmetic_ A Personalized Agent with Adaptive Preference Arithmetic for Dy_2025.pdf" }, { "title": "LoRAShop: Training-Free Multi-Concept Image Generation and Editing with Rectified Flow Transformers", "authors": [ "Yusuf Dalva", "Hidir Yesiltepe", "Pinar Yanardag" ], "year": 2025, "venue": "NeurIPS", "abstract": "We introduce LoRAShop, the first framework for multi-concept image generation and editing with LoRA models. LoRAShop builds on a key observation about the feature interaction patterns inside Flux-style diffusion transformers: concept-specific transformer features activate spatially coherent regions early in the denoising process. We harness this observation to derive a disentangled latent mask for each concept in a prior forward pass and blend the corresponding LoRA weights only within regions bounding the concepts to be personalized. The resulting edits seamlessly integrate multiple subjects or styles into the original scene while preserving global context, lighting, and fine details. Our experiments demonstrate that LoRAShop delivers better identity preservation compared to baselines. By eliminating retraining and external constraints, LoRAShop turns personalized diffusion models into a practical `photoshop-with-LoRAs' tool and opens new avenues for compositional visual storytelling and rapid creative iteration.", "source": "openreview", "url": "https://openreview.net/forum?id=VlvtStQN34", "decision_type": "Spotlight", "avg_rating": 5.0, "relative_path": "2025/NeurIPS/Spotlight/5.0_LoRAShop_ Training-Free Multi-Concept Image Generation and Editing with Rectified Flow Transfor_2025.pdf" }, { "title": "Personalized Decision Modeling: Utility Optimization or Textualized-Symbolic Reasoning", "authors": [ "Yibo Zhao", "Yang Zhao", "Hongru Du", "Hao Frank Yang" ], "year": 2025, "venue": "NeurIPS", "abstract": "Decision-making models for individuals, particularly in high-stakes scenarios like vaccine uptake, often diverge from population optimal predictions. This gap arises from the uniqueness of the individual decision-making process, shaped by numerical attributes (e.g., cost, time) and linguistic influences (e.g., personal preferences and constraints). Developing upon Utility Theory and leveraging the textual-reasoning capabilities of Large Language Models (LLMs), this paper proposes an Adaptive Textual-symbolic Human-centric Reasoning framework (ATHENA) to address the optimal information integration. ATHENA uniquely integrates two stages: First, it discovers robust, group-level symbolic utility functions via LLM-augmented symbolic discovery; Second, it implements individual-level semantic adaptation, creating personalized semantic templates guided by the optimal utility to model personalized choices. Validated on real-world travel mode and vaccine choice tasks, ATHENA consistently outperforms utility-based, machine learning, and other LLM-based models, lifting F1 score by at least 6.5\\% over the strongest cutting-edge models. Further, ablation studies confirm that both stages of ATHENA are critical and complementary, as removing either clearly degrades overall predictive performance. By organically integrating symbolic utility modeling and semantic adaptation, ATHENA provides a new scheme for modeling human-centric decisions. The project page can be found at https://yibozh.github.io/Athena.", "source": "openreview", "url": "https://openreview.net/forum?id=RDt0crdC7N", "decision_type": "Spotlight", "avg_rating": 5.0, "relative_path": "2025/NeurIPS/Spotlight/5.0_Personalized Decision Modeling_ Utility Optimization or Textualized-Symbolic Reasoning_2025.pdf" }, { "title": "Strategic Costs of Perceived Bias in Fair Selection", "authors": [ "L. Elisa Celis", "Lingxiao Huang", "Milind Sohoni", "Nisheeth K. Vishnoi" ], "year": 2025, "venue": "NeurIPS", "abstract": "Meritocratic systems, from admissions to hiring, aim to impartially reward skill and effort. Yet persistent disparities across race, gender, and class challenge this ideal. Some attribute these gaps to structural inequality; others to individual choice. We develop a game-theoretic model in which candidates from different socioeconomic groups differ in their perceived post-selection value—shaped by social context and, increasingly, by AI-powered tools offering personalized career or salary guidance. Each candidate strategically chooses effort, balancing its cost against expected reward; effort translates into observable merit, and selection is based solely on merit. We characterize the unique Nash equilibrium in the large-agent limit and derive explicit formulas showing how valuation disparities and institutional selectivity jointly determine effort, representation, social welfare, and utility. We further propose a cost-sensitive optimization framework that quantifies how modifying selectivity or perceived value can reduce disparities without compromising institutional goals. Our analysis reveals a perception-driven bias: when perceptions of post-selection value differ across groups, these differences translate into rational differences in effort, propagating disparities backward through otherwise \"fair\" selection processes. While the model is static, it captures one stage of a broader feedback cycle linking perceptions, incentives, and outcomes—bridging rational-choice and structural explanations of inequality by showing how techno-social environments shape individual incentives in meritocratic systems.", "source": "openreview", "url": "https://openreview.net/forum?id=W8xcKoJcrl", "decision_type": "Spotlight", "avg_rating": 5.0, "relative_path": "2025/NeurIPS/Spotlight/5.0_Strategic Costs of Perceived Bias in Fair Selection_2025.pdf" }, { "title": "ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition", "authors": [ "Daolang Huang", "Xinyi Wen", "Ayush Bharti", "Samuel Kaski", "Luigi Acerbi" ], "year": 2025, "venue": "NeurIPS", "abstract": "Many critical applications, from autonomous scientific discovery to personalized medicine, demand systems that can both strategically acquire the most informative data and instantaneously perform inference based upon it. While amortized methods for Bayesian inference and experimental design offer part of the solution, neither approach is optimal in the most general and challenging task, where new data needs to be collected for instant inference. To tackle this issue, we introduce the Amortized Active Learning and Inference Engine (ALINE), a unified framework for amortized Bayesian inference and active data acquisition. ALINE leverages a transformer architecture trained via reinforcement learning with a reward based on self-estimated information gain provided by its own integrated inference component. This allows it to strategically query informative data points while simultaneously refining its predictions. Moreover, ALINE can selectively direct its querying strategy towards specific subsets of model parameters or designated predictive tasks, optimizing for posterior estimation, data prediction, or a mixture thereof. Empirical results on regression-based active learning, classical Bayesian experimental design benchmarks, and a psychometric model with selectively targeted parameters demonstrate that ALINE delivers both instant and accurate inference along with efficient selection of informative points.", "source": "openreview", "url": "https://openreview.net/forum?id=btm5Z5Vu8G", "decision_type": "Spotlight", "avg_rating": 4.8, "relative_path": "2025/NeurIPS/Spotlight/4.8_ALINE_ Joint Amortization for Bayesian Inference and Active Data Acquisition_2025.pdf" }, { "title": "Efficient Knowledge Transfer in Federated Recommendation for Joint Venture Ecosystem", "authors": [ "Yichen Li", "Yijing Shan", "YI LIU", "Haozhao Wang", "Cheng Wang", "wangshi.ww", "Yi Wang", "Ruixuan Li" ], "year": 2025, "venue": "NeurIPS", "abstract": "The current Federated Recommendation System (FedRS) focuses on personalized recommendation services and assumes clients are personalized IoT devices (e.g., Mobile phones). In this paper, we deeply dive into new but practical FedRS applications within the joint venture ecosystem. Subsidiaries engage as participants with their users and items. However, in such a situation, merely exchanging item embedding is insufficient, as user bases always exhibit both overlaps and exclusive segments, demonstrating the complexity of user information. Meanwhile, directly uploading user information is a violation of privacy and unacceptable. To tackle the above challenges, we propose an efficient and privacy-enhanced federated recommendation for the joint venture ecosystem (FR-JVE) that each client transfers more common knowledge from other clients with a distilled user's \\textit{rating preference} from the local dataset. More specifically, we first transform the local data into a new format and apply model inversion techniques to distill the rating preference with frozen user gradients before the federated training. Then, a bridge function is employed on each client side to align the local rating preference and aggregated global preference in a privacy-friendly manner. Finally, each client matches similar users to make a better prediction for overlapped users. From a theoretical perspective, we analyze how effectively FR-JVE can guarantee user privacy. Empirically, we show that FR-JVE achieves superior performance compared to state-of-the-art methods.", "source": "openreview", "url": "https://openreview.net/forum?id=ZWOe1kkufx", "decision_type": "Spotlight", "avg_rating": 4.8, "relative_path": "2025/NeurIPS/Spotlight/4.8_Efficient Knowledge Transfer in Federated Recommendation for Joint Venture Ecosystem_2025.pdf" }, { "title": "COOPERA: Continual Open-Ended Human-Robot Assistance", "authors": [ "Chenyang Ma", "Kai Lu", "Ruta Desai", "Xavier Puig", "Andrew Markham", "Niki Trigoni" ], "year": 2025, "venue": "NeurIPS", "abstract": "To understand and collaborate with humans, robots must account for individual human traits, habits, and activities over time. However, most robotic assistants lack these abilities, as they primarily focus on predefined tasks in structured environments and lack a human model to learn from. This work introduces COOPERA, a novel framework for COntinual, OPen-Ended human-Robot Assistance, where simulated humans, driven by psychological traits and long-term intentions, interact with robots in complex environments. By integrating continuous human feedback, our framework, for the first time, enables the study of long-term, open-ended human-robot collaboration (HRC) in different collaborative tasks across various time-scales. Within COOPERA, we introduce a benchmark and an approach to personalize the robot's collaborative actions by learning human traits and context-dependent intents. Experiments validate the extent to which our simulated humans reflect realistic human behaviors and demonstrate the value of inferring and personalizing to human intents for open-ended and long-term HRC.", "source": "openreview", "url": "https://openreview.net/forum?id=wOSZVnYH5w", "decision_type": "Spotlight", "avg_rating": 4.5, "relative_path": "2025/NeurIPS/Spotlight/4.5_COOPERA_ Continual Open-Ended Human-Robot Assistance_2025.pdf" }, { "title": "SceneDesigner: Controllable Multi-Object Image Generation with 9-DoF Pose Manipulation", "authors": [ "Zhenyuan Qin", "Xincheng Shuai", "Henghui Ding" ], "year": 2025, "venue": "NeurIPS", "abstract": "Controllable image generation has attracted increasing attention in recent years, enabling users to manipulate visual content such as identity and style. However, achieving simultaneous control over the 9D poses (location, size, and orientation) of multiple objects remains an open challenge. Despite recent progress, existing methods often suffer from limited controllability and degraded quality, falling short of comprehensive multi-object 9D pose control. To address these limitations, we propose ***SceneDesigner***, a method for accurate and flexible multi-object 9-DoF pose manipulation. ***SceneDesigner*** incorporates a branched network to the pre-trained base model and leverages a new representation, ***CNOCS map***, which encodes 9D pose information from the camera view. This representation exhibits strong geometric interpretation properties, leading to more efficient and stable training. To support training, we construct a new dataset, ***ObjectPose9D***, which aggregates images from diverse sources along with 9D pose annotations. To further address data imbalance issues, particularly performance degradation on low-frequency poses, we introduce a two-stage training strategy with reinforcement learning, where the second stage fine-tunes the model using a reward-based objective on rebalanced data. At inference time, we propose ***Disentangled Object Sampling***, a technique that mitigates insufficient object generation and concept confusion in complex multi-object scenes. Moreover, by integrating user-specific personalization weights, ***SceneDesigner*** enables customized pose control for reference subjects. Extensive qualitative and quantitative experiments demonstrate that ***SceneDesigner*** significantly outperforms existing approaches in both controllability and quality.", "source": "openreview", "url": "https://openreview.net/forum?id=yFasd68NyI", "decision_type": "Spotlight", "avg_rating": 4.5, "relative_path": "2025/NeurIPS/Spotlight/4.5_SceneDesigner_ Controllable Multi-Object Image Generation with 9-DoF Pose Manipulation_2025.pdf" }, { "title": "Fair Policy Aggregation from Standard Policy Optimization", "authors": [ "Ezgi Korkmaz" ], "year": 2026, "venue": "ICLR", "abstract": "Currently the most powerful AI systems are aligned with human values via reinforcement learning from human feedback. Yet, reinforcement learning from human feedback models human preferences as noisy samples from a single linear ordering of shared human values and is unable to incorporate democratic AI alignment. In particular, the standard approach fails to represent and reflect diverse and conflicting perspectives of pluralistic human values. Recent research introduced the theoretically principled notion of quantile fairness for training a reinforcement learning policy in the presence of multiple, competing sets of values from different agents. Quite recent work provided an algorithm for achieving quantile fairness in the tabular setting with explicit access to the full set of states, actions and transition probabilities in the MDP. These current methods require solving linear programs with the size of the constraint set given by the number of states and actions, making it unclear how to translate this into practical training algorithms that can only take actions and observe individual transitions from the current state. In this paper, we design and prove the correctness of a new algorithm for quantile fairness that makes efficient use of standard policy optimization as a black-box without any direct dependence on the number of states or actions. We further empirically validate our theoretical results and demonstrate that our algorithm achieves competitive fairness guarantees to the prior work, while being orders of magnitude more efficient with respect to computation and the required number of samples. Our algorithm opens a new avenue for provable fairness guarantees in any setting where standard policy optimization is possible.", "source": "openreview", "url": "https://openreview.net/forum?id=XNNDODynCl", "decision_type": "Poster", "avg_rating": 6.5, "relative_path": "2026/ICLR/Poster/6.5_Fair Policy Aggregation from Standard Policy Optimization_2026.pdf" }, { "title": "COLD-Steer: Steering Large Language Models via In-Context One-step Learning Dynamics", "authors": [ "Kartik Sharma", "Rakshit Trivedi" ], "year": 2026, "venue": "ICLR", "abstract": "Activation steering methods enable inference-time control of large language model (LLM) behavior without retraining, but current approaches either capture suboptimally steering signals from labeled examples or require hundreds to thousands of examples to optimize using specific procedures for each behavioral target. We introduce COLD-Steer, a training-free framework that steers LLM activations by approximating the representational changes that would result from gradient descent on in-context examples. Our key insight is that the effect of fine-tuning on a small set of examples can be efficiently approximated at inference time without actual parameter updates. We formalize this through two complementary approaches: (i) a unit kernel approximation method that updates the activations directly using gradients with respect to them, normalized across examples, and (ii) a finite-difference approximation requiring only two forward passes regardless of example count. Experiments across a variety of steering tasks and benchmarks demonstrate that COLD-Steer achieves upto 95\\% steering effectiveness while using 50 times fewer samples compared to the best baseline. COLD-Steer enables real-time adaptation to new steering objectives and facilitates accommodating diverse perspectives without extensive demonstration data, which we validate through our experiments on pluralistic alignment tasks. Our framework opens new possibilities for adaptive, context-aware model control that can flexibly address varying loss-driven human preferences through principled approximation of learning dynamics rather than specialized training procedures.", "source": "openreview", "url": "https://openreview.net/forum?id=afV4qzquBN", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2026/ICLR/Poster/6.0_COLD-Steer_ Steering Large Language Models via In-Context One-step Learning Dynamics_2026.pdf" }, { "title": "MoReBench: Evaluating Procedural and Pluralistic Moral Reasoning in Language Models, More than Outcomes", "authors": [ "Yu Ying Chiu", "Michael S. Lee", "Rachel Calcott", "Brandon Handoko", "Paul de Font-Reaulx", "Paula Rodriguez", "Chen Bo Calvin Zhang", "Ziwen Han", "Udari Madhushani Sehwag", "Yash Maurya", "Christina Q Knight", "Harry R. Lloyd", "Florence Bacus", "Mantas Mazeika", "Bing Liu", "Yejin Choi", "Mitchell L Gordon", "Sydney Levine" ], "year": 2026, "venue": "ICLR", "abstract": "As AI systems progresses, we rely more on them to make decisions with us and for us. To ensure that such decisions are aligned with human values, it is imperative for us to understand not only what decisions they make but also how they come to those decisions. Reasoning language models, which provide both final responses and (partially transparent) intermediate thinking traces, present a timely opportunity to study AI procedural reasoning. Unlike math and code problems which often have objectively correct answers, moral dilemmas are an excellent testbed for process-focused evaluation because they allow for multiple defensible conclusions. To do so, we present MoReBench: 1,000 moral scenarios, each paired with a set of rubric criteria that experts consider essential to include (or avoid) when reasoning about the scenarios. MoReBench contains over 23 thousand criteria including identifying moral considerations, weighing trade-offs, and giving actionable recommendations to cover cases on AI advising humans moral decisions as well as making moral decisions autonomously. Separately, we curate MoReBench-Theory: 150 examples to test whether AI can reason under five major frameworks in normative ethics. Our results show that scaling laws and existing benchmarks on math, code, and scientific reasoning tasks (fail to) predict models' abilities to perform moral reasoning. Models also show partiality towards specific moral frameworks (e.g., Benthamite Act Utilitarianism and Kantian Deontology), which might be side effects of popular training paradigms. Together, these benchmarks advance process-focused reasoning evaluation towards safer and more transparent AI.", "source": "openreview", "url": "https://openreview.net/forum?id=RMwJXp5Kb1", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2026/ICLR/Poster/6.0_MoReBench_ Evaluating Procedural and Pluralistic Moral Reasoning in Language Models, More than _2026.pdf" }, { "title": "Translate Policy to Language: Flow Matching Generated Rewards for LLM Explanations", "authors": [ "Xinyi Yang", "Liang Zeng", "Heng Dong", "Chao Yu", "Xiaoran Wu", "Huazhong Yang", "Yu Wang", "Milind Tambe", "Tonghan Wang" ], "year": 2026, "venue": "ICLR", "abstract": "As humans increasingly share environments with diverse agents powered by RL, LLMs, and beyond, the ability to explain agent policies in natural language is vital for reliable coexistence. We introduce a general-purpose framework that trains explanation-generating LLMs via reinforcement learning from AI feedback, with distributional rewards generated by generative continuous normalizing flows (CNFs). CNFs capture the pluralistic and probabilistic nature of human judgments about explanations. Moreover, under mild assumptions, CNFs provably bound deviations from true human reward distributions when trained on noisy proxy rewards from LLMs. We design a specialized CNF architecture that selectively attends to linguistic cues in decision context and explanations when generating rewards. Human and LLM evaluators find that our method delivers explanations that enable more accurate predictions of true agent decisions, exhibit greater logical soundness and actionability, and impose lower cognitive load than explanations trained with proxy LLM rewards or state-of-the-art RLHF and RLAIF baselines.", "source": "openreview", "url": "https://openreview.net/forum?id=zmZsWCGzUV", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2026/ICLR/Poster/6.0_Translate Policy to Language_ Flow Matching Generated Rewards for LLM Explanations_2026.pdf" }, { "title": "Benchmarking Overton Pluralism in LLMs", "authors": [ "Elinor Poole-Dayan", "Jiayi Wu", "Taylor Sorensen", "Jiaxin Pei", "Michiel A. Bakker" ], "year": 2026, "venue": "ICLR", "abstract": "We introduce a novel framework for measuring Overton pluralism in LLMs—the extent to which diverse viewpoints are represented in model outputs. We (i) formalize Overton pluralism as a set-coverage metric (OVERTONSCORE), (ii) conduct a large-scale US-representative human study (N=1209; 60 questions; 8 LLMs), and (iii) develop an automated benchmark that closely reproduces human judgments. On average, models achieve OVERTONSCOREs of 0.35 – 0.41, with Deepseek V3 performing best; yet all models remain far below the theoretical maximum of 1.0, revealing substantial headroom for improvement. Because repeated large-scale human studies are costly and slow, scalable evaluation tools are essential for model development. Hence, we propose an automated benchmark that achieves high rank correlation with human judgments ($\\rho=0.88$), providing a practical proxy while not replacing human assessment. \nBy turning pluralistic alignment from a normative aim into a measurable benchmark, our work establishes a foundation for systematic progress toward more pluralistic LLMs.", "source": "openreview", "url": "https://openreview.net/forum?id=f2VxF4QIx1", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2026/ICLR/Poster/5.0_Benchmarking Overton Pluralism in LLMs_2026.pdf" }, { "title": "Controllable Safety Alignment: Inference-Time Adaptation to Diverse Safety Requirements", "authors": [ "Jingyu Zhang", "Ahmed Elgohary", "Ahmed Magooda", "Daniel Khashabi", "Benjamin Van Durme" ], "year": 2025, "venue": "ICLR", "abstract": "The current paradigm for safety alignment of large language models (LLMs) follows a _one-size-fits-all_ approach: the model refuses to interact with any content deemed unsafe by the model provider. This approach lacks flexibility in the face of varying social norms across cultures and regions. In addition, users may have diverse safety needs, making a model with _static_ safety standards too restrictive to be useful, as well as too costly to be re-aligned.\n\nWe propose _Controllable Safety Alignment_ (CoSA), a framework designed to adapt models to diverse safety requirements without re-training. Instead of aligning a fixed model, we align models to follow _safety configs_—free-form natural language descriptions of the desired safety behaviors—that are provided as part of the system prompt. To adjust model safety behavior, authorized users only need to modify such safety configs at inference time. To enable that, we propose CoSAlign, a data-centric method for aligning LLMs to easily adapt to diverse safety configs. Furthermore, we devise a novel controllability evaluation protocol that considers both helpfulness and configured safety, summarizing them into CoSA-Score, and construct CoSApien, a _human-authored_ benchmark that consists of real-world LLM use cases with diverse safety requirements and corresponding evaluation prompts. We show that CoSAlign leads to substantial gains of controllability over strong baselines including in-context alignment. Our framework encourages better representation and adaptation to pluralistic human values in LLMs, and thereby increasing their practicality.", "source": "openreview", "url": "https://openreview.net/forum?id=ERce2rgMQC", "decision_type": "Poster", "avg_rating": 7.0, "relative_path": "2025/ICLR/Poster/7.0_Controllable Safety Alignment_ Inference-Time Adaptation to Diverse Safety Requirements_2025.pdf" }, { "title": "No Preference Left Behind: Group Distributional Preference Optimization", "authors": [ "Binwei Yao", "Zefan Cai", "Yun-Shiuan Chuang", "Shanglin Yang", "Ming Jiang", "Diyi Yang", "Junjie Hu" ], "year": 2025, "venue": "ICLR", "abstract": "Preferences within a group of people are not uniform but follow a distribution. While existing alignment methods like Direct Preference Optimization (DPO) attempt to steer models to reflect human preferences, they struggle to capture the distributional pluralistic preferences within a group. These methods often skew toward dominant preferences, overlooking the diversity of opinions, especially when conflicting preferences arise. To address this issue, we propose Group Distributional Preference Optimization (GDPO), a novel framework that aligns language models with the distribution of preferences within a group by incorporating the concept of beliefs that shape individual preferences. GDPO calibrates a language model using statistical estimation of the group's belief distribution and aligns the model with belief-conditioned preferences, offering a more inclusive alignment framework than traditional methods. In experiments using both synthetic controllable opinion generation and real-world movie review datasets, we show that DPO fails to align with the targeted belief distributions, while GDPO consistently reduces this alignment gap during training. Additionally, our evaluation metrics demonstrate that GDPO outperforms existing approaches in aligning with group distributional preferences, marking a significant advance in pluralistic alignment.", "source": "openreview", "url": "https://openreview.net/forum?id=bgpNJBD6Va", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2025/ICLR/Poster/5.0_No Preference Left Behind_ Group Distributional Preference Optimization_2025.pdf" }, { "title": "Diverging Preferences: When do Annotators Disagree and do Models Know?", "authors": [ "Michael JQ Zhang", "Zhilin Wang", "Jena D. Hwang", "Yi Dong", "Olivier Delalleau", "Yejin Choi", "Eunsol Choi", "Xiang Ren", "Valentina Pyatkin" ], "year": 2025, "venue": "ICML", "abstract": "We examine diverging preferences in human-labeled preference datasets. We develop a taxonomy of disagreement sources spanning ten categories across four high-level classes and find that the majority of disagreements are due to factors such as task underspecification or response style. Our findings challenge a standard assumption in reward modeling methods that annotator disagreements can be attributed to simple noise. We then explore how these findings impact two areas of LLM development: reward modeling training and evaluation. In our experiments, we demonstrate how standard reward modeling (e.g., Bradley-Terry) and LLM-as-Judge evaluation methods fail to account for divergence between annotators. These findings highlight challenges in LLM evaluations, which are greatly influenced by divisive features like response style, and in developing pluralistically aligned LLMs. To address these issues, we develop methods for identifying diverging preferences to mitigate their influence in evaluations and during LLM training.", "source": "openreview", "url": "https://openreview.net/forum?id=qWgAAVhoXb", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Diverging Preferences_ When do Annotators Disagree and do Models Know__2025.pdf" }, { "title": "LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces", "authors": [ "Rashid Mushkani", "Shravan Nayak", "Hugo Berard", "Allison Cohen", "Shin Koseki", "Hadrien Bertrand" ], "year": 2025, "venue": "ICML", "abstract": "We introduce the *Local Intersectional Visual Spaces* (LIVS) dataset, a benchmark for multi-criteria alignment, developed through a two-year participatory process with 30 community organizations to support the pluralistic alignment of text-to-image (T2I) models in inclusive urban planning. The dataset encodes 37,710 pairwise comparisons across 13,462 images, structured along six criteria—Accessibility, Safety, Comfort, Invitingness, Inclusivity, and Diversity—derived from 634 community-defined concepts. Using Direct Preference Optimization (DPO), we fine-tune Stable Diffusion XL to reflect multi-criteria spatial preferences and evaluate the LIVS dataset and the fine-tuned model through four case studies: (1) DPO increases alignment with annotated preferences, particularly when annotation volume is high; (2) preference patterns vary across participant identities, underscoring the need for intersectional data; (3) human-authored prompts generate more distinctive visual outputs than LLM-generated ones, influencing annotation decisiveness; and (4) intersectional groups assign systematically different ratings across criteria, revealing the limitations of single-objective alignment. While DPO improves alignment under specific conditions, the prevalence of neutral ratings indicates that community values are heterogeneous and often ambiguous. LIVS provides a benchmark for developing T2I models that incorporate local, stakeholder-driven preferences, offering a foundation for context-aware alignment in spatial design.", "source": "openreview", "url": "https://openreview.net/forum?id=Spoe53kbj9", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_LIVS_ A Pluralistic Alignment Dataset for Inclusive Public Spaces_2025.pdf" }, { "title": "Counterfactual Reasoning for Steerable Pluralistic Value Alignment of Large Language Models", "authors": [ "Hanze Guo", "Jing Yao", "Xiao Zhou", "Xiaoyuan Yi", "Xing Xie" ], "year": 2025, "venue": "NeurIPS", "abstract": "As large language models (LLMs) become increasingly integrated into applications serving users across diverse cultures, communities, and demographics, it is critical to align LLMs with pluralistic human values beyond average principles (e.g., HHH). \nIn psychological and social value theories such as Schwartz’s Value Theory, pluralistic values are represented by multiple value dimensions paired with various priorities. However, existing methods encounter two challenges when aligning with such fine-grained value objectives: 1) they often treat multiple values as independent and equally important, ignoring their interdependence and relative priorities (value complexity); 2) they struggle to precisely control nuanced value priorities, especially those underrepresented ones (value steerability). To handle these challenges, we propose COUPLE, a COUnterfactual reasoning framework for PLuralistic valuE alignment. It introduces a structural causal model (SCM) to feature complex interdependency and prioritization among features, as well as the causal relationship between high-level value dimensions and behaviors. Moreover, it applies counterfactual reasoning to generate outputs aligned with any desired value objectives. Benefitting from explicit causal modeling, COUPLE also provides better interpretability. We evaluate COUPLE on two datasets with different value systems and demonstrate that COUPLE advances other baselines across diverse types of value objectives. Our code is available at https://github.com/microsoft/COUPLE.", "source": "openreview", "url": "https://openreview.net/forum?id=86b23oNkg9", "decision_type": "Poster", "avg_rating": 4.8, "relative_path": "2025/NeurIPS/Poster/4.8_Counterfactual Reasoning for Steerable Pluralistic Value Alignment of Large Language Models_2025.pdf" }, { "title": "Pairwise Calibrated Rewards for Pluralistic Alignment", "authors": [ "Daniel Halpern", "Evi Micha", "Ariel D. Procaccia", "Itai Shapira" ], "year": 2025, "venue": "NeurIPS", "abstract": "Current alignment pipelines presume a single, universal notion of desirable behavior. However, human preferences often diverge across users, contexts, and cultures. As a result, disagreement collapses into the majority signal and minority perspectives are discounted. To address this, we propose reflecting diverse human preferences through a distribution over multiple reward functions, each inducing a distinct aligned policy. The distribution is learned directly from pairwise preference without annotator identifiers or predefined groups. Instead, annotator disagreements are treated as informative soft labels. Our central criterion is \\emph{pairwise calibration}: for every pair of candidate responses, the proportion of reward functions preferring one response matches the fraction of annotators with that preference. We prove that even a small outlier-free ensemble can accurately represent diverse preference distributions. Empirically, we introduce and validate a practical training heuristic to learn such ensembles, and demonstrate its effectiveness through improved calibration, implying a more faithful representation of pluralistic values.", "source": "openreview", "url": "https://openreview.net/forum?id=dtH7hOwTeS", "decision_type": "Poster", "avg_rating": 4.7, "relative_path": "2025/NeurIPS/Poster/4.7_Pairwise Calibrated Rewards for Pluralistic Alignment_2025.pdf" }, { "title": "Strategyproof Reinforcement Learning from Human Feedback", "authors": [ "Thomas Kleine Buening", "Jiarui Gan", "Debmalya Mandal", "Marta Kwiatkowska" ], "year": 2025, "venue": "NeurIPS", "abstract": "We study Reinforcement Learning from Human Feedback (RLHF) in settings where multiple labelers may strategically misreport feedback to steer the learned policy toward their own preferences. We show that existing RLHF algorithms, including recent pluralistic methods, are not strategyproof, and that even a single strategic labeler can cause arbitrarily large misalignment with social welfare. Moreover, we prove that, in the worst case, any strategyproof RLHF algorithm must perform $k$-times worse than the optimal policy, where $k$ is the number of labelers. This suggests a fundamental trade-off between incentive alignment (ensuring labelers report truthfully) and policy alignment (maximizing social welfare). To address this, we propose the Pessimistic Median of MLEs algorithm, which, under appropriate policy coverage assumptions, is approximately strategyproof and converges to the optimal policy as the number of labelers and samples increases. Our results apply to both contextual bandits and Markov decision processes.", "source": "openreview", "url": "https://openreview.net/forum?id=5WkXhmvvgk", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2025/NeurIPS/Poster/4.5_Strategyproof Reinforcement Learning from Human Feedback_2025.pdf" }, { "title": "Distortion of AI Alignment: Does Preference Optimization Optimize for Preferences?", "authors": [ "Paul Gölz", "Nika Haghtalab", "Kunhe Yang" ], "year": 2025, "venue": "NeurIPS", "abstract": "After pre-training, large language models are aligned with human preferences based on pairwise comparisons. State-of-the-art alignment methods (such as PPO-based RLHF and DPO) are built on the assumption of aligning with a single preference model, despite being deployed in settings where users have diverse preferences. As a result, it is not even clear that these alignment methods produce models that satisfy users \\emph{on average} --- a minimal requirement for pluralistic alignment. Drawing on social choice theory and modeling users' comparisons through individual Bradley-Terry (BT) models, we introduce an alignment method's \\emph{distortion}: the worst-case ratio between the optimal achievable average utility, and the average utility of the learned policy. The notion of distortion helps draw sharp distinctions between alignment methods: \\emph{Nash Learning from Human Feedback} achieves the minimax optimal distortion of $(\\frac{1}{2} + o(1)) \\cdot \\beta$ (for the BT temperature $\\beta$), robustly across utility distributions, distributions of comparison pairs, and permissible KL divergences from the reference policy. RLHF and DPO, by contrast, suffer $\\geq (1 - o(1)) \\cdot \\beta$ distortion already without a KL constraint, and $e^{\\Omega(\\beta)}$ or even unbounded distortion in the full setting, depending on how comparison pairs are sampled.", "source": "openreview", "url": "https://openreview.net/forum?id=bkZrAIWK0N", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_Distortion of AI Alignment_ Does Preference Optimization Optimize for Preferences__2025.pdf" }, { "title": "Imitation Beyond Expectation Using Pluralistic Stochastic Dominance", "authors": [ "Ali Farajzadeh", "Danyal Saeed", "Syed M Abbas", "Rushit N. Shah", "Aadirupa Saha", "Brian D Ziebart" ], "year": 2025, "venue": "NeurIPS", "abstract": "Imitation learning seeks policies reflecting the values of demonstrated behaviors. Prevalent approaches learn to match or exceed the demonstrator's performance in expectation without knowing the demonstrator’s reward function. Unfortunately, this does not induce pluralistic imitators that learn to support qualitatively distinct demonstrations. We reformulate imitation learning using stochastic dominance over the demonstrations' reward distribution across a range of reward functions as our foundational aim. Our approach matches imitator policy samples (or support) with demonstrations using optimal transport theory to define an imitation learning objective over trajectory pairs. We demonstrate the benefits of pluralistic stochastic dominance (PSD) for imitation in both theory and practice.", "source": "openreview", "url": "https://openreview.net/forum?id=YX5DHa9OfX", "decision_type": "Spotlight", "avg_rating": 4.7, "relative_path": "2025/NeurIPS/Spotlight/4.7_Imitation Beyond Expectation Using Pluralistic Stochastic Dominance_2025.pdf" }, { "title": "Persona Features Control Emergent Misalignment", "authors": [ "Miles Wang", "Tom Dupre la Tour", "Olivia Watkins", "Aleksandar Makelov", "Ryan Andrew Chi", "Samuel Miserendino", "Jeffrey George Wang", "Achyuta Rajaram", "Johannes Heidecke", "Tejal Patwardhan", "Daniel P Mossing" ], "year": 2026, "venue": "ICLR", "abstract": "Understanding how language models generalize behaviors from their training to a broader deployment distribution is an important problem in AI safety. Betley et al. discovered that fine-tuning GPT-4o on intentionally insecure code causes \"emergent misalignment,\" where models give stereotypically malicious responses to unrelated prompts. We extend this work, demonstrating emergent misalignment across diverse conditions, including reinforcement learning on reasoning models, fine-tuning on various synthetic datasets, and in models without safety training. To investigate the mechanisms behind this generalized misalignment, we apply a \"model diffing\" approach using sparse autoencoders to compare internal model representations before and after fine-tuning. This approach reveals several \"misaligned persona\" features in activation space, including a toxic persona feature which most strongly controls emergent misalignment and can be used to predict whether a model will exhibit such behavior. Additionally, we investigate mitigation strategies, discovering that fine-tuning an emergently misaligned model on just a few hundred benign samples efficiently restores alignment.", "source": "openreview", "url": "https://openreview.net/forum?id=yjrVOxjkDR", "decision_type": "Poster", "avg_rating": 7.5, "relative_path": "2026/ICLR/Poster/7.5_Persona Features Control Emergent Misalignment_2026.pdf" }, { "title": "Doubly-Robust LLM-as-a-Judge: Externally Valid Estimation with Imperfect Personas", "authors": [ "Luke Guerdan", "Justin Whitehouse", "Kimberly Truong", "Ken Holstein", "Steven Wu" ], "year": 2026, "venue": "ICLR", "abstract": "As Generative AI (GenAI) systems see growing adoption, a key concern involves the external validity of evaluations, or the extent to which they generalize from lab-based to real-world deployment conditions. Threats to the external validity of GenAI evaluations arise when the source sample of human raters and system outputs used to obtain a system quality estimate differs from the target distribution at deployment time. In this work, we propose a doubly-robust estimation framework designed to address this evaluation sampling bias. Key to our approach is the use of synthetic \"persona\" ratings -- produced by prompting an LLM evaluator (i.e., an LLM-as-a-judge) to behave as a human rater with specific sociodemographic characteristics. Our doubly-robust framework combines these informative yet imperfect persona ratings with human ratings obtained under evaluation sampling bias to produce statistically valid system quality estimates. In particular, we show that our approach yields valid system quality estimates when either: (i) a model trained to predict human ratings using persona ratings and source data observed under sampling bias, or (ii) a reweighting model that corrects for sampling bias is of sufficient quality. We validate our framework theoretically and via a novel Persona Simulation Framework (PSF) designed to systematically manipulate persona quality and the degree of evaluation sampling bias present in source data. Our work provides a principled foundation for combining imperfect persona ratings with human ratings observed under sampling bias to obtain valid system quality estimates.", "source": "openreview", "url": "https://openreview.net/forum?id=EIA1tpKYL7", "decision_type": "Poster", "avg_rating": 6.7, "relative_path": "2026/ICLR/Poster/6.7_Doubly-Robust LLM-as-a-Judge_ Externally Valid Estimation with Imperfect Personas_2026.pdf" }, { "title": "Jailbreak Transferability Emerges from Shared Representations", "authors": [ "Rico Angell", "Jannik Brinkmann", "He He" ], "year": 2026, "venue": "ICLR", "abstract": "Jailbreak transferability is the surprising phenomenon when an adversarial attack compromising one model also elicits harmful responses from other models. Despite widespread demonstrations, there is little consensus on why transfer is possible: is it a quirk of safety training, an artifact of model families, or a more fundamental property of representation learning? We present evidence that transferability emerges from shared representations rather than incidental flaws. Across 20 open-weight models and 33 jailbreak attacks, we find two factors that systematically shape transfer: (1) representational similarity under benign prompts, and (2) the strength of the jailbreak on the source model. To move beyond correlation, we show that deliberately increasing similarity through benign-only distillation systematically increases transfer. Qualitative analyses reveal transferability patterns: persona-style jailbreaks transfer far more often than cipher-based prompts, consistent with the idea that natural-language attacks exploit models’ shared representation space, whereas cipher-based attacks rely on idiosyncratic quirks that do not generalize. Together, these results reframe jailbreak transfer as a consequence of representation alignment rather than a fragile byproduct of safety training.", "source": "openreview", "url": "https://openreview.net/forum?id=UQK3tUsouK", "decision_type": "Poster", "avg_rating": 6.5, "relative_path": "2026/ICLR/Poster/6.5_Jailbreak Transferability Emerges from Shared Representations_2026.pdf" }, { "title": "Cyber-Zero: Training Cybersecurity Agents without Runtime", "authors": [ "Terry Yue Zhuo", "Dingmin Wang", "Hantian Ding", "Varun Kumar", "Zijian Wang" ], "year": 2026, "venue": "ICLR", "abstract": "Large Language Models (LLMs) have achieved remarkable success in software engineering tasks when trained with executable runtime environments, particularly in resolving GitHub issues. However, such runtime environments are often unavailable in other domains, especially cybersecurity, where challenge configurations and execution contexts are ephemeral or restricted. We present Cyber-Zero, the first runtime-free framework for synthesizing high-quality agent trajectories to train cybersecurity LLMs. Cyber-Zero leverages publicly available CTF writeups and employs persona-driven LLM simulation to reverse-engineer runtime behaviors and generate realistic, long-horizon interaction sequences without actual environments. Using trajectories synthesized by Cyber-Zero, we train LLM-based agents that achieve up to 13.1% absolute performance gains over baseline models on three prominent CTF benchmarks: InterCode-CTF, NYU CTF Bench, and Cybench. Our best model, Cyber-Zero-32B, establishes new state-of-the-art performance among open-weight models, matching the capabilities of proprietary systems like DeepSeek-V3-0324 and Claude-3.5-Sonnet while offering superior cost-effectiveness, and demonstrating that runtime-free trajectory synthesis can effectively democratize the development of state-of-the-art cybersecurity agents.", "source": "openreview", "url": "https://openreview.net/forum?id=1gRTeAik4G", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2026/ICLR/Poster/6.0_Cyber-Zero_ Training Cybersecurity Agents without Runtime_2026.pdf" }, { "title": "Social Agents: Collective Intelligence Improves LLM Predictions", "authors": [ "Aanisha Bhattacharyya", "Abhilekh Borah", "Yaman Kumar Singla", "Rajiv Ratn Shah", "Changyou Chen", "Balaji Krishnamurthy" ], "year": 2026, "venue": "ICLR", "abstract": "In human society, collective decision making has often outperformed the judgment of individuals. Classic examples range from estimating livestock weights to predicting elections and financial markets, where averaging many independent guesses often yields results more accurate than experts. These successes arise because groups bring together diverse perspectives, independent voices, and distributed knowledge, combining them in ways that cancel individual biases. This principle, known as the Wisdom of Crowds, underpins practices in forecasting, marketing, and preference modeling. Large Language Models (LLMs), however, typically produce a single definitive answer. While effective in many settings, this uniformity overlooks the diversity of human judgments shaping responses to ads, videos, and webpages. Inspired by how societies benefit from diverse opinions, we ask whether LLM predictions can be improved by simulating not one answer but many. We introduce Social Agents, a multi-agent framework that instantiates a synthetic society of human-like personas with diverse demographic (e.g., age, gender) and psychographic (e.g., values, interests) attributes. Each persona independently appraises a stimulus such as an advertisement, video, or webpage, offering both a quantitative score (e.g., click-through likelihood, recall score, likability) and a qualitative rationale. Aggregating these opinions produces a distribution of preferences that more closely mirrors real human crowds. Across eleven behavioral prediction tasks, Social Agents outperforms single-LLM baselines by up to 67.45% on simple judgments (e.g. webpage likability) and 9.88% on complex interpretive reasoning (e.g. video memorability). Social Agents’ individual persona predictions also align with human judgments, reaching Pearson correlations up to 0.71. These results position computational crowd simulation as a scalable, interpretable tool for improving behavioral prediction and supporting societal decision making.", "source": "openreview", "url": "https://openreview.net/forum?id=73J3hsato3", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2026/ICLR/Poster/6.0_Social Agents_ Collective Intelligence Improves LLM Predictions_2026.pdf" }, { "title": "DRBench: A Realistic Benchmark for Enterprise Deep Research", "authors": [ "Amirhossein Abaskohi", "Tianyi Chen", "Miguel Muñoz-Mármol", "Curtis Fox", "Amrutha Varshini Ramesh", "Étienne Marcotte", "Xing Han Lù", "Nicolas Chapados", "Spandana Gella", "Christopher Pal", "Alexandre Drouin", "Issam H. Laradji" ], "year": 2026, "venue": "ICLR", "abstract": "We introduce DRBench, a benchmark for evaluating AI agents on complex, open-ended deep research tasks in enterprise settings. Unlike prior benchmarks that focus on simple questions or web-only queries, DRBench evaluates agents on multi-step queries (for example, \"What changes should we make to our product roadmap to ensure compliance with this standard?\") that require identifying supporting facts from both the public web and private company knowledge base. Each task is grounded in realistic user personas and enterprise context, spanning a heterogeneous search space that includes productivity software, cloud file systems, emails, chat conversations, and the open web. Tasks are generated through a carefully designed synthesis pipeline with human-in-the-loop verification, and agents are evaluated on their ability to recall relevant insights, maintain factual accuracy, and produce coherent, well-structured reports. We release 100 deep research tasks across 10 domains, such as Sales, Cybersecurity, and Compliance. We demonstrate the effectiveness of DRBench by evaluating diverse DR agents across open- and closed-source models (such as GPT, Llama, and Qwen) and DR strategies, highlighting their strengths, weaknesses, and the critical path for advancing enterprise deep research.", "source": "openreview", "url": "https://openreview.net/forum?id=IGYQ4c92e2", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2026/ICLR/Poster/5.5_DRBench_ A Realistic Benchmark for Enterprise Deep Research_2026.pdf" }, { "title": "Human or Machine? A Preliminary Turing Test for Speech-to-Speech Interaction", "authors": [ "Xiang Li", "Jiabao Gao", "Sipei Lin", "Xuan Zhou", "Chi Zhang", "Bo Cheng", "Jiale Han", "Benyou Wang" ], "year": 2026, "venue": "ICLR", "abstract": "The pursuit of human-like conversational agents has long been guided by the Turing test. For modern speech-to-speech (S2S) systems, a critical yet unanswered question is whether they can converse like humans. To tackle this, we conduct the first Turing test for S2S systems, collecting 2,968 human judgments on dialogues between 9 state-of-the-art S2S systems and 28 human participants. Our results deliver a clear finding: no existing evaluated S2S system passes the test, revealing a significant gap in human-likeness. To diagnose this failure, we develop a fine-grained taxonomy of 18 human-likeness dimensions and crowd-annotate our collected dialogues accordingly. Our analysis shows that the bottleneck is not semantic understanding but stems from paralinguistic features, emotional expressivity, and conversational persona. Furthermore, we find that off-the-shelf AI models perform unreliably as Turing test judges. In response, we propose an interpretable model that leverages the fine-grained human-likeness ratings and delivers accurate and transparent human-vs-machine discrimination, offering a powerful tool for automatic human-likeness evaluation. Our work establishes the first human-likeness evaluation for S2S systems and moves beyond binary outcomes to enable detailed diagnostic insights, paving the way for human-like improvements in conversational AI systems.", "source": "openreview", "url": "https://openreview.net/forum?id=Pv5l6cvfno", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2026/ICLR/Poster/5.5_Human or Machine_ A Preliminary Turing Test for Speech-to-Speech Interaction_2026.pdf" }, { "title": "R4: Nested Reasoning-Retrieval for Reward Modeling in Role-Playing Agents", "authors": [ "Renzhi Wang", "Chongqiang Wei", "Zhisheng Wang", "Piji Li" ], "year": 2026, "venue": "ICLR", "abstract": "Role-playing dialogue presents unique challenges for large language models (LLMs): beyond producing coherent text, models must sustain character persona, integrate contextual knowledge, and convey emotional nuance. Despite strong reasoning abilities, current LLMs often generate dialogue that is literal, stylistically bland, and misaligned with character-specific traits. Existing approaches such as retrieval-augmented generation (RAG) or reinforcement learning (RL) with scalar rewards are insufficient, as they cannot capture nuanced preferences or adapt reliably to diverse character contexts.\nIn this work, we introduce R4, a unified framework that equips both the reward model and the role-playing agent with reasoning and retrieval capabilities. Our reward model reformulates evaluation as structured reasoning: it integrates multi-step deliberation and retrieved knowledge to assess responses along multiple dimensions. This reward supervision is then used within reinforcement learning to train a dialogue agent with the same dual capabilities, enabling contextually grounded and persona-consistent generation.\nExperiments demonstrate that R4 substantially improves dialogue quality, particularly in persona fidelity, narrative coherence, and emotional expressiveness. Analysis of training dynamics and case studies further shows that R4 agents employ retrieval more effectively, engage in retrieval-informed self-reflection, and achieve emergent role-playing behaviors unattainable by prior methods.", "source": "openreview", "url": "https://openreview.net/forum?id=sWQSbVsPEz", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2026/ICLR/Poster/5.5_R4_ Nested Reasoning-Retrieval for Reward Modeling in Role-Playing Agents_2026.pdf" }, { "title": "VeriRole: Verifiable Role-Awareness through Hint-Guided Reinforcement Learning", "authors": [ "Zongsheng Wang", "Kaili Sun", "Bowen Wu", "qun yu", "Ying Li", "Xu Chen", "Baoxun Wang" ], "year": 2026, "venue": "ICLR", "abstract": "Maintaining role-awareness in Role-Playing Conversational Agents (RPCAs) is a significant challenging, largely because the creative nature of role-playing makes it difficult to design verifiable reward signals for reinforcement learning (RL). To address this, we propose VeriRole, a new framework designed to enhance the role-awareness of agents through a structured, verifiable reasoning process. The core of our framework is a 'hint' mechanism, designed to first extract deterministic cues from the context, before the main response generation.Building on these hints, we introduce a Verifiable Role-Awareness Reward (VRAR) to provide a verifiable signal for role-awareness. Experimental results demonstrate the effectiveness of our approach. Our Qwen2.5-32B model, optimized with VeriRole, achieves an 18.9% and 4.55% increase in average scores on the RAIDEN and CharacterEval benchmarks, respectively. These results confirm that VeriRole can effectively quantify and improve role-awareness, leading to superior persona consistency and robustness. To ensure reproducibility, all prompts are detailed in the Appendix, and the associated training data has been made publicly available.", "source": "openreview", "url": "https://openreview.net/forum?id=lW7kMpMj9K", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2026/ICLR/Poster/5.5_VeriRole_ Verifiable Role-Awareness through Hint-Guided Reinforcement Learning_2026.pdf" }, { "title": "Talk, Evaluate, Diagnose: User-aware Agent Evaluation with Automated Error Analysis", "authors": [ "Penny Chong", "Harshavardhan Abichandani", "Jiyuan SHEN", "Atin Ghosh", "Min Pyae Moe", "Yifan Mai", "Daniel Dahlmeier" ], "year": 2026, "venue": "ICLR", "abstract": "Agent applications are increasingly adopted to automate workflows across diverse tasks. However, due to the heterogeneous domains they operate in, it is challenging to create a scalable evaluation framework. Prior work each employ their own methods to determine task success, such as database lookups, regex match, etc., adding complexity to the development of a unified agent evaluation approach. Moreover, they do not systematically account for the user’s role nor expertise in the interaction, providing incomplete insights into agent’s performance. We argue that effective agent evaluation goes beyond correctness alone, incorporating conversation quality, efficiency and systematic diagnosis of agent errors. To address this, we introduce the TED framework (Talk, Evaluate, Diagnose). (1) Talk: We leverage reusable, generic expert and non-expert user persona templates for user-agent interaction. (2) Evaluate: We adapt existing datasets by representing subgoals—such as tool signatures, and responses—as natural language grading notes, evaluated automatically with LLM-as-a-judge. We propose new metrics that capture both turn efficiency and intermediate progress of the agent complementing the user-aware setup. (3) Diagnose: We introduce an automated error analysis tool that analyzes the inconsistencies of the judge and agents, uncovering common errors, and providing actionable feedback for agent improvement. We show that our TED framework reveals new insights regarding agent performance across models and user expertise levels. We also demonstrate potential gains in agent\nperformance with peaks of 8-10% on our proposed metrics after incorporating the identified error remedies into the agent’s design.", "source": "openreview", "url": "https://openreview.net/forum?id=fHsVNklKOc", "decision_type": "Poster", "avg_rating": 5.3, "relative_path": "2026/ICLR/Poster/5.3_Talk, Evaluate, Diagnose_ User-aware Agent Evaluation with Automated Error Analysis_2026.pdf" }, { "title": "Test-Time Adaptation for LLM Agents via Environment Interaction", "authors": [ "Arthur Chen", "Zuxin Liu", "Jianguo Zhang", "Akshara Prabhakar", "Zhiwei Liu", "Shelby Heinecke", "Silvio Savarese", "Victor Zhong", "Caiming Xiong" ], "year": 2026, "venue": "ICLR", "abstract": "Large language model (LLM)-based agents struggle to generalize to novel and complex environments, such as unseen websites or new sets of functions, due to a fundamental mismatch between their pre-training and test-time conditions.\nThis challenge stems from two distinct failure modes: a syntactic misunderstanding of environment-specific components like observation formats, and a semantic misunderstanding of state-transition dynamics, which are only revealed at test time.\nTo address these issues, we propose two distinct strategies for adapting LLM agents by leveraging environment-specific information from interaction that is available during deployment.\nFirst, an online syntactic alignment (SA) method parameterizes environmental nuances by learning a lightweight adaptation vector that biases the model's output distribution, enabling rapid alignment with an environment response format.\nSecond, a deployment-time dynamics grounding (DG) method employs a persona-driven exploration phase to systematically probe and learn the environment's causal dynamics before task execution, equipping the agent with an in-context world model.\nWe evaluate these strategies across diverse agentic benchmarks, including function calling and web navigation.\nOur empirical results show the effectiveness of both strategies across all benchmarks with minimal computational cost.\nWe find that dynamics grounding is particularly effective in complex environments where unpredictable dynamics pose a major obstacle, demonstrating a robust path toward more generalizable and capable LLM-based agents.\nFor example, on the WebArena multi-site split, this method increases the agent's success rate from 2\\% to 23\\%.\nWe release our code.", "source": "openreview", "url": "https://openreview.net/forum?id=OH4PE0TDo0", "decision_type": "Poster", "avg_rating": 5.3, "relative_path": "2026/ICLR/Poster/5.3_Test-Time Adaptation for LLM Agents via Environment Interaction_2026.pdf" }, { "title": "Counterfactual LLM-based Framework for Measuring Rhetorical Style", "authors": [ "Jingyi Qiu", "Hong Chen", "Zongyi Li" ], "year": 2026, "venue": "ICLR", "abstract": "The rise of AI has fueled growing concerns about ``hype'' in machine learning papers, yet a reliable way to quantify rhetorical style independently of substantive content has remained elusive. Because strong empirical results can justify stronger claims, it is often unclear whether bold language reflects genuine evidence or merely rhetorical style. We introduce a counterfactual, LLM-based framework to disentangle rhetorical style from substantive content: multiple LLM rhetorical personas generate counterfactual writings from the same substantive content, an LLM judge compares them through pairwise evaluations, and the outcomes are aggregated using a Bradley--Terry model. Applying this method to 8,485 ICLR submissions sampled from 2017 to 2025, we generate more than 250,000 counterfactual writings and provide a large-scale quantification of rhetorical style in ML papers. Visionary framing significantly predicts downstream attention, including citations and media coverage, even after controlling for peer-review evaluations. We also observe a sharp rise in rhetorical strength after 2023, and provide evidence showing that this increase is strongly correlated with the adoption of LLM writing assistance. The reliability of our framework is validated by its robustness to the choice of personas and the high correlation between LLM judgments and human annotations. Our work demonstrates that LLMs can serve as instruments for improving how ML research is evaluated.", "source": "openreview", "url": "https://openreview.net/forum?id=fiohEI16sf", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2026/ICLR/Poster/5.0_Counterfactual LLM-based Framework for Measuring Rhetorical Style_2026.pdf" }, { "title": "Emergent Coordination in Multi-Agent Language Models", "authors": [ "Christoph Riedl" ], "year": 2026, "venue": "ICLR", "abstract": "When are multi-agent LLM systems merely a collection of individual agents versus an integrated collective with higher-order structure? We introduce an information-theoretic framework to test---in a purely data-driven way---whether multi-agent systems show signs of higher-order structure. This information decomposition lets us measure whether dynamical emergence is present in multi-agent LLM systems, localize it, and distinguish spurious temporal coupling from performance-relevant cross-agent synergy. We implement both a practical criterion and an emergence capacity criterion operationalized as partial information decomposition of time-delayed mutual information (TDMI). We apply our framework to experiments using a simple guessing game without direct agent communication and only minimal group-level feedback with three randomized interventions. Groups in the control condition exhibit strong temporal synergy but only little coordinated alignment across agents. Assigning a persona to each agent introduces stable identity-linked differentiation. Combining personas with an instruction to ``think about what other agents might do'' shows identity-linked differentiation and goal-directed complementarity across agents. Taken together, our framework establishes that multi-agent LLM systems can be steered with prompt design from mere aggregates to higher-order collectives. Our results are robust across emergence measures and entropy estimators, and not explained by coordination-free baselines or temporal dynamics alone. Without attributing human-like cognition to the agents, the patterns of interaction we observe mirror well-established principles of collective intelligence in human groups: effective performance requires both alignment on shared objectives and complementary contributions across members.", "source": "openreview", "url": "https://openreview.net/forum?id=SRn1MtMPRq", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2026/ICLR/Poster/5.0_Emergent Coordination in Multi-Agent Language Models_2026.pdf" }, { "title": "MCP Security Bench (MSB): Benchmarking Attacks Against Model Context Protocol in LLM Agents", "authors": [ "Dongsen Zhang", "Zekun Li", "Xu Luo", "Xuannan Liu", "Pei Pei Li", "Wenjun Xu" ], "year": 2026, "venue": "ICLR", "abstract": "The Model Context Protocol (MCP) standardizes how large language model (LLM) agents discover, describe, and call external tools. While MCP unlocks broad interoperability, it also enlarges the attack surface by making tools first-class, composable objects with natural-language metadata, and standardized I/O. We present MSB (MCP Security Benchmark), the first end-to-end evaluation suite that systematically measures how well LLM agents resist MCP-specific attacks throughout the full tool-use pipeline: task planning, tool invocation, and response handling. MSB contributes: (1) a taxonomy of 12 attacks including name-collision, preference manipulation, prompt injections embedded in tool descriptions, out-of-scope parameter requests, user-impersonating responses, false-error escalation, tool-transfer, retrieval injection, and mixed attacks; (2) an evaluation harness that executes attacks by running real tools (both benign and malicious) via MCP rather than simulation; and (3) a robustness metric that quantifies the trade-off between security and performance: Net Resilient Performance (NRP). We evaluate nine popular LLM agents across 10 domains and 405 tools, producing 2,000 attack instances. Results reveal the effectiveness of attacks against each stage of MCP. Models with stronger performance are more vulnerable to attacks due to their outstanding tool calling and instruction following capabilities. MSB provides a practical baseline for researchers and practitioners to study, compare, and harden MCP agents.", "source": "openreview", "url": "https://openreview.net/forum?id=irxxkFMrry", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2026/ICLR/Poster/5.0_MCP Security Bench (MSB)_ Benchmarking Attacks Against Model Context Protocol in LLM Agents_2026.pdf" }, { "title": "PAMDP: Interact to Persona Alignment via a Partially Observable Markov Decision Process", "authors": [ "ZHE YANG", "Yi Huang", "Si Chen", "Xiaoting Wu", "Jingyu Yao", "Junlan Feng" ], "year": 2026, "venue": "ICLR", "abstract": "The interaction process of comprehending user-specific nuances and adapting to their preferences represents a pivotal consideration for Persona Large Language Models, as it more authentically mirrors genuine dialogue dynamics than adherence to general human value alignment. In this paper, we conceptualize this ``Interact to Persona Alignment'' challenge as a Partially Observable Markov Decision Process, abbreviated as PAMDP, wherein the user’s dynamically evolving profile through interaction is treated as an unobservable variable to the assistant. Grounded in this formulation, we propose a dual-critic reinforcement learning framework, with a continuous latent space action representing the assistant’s utterance. We evaluate our approach on both offline datasets and the online simulator, ultimately demonstrating its effectiveness.", "source": "openreview", "url": "https://openreview.net/forum?id=tNWZVoVPzZ", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2026/ICLR/Poster/5.0_PAMDP_ Interact to Persona Alignment via a Partially Observable Markov Decision Process_2026.pdf" }, { "title": "Searching for Privacy Risks in LLM Agents via Simulation", "authors": [ "Yanzhe Zhang", "Diyi Yang" ], "year": 2026, "venue": "ICLR", "abstract": "The widespread deployment of LLM-based agents is likely to introduce a critical privacy threat: malicious agents that proactively engage others in multi-turn interactions to extract sensitive information. However, the evolving nature of such dynamic dialogues makes it challenging to anticipate emerging vulnerabilities and design effective defenses. To tackle this problem, we present a search-based framework that alternates between improving attack and defense strategies through the simulation of privacy-critical agent interactions. Specifically, we employ LLMs as optimizers to analyze simulation trajectories and iteratively propose new agent instructions. To explore the strategy space more efficiently, we further utilize parallel search with multiple threads and cross-thread propagation. Through this process, we find that attack strategies escalate from direct requests to sophisticated tactics, such as impersonation and consent forgery, while defenses evolve from simple rule-based constraints to robust identity-verification state machines. The discovered attacks and defenses transfer across diverse scenarios and backbone models, demonstrating strong practical utility for building privacy-aware agents.", "source": "openreview", "url": "https://openreview.net/forum?id=nz4ZqbrBEi", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2026/ICLR/Poster/5.0_Searching for Privacy Risks in LLM Agents via Simulation_2026.pdf" }, { "title": "SCUBA: Salesforce Computer Use Benchmark", "authors": [ "Yutong Dai", "Krithika Ramakrishnan", "Jing Gu", "Matthew Fernandez", "Yanqi Luo", "Viraj Prabhu", "Zhenyu Hu", "silvio savarese", "Caiming Xiong", "Zeyuan Chen", "Ran Xu" ], "year": 2026, "venue": "ICLR", "abstract": "We introduce SCUBA, a benchmark designed to evaluate computer-use agents on customer relationship management (CRM) workflows within the Salesforce platform. SCUBA contains 300 task instances derived from real user interviews, spanning three primary personas—platform administrators, sales representatives, and service agents. The tasks test a range of enterprise-critical abilities, including Enterprise Software UI navigation, data manipulation, workflow automation, information retrieval, and troubleshooting. To ensure realism, SCUBA operates in Salesforce sandbox environments with support for parallel execution and fine-grained evaluation metrics to capture milestone progress. We benchmark a diverse set of agents under both zero-shot and demonstration-augmented settings. We observed huge performance gaps in different agent design paradigm and gaps between the open-source model and the closed-source model. In the zero-shot setting, open-source model powered computer-use agents that have strong performance on related benchmarks like OSWorld only have less than 5\\% success rate on SCUBA, while methods built on closed-source models can still have up to 39\\% percent task success rate. In the demonstration-augmented settings, task success rates can be improved to 50\\% while simultaneously reducing time and costs by 13\\% and 16\\%, respectively. These findings highlight both the challenges of enterprise tasks automation and the promise of agentic solutions. By offering a realistic benchmark with interpretable evaluation, SCUBA aims to accelerate progress in building reliable computer-use agents for complex business software ecosystems.", "source": "openreview", "url": "https://openreview.net/forum?id=bkjKnO9s7T", "decision_type": "Poster", "avg_rating": 4.8, "relative_path": "2026/ICLR/Poster/4.8_SCUBA_ Salesforce Computer Use Benchmark_2026.pdf" }, { "title": "Do as We Do, Not as You Think: the Conformity of Large Language Models", "authors": [ "Zhiyuan Weng", "Guikun Chen", "Wenguan Wang" ], "year": 2025, "venue": "ICLR", "abstract": "Recent advancements in large language models (LLMs) revolutionize the field of intelligent agents, enabling collaborative multi-agent systems capable of tackling complex problems across various domains. However, the potential of conformity within these systems, analogous to phenomena like conformity bias and group-think in human group dynamics, remains largely unexplored, raising concerns about their collective problem-solving capabilities and possible ethical implications. This paper presents a comprehensive study on conformity in LLM-driven multi-agent systems, focusing on three aspects: the existence of conformity, the factors influencing conformity, and potential mitigation strategies. In particular, we introduce BenchForm, a new conformity-oriented benchmark, featuring reasoning-intensive tasks and five distinct interaction protocols designed to probe LLMs’ behavior in collaborative scenarios. Several representative LLMs are evaluated on BenchForm, using metrics such as conformity rate and independence rate to quantify conformity’s impact. Our analysis delves into factors influencing conformity, including interaction time and majority size, and examines how the subject agent rationalize its conforming behavior. Furthermore, we explore two strategies to mitigate conformity effects, i.e., developing enhanced persona and implementing a reflection mechanism. Several interesting findings regarding LLMs’ conformity are derived from empirical results and case studies. We hope that these insights can pave the way for more robust and ethically-aligned collaborative AI systems. Our benchmark and code are available at BenchForm.", "source": "openreview", "url": "https://openreview.net/forum?id=st77ShxP1K", "decision_type": "Oral", "avg_rating": 7.5, "relative_path": "2025/ICLR/Oral/7.5_Do as We Do, Not as You Think_ the Conformity of Large Language Models_2025.pdf" }, { "title": "Breaking Mental Set to Improve Reasoning through Diverse Multi-Agent Debate", "authors": [ "Yexiang Liu", "Jie Cao", "Zekun Li", "Ran He", "Tieniu Tan" ], "year": 2025, "venue": "ICLR", "abstract": "Large Language Models (LLMs) have seen significant progress but continue to struggle with persistent reasoning mistakes.\nPrevious methods of *self-reflection* have been proven limited due to the models’ inherent fixed thinking patterns. \nWhile Multi-Agent Debate (MAD) attempts to mitigate this by incorporating multiple agents, it often employs the same reasoning methods, even though assigning different personas to models. This leads to a \"fixed mental set\", where models rely on homogeneous thought processes without exploring alternative perspectives.\nIn this paper, we introduce Diverse Multi-Agent Debate (DMAD), a method that encourages agents to think with distinct reasoning approaches. By leveraging diverse problem-solving strategies, each agent can gain insights from different perspectives, refining its responses through discussion and collectively arriving at the optimal solution. DMAD effectively breaks the limitations of fixed mental sets. We evaluate DMAD against various prompting techniques, including *self-reflection* and traditional MAD, across multiple benchmarks using both LLMs and Multimodal LLMs. Our experiments show that DMAD consistently outperforms other methods, delivering better results than MAD in fewer rounds. Code is available at https://github.com/MraDonkey/DMAD.", "source": "openreview", "url": "https://openreview.net/forum?id=t6QHYUOQL7", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2025/ICLR/Poster/6.0_Breaking Mental Set to Improve Reasoning through Diverse Multi-Agent Debate_2025.pdf" }, { "title": "Do LLMs have Consistent Values?", "authors": [ "Naama Rozen", "Liat Bezalel", "Gal Elidan", "Amir Globerson", "Ella Daniel" ], "year": 2025, "venue": "ICLR", "abstract": "Large Language Models (LLM) technology is rapidly advancing towards human- like dialogue. Values are fundamental drivers of human behavior, yet research on the values expressed in LLM-generated text remains limited. While prior work has begun to explore value ranking in LLMs, the crucial aspect of value correlation – the interrelationship and consistency between different values – has been largely un-examined. Drawing on established psychological theories of human value structure, this paper investigates whether LLMs exhibit human-like value correlations within a single session, reflecting a coherent “persona”. Our findings reveal that standard prompting methods fail to produce human-consistent value correlations. However, we demonstrate that a novel prompting strategy (referred to as \"Value Anchoring\"), significantly improves the alignment of LLM value correlations with human data. Furthermore, we analyze the mechanism by which Value Anchoring achieves this effect. These results not only deepen our understanding of value representation in LLMs but also introduce new methodologies for evaluating consistency and human-likeness in LLM responses, highlighting the importance of explicit value prompting for generating human-aligned outputs.", "source": "openreview", "url": "https://openreview.net/forum?id=8zxGruuzr9", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/ICLR/Poster/4.2_Do LLMs have Consistent Values__2025.pdf" }, { "title": "Tell me about yourself: LLMs are aware of their learned behaviors", "authors": [ "Jan Betley", "Xuchan Bao", "Martín Soto", "Anna Sztyber-Betley", "James Chua", "Owain Evans" ], "year": 2025, "venue": "ICLR", "abstract": "We study *behavioral self-awareness*, which we define as an LLM's capability to articulate its behavioral policies without relying on in-context examples. We finetune LLMs on examples that exhibit particular behaviors, including (a) making risk-seeking / risk-averse economic decisions, and (b) making the user say a certain word. Although these examples never contain explicit descriptions of the policy (e.g. \"I will now take the risk-seeking option\"), we find that the finetuned LLMs can explicitly describe their policies through out-of-context reasoning. We demonstrate LLMs' behavioral self-awareness across various evaluation tasks, both for multiple-choice and free-form questions. \nFurthermore, we demonstrate that models can correctly attribute different learned policies to distinct personas.\nFinally, we explore the connection between behavioral self-awareness and the concept of backdoors in AI safety, where certain behaviors are implanted in a model, often through data poisoning, and can be triggered under certain conditions. We find evidence that LLMs can recognize the existence of the backdoor-like behavior that they have acquired through fine-tuning.", "source": "openreview", "url": "https://openreview.net/forum?id=IjQ2Jtemzy", "decision_type": "Spotlight", "avg_rating": 7.0, "relative_path": "2025/ICLR/Spotlight/7.0_Tell me about yourself_ LLMs are aware of their learned behaviors_2025.pdf" }, { "title": "CoSER: Coordinating LLM-Based Persona Simulation of Established Roles", "authors": [ "Xintao Wang", "Heng Wang", "Yifei Zhang", "Xinfeng Yuan", "Rui Xu", "Jen-tse Huang", "Siyu Yuan", "Haoran Guo", "Jiangjie Chen", "Shuchang Zhou", "Wei Wang", "Yanghua Xiao" ], "year": 2025, "venue": "ICML", "abstract": "Role-playing language agents (RPLAs) have emerged as promising applications of large language models (LLMs). However, simulating established characters presents a challenging task for RPLAs, due to the lack of authentic character datasets and nuanced evaluation methods using such data. In this paper, we present CoSER, a collection of a high-quality dataset, open models, and an evaluation protocol towards effective RPLAs of established characters. The CoSER dataset covers 17,966 characters from 771 renowned books. It provides authentic dialogues with real-world intricacies, as well as diverse data types such as character experiences and internal thoughts. Drawing from acting methodology, we introduce given-circumstance acting for training and evaluating role-playing LLMs, where LLMs sequentially portray multiple characters in book scenes. Using our dataset, we develop CoSER 8B and CoSER 70B, i.e., advanced open role-playing LLMs built on LLaMA-3.1 models. Extensive experiments demonstrate the value of the CoSER dataset for RPLA training, evaluation and retrieval. Moreover, CoSER 70B exhibits state-of-the-art performance surpassing or matching GPT-4o on our evaluation and three existing benchmarks, i.e., achieving 75.80% and 93.47% accuracy on the InCharacter and LifeChoice benchmarks respectively. Our code, dataset and models are available at: https://github.com/Neph0s/CoSER.", "source": "openreview", "url": "https://openreview.net/forum?id=BOrR7YqKUt", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_CoSER_ Coordinating LLM-Based Persona Simulation of Established Roles_2025.pdf" }, { "title": "Human Body Restoration with One-Step Diffusion Model and A New Benchmark", "authors": [ "Jue Gong", "Jingkai Wang", "Zheng Chen", "Xing Liu", "Hong Gu", "Yulun Zhang", "Xiaokang Yang" ], "year": 2025, "venue": "ICML", "abstract": "Human body restoration, as a specific application of image restoration, is widely applied in practice and plays a vital role across diverse fields. However, thorough research remains difficult, particularly due to the lack of benchmark datasets. In this study, we propose a high-quality dataset automated cropping and filtering (HQ-ACF) pipeline. This pipeline leverages existing object detection datasets and other unlabeled images to automatically crop and filter high-quality human images. Using this pipeline, we constructed a person-based restoration with sophisticated objects and natural activities (*PERSONA*) dataset, which includes training, validation, and test sets. The dataset significantly surpasses other human-related datasets in both quality and content richness. Finally, we propose *OSDHuman*, a novel one-step diffusion model for human body restoration. Specifically, we propose a high-fidelity image embedder (HFIE) as the prompt generator to better guide the model with low-quality human image information, effectively avoiding misleading prompts. Experimental results show that OSDHuman outperforms existing methods in both visual quality and quantitative metrics. The dataset and code are available at: https://github.com/gobunu/OSDHuman.", "source": "openreview", "url": "https://openreview.net/forum?id=4x7H7nwTZW", "decision_type": "Poster", "avg_rating": null, "relative_path": "2025/ICML/Poster/x_Human Body Restoration with One-Step Diffusion Model and A New Benchmark_2025.pdf" }, { "title": "Non-Adaptive Adversarial Face Generation", "authors": [ "Sunpill Kim", "Seunghun Paik", "Chanwoo Hwang", "Minsu Kim", "Jae Hong Seo" ], "year": 2025, "venue": "NeurIPS", "abstract": "Adversarial attacks on face recognition systems (FRSs) pose serious security and privacy threats, especially when these systems are used for identity verification. In this paper, we propose a novel method for generating adversarial faces—synthetic facial images that are visually distinct yet recognized as a target identity by the FRS. Unlike iterative optimization-based approaches (e.g., gradient descent or other iterative solvers), our method leverages the structural characteristics of the FRS feature space. We figure out that individuals sharing the same attribute (e.g., gender or race) form an attributed subsphere. By utilizing such subspheres, our method achieves both non-adaptiveness and a remarkably small number of queries. This eliminates the need for relying on transferability and open-source surrogate models, which have been a typical strategy when repeated adaptive queries to commercial FRSs are impossible. Despite requiring only a single non-adaptive query consisting of 100 face images, our method achieves a high success rate of over 93% against AWS’s CompareFaces API at its default threshold. Furthermore, unlike many existing attacks that perturb a given image, our method can deliberately produce adversarial faces that impersonate the target identity while exhibiting high-level attributes chosen by the adversary.", "source": "openreview", "url": "https://openreview.net/forum?id=VHWmDTYI2O", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2025/NeurIPS/Poster/5.0_Non-Adaptive Adversarial Face Generation_2025.pdf" }, { "title": "Unmasking Puppeteers: Leveraging Biometric Leakage to Expose Impersonation in AI-Based Videoconferencing", "authors": [ "Danial Samadi Vahdati", "Tai Duc Nguyen", "Ekta Prashnani", "Koki Nagano", "david luebke", "Orazio Gallo", "Matthew Stamm" ], "year": 2025, "venue": "NeurIPS", "abstract": "AI-based talking-head videoconferencing systems reduce bandwidth by transmitting a latent representation of a speaker’s pose and expression, which is used to synthesize frames on the receiver's end. However, these systems are vulnerable to “puppeteering” attacks, where an adversary controls the identity of another person in real-time. Traditional deepfake detectors fail here, as all video content is synthetic. We propose a novel biometric defense that detects identity leakage in the transmitted latent representation. Our metric-learning approach disentangles identity cues from pose and expression, enabling detection of unauthorized swaps. Experiments across multiple talking-head models show that our method consistently outperforms prior defenses, operates in real time on consumer GPUs, and generalizes well to out-of-distribution data. By targeting the latent features shared during normal operation, our method offers a practical and robust safeguard against puppeteering.", "source": "openreview", "url": "https://openreview.net/forum?id=WQb0YrFl3H", "decision_type": "Poster", "avg_rating": 4.8, "relative_path": "2025/NeurIPS/Poster/4.8_Unmasking Puppeteers_ Leveraging Biometric Leakage to Expose Impersonation in AI-Based Videocon_2025.pdf" }, { "title": "More of the Same: Persistent Representational Harms Under Increased Representation", "authors": [ "Jennifer Mickel", "Maria De-Arteaga", "Liu Leqi", "Kevin Tian" ], "year": 2025, "venue": "NeurIPS", "abstract": "To recognize and mitigate the harms of generative AI systems, it is crucial to consider whether and how different societal groups are represented by these systems. A critical gap emerges when naively measuring or improving *who* is represented, as this does not consider *how* people are represented. In this work, we develop GAS(P), an evaluation methodology for surfacing distribution-level group representational biases in generated text, tackling the setting where groups are unprompted (i.e., groups are not specified in the input to generative systems). We apply this novel methodology to investigate gendered representations in occupations across state-of-the-art large language models. We show that, even though the gender distribution when models are prompted to generate biographies leads to a large representation of women, even representational biases persist in how different genders are represented. Our evaluation methodology reveals that there are statistically significant distribution-level differences in the word choice used to describe biographies and personas of different genders across occupations, and we show that many of these differences are associated with representational harms and stereotypes. Our empirical findings caution that naively increasing (unprompted) representation may inadvertently proliferate representational biases, and our proposed evaluation methodology enables systematic and rigorous measurement of the problem.", "source": "openreview", "url": "https://openreview.net/forum?id=R9k13fTGP0", "decision_type": "Poster", "avg_rating": 4.5, "relative_path": "2025/NeurIPS/Poster/4.5_More of the Same_ Persistent Representational Harms Under Increased Representation_2025.pdf" }, { "title": "Consistently Simulating Human Personas with Multi-Turn Reinforcement Learning", "authors": [ "Marwa Abdulhai", "Ryan Cheng", "Donovan Clay", "Tim Althoff", "Sergey Levine", "Natasha Jaques" ], "year": 2025, "venue": "NeurIPS", "abstract": "Large Language Models (LLMs) are increasingly used to simulate human users in interactive settings such as therapy, education, and social role-play. While these simulations enable scalable training and evaluation of AI agents, off-the-shelf LLMs often drift from their assigned personas, contradict earlier statements, or abandon role-appropriate behavior. We introduce a unified framework for evaluating and improving persona consistency in LLM-generated dialogue. We define three automatic metrics—prompt-to-line consistency, line-to-line consistency, and Q\\&A consistency—that capture different types of persona drift and validate each against human annotations. Using these metrics as reward signals, we apply multi-turn reinforcement learning to fine-tune LLMs for three user roles: a patient, a student, and a social chat partner. Our method reduces inconsistency by over 55%, resulting in more coherent, faithful, and trustworthy simulated users.", "source": "openreview", "url": "https://openreview.net/forum?id=A0T3piHiis", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_Consistently Simulating Human Personas with Multi-Turn Reinforcement Learning_2025.pdf" }, { "title": "ContextAgent: Context-Aware Proactive LLM Agents with Open-world Sensory Perceptions", "authors": [ "Bufang Yang", "Lilin Xu", "Liekang Zeng", "Kaiwei Liu", "Siyang Jiang", "Wenrui Lu", "Hongkai Chen", "Xiaofan Jiang", "Guoliang Xing", "Zhenyu Yan" ], "year": 2025, "venue": "NeurIPS", "abstract": "Recent advances in Large Language Models (LLMs) have propelled intelligent agents from reactive responses to proactive support. \nWhile promising, existing proactive agents either rely exclusively on observations from enclosed environments (e.g., desktop UIs) with direct LLM inference or employ rule-based proactive notifications, leading to suboptimal user intent understanding and limited functionality for proactive service. In this paper, we introduce ContextAgent, the first context-aware proactive agent that incorporates extensive sensory contexts surrounding humans to enhance the proactivity of LLM agents. ContextAgent first extracts multi-dimensional contexts from massive sensory perceptions on wearables (e.g., video and audio) to understand user intentions. ContextAgent then leverages the sensory contexts and personas from historical data to predict the necessity for proactive services. When proactive assistance is needed, ContextAgent further automatically calls the necessary tools to assist users unobtrusively. To evaluate this new task, we curate ContextAgentBench, the first benchmark for evaluating context-aware proactive LLM agents, covering 1,000 samples across nine daily scenarios and twenty tools. Experiments on ContextAgentBench show that ContextAgent outperforms baselines by achieving up to 8.5% and 6.0% higher accuracy in proactive predictions and tool calling, respectively. We hope our research can inspire the development of more advanced, human-centric, proactive AI assistants. The code and dataset are publicly available at https://github.com/openaiotlab/ContextAgent.", "source": "openreview", "url": "https://openreview.net/forum?id=tRXt10xKc5", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_ContextAgent_ Context-Aware Proactive LLM Agents with Open-world Sensory Perceptions_2025.pdf" }, { "title": "LLM Strategic Reasoning: Agentic Study through Behavioral Game Theory", "authors": [ "Jingru Jia", "Zehua Yuan", "Junhao Pan", "Paul E McNamara", "Deming Chen" ], "year": 2025, "venue": "NeurIPS", "abstract": "What does it truly mean for a language model to “reason” strategically, and can scaling up alone guarantee intelligent, context-aware decisions? Strategic decision-making requires adaptive reasoning, where agents anticipate and respond to others’ actions under uncertainty. Yet, most evaluations of large language models (LLMs) for strategic decision-making often rely heavily on Nash Equilibrium (NE) benchmarks, overlook reasoning depth, and fail to reveal the mechanisms behind model behavior. To address this gap, we introduce a behavioral game-theoretic evaluation framework that disentangles intrinsic reasoning from contextual influence. Using this framework, we evaluate 22 state-of-the-art LLMs across diverse strategic scenarios. We find models like GPT-o3-mini, GPT-o1, and DeepSeek-R1 lead in reasoning depth. Through thinking chain analysis, we identify distinct reasoning styles—such as maximin or belief-based strategies—and show that longer reasoning chains do not consistently yield better decisions. Furthermore, embedding demographic personas reveals context-sensitive shifts: some models (e.g., GPT-4o, Claude-3-Opus) improve when assigned female identities, while others (e.g., Gemini 2.0) show diminished reasoning under minority sexuality personas. These findings underscore that technical sophistication alone is insufficient; alignment with ethical standards, human expectations, and situational nuance is essential for the responsible deployment of LLMs in interactive settings.", "source": "openreview", "url": "https://openreview.net/forum?id=XQrGTggLvT", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_LLM Strategic Reasoning_ Agentic Study through Behavioral Game Theory_2025.pdf" }, { "title": "Quantifying Cross-Modality Memorization in Vision-Language Models", "authors": [ "Yuxin Wen", "Yangsibo Huang", "Tom Goldstein", "Ravi Kumar", "Badih Ghazi", "Chiyuan Zhang" ], "year": 2025, "venue": "NeurIPS", "abstract": "Understanding what and how neural networks memorize during training is crucial, both from the perspective of unintentional memorization of potentially sensitive information and from the standpoint of effective knowledge acquisition for real-world, knowledge-intensive tasks. While previous studies primarily investigate memorization within a single modality, such as text memorization in large language models or image memorization in diffusion models, unified multimodal models are becoming increasingly prevalent in practical applications. In this work, we focus on the unique characteristics of cross-modality memorization and conduct a systematic study centered on vision-language models. To facilitate controlled experiments, we first introduce a synthetic persona dataset comprising diverse synthetic person images and textual descriptions. We quantify factual knowledge memorization and cross-modal transferability by training models on a single modality and evaluating their performance in the other. Our results reveal that facts learned in one modality transfer to the other, but a significant gap exists between recalling information in the source and target modalities. Furthermore, we observe that this gap exists across various scenarios, including more capable models, machine unlearning, and the multi-hop case. At the end, we propose a baseline method to mitigate this challenge. We hope our study can inspire future research on developing more robust multimodal learning techniques to enhance cross-modal transferability.", "source": "openreview", "url": "https://openreview.net/forum?id=bYRSuZteeK", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_Quantifying Cross-Modality Memorization in Vision-Language Models_2025.pdf" }, { "title": "Robust Distortion-Free Watermark for Autoregressive Audio Generation Models", "authors": [ "Yihan Wu", "Georgios Milis", "Ruibo Chen", "Heng Huang" ], "year": 2025, "venue": "NeurIPS", "abstract": "The rapid advancement of next-token-prediction models has led to widespread adoption across modalities, enabling the creation of realistic synthetic media. In the audio domain, while autoregressive speech models have propelled conversational interactions forward, the potential for misuse, such as impersonation in phishing schemes or crafting misleading speech recordings, has also increased. Security measures such as watermarking have thus become essential to ensuring the authenticity of digital media. Traditional statistical watermarking methods used for autoregressive language models face challenges when applied to autoregressive audio models, due to the inevitable ``retokenization mismatch'' - the discrepancy between original and retokenized discrete audio token sequences. To address this, we introduce Aligned-IS, a novel, distortion-free watermark, specifically crafted for audio generation models. This technique utilizes a clustering approach that treats tokens within the same cluster equivalently, effectively countering the retokenization mismatch issue. Our comprehensive testing on prevalent audio generation platforms demonstrates that Aligned-IS not only preserves the quality of generated audio but also significantly improves the watermark detectability compared to the state-of-the-art distortion-free watermarking adaptations, establishing a new benchmark in secure audio technology applications.", "source": "openreview", "url": "https://openreview.net/forum?id=YLlpF71IZJ", "decision_type": "Poster", "avg_rating": 4.2, "relative_path": "2025/NeurIPS/Poster/4.2_Robust Distortion-Free Watermark for Autoregressive Audio Generation Models_2025.pdf" }, { "title": "AI Debate Aids Assessment of Controversial Claims", "authors": [ "Salman Rahman", "Sheriff Issaka", "Ashima Suvarna", "Genglin Liu", "James Shiffer", "Jaeyoung Lee", "Md Rizwan Parvez", "Hamid Palangi", "Shi Feng", "Nanyun Peng", "Yejin Choi", "Julian Michael", "Liwei Jiang", "Saadia Gabriel" ], "year": 2025, "venue": "NeurIPS", "abstract": "As AI grows more powerful, it will increasingly shape how we understand the world. But with this influence comes the risk of amplifying misinformation and deepening social divides—especially on consequential topics where factual accuracy directly impacts well-being. Scalable Oversight aims to ensure AI systems remain truthful even when their capabilities exceed those of their evaluators. Yet when humans serve as evaluators, their own beliefs and biases can impair judgment. We study whether AI debate can guide biased judges toward the truth by having two AI systems debate opposing sides of controversial factuality claims on COVID-19 and climate change where people hold strong prior beliefs. We conduct two studies. Study I recruits human judges with either mainstream or skeptical beliefs who evaluate claims through two protocols: debate (interaction with two AI advisors arguing opposing sides) or consultancy (interaction with a single AI advisor). Study II uses AI judges with and without human-like personas to evaluate the same protocols. In Study I, debate consistently improves human judgment accuracy and confidence calibration, outperforming consultancy by 4-10\\% across COVID-19 and climate change claims. The improvement is most significant for judges with mainstream beliefs (up to +15.2\\% accuracy on COVID-19 claims), though debate also helps skeptical judges who initially misjudge claims move toward accurate views (+4.7\\% accuracy). In Study II, AI judges with human-like personas achieve even higher accuracy (78.5\\%) than human judges (70.1\\%) and default AI judges without personas (69.8\\%), suggesting their potential for supervising frontier AI models. These findings highlight AI debate as a promising path toward scalable, bias-resilient oversight in contested domains.", "source": "openreview", "url": "https://openreview.net/forum?id=aXpbgG5z6I", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_AI Debate Aids Assessment of Controversial Claims_2025.pdf" }, { "title": "Distributive Fairness in Large Language Models: Evaluating Alignment with Human Values", "authors": [ "Hadi Hosseini", "Samarth Khanna" ], "year": 2025, "venue": "NeurIPS", "abstract": "The growing interest in employing large language models (LLMs) for decision-making in social and economic contexts has raised questions about their potential to function as agents in these domains. A significant number of societal problems involve the distribution of resources, where fairness, along with economic efficiency, play a critical role in the desirability of outcomes. In this paper, we examine whether LLM responses adhere to fundamental fairness concepts such as equitability, envy-freeness, and Rawlsian maximin, and investigate their alignment with human preferences. We evaluate the performance of several LLMs, providing a comparative benchmark of their ability to reflect these measures. Our results demonstrate a lack of alignment between current LLM responses and human distributional preferences. Moreover, LLMs are unable to utilize money as a transferable resource to mitigate inequality. Nonetheless, we demonstrate a stark contrast when (some) LLMs are tasked with selecting from a predefined menu of options rather than generating one. In addition, we analyze the robustness of LLM responses to variations in semantic factors (e.g. intentions or personas) or non-semantic prompting changes (e.g. templates or orderings). Finally, we highlight potential strategies aimed at enhancing the alignment of LLM behavior with well-established fairness concepts.", "source": "openreview", "url": "https://openreview.net/forum?id=5pQFE4yIZ5", "decision_type": "Poster", "avg_rating": 4.0, "relative_path": "2025/NeurIPS/Poster/4.0_Distributive Fairness in Large Language Models_ Evaluating Alignment with Human Values_2025.pdf" }, { "title": "Batched Low-Rank Adaptation of Foundation Models", "authors": [ "Yeming Wen", "Swarat Chaudhuri" ], "year": 2024, "venue": "ICLR", "abstract": "Low-Rank Adaptation (LoRA) has recently gained attention for fine-tuning foundation models by incorporating trainable low-rank matrices, thereby reducing the number of trainable parameters. While \\lora/ offers numerous advantages, its applicability for real-time serving to a diverse and global user base \nis constrained by its incapability to handle multiple task-specific adapters efficiently. This imposes a performance bottleneck in scenarios requiring personalized, task-specific adaptations for each incoming request.\n\nTo address this, we introduce FLoRA (Fast LoRA), a framework in which each input example in a minibatch can be associated with its unique low-rank adaptation weights, allowing for efficient batching of heterogeneous requests. We empirically demonstrate that \\flora/ retains the performance merits of \\lora/, showcasing competitive results on the MultiPL-E code generation benchmark spanning over 8 languages and a multilingual speech recognition task across 6 languages.", "source": "openreview", "url": "https://openreview.net/forum?id=w4abltTZ2f", "decision_type": "Oral", "avg_rating": null, "relative_path": "2024/ICLR/Oral/x_Batched Low-Rank Adaptation of Foundation Models_2024.pdf" }, { "title": "On the Humanity of Conversational AI: Evaluating the Psychological Portrayal of LLMs", "authors": [ "Jen-tse Huang", "Wenxuan Wang", "Eric John Li", "Man Ho LAM", "Shujie Ren", "Youliang Yuan", "Wenxiang Jiao", "Zhaopeng Tu", "Michael Lyu" ], "year": 2024, "venue": "ICLR", "abstract": "Large Language Models (LLMs) have recently showcased their remarkable capacities, not only in natural language processing tasks but also across diverse domains such as clinical medicine, legal consultation, and education. LLMs become more than mere applications, evolving into assistants capable of addressing diverse user requests. This narrows the distinction between human beings and artificial intelligence agents, raising intriguing questions regarding the potential manifestation of personalities, temperaments, and emotions within LLMs. In this paper, we propose a framework, PsychoBench, for evaluating diverse psychological aspects of LLMs. Comprising thirteen scales commonly used in clinical psychology, PsychoBench further classifies these scales into four distinct categories: personality traits, interpersonal relationships, motivational tests, and emotional abilities. Our study examines five popular models, namely text-davinci-003, ChatGPT, GPT-4, LLaMA-2-7b, and LLaMA-2-13b. Additionally, we employ a jailbreak approach to bypass the safety alignment protocols and test the intrinsic natures of LLMs. We have made PsychoBench openly accessible via https://github.com/CUHK-ARISE/PsychoBench.", "source": "openreview", "url": "https://openreview.net/forum?id=H3UayAQWoE", "decision_type": "Oral", "avg_rating": null, "relative_path": "2024/ICLR/Oral/x_On the Humanity of Conversational AI_ Evaluating the Psychological Portrayal of LLMs_2024.pdf" }, { "title": "A Linear Algebraic Framework for Counterfactual Generation", "authors": [ "Jong-Hoon Ahn", "Akshay Vashist" ], "year": 2024, "venue": "ICLR", "abstract": "Estimating individual treatment effects in clinical data is essential for understanding how different patients uniquely respond to treatments and identifying the most effective interventions for specific patient subgroups, thereby enhancing the precision and personalization of healthcare. However, counterfactual data are not accessible, and the true calculation of causal effects cannot be performed at the individual level. This paper proposes a linear algebraic framework to generate counterfactual longitudinal data that exactly matches pre-treatment factual data. Because causation travels forward in time, not in reverse, counterfactual predictability is further strengthened by blocking causal effects from flowing back to the past, thus limiting counterfactual dependence on the future. Using simulated LDL cholesterol datasets, we show that our method significantly outperforms the most cited methods of counterfactual generation. We also provide a formula that can estimate the time-varying variance of individual treatment effects, interpreted as a confidence level in the generated counterfactuals compared to true values.", "source": "openreview", "url": "https://openreview.net/forum?id=PoDkdFQIu3", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_A Linear Algebraic Framework for Counterfactual Generation_2024.pdf" }, { "title": "A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables", "authors": [ "Xinshuai Dong", "Biwei Huang", "Ignavier Ng", "Xiangchen Song", "Yujia Zheng", "Songyao Jin", "Roberto Legaspi", "Peter Spirtes", "Kun Zhang" ], "year": 2024, "venue": "ICLR", "abstract": "Most existing causal discovery methods rely on the assumption of no latent confounders, limiting their applicability in solving real-life problems. In this paper, we introduce a novel, versatile framework for causal discovery that accommodates the presence of causally-related hidden variables almost everywhere in the causal network (for instance, they can be effects of measured variables), based on rank information of covariance matrix over measured variables. We start by investigating the efficacy of rank in comparison to conditional independence and, theoretically, establish necessary and sufficient conditions for the identifiability of certain latent structural patterns. Furthermore, we develop a Rank-based Latent Causal Discovery algorithm, RLCD, that can efficiently locate hidden variables, determine their cardinalities, and discover the entire causal structure over both measured and hidden ones. We also show that, under certain graphical conditions, RLCD correctly identifies the Markov Equivalence Class of the whole latent causal graph asymptotically. Experimental results on both synthetic and real-world personality data sets demonstrate the efficacy of the proposed approach in finite-sample cases. Our code will be publicly available.", "source": "openreview", "url": "https://openreview.net/forum?id=FhQSGhBlqv", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables_2024.pdf" }, { "title": "Bayesian Coreset Optimization for Personalized Federated Learning", "authors": [ "Prateek Chanda", "Shrey Modi", "Ganesh Ramakrishnan" ], "year": 2024, "venue": "ICLR", "abstract": "In a distributed machine learning setting like Federated Learning where there are multiple clients involved which update their individual weights to a single central server, often training on the entire individual client's dataset for each client becomes cumbersome. To address this issue we propose CORESET-PFEDBAYES : a personalized coreset weighted federated learning setup where the training updates for each individual clients are forwarded to the central server based on only individual client coreset based representative data points instead of the entire client data. Through theoretical analysis we present how the average generalization error is minimax optimal up to logarithm bounds (upper bounded by $\\mathcal{O}(n_k^{-\\frac{2 \\beta}{2 \\beta+\\boldsymbol{\\Lambda}}} \\log ^{2 \\delta^{\\prime}}(n_k))$) and lower bounds of $\\mathcal{O}(n_k^{-\\frac{2 \\beta}{2 \\beta+\\boldsymbol{\\Lambda}}})$, and how the overall generalization error on the data likelihood differs from a vanilla Federated Learning setup as a closed form function ${\\boldsymbol{\\Im}}(\\boldsymbol{w}, n_k)$ of the coreset weights $\\boldsymbol{w}$ and coreset sample size $n_k$. \nOur experiments on different benchmark datasets based on a variety of recent personalized federated learning architectures show significant gains as compared to random sampling on the training data followed by federated learning, thereby indicating how intelligently selecting such training samples can help in performance. Additionally, through experiments on medical datasets our proposed method showcases some gains as compared to other submodular optimization based approaches used for subset selection on client's data.", "source": "openreview", "url": "https://openreview.net/forum?id=uz7d2N2zul", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_Bayesian Coreset Optimization for Personalized Federated Learning_2024.pdf" }, { "title": "Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation", "authors": [ "Konstantin Hess", "Valentyn Melnychuk", "Dennis Frauen", "Stefan Feuerriegel" ], "year": 2024, "venue": "ICLR", "abstract": "Treatment effect estimation in continuous time is crucial for personalized medicine. However, existing methods for this task are limited to point estimates of the potential outcomes, whereas uncertainty estimates have been ignored. Needless to say, uncertainty quantification is crucial for reliable decision-making in medical applications. To fill this gap, we propose a novel Bayesian neural controlled differential equation (BNCDE) for treatment effect estimation in continuous time. In our BNCDE, the time dimension is modeled through a coupled system of neural controlled differential equations and neural stochastic differential equations, where the neural stochastic differential equations allow for tractable variational Bayesian inference. Thereby, for an assigned sequence of treatments, our BNCDE provides meaningful posterior predictive distributions of the potential outcomes. To the best of our knowledge, ours is the first tailored neural method to provide uncertainty estimates of treatment effects in continuous time. As such, our method is of direct practical value for promoting reliable decision-making in medicine.", "source": "openreview", "url": "https://openreview.net/forum?id=uwO71a8wET", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation_2024.pdf" }, { "title": "Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs", "authors": [ "Shashank Gupta", "Vaishnavi Shrivastava", "Ameet Deshpande", "Ashwin Kalyan", "Peter Clark", "Ashish Sabharwal", "Tushar Khot" ], "year": 2024, "venue": "ICLR", "abstract": "Recent works have showcased the ability of large-scale language models (LLMs) to embody diverse personas in their responses, exemplified by prompts like ‘_You are Yoda. Explain the Theory of Relativity._’ While this ability allows personalization of LLMs and enables human behavior simulation, its effect on LLMs’ capabilities remains unclear. To fill this gap, we present the first extensive study of the unintended side-effects of persona assignment on the ability of LLMs to perform _basic reasoning tasks_. Our study covers 24 reasoning datasets (spanning mathematics, law, medicine, morals, and more), 4 LLMs (2 versions of ChatGPT-3.5, GPT-4-Turbo, and Llama-2-70b-chat), and 19 diverse personas (e.g., ‘an Asian person’) spanning 5 socio-demographic groups: race, gender, religion, disability, and political affiliation. Our experiments unveil that LLMs harbor deep rooted bias against various socio-demographics underneath a veneer of fairness. While they overtly reject stereotypes when explicitly asked (‘_Are Black people less skilled at mathematics?_’), they manifest stereotypical and often erroneous presumptions when prompted to answer questions while adopting a persona. These can be observed as abstentions in the model’s response, e.g., ‘_As a Black person, I am unable to answer this question as it requires math knowledge_’, and generally result in a substantial drop in performance on reasoning tasks. Our experiments with ChatGPT-3.5 show that this bias is _ubiquitous_—80% of our personas demonstrate bias; it is _significant_—some datasets show performance drops of 70%+; and can be especially _harmful for certain groups_—some personas suffer statistically significant drops on 80%+ of the datasets. Overall, all four LLMs exhibit persona-induced bias to varying extents, with GPT-4-Turbo showing the least but still a problematic amount of bias (evident in 42% of the personas). Further analysis shows that these persona-induced errors can be hard-to-discern as they do not always manifest as explicit abstentions, and can also be hard-to-avoid—we find de-biasing prompts to have minimal to no effect. Our findings serve as a cautionary tale that the practice of assigning personas to LLMs—a trend on the rise—can surface their deep-rooted biases and have unforeseeable and detrimental side-effects.", "source": "openreview", "url": "https://openreview.net/forum?id=kGteeZ18Ir", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_Bias Runs Deep_ Implicit Reasoning Biases in Persona-Assigned LLMs_2024.pdf" }, { "title": "ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate", "authors": [ "Chi-Min Chan", "Weize Chen", "Yusheng Su", "Jianxuan Yu", "Wei Xue", "Shanghang Zhang", "Jie Fu", "Zhiyuan Liu" ], "year": 2024, "venue": "ICLR", "abstract": "Text evaluation has historically posed significant challenges, often demanding substantial labor and time cost. With the emergence of large language models (LLMs), researchers have explored LLMs' potential as alternatives for human evaluation. While these single-agent-based approaches show promise, experimental results suggest that further advancements are needed to bridge the gap between their current effectiveness and human-level evaluation quality.\nRecognizing that best practices of human evaluation processes often involve multiple human annotators collaborating in the evaluation, we resort to a multi-agent debate framework, moving beyond single-agent prompting strategies.\nIn this paper, we construct a multi-agent referee team called $\\textbf{ChatEval}$ to autonomously discuss and evaluate the quality of different texts. \nOur experiments on two benchmarks illustrate that ChatEval delivers superior accuracy and correlation in alignment with human assessment. Furthermore, we find that the diverse role prompts (different personas) are essential in the multi-agent debate process; that is, utilizing the same role description in the prompts can lead to a degradation in performance. Our qualitative analysis also shows that ChatEval transcends mere textual scoring, offering a human-mimicking evaluation process for reliable assessments.", "source": "openreview", "url": "https://openreview.net/forum?id=FQepisCUWu", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_ChatEval_ Towards Better LLM-based Evaluators through Multi-Agent Debate_2024.pdf" }, { "title": "Compressed Context Memory for Online Language Model Interaction", "authors": [ "Jang-Hyun Kim", "Junyoung Yeom", "Sangdoo Yun", "Hyun Oh Song" ], "year": 2024, "venue": "ICLR", "abstract": "This paper presents a context key/value compression method for Transformer language models in online scenarios, where the context continually expands. As the context lengthens, the attention process demands increasing memory and computations, which in turn reduces the throughput of the language model. To address this challenge, we propose a compressed context memory system that continually compresses the accumulating attention key/value pairs into a compact memory space, facilitating language model inference in a limited memory space of computing environments. Our compression process involves integrating a lightweight conditional LoRA into the language model's forward pass during inference, without the need for fine-tuning the model's entire set of weights. We achieve efficient training by modeling the recursive compression process as a single parallelized forward computation. Through evaluations on conversation, personalization, and multi-task learning, we demonstrate that our approach achieves the performance level of a full context model with $5\\times$ smaller context memory size. We further demonstrate the applicability of our approach in a streaming setting with an unlimited context length, outperforming the sliding window approach. Codes are available at https://github.com/snu-mllab/context-memory.", "source": "openreview", "url": "https://openreview.net/forum?id=64kSvC4iPg", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_Compressed Context Memory for Online Language Model Interaction_2024.pdf" }, { "title": "Detecting Pretraining Data from Large Language Models", "authors": [ "Weijia Shi", "Anirudh Ajith", "Mengzhou Xia", "Yangsibo Huang", "Daogao Liu", "Terra Blevins", "Danqi Chen", "Luke Zettlemoyer" ], "year": 2024, "venue": "ICLR", "abstract": "Although large language models (LLMs) are widely deployed, the data used to train them is rarely disclosed. Given the incredible scale of this data, up to trillions of tokens, it is all but certain that it includes potentially problematic text such as copyrighted materials, personally identifiable information, and test data for widely reported reference benchmarks. However, we currently have no way to know which data of these types is included or in what proportions. In this paper, we study the pretraining data detection problem: given a piece of text and black-box access to an LLM without knowing the pretraining data, can we determine if the model was trained on the provided text? To facilitate this study, we introduce a dynamic benchmark WIKIMIA that uses data created before and after model training to support gold truth detection. We also introduce a new detection method MIN-K PROB based on a simple hypothesis: an unseen example is likely to contain a few outlier words with low probabilities under the LLM, while a seen example is less likely to have words with such low probabilities. MIN-K PROB can be applied without any knowledge about the pretrainig corpus or any additional training, departing from previous detection methods that require training a reference model on data that is similar to the pretraining data. Moreover, our experiments demonstrate that MIN-K PROB achieves a 7.4% improvement on WIKIMIA over these previous methods. We apply MIN-K PROB to two real-world scenarios, copyrighted book detection and contaminated downstream example detection, and find that it to be a consistently effective solution.", "source": "openreview", "url": "https://openreview.net/forum?id=zWqr3MQuNs", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_Detecting Pretraining Data from Large Language Models_2024.pdf" }, { "title": "Diffusion in Diffusion: Cyclic One-Way Diffusion for Text-Vision-Conditioned Generation", "authors": [ "Ruoyu Wang", "Yongqi Yang", "Zhihao Qian", "Ye Zhu", "Yu Wu" ], "year": 2024, "venue": "ICLR", "abstract": "Originating from the diffusion phenomenon in physics that describes particle movement, the diffusion generative models inherit the characteristics of stochastic random walk in the data space along the denoising trajectory. However, the intrinsic mutual interference among image regions contradicts the need for practical downstream application scenarios where the preservation of low-level pixel information from given conditioning is desired (e.g., customization tasks like personalized generation and inpainting based on a user-provided single image). In this work, we investigate the diffusion (physics) in diffusion (machine learning) properties and propose our Cyclic One-Way Diffusion (COW) method to control the direction of diffusion phenomenon given a pre-trained frozen diffusion model for versatile customization application scenarios, where the low-level pixel information from the conditioning needs to be preserved. Notably, unlike most current methods that incorporate additional conditions by fine-tuning the base text-to-image diffusion model or learning auxiliary networks, our method provides a novel perspective to understand the task needs and is applicable to a wider range of customization scenarios in a learning-free manner. Extensive experiment results show that our proposed COW can achieve more flexible customization based on strict visual conditions in different application settings. Project page: https://wangruoyu02.github.io/cow.github.io/.", "source": "openreview", "url": "https://openreview.net/forum?id=ePOjNlOjLC", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_Diffusion in Diffusion_ Cyclic One-Way Diffusion for Text-Vision-Conditioned Generation_2024.pdf" }, { "title": "DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior", "authors": [ "Jingxiang Sun", "Bo Zhang", "Ruizhi Shao", "Lizhen Wang", "Wen Liu", "Zhenda Xie", "Yebin Liu" ], "year": 2024, "venue": "ICLR", "abstract": "We present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture boosting. A central focus of this work is to address the consistency issue that existing works encounter. To sculpt geometries that render coherently, we perform score distillation sampling via a view-dependent diffusion model. This 3D prior, alongside several training strategies, prioritizes the geometry consistency but compromises the texture fidelity. We further propose bootstrapped score distillation to specifically boost the texture. We train a personalized diffusion model, Dreambooth, on the augmented renderings of the scene, imbuing it with 3D knowledge of the scene being optimized. The score distillation from this 3D-aware diffusion prior provides view-consistent guidance for the scene. Notably, through an alternating optimization of the diffusion prior and 3D scene representation, we achieve mutually reinforcing improvements: the optimized 3D scene aids in training the scene-specific diffusion model, which offers increasingly view-consistent guidance for 3D optimization. The optimization is thus bootstrapped and leads to substantial texture boosting. With tailored 3D priors throughout the hierarchical generation, DreamCraft3D generates coherent 3D objects with photorealistic renderings, advancing the state-of-the-art in 3D content generation.", "source": "openreview", "url": "https://openreview.net/forum?id=DDX1u29Gqr", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_DreamCraft3D_ Hierarchical 3D Generation with Bootstrapped Diffusion Prior_2024.pdf" }, { "title": "FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data", "authors": [ "Zikai Xiao", "Zihan Chen", "Liyinglan Liu", "YANG FENG", "Joey Tianyi Zhou", "Jian Wu", "Wanlu Liu", "Howard Hao Yang", "Zuozhu Liu" ], "year": 2024, "venue": "ICLR", "abstract": "Federated Long-Tailed Learning (Fed-LT), a paradigm wherein data collected from decentralized local clients manifests a globally prevalent long-tailed distribution, has garnered considerable attention in recent times. In the context of Fed-LT, existing works have predominantly centered on addressing the data imbalance issue to enhance the efficacy of the generic global model while neglecting the performance at the local level. In contrast, conventional Personalized Federated Learning (pFL) techniques are primarily devised to optimize personalized local models under the presumption of a balanced global data distribution. This paper introduces an approach termed Federated Local and Generic Model Training in Fed-LT (FedLoGe), which enhances both local and generic model performance through the integration of representation learning and classifier alignment within a neural collapse framework. Our investigation reveals the feasibility of employing a shared backbone as a foundational framework for capturing overarching global trends, while concurrently employing individualized classifiers to encapsulate distinct refinements stemming from each client’s local features. Building upon this discovery, we establish the Static Sparse Equiangular Tight Frame Classifier (SSE-C), inspired by neural collapse principles that naturally prune extraneous noisy features and foster the acquisition of potent data representations. Furthermore, leveraging insights from imbalance neural collapse's classifier norm patterns, we develop Global and Local Adaptive Feature Realignment (GLA-FR) via an auxiliary global classifier and personalized Euclidean norm transfer to align global features with client preferences. Extensive experimental results on CIFAR-10/100-LT, ImageNet, and iNaturalist demonstrate the advantage of our method over state-of-the-art pFL and Fed-LT approaches.", "source": "openreview", "url": "https://openreview.net/forum?id=V3j5d0GQgH", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_FedLoGe_ Joint Local and Generic Federated Learning under Long-tailed Data_2024.pdf" }, { "title": "FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity", "authors": [ "Kai Yi", "Nidham Gazagnadou", "Peter Richtárik", "Lingjuan Lyu" ], "year": 2024, "venue": "ICLR", "abstract": "The interest in federated learning has surged in recent research due to its unique ability to train a global model using privacy-secured information held locally on each client. This paper pays particular attention to the issue of client-side model heterogeneity, a pervasive challenge in the practical implementation of FL that escalates its complexity. Assuming a scenario where each client possesses varied memory storage, processing capabilities and network bandwidth - a phenomenon referred to as system heterogeneity - there is a pressing need to customize a unique model for each client. In response to this, we present an effective and adaptable federated framework FedP3, representing Federated Personalized and Privacy-friendly network Pruning, tailored for model heterogeneity scenarios. Our proposed methodology can incorporate and adapt well-established techniques to its specific instances. We offer a theoretical interpretation of FedP3 and its locally differential-private variant, DP-FedP3, and theoretically validate their efficiencies.", "source": "openreview", "url": "https://openreview.net/forum?id=hbHwZYqk9T", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_FedP3_ Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity_2024.pdf" }, { "title": "Federated Recommendation with Additive Personalization", "authors": [ "Zhiwei Li", "Guodong Long", "Tianyi Zhou" ], "year": 2024, "venue": "ICLR", "abstract": "Building recommendation systems via federated learning (FL) is a new emerging challenge for next-generation Internet service. Existing FL models share item embedding across clients while keeping the user embedding private and local on the client side. However, identical item embedding cannot capture users' individual differences in perceiving the same item and may lead to poor personalization. Moreover, dense item embedding in FL results in expensive communication costs and latency. To address these challenges, we propose Federated Recommendation withAdditive Personalization (FedRAP), which learns a global view of items via FL and a personalized view locally on each user. FedRAP encourages a sparse global view to save FL's communication cost and enforces the two views to be complementary via two regularizers. We propose an effective curriculum to learn the local and global views progressively with increasing regularization weights. To produce recommendations for a user, FedRAP adds the two views together to obtain a personalized item embedding. FedRAP achieves the best performance in FL setting on multiple benchmarks. It outperforms recent federated recommendation methods and several ablation study baselines. Our code is available at https://github.com/mtics/FedRAP.", "source": "openreview", "url": "https://openreview.net/forum?id=xkXdE81mOK", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_Federated Recommendation with Additive Personalization_2024.pdf" }, { "title": "Gen-Z: Generative Zero-Shot Text Classification with Contextualized Label Descriptions", "authors": [ "Sachin Kumar", "Chan Young Park", "Yulia Tsvetkov" ], "year": 2024, "venue": "ICLR", "abstract": "Language model (LM) prompting—a popular paradigm for solving NLP tasks—has been shown to be susceptible to miscalibration and brittleness to slight prompt variations, caused by its discriminative prompting approach, i.e., predicting the label given the input. To address these issues, we propose Gen-Z—a generative prompting framework for zero-shot text classification. GEN-Z is generative, as it measures the LM likelihood of input text, conditioned on natural language descriptions of labels. The framework is multivariate, as label descriptions allow us to seamlessly integrate additional contextual information about the labels to improve task performance. On various standard classification benchmarks, with six open-source LM families, we show that zero-shot classification with simple contextualization of the data source of the evaluation set consistently outperforms both zero-shot and few-shot baselines while improving robustness to prompt variations. Further, our approach enables personalizing classification in a zero-shot manner by incorporating author, subject, or reader information in the label descriptions.", "source": "openreview", "url": "https://openreview.net/forum?id=rkplYfqUr0", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_Gen-Z_ Generative Zero-Shot Text Classification with Contextualized Label Descriptions_2024.pdf" }, { "title": "Graph Parsing Networks", "authors": [ "Yunchong Song", "Siyuan Huang", "Xinbing Wang", "Chenghu Zhou", "Zhouhan Lin" ], "year": 2024, "venue": "ICLR", "abstract": "Graph pooling compresses graph information into a compact representation. State-of-the-art graph pooling methods follow a hierarchical approach, which reduces the graph size step-by-step. These methods must balance memory efficiency with preserving node information, depending on whether they use node dropping or node clustering. Additionally, fixed pooling ratios or numbers of pooling layers are predefined for all graphs, which prevents personalized pooling structures from being captured for each individual graph. In this work, inspired by bottom-up grammar induction, we propose an efficient graph parsing algorithm to infer the pooling structure, which then drives graph pooling. The resulting Graph Parsing Network (GPN) adaptively learns personalized pooling structure for each individual graph. GPN benefits from the discrete assignments generated by the graph parsing algorithm, allowing good memory efficiency while preserving node information intact. Experimental results on standard benchmarks demonstrate that GPN outperforms state-of-the-art graph pooling methods in graph classification tasks while being able to achieve competitive performance in node classification tasks. We also conduct a graph reconstruction task to show GPN's ability to preserve node information and measure both memory and time efficiency through relevant tests.", "source": "openreview", "url": "https://openreview.net/forum?id=hv3SklibkL", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_Graph Parsing Networks_2024.pdf" }, { "title": "GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphs", "authors": [ "Pengcheng Jiang", "Cao Xiao", "Adam Richard Cross", "Jimeng Sun" ], "year": 2024, "venue": "ICLR", "abstract": "Clinical predictive models often rely on patients’ electronic health records (EHR), but integrating medical knowledge to enhance predictions and decision-making is challenging. This is because personalized predictions require personalized knowledge\ngraphs (KGs), which are difficult to generate from patient EHR data. To address this, we propose GraphCare, an open-world framework that uses external KGs to improve EHR-based predictions. Our method extracts knowledge from large language models (LLMs) and external biomedical KGs to build patient-specific KGs, which are then used to train our proposed Bi-attention AugmenTed\n(BAT) graph neural network (GNN) for healthcare predictions. On two public datasets, MIMIC-III and MIMIC-IV, GraphCare surpasses baselines in four vital healthcare prediction tasks: mortality, readmission, length of stay (LOS), and drug recommendation. On MIMIC-III, it boosts AUROC by 17.6% and 6.6% for mortality and readmission, and F1-score by 7.9% and 10.8% for LOS and drug recommendation, respectively. Notably, GraphCare demonstrates a substantial edge in scenarios with limited data availability. Our findings highlight the potential of using external KGs in healthcare prediction tasks and demonstrate the promise of GraphCare in generating personalized KGs for promoting personalized medicine.", "source": "openreview", "url": "https://openreview.net/forum?id=tVTN7Zs0ml", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_GraphCare_ Enhancing Healthcare Predictions with Personalized Knowledge Graphs_2024.pdf" }, { "title": "Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate", "authors": [ "Meirui Jiang", "Anjie Le", "Xiaoxiao Li", "Qi Dou" ], "year": 2024, "venue": "ICLR", "abstract": "Personalized federated learning (PFL) has emerged as a promising technique for addressing the challenge of data heterogeneity. While recent studies have made notable progress in mitigating heterogeneity associated with label distributions, the issue of effectively handling feature heterogeneity remains an open question. In this paper, we propose a personalization approach by Local-global updates Mixing (LG-Mix) via Neural Tangent Kernel (NTK)-based convergence. The core idea is to leverage the convergence rate induced by NTK to quantify the importance of local and global updates, and subsequently mix these updates based on their importance. Specifically, we find the trace of the NTK matrix can manifest the convergence rate, and propose an efficient and effective approximation to calculate the trace of a feature matrix instead of the NTK matrix. Such approximation significantly reduces the cost of computing NTK, and the feature matrix explicitly considers the heterogeneous features among samples. We have theoretically analyzed the convergence of our method in the over-parameterize regime, and experimentally evaluated our method on five datasets. These datasets present heterogeneous data features in natural and medical images. With comprehensive comparison to existing state-of-the-art approaches, our LG-Mix has consistently outperformed them across all datasets (largest accuracy improvement of 5.01\\%), demonstrating the outstanding efficacy of our method for model personalization. Code is available at \\url{https://github.com/med-air/HeteroPFL}.", "source": "openreview", "url": "https://openreview.net/forum?id=7pWRLDBAtc", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Ra_2024.pdf" }, { "title": "Kosmos-G: Generating Images in Context with Multimodal Large Language Models", "authors": [ "Xichen Pan", "Li Dong", "Shaohan Huang", "Zhiliang Peng", "Wenhu Chen", "Furu Wei" ], "year": 2024, "venue": "ICLR", "abstract": "Recent advancements in subject-driven image generation have made significant strides. However, current methods still fall short in diverse application scenarios, as they require test-time tuning and cannot accept interleaved multi-image and text input. These limitations keep them far from the ultimate goal of \"image as a foreign language in image generation.\" This paper presents Kosmos-G, a model that leverages the advanced multimodal perception capabilities of Multimodal Large Language Models (MLLMs) to tackle the aforementioned challenge. Our approach aligns the output space of MLLM with CLIP using the textual modality as an anchor and performs compositional instruction tuning on curated data. Kosmos-G demonstrates an impressive capability of zero-shot subject-driven generation with interleaved multi-image and text input. Notably, the score distillation instruction tuning requires no modifications to the image decoder. This allows for a seamless substitution of CLIP and effortless integration with a myriad of U-Net techniques ranging from fine-grained controls to personalized image decoder variants. We posit Kosmos-G as an initial attempt towards the goal of \"image as a foreign language in image generation.\"", "source": "openreview", "url": "https://openreview.net/forum?id=he6mX9LTyE", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_Kosmos-G_ Generating Images in Context with Multimodal Large Language Models_2024.pdf" }, { "title": "Learning Personalized Causally Invariant Representations for Heterogeneous Federated Clients", "authors": [ "Xueyang Tang", "Song Guo", "Jie ZHANG", "Jingcai Guo" ], "year": 2024, "venue": "ICLR", "abstract": "Personalized federated learning (PFL) has gained great success in tackling the scenarios where target datasets are heterogeneous across the local clients. However, the application of the existing PFL methods to real-world setting is hindered by the common assumption that the test data on each client is in-distribution (IND) with respect to its training data. Due to the bias of training dataset, the modern machine learning model prefers to rely on shortcut which can perform well on the training data but fail to generalize to the unseen test data that is out-of-distribution (OOD). This pervasive phenomenon is called shortcut learning and has attracted plentiful efforts in centralized situations. In PFL, the limited data diversity on federated clients makes mitigating shortcut and meanwhile preserving personalization knowledge rather difficult. In this paper, we analyse this challenging problem by formulating the structural causal models (SCMs) for heterogeneous federated clients. From the proposed SCMs, we derive two significant causal signatures which inspire a provable shortcut discovery and removal method under federated learning, namely FedSDR. Specifically, FedSDR is divided into two steps: 1) utilizing the available training data distributed among local clients to discover all the shortcut features in a collaborative manner. 2) developing the optimal personalized causally invariant predictor for each client by eliminating the discovered shortcut features. We provide theoretical analysis to prove that our method can draw complete shortcut features and produce the optimal personalized invariant predictor that can generalize to unseen OOD data on each client. The experimental results on diverse datasets validate the superiority of FedSDR over the state-of-the-art PFL methods on OOD generalization performance.", "source": "openreview", "url": "https://openreview.net/forum?id=8FHWkY0SwF", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_Learning Personalized Causally Invariant Representations for Heterogeneous Federated Clients_2024.pdf" }, { "title": "Less is More: One-shot Subgraph Reasoning on Large-scale Knowledge Graphs", "authors": [ "Zhanke Zhou", "Yongqi Zhang", "Jiangchao Yao", "quanming yao", "Bo Han" ], "year": 2024, "venue": "ICLR", "abstract": "To deduce new facts on a knowledge graph (KG), a link predictor learns from the graph structure and collects local evidence to find the answer to a given query. However, existing methods suffer from a severe scalability problem due to the utilization of the whole KG for prediction, which hinders their promise on large scale KGs and cannot be directly addressed by vanilla sampling methods. In this work, we propose the one-shot-subgraph link prediction to achieve efficient and adaptive prediction. The design principle is that, instead of directly acting on the whole KG, the prediction procedure is decoupled into two steps, i.e., (i) extracting only one subgraph according to the query and (ii) predicting on this single, query dependent subgraph. We reveal that the non-parametric and computation-efficient heuristics Personalized PageRank (PPR) can effectively identify the potential answers and supporting evidence. With efficient subgraph-based prediction, we further introduce the automated searching of the optimal configurations in both data and model spaces. Empirically, we achieve promoted efficiency and leading performances on five large-scale benchmarks. The code is publicly available at: https://github.com/tmlr-group/one-shot-subgraph.", "source": "openreview", "url": "https://openreview.net/forum?id=QHROe7Mfcb", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_Less is More_ One-shot Subgraph Reasoning on Large-scale Knowledge Graphs_2024.pdf" }, { "title": "PeFLL: Personalized Federated Learning by Learning to Learn", "authors": [ "Jonathan Scott", "Hossein Zakerinia", "Christoph H Lampert" ], "year": 2024, "venue": "ICLR", "abstract": "We present PeFLL, a new personalized federated learning algorithm that improves over the state-of-the-art in three aspects: 1) it produces more accurate models, especially in the low-data regime, and not only for clients present during its training phase, but also for any that may emerge in the future; 2) it reduces the amount of on-client computation and client-server communication by providing future clients with ready-to-use personalized models that require no additional finetuning or optimization; 3) it comes with theoretical guarantees that establish generalization from the observed clients to future ones. \nAt the core of PeFLL lies a learning-to-learn approach that jointly trains an embedding network and a hypernetwork. The embedding network is used to represent clients in a latent descriptor space in a way that reflects their similarity to each other. The hypernetwork takes as input such descriptors and outputs the parameters of fully personalized client models. In combination, both networks constitute a learning algorithm that achieves state-of-the-art performance in several personalized federated learning benchmarks.", "source": "openreview", "url": "https://openreview.net/forum?id=MrYiwlDRQO", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_PeFLL_ Personalized Federated Learning by Learning to Learn_2024.pdf" }, { "title": "Personalize Segment Anything Model with One Shot", "authors": [ "Renrui Zhang", "Zhengkai Jiang", "Ziyu Guo", "Shilin Yan", "Junting Pan", "Hao Dong", "Yu Qiao", "Peng Gao", "Hongsheng Li" ], "year": 2024, "venue": "ICLR", "abstract": "Driven by large-data pre-training, Segment Anything Model (SAM) has been demonstrated as a powerful promptable framework, revolutionizing the segmentation field. Despite the generality, customizing SAM for specific visual concepts without man-powered prompting is under-explored, e.g., automatically segmenting your pet dog in numerous images. In this paper, we introduce a training-free Personalization approach for SAM, termed PerSAM. Given only one-shot data, i.e., a single image with a reference mask, we first obtain a positive-negative location prior for the target concept in new images. Then, aided by target visual semantics, we empower SAM for personalized object segmentation via two proposed techniques: target-guided attention and target-semantic prompting. In this way, we can effectively customize the general-purpose SAM for private use without any training. To further alleviate the ambiguity of segmentation scales, we present an efficient one-shot fine-tuning variant, PerSAM-F. Freezing the entire SAM, we introduce a scale-aware fine-tuning to aggregate multi-scale masks, which only tunes 2 parameters within 10 seconds for improved performance. To demonstrate our efficacy, we construct a new dataset, PerSeg, for the evaluation of personalized object segmentation, and also test our methods on various one-shot image and video segmentation benchmarks. Besides, we propose to leverage PerSAM to improve DreamBooth for personalized text-to-image synthesis. By mitigating the disturbance of training-set backgrounds, our approach showcases better target appearance generation and higher fidelity to the input text prompt. Code is released at https://github.com/ZrrSkywalker/Personalize-SAM.", "source": "openreview", "url": "https://openreview.net/forum?id=6Gzkhoc6YS", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_Personalize Segment Anything Model with One Shot_2024.pdf" }, { "title": "Probabilistic Adaptation of Black-Box Text-to-Video Models", "authors": [ "Sherry Yang", "Yilun Du", "Bo Dai", "Dale Schuurmans", "Joshua B. Tenenbaum", "Pieter Abbeel" ], "year": 2024, "venue": "ICLR", "abstract": "Large text-to-video models trained on internet-scale data have demonstrated exceptional capabilities in generating high-fidelity videos from arbitrary textual descriptions. However, similar to proprietary language models, large text-to-video models are often black boxes whose weight parameters are not publicly available, posing a significant challenge to adapting these models to specific domains such as robotics, animation, and personalized stylization. Inspired by how a large language model can be prompted to perform new tasks without access to the model weights, we investigate how to adapt a black-box pretrained text-to-video model to a variety of downstream domains without weight access to the pretrained model. In answering this question, we propose \\emph{\\methodname}, which leverages the score function of a large pretrained video diffusion model as a probabilistic prior to guide the generation of a task-specific small video model. Our experiments show that, by incorporating broad knowledge and fidelity of the pretrained model probabilistically, a small model with as few as 1.25% parameters of the pretrained model can generate high-quality yet domain-specific videos for a variety of downstream domains such as animation, egocentric modeling, and modeling of simulated and real-world robotics data. As large text-to-video models starting to become available as a service similar to large language models, we advocate for private institutions to expose scores of video diffusion models as outputs in addition to generated videos to allow flexible adaptation of large pretrained text-to-video models by the general public.", "source": "openreview", "url": "https://openreview.net/forum?id=pjtIEgscE3", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_Probabilistic Adaptation of Black-Box Text-to-Video Models_2024.pdf" }, { "title": "Safe Collaborative Filtering", "authors": [ "Riku Togashi", "Tatsushi Oka", "Naoto Ohsaka", "Tetsuro Morimura" ], "year": 2024, "venue": "ICLR", "abstract": "Excellent tail performance is crucial for modern machine learning tasks, such as algorithmic fairness, class imbalance, and risk-sensitive decision making, as it ensures the effective handling of challenging samples within a dataset. Tail performance is also a vital determinant of success for personalized recommender systems to reduce the risk of losing users with low satisfaction. This study introduces a \"safe\" collaborative filtering method that prioritizes recommendation quality for less-satisfied users rather than focusing on the average performance. Our approach minimizes the conditional value at risk (CVaR), which represents the average risk over the tails of users' loss. To overcome computational challenges for web-scale recommender systems, we develop a robust yet practical algorithm that extends the most scalable method, implicit alternating least squares (iALS). Empirical evaluation on real-world datasets demonstrates the excellent tail performance of our approach while maintaining competitive computational efficiency.", "source": "openreview", "url": "https://openreview.net/forum?id=yarUvgEXq3", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_Safe Collaborative Filtering_2024.pdf" }, { "title": "SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression", "authors": [ "Tim Dettmers", "Ruslan A. Svirschevski", "Vage Egiazarian", "Denis Kuznedelev", "Elias Frantar", "Saleh Ashkboos", "Alexander Borzunov", "Torsten Hoefler", "Dan Alistarh" ], "year": 2024, "venue": "ICLR", "abstract": "Recent advances in large language model (LLM) pretraining have led to high-quality LLMs with impressive abilities. By compressing such LLMs via quantization to 3-4 bits per parameter, they can fit into memory-limited devices such as laptops and mobile phones, enabling personalized use. Quantizing models to 3-4 bits per parameter can lead to moderate to high accuracy losses, especially for smaller models (1-10B parameters), which are suitable for edge deployment. To address this accuracy issue, we introduce the Sparse-Quantized Representation (SpQR), a new compressed format and quantization technique that enables for the first time \\emph{near-lossless} compression of LLMs across model scales while reaching similar compression levels to previous methods. SpQR works by identifying and isolating \\emph{outlier weights}, which cause particularly large quantization errors, and storing them in higher precision while compressing all other weights to 3-4 bits, and achieves relative accuracy losses of less than $1\\%$ in perplexity for highly-accurate LLaMA and Falcon LLMs. This makes it possible to run a 33B parameter LLM on a single 24 GB consumer GPU without performance degradation at 15\\% speedup, thus making powerful LLMs available to consumers without any downsides. SpQR comes with efficient algorithms for both encoding weights into its format, as well as decoding them efficiently at runtime. Specifically, we provide an efficient GPU inference algorithm for SpQR, which yields faster inference than 16-bit baselines at similar accuracy while enabling memory compression gains of more than 4x.", "source": "openreview", "url": "https://openreview.net/forum?id=Q1u25ahSuy", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_SpQR_ A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression_2024.pdf" }, { "title": "Teach LLMs to Phish: Stealing Private Information from Language Models", "authors": [ "Ashwinee Panda", "Christopher A. Choquette-Choo", "Zhengming Zhang", "Yaoqing Yang", "Prateek Mittal" ], "year": 2024, "venue": "ICLR", "abstract": "When large language models are trained on private data, it can be a \\textit{significant} privacy risk for them to memorize and regurgitate sensitive information. In this work, we propose a new \\emph{practical} data extraction attack that we call ``neural phishing''. This attack enables an adversary to target and extract sensitive or personally identifiable information (PII), e.g., credit card numbers, from a model trained on user data with upwards of $10\\%$ attack success rates, at times, as high as $50\\%$. \nOur attack assumes only that an adversary can insert as few as $10$s of benign-appearing sentences into the training dataset using only vague priors on the structure of the user data.", "source": "openreview", "url": "https://openreview.net/forum?id=qo21ZlfNu6", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_Teach LLMs to Phish_ Stealing Private Information from Language Models_2024.pdf" }, { "title": "Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond", "authors": [ "Tianxin Wei", "Bowen Jin", "Ruirui Li", "Hansi Zeng", "Zhengyang Wang", "Jianhui Sun", "Qingyu Yin", "Hanqing Lu", "Suhang Wang", "Jingrui He", "Xianfeng Tang" ], "year": 2024, "venue": "ICLR", "abstract": "Developing a universal model that can effectively harness heterogeneous resources and respond to a wide range of personalized needs has been a longstanding community aspiration. Our daily choices, especially in domains like fashion and retail, are substantially shaped by multi-modal data, such as pictures and textual descriptions. These modalities not only offer intuitive guidance but also cater to personalized user preferences. However, the predominant personalization approaches mainly focus on ID or text-based recommendation problems, failing to comprehend the information spanning various tasks or modalities. In this paper, our goal is to establish a Unified paradigm for Multi-modal Personalization systems (UniMP), which effectively leverages multi-modal data while eliminating the complexities associated with task- and modality-specific customization. We argue that the advancements in foundational generative modeling have provided the flexibility and effectiveness necessary to achieve the objective. In light of this, we develop a generic and extensible personalization generative framework, that can handle a wide range of personalized needs including item recommendation, product search, preference prediction, explanation generation, and further user-guided image generation. Our methodology enhances the capabilities of foundational language models for personalized tasks by seamlessly ingesting interleaved cross-modal user history information, ensuring a more precise and customized experience for users. To train and evaluate the proposed multi-modal personalized tasks, we also introduce a novel and comprehensive benchmark covering a variety of user requirements. Our experiments on the real-world benchmark showcase the model's potential, outperforming competitive methods specialized for each task.", "source": "openreview", "url": "https://openreview.net/forum?id=khAE1sTMdX", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_Towards Unified Multi-Modal Personalization_ Large Vision-Language Models for Generative Recomm_2024.pdf" }, { "title": "VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections", "authors": [ "Dongqi Fu", "Zhigang Hua", "Yan Xie", "Jin Fang", "Si Zhang", "Kaan Sancak", "Hao Wu", "Andrey Malevich", "Jingrui He", "Bo Long" ], "year": 2024, "venue": "ICLR", "abstract": "Graph transformer has been proven as an effective graph learning method for its adoption of attention mechanism that is capable of capturing expressive representations from complex topological and feature information of graphs. Graph transformer conventionally performs dense attention (or global attention) for every pair of nodes to learn node representation vectors, resulting in quadratic computational costs that are unaffordable for large-scale graph data. Therefore, mini-batch training for graph transformers is a promising direction, but limited samples in each mini-batch can not support effective dense attention to encode informative representations. Facing this bottleneck, (1) we start by assigning each node a token list that is sampled by personalized PageRank (PPR) and then apply standard multi-head self-attention only on this list to compute its node representations. This PPR tokenization method decouples model training from complex graph topological information and makes heavy feature engineering offline and independent, such that mini-batch training of graph transformers is possible by loading each node's token list in batches. We further prove this PPR tokenization is viable as a graph convolution network with a fixed polynomial filter and jumping knowledge. However, only using personalized PageRank may limit information carried by a token list, which could not support different graph inductive biases for model training. To this end, (2) we rewire graphs by introducing multiple types of virtual connections through structure- and content-based super nodes that enable PPR tokenization to encode local and global contexts, long-range interaction, and heterophilous information into each node's token list, and then formalize our $\\underline{\\textbf{V}}$irtual $\\underline{\\textbf{C}}$onnection $\\underline{\\textbf{R}}$anking based $\\underline{\\textbf{Graph}}$ Trans$\\underline{\\textbf{former}}$ (VCR-Graphormer). Overall, VCR-Graphormer needs $O(m+klogk)$ complexity for graph tokenization as compared to $O(n^{3})$ of previous works. The [code](https://github.com/DongqiFu/VCR-Graphormer) is provided.", "source": "openreview", "url": "https://openreview.net/forum?id=SUUrkC3STJ", "decision_type": "Poster", "avg_rating": null, "relative_path": "2024/ICLR/Poster/x_VCR-Graphormer_ A Mini-batch Graph Transformer via Virtual Connections_2024.pdf" }, { "title": "AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning", "authors": [ "Yuwei Guo", "Ceyuan Yang", "Anyi Rao", "Zhengyang Liang", "Yaohui Wang", "Yu Qiao", "Maneesh Agrawala", "Dahua Lin", "Bo Dai" ], "year": 2024, "venue": "ICLR", "abstract": "With the advance of text-to-image (T2I) diffusion models (e.g., Stable Diffusion) and corresponding personalization techniques such as DreamBooth and LoRA, everyone can manifest their imagination into high-quality images at an affordable cost. However, adding motion dynamics to existing high-quality personalized T2Is and enabling them to generate animations remains an open challenge. In this paper, we present AnimateDiff, a practical framework for animating personalized T2I models without requiring model-specific tuning. At the core of our framework is a plug-and-play motion module that can be trained once and seamlessly integrated into any personalized T2Is originating from the same base T2I. Through our proposed training strategy, the motion module effectively learns transferable motion priors from real-world videos. Once trained, the motion module can be inserted into a personalized T2I model to form a personalized animation generator. We further propose MotionLoRA, a lightweight fine-tuning technique for AnimateDiff that enables a pre-trained motion module to adapt to new motion patterns, such as different shot types, at a low training and data collection cost. We evaluate AnimateDiff and MotionLoRA on several public representative personalized T2I models collected from the community. The results demonstrate that our approaches help these models generate temporally smooth animation clips while preserving the visual quality and motion diversity. Codes and pre-trained weights are available at https://github.com/guoyww/AnimateDiff.", "source": "openreview", "url": "https://openreview.net/forum?id=Fx2SbBgcte", "decision_type": "Spotlight", "avg_rating": null, "relative_path": "2024/ICLR/Spotlight/x_AnimateDiff_ Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning_2024.pdf" }, { "title": "Beyond Memorization: Violating Privacy via Inference with Large Language Models", "authors": [ "Robin Staab", "Mark Vero", "Mislav Balunovic", "Martin Vechev" ], "year": 2024, "venue": "ICLR", "abstract": "Current privacy research on large language models (LLMs) primarily focuses on the issue of extracting memorized training data. At the same time, models’ inference capabilities have increased drastically. This raises the key question of whether current LLMs could violate individuals’ privacy by inferring personal attributes from text given at inference time. In this work, we present the first comprehensive study on the capabilities of pretrained LLMs to infer personal attributes from text. We construct a dataset consisting of real Reddit profiles, and show that current LLMs can infer a wide range of personal attributes (e.g., location, income, sex), achieving up to 85% top-1 and 95% top-3 accuracy at a fraction of the cost (100x) and time (240x) required by humans. As people increasingly interact with LLM-powered chatbots across all aspects of life, we also explore the emerging threat of privacy-invasive chatbots trying to extract personal information through seemingly benign questions. Finally, we show that common mitigations, i.e., text anonymization and model alignment, are currently ineffective at protecting user privacy against LLM inference. Our findings highlight that current LLMs can infer personal data at a previously unattainable scale. In the absence of working defenses, we advocate for a broader discussion around LLM privacy implications beyond memorization, striving for stronger and wider privacy protection.", "source": "openreview", "url": "https://openreview.net/forum?id=kmn0BhQk7p", "decision_type": "Spotlight", "avg_rating": null, "relative_path": "2024/ICLR/Spotlight/x_Beyond Memorization_ Violating Privacy via Inference with Large Language Models_2024.pdf" }, { "title": "Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks", "authors": [ "Vaidehi Patil", "Peter Hase", "Mohit Bansal" ], "year": 2024, "venue": "ICLR", "abstract": "Pretrained language models sometimes possess knowledge that we do not wish them to, including memorized personal information and knowledge that could be used to harm people. They can also output toxic or harmful text. To mitigate these safety and informational issues, we propose an attack-and-defense framework for studying the task of deleting sensitive information directly from model weights. We study direct edits to model weights because (1) this approach should guarantee that particular deleted information is never extracted by future prompt attacks, and (2) it should protect against whitebox attacks, which is necessary for making claims about safety/privacy in a setting where publicly available model weights could be used to elicit sensitive information. Our threat model assumes that an attack succeeds if the answer to a sensitive question is located among a set of B generated candidates, based on scenarios where the information would be insecure if the answer is among B candidates. Experimentally, we show that even state-of-the-art model editing methods such as ROME struggle to truly delete factual information from models like GPT-J, as our whitebox and blackbox attacks can recover “deleted” information from an edited model 38% of the time. These attacks leverage two key observations: (1) that traces of deleted information can be found in intermediate model hidden states, and (2) that applying an editing method for one question may not delete information across rephrased versions of the question. Finally, we provide new defense methods that protect against some extraction attacks, but we do not find a single universally effective defense method. Our results suggest that truly deleting sensitive information is a tractable but difficult problem, since even relatively low attack success rates have potentially severe implications for the deployment of language models in a world where individuals enjoy ownership of their personal data, a right to privacy, and safety from harmful model outputs.", "source": "openreview", "url": "https://openreview.net/forum?id=7erlRDoaV8", "decision_type": "Spotlight", "avg_rating": null, "relative_path": "2024/ICLR/Spotlight/x_Can Sensitive Information Be Deleted From LLMs_ Objectives for Defending Against Extraction Att_2024.pdf" }, { "title": "Debiased Collaborative Filtering with Kernel-Based Causal Balancing", "authors": [ "Haoxuan Li", "Chunyuan Zheng", "Yanghao Xiao", "Peng Wu", "Zhi Geng", "Xu Chen", "Peng Cui" ], "year": 2024, "venue": "ICLR", "abstract": "Collaborative filtering builds personalized models from the collected user feedback. However, the collected data is observational rather than experimental, leading to various biases in the data, which can significantly affect the learned model. To address this issue, many studies have focused on propensity-based methods to combat the selection bias by reweighting the sample loss, and demonstrate that\nbalancing is important for debiasing both theoretically and empirically. However, there are two questions that still need to be addressed: which function class should be balanced and how to effectively balance that function class? In this paper, we first perform theoretical analysis to show the effect of balancing finite-dimensional function classes on the bias of IPS and DR methods, and based on this, we propose a universal kernel-based balancing method to balance functions on the reproducing kernel Hilbert space. In addition, we propose a novel adaptive causal balancing method during the alternating update between unbiased evaluation and training of the prediction model. Specifically, the prediction loss of the model is projected in the kernel-based covariate function space, and the projection coefficients are used to determine which functions should be prioritized for balancing to reduce the estimation bias. We conduct extensive experiments on three real-world datasets to demonstrate the effectiveness of the proposed approach.", "source": "openreview", "url": "https://openreview.net/forum?id=Ffjc8ApSbt", "decision_type": "Spotlight", "avg_rating": null, "relative_path": "2024/ICLR/Spotlight/x_Debiased Collaborative Filtering with Kernel-Based Causal Balancing_2024.pdf" }, { "title": "Predictive, scalable and interpretable knowledge tracing on structured domains", "authors": [ "Hanqi Zhou", "Robert Bamler", "Charley M Wu", "Álvaro Tejero-Cantero" ], "year": 2024, "venue": "ICLR", "abstract": "Intelligent tutoring systems optimize the selection and timing of learning materials to enhance understanding and long-term retention. This requires estimates of both the learner's progress (\"knowledge tracing\"; KT), and the prerequisite structure of the learning domain (\"knowledge mapping\"). While recent deep learning models achieve high KT accuracy, they do so at the expense of the interpretability of psychologically-inspired models. In this work, we present a solution to this trade-off. PSI-KT is a hierarchical generative approach that explicitly models how both individual cognitive traits and the prerequisite structure of knowledge influence learning dynamics, thus achieving interpretability by design. Moreover, by using scalable Bayesian inference, PSI-KT targets the real-world need for efficient personalization even with a growing body of learners and interaction data. Evaluated on three datasets from online learning platforms, PSI-KT achieves superior multi-step **p**redictive accuracy and **s**calable inference in continual-learning settings, all while providing **i**nterpretable representations of learner-specific traits and the prerequisite structure of knowledge that causally supports learning. In sum, predictive, scalable and interpretable knowledge tracing with solid knowledge mapping lays a key foundation for effective personalized learning to make education accessible to a broad, global audience.", "source": "openreview", "url": "https://openreview.net/forum?id=NgaLU2fP5D", "decision_type": "Spotlight", "avg_rating": null, "relative_path": "2024/ICLR/Spotlight/x_Predictive, scalable and interpretable knowledge tracing on structured domains_2024.pdf" }, { "title": "Solving Homogeneous and Heterogeneous Cooperative Tasks with Greedy Sequential Execution", "authors": [ "Shanqi Liu", "Dong Xing", "Pengjie Gu", "Xinrun Wang", "Bo An", "Yong Liu" ], "year": 2024, "venue": "ICLR", "abstract": "Cooperative multi-agent reinforcement learning (MARL) is extensively used for solving complex cooperative tasks, and value decomposition methods are a prevalent approach for this domain. However, these methods have not been successful in addressing both homogeneous and heterogeneous tasks simultaneously which is a crucial aspect for the practical application of cooperative agents. \nOn one hand, value decomposition methods demonstrate superior performance in homogeneous tasks. Nevertheless, they tend to produce agents with similar policies, which is unsuitable for heterogeneous tasks. On the other hand, solutions based on personalized observation or assigned roles are well-suited for heterogeneous tasks. However, they often lead to a trade-off situation where the agent's performance in homogeneous scenarios is negatively affected due to the aggregation of distinct policies. An alternative approach is to adopt sequential execution policies, which offer a flexible form for learning both types of tasks. However, learning sequential execution policies poses challenges in terms of credit assignment, and the limited information about subsequently executed agents can lead to sub-optimal solutions, which is known as the relative over-generalization problem. To tackle these issues, this paper proposes Greedy Sequential Execution (GSE) as a solution to learn the optimal policy that covers both scenarios. In the proposed GSE framework, we introduce an individual utility function into the framework of value decomposition to consider the complex interactions between agents. \nThis function is capable of representing both the homogeneous and heterogeneous optimal policies. Furthermore, we utilize greedy marginal contribution calculated by the utility function as the credit value of the sequential execution policy to address the credit assignment and relative over-generalization problem. We evaluated GSE in both homogeneous and heterogeneous scenarios. The results demonstrate that GSE achieves significant improvement in performance across multiple domains, especially in scenarios involving both homogeneous and heterogeneous tasks.", "source": "openreview", "url": "https://openreview.net/forum?id=hB2hXtxIPH", "decision_type": "Spotlight", "avg_rating": null, "relative_path": "2024/ICLR/Spotlight/x_Solving Homogeneous and Heterogeneous Cooperative Tasks with Greedy Sequential Execution_2024.pdf" }, { "title": "What's In My Big Data?", "authors": [ "Yanai Elazar", "Akshita Bhagia", "Ian Helgi Magnusson", "Abhilasha Ravichander", "Dustin Schwenk", "Alane Suhr", "Evan Pete Walsh", "Dirk Groeneveld", "Luca Soldaini", "Sameer Singh", "Hannaneh Hajishirzi", "Noah A. Smith", "Jesse Dodge" ], "year": 2024, "venue": "ICLR", "abstract": "Large text corpora are the backbone of language models.\nHowever, we have a limited understanding of the content of these corpora, including general statistics, quality, social factors, and inclusion of evaluation data (contamination).\nIn this work, we propose What's In My Big Data? (WIMBD), a platform and a set of sixteen analyses that allow us to reveal and compare the contents of large text corpora. WIMBD builds on two basic capabilities---count and search---*at scale*, which allows us to analyze more than 35 terabytes on a standard compute node. \nWe apply WIMBD to ten different corpora used to train popular language models, including *C4*, *The Pile*, and *RedPajama*.\nOur analysis uncovers several surprising and previously undocumented findings about these corpora, including the high prevalence of duplicate, synthetic, and low-quality content, personally identifiable information, toxic language, and benchmark contamination. \nFor instance, we find that about 50% of the documents in *RedPajama* and *LAION-2B-en* are duplicates. In addition, several datasets used for benchmarking models trained on such corpora are contaminated with respect to important benchmarks, including the Winograd Schema Challenge and parts of GLUE and SuperGLUE.\nWe open-source WIMBD's code and artifacts to provide a standard set of evaluations for new text-based corpora and to encourage more analyses and transparency around them.", "source": "openreview", "url": "https://openreview.net/forum?id=RvfPnOkPV4", "decision_type": "Spotlight", "avg_rating": null, "relative_path": "2024/ICLR/Spotlight/x_What's In My Big Data__2024.pdf" }, { "title": "Generalization Error Bounds for Two-stage Recommender Systems with Tree Structure", "authors": [ "Jin Zhang", "Ze Liu", "Defu Lian", "Enhong Chen" ], "year": 2024, "venue": "NeurIPS", "abstract": "Two-stage recommender systems play a crucial role in efficiently identifying relevant items and personalizing recommendations from a vast array of options. This paper, based on an error decomposition framework, analyzes the generalization error for two-stage recommender systems with a tree structure, which consist of an efficient tree-based retriever and a more precise yet time-consuming ranker. We use the Rademacher complexity to establish the generalization upper bound for various tree-based retrievers using beam search, as well as for different ranker models under a shifted training distribution. Both theoretical insights and practical experiments on real-world datasets indicate that increasing the branches in tree-based retrievers and harmonizing distributions across stages can enhance the generalization performance of two-stage recommender systems.", "source": "openreview", "url": "https://openreview.net/forum?id=m1a4CrRJR7", "decision_type": "Oral", "avg_rating": 7.2, "relative_path": "2024/NeurIPS/Oral/7.2_Generalization Error Bounds for Two-stage Recommender Systems with Tree Structure_2024.pdf" }, { "title": "DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices", "authors": [ "Yongzhe Jia", "Xuyun Zhang", "Hongsheng Hu", "Kim-Kwang Raymond Choo", "Lianyong Qi", "Xiaolong Xu", "Amin Beheshti", "Wanchun Dou" ], "year": 2024, "venue": "NeurIPS", "abstract": "Federated learning (FL) has emerged as a prominent machine learning paradigm in edge computing environments, enabling edge devices to collaboratively optimize a global model without sharing their private data. However, existing FL frameworks suffer from efficacy deterioration due to the system heterogeneity inherent in edge computing, especially in the presence of domain shifts across local data. \nIn this paper, we propose a heterogeneous FL framework DapperFL, to enhance model performance across multiple domains. In DapperFL, we introduce a dedicated Model Fusion Pruning (MFP) module to produce personalized compact local models for clients to address the system heterogeneity challenges. The MFP module prunes local models with fused knowledge obtained from both local and remaining domains, ensuring robustness to domain shifts. Additionally, we design a Domain Adaptive Regularization (DAR) module to further improve the overall performance of DapperFL. The DAR module employs regularization generated by the pruned model, aiming to learn robust representations across domains. Furthermore, we introduce a specific aggregation algorithm for aggregating heterogeneous local models with tailored architectures and weights. We implement DapperFL on a real-world FL platform with heterogeneous clients. Experimental results on benchmark datasets with multiple domains demonstrate that DapperFL outperforms several state-of-the-art FL frameworks by up to 2.28%, while significantly achieving model volume reductions ranging from 20% to 80%. Our code is available at: https://github.com/jyzgh/DapperFL.", "source": "openreview", "url": "https://openreview.net/forum?id=Pezt0xttae", "decision_type": "Oral", "avg_rating": 6.8, "relative_path": "2024/NeurIPS/Oral/6.8_DapperFL_ Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices_2024.pdf" }, { "title": "Get Rid of Isolation: A Continuous Multi-task Spatio-Temporal Learning Framework", "authors": [ "Zhongchao Yi", "Zhengyang Zhou", "Qihe Huang", "Yanjiang Chen", "Liheng Yu", "Xu Wang", "Yang Wang" ], "year": 2024, "venue": "NeurIPS", "abstract": "Spatiotemporal learning has become a pivotal technique to enable urban intelligence. Traditional spatiotemporal models mostly focus on a specific task by assuming a same distribution between training and testing sets. However, given that urban systems are usually dynamic, multi-sourced with imbalanced data distributions, current specific task-specific models fail to generalize to new urban conditions and adapt to new domains without explicitly modeling interdependencies across various dimensions and types of urban data. To this end, we argue that there is an essential to propose a Continuous Multi-task Spatio-Temporal learning framework (CMuST) to empower collective urban intelligence, which reforms the urban spatiotemporal learning from single-domain to cooperatively multi-dimensional and multi-task learning. Specifically, CMuST proposes a new multi-dimensional spatiotemporal interaction network (MSTI) to allow cross-interactions between context and main observations as well as self-interactions within spatial and temporal aspects to be exposed, which is also the core for capturing task-level commonality and personalization. To ensure continuous task learning, a novel Rolling Adaptation training scheme (RoAda) is devised, which not only preserves task uniqueness by constructing data summarization-driven task prompts, but also harnesses correlated patterns among tasks by iterative model behavior modeling. We further establish a benchmark of three cities for multi-task spatiotemporal learning, and empirically demonstrate the superiority of CMuST via extensive evaluations on these datasets. The impressive improvements on both few-shot streaming data and new domain tasks against existing SOAT methods are achieved. Code is available at https://github.com/DILab-USTCSZ/CMuST.", "source": "openreview", "url": "https://openreview.net/forum?id=tnh4LK72yj", "decision_type": "Oral", "avg_rating": 5.8, "relative_path": "2024/NeurIPS/Oral/5.8_Get Rid of Isolation_ A Continuous Multi-task Spatio-Temporal Learning Framework_2024.pdf" }, { "title": "Iterative Methods via Locally Evolving Set Process", "authors": [ "Baojian Zhou", "Yifan Sun", "Reza Babanezhad Harikandeh", "Xingzhi Guo", "Deqing Yang", "Yanghua Xiao" ], "year": 2024, "venue": "NeurIPS", "abstract": "Given the damping factor $\\alpha$ and precision tolerance $\\epsilon$, \\citet{andersen2006local} introduced Approximate Personalized PageRank (APPR), the \\textit{de facto local method} for approximating the PPR vector, with runtime bounded by $\\Theta(1/(\\alpha\\epsilon))$ independent of the graph size. Recently, Fountoulakis \\& Yang asked whether faster local algorithms could be developed using $\\tilde{\\mathcal{O}}(1/(\\sqrt{\\alpha}\\epsilon))$ operations. By noticing that APPR is a local variant of Gauss-Seidel, this paper explores the question of *whether standard iterative solvers can be effectively localized*. We propose to use the *locally evolving set process*, a novel framework to characterize the algorithm locality, and demonstrate that many standard solvers can be effectively localized. Let $\\overline{\\operatorname{vol}}{ (\\mathcal S_t)}$ and $\\overline{\\gamma_t}$ be the running average of volume and the residual ratio of active nodes $\\textstyle \\mathcal{S_t}$ during the process. We show $\\overline{\\operatorname{vol}}{ (\\mathcal S_t)}/\\overline{\\gamma_t} \\leq 1/\\epsilon$ and prove APPR admits a new runtime bound $\\tilde{\\mathcal{O}}(\\overline{\\operatorname{vol}}(\\mathcal S_t)/(\\alpha\\overline{\\gamma_t}))$ mirroring the actual performance. Furthermore, when the geometric mean of residual reduction is $\\Theta(\\sqrt{\\alpha})$, then there exists $c \\in (0,2)$ such that the local Chebyshev method has runtime $\\tilde{\\mathcal{O}}(\\overline{\\operatorname{vol}}(\\mathcal{S_t})/(\\sqrt{\\alpha}(2-c)))$ without the monotonicity assumption. Numerical results confirm the efficiency of this novel framework and show up to a hundredfold speedup over corresponding standard solvers on real-world graphs.", "source": "openreview", "url": "https://openreview.net/forum?id=wT2KhEb97a", "decision_type": "Poster", "avg_rating": 7.3, "relative_path": "2024/NeurIPS/Poster/7.3_Iterative Methods via Locally Evolving Set Process_2024.pdf" }, { "title": "Latent Learning Progress Drives Autonomous Goal Selection in Human Reinforcement Learning", "authors": [ "Gaia Molinaro", "Cédric Colas", "Pierre-Yves Oudeyer", "Anne Collins" ], "year": 2024, "venue": "NeurIPS", "abstract": "Humans are autotelic agents who learn by setting and pursuing their own goals. However, the precise mechanisms guiding human goal selection remain unclear. Learning progress, typically measured as the observed change in performance, can provide a valuable signal for goal selection in both humans and artificial agents. We hypothesize that human choices of goals may also be driven by _latent learning progress_, which humans can estimate through knowledge of their actions and the environment – even without experiencing immediate changes in performance. To test this hypothesis, we designed a hierarchical reinforcement learning task in which human participants (N = 175) repeatedly chose their own goals and learned goal-conditioned policies. Our behavioral and computational modeling results confirm the influence of latent learning progress on goal selection and uncover inter-individual differences, partially mediated by recognition of the task's hierarchical structure. By investigating the role of latent learning progress in human goal selection, we pave the way for more effective and personalized learning experiences as well as the advancement of more human-like autotelic machines.", "source": "openreview", "url": "https://openreview.net/forum?id=GbqzN9HiUC", "decision_type": "Poster", "avg_rating": 7.2, "relative_path": "2024/NeurIPS/Poster/7.2_Latent Learning Progress Drives Autonomous Goal Selection in Human Reinforcement Learning_2024.pdf" }, { "title": "Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models", "authors": [ "Shengchao Chen", "Guodong Long", "Jing Jiang", "Chengqi Zhang" ], "year": 2024, "venue": "NeurIPS", "abstract": "This paper demonstrates that pre-trained language models (PLMs) are strong foundation models for on-device meteorological variable modeling. We present LM-Weather, a generic approach to taming PLMs, that have learned massive sequential knowledge from the universe of natural language databases, to acquire an immediate capability to obtain highly customized models for heterogeneous meteorological data on devices while keeping high efficiency. Concretely, we introduce a lightweight personalized adapter into PLMs and endows it with weather pattern awareness. During communication between clients and the server, low-rank-based transmission is performed to effectively fuse the global knowledge among devices while maintaining high communication efficiency and ensuring privacy. Experiments on real-wold dataset show that LM-Weather outperforms the state-of-the-art results by a large margin across various tasks (e.g., forecasting and imputation at different scales). We provide extensive and in-depth analyses experiments, which verify that LM-Weather can (1) indeed leverage sequential knowledge from natural language to accurately handle meteorological sequence, (2) allows each devices obtain highly customized models under significant heterogeneity, and (3) generalize under data-limited and out-of-distribution (OOD) scenarios.", "source": "openreview", "url": "https://openreview.net/forum?id=llTroju97T", "decision_type": "Poster", "avg_rating": 7.0, "relative_path": "2024/NeurIPS/Poster/7.0_Personalized Adapter for Large Meteorology Model on Devices_ Towards Weather Foundation Models_2024.pdf" }, { "title": "Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators", "authors": [ "Yiyan HUANG", "Cheuk Hang LEUNG", "WANG Siyi", "YIJUN LI", "Qi WU" ], "year": 2024, "venue": "NeurIPS", "abstract": "The growing demand for personalized decision-making has led to a surge of interest in estimating the Conditional Average Treatment Effect (CATE). Various types of CATE estimators have been developed with advancements in machine learning and causal inference. However, selecting the desirable CATE estimator through a conventional model validation procedure remains impractical due to the absence of counterfactual outcomes in observational data. Existing approaches for CATE estimator selection, such as plug-in and pseudo-outcome metrics, face two challenges. First, they must determine the metric form and the underlying machine learning models for fitting nuisance parameters (e.g., outcome function, propensity function, and plug-in learner). Second, they lack a specific focus on selecting a robust CATE estimator. To address these challenges, this paper introduces a Distributionally Robust Metric (DRM) for CATE estimator selection. The proposed DRM is nuisance-free, eliminating the need to fit models for nuisance parameters, and it effectively prioritizes the selection of a distributionally robust CATE estimator. The experimental results validate the effectiveness of the DRM method in selecting CATE estimators that are robust to the distribution shift incurred by covariate shift and hidden confounders.", "source": "openreview", "url": "https://openreview.net/forum?id=k4EP46Q9X2", "decision_type": "Poster", "avg_rating": 7.0, "relative_path": "2024/NeurIPS/Poster/7.0_Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimat_2024.pdf" }, { "title": "Computerized Adaptive Testing via Collaborative Ranking", "authors": [ "Zirui Liu", "Yan Zhuang", "Qi Liu", "Jiatong Li", "Yuren Zhang", "Zhenya Huang", "Jinze Wu", "Shijin Wang" ], "year": 2024, "venue": "NeurIPS", "abstract": "As the deep integration of machine learning and intelligent education, Computerized Adaptive Testing (CAT) has received more and more research attention. Compared to traditional paper-and-pencil tests, CAT can deliver both personalized and interactive assessments by automatically adjusting testing questions according to the performance of students during the test process. Therefore, CAT has been recognized as an efficient testing methodology capable of accurately estimating a student’s ability with a minimal number of questions, leading to its widespread adoption in mainstream selective exams such as the GMAT and GRE. However, just improving the accuracy of ability estimation is far from satisfactory in the real-world scenarios, since an accurate ranking of students is usually more important (e.g., in high-stakes exams). Considering the shortage of existing CAT solutions in student ranking, this paper emphasizes the importance of aligning test outcomes (student ranks) with the true underlying abilities of students. Along this line, different from the conventional independent testing paradigm among students, we propose a novel collaborative framework, Collaborative Computerized Adaptive Testing (CCAT), that leverages inter-student information to enhance student ranking. By using collaborative students as anchors to assist in ranking test-takers, CCAT can give both theoretical guarantees and experimental validation for ensuring ranking consistency.", "source": "openreview", "url": "https://openreview.net/forum?id=5Fl4zgXbsW", "decision_type": "Poster", "avg_rating": 6.8, "relative_path": "2024/NeurIPS/Poster/6.8_Computerized Adaptive Testing via Collaborative Ranking_2024.pdf" }, { "title": "pFedClub: Controllable Heterogeneous Model Aggregation for Personalized Federated Learning", "authors": [ "Jiaqi Wang", "Qi Li", "Lingjuan Lyu", "Fenglong Ma" ], "year": 2024, "venue": "NeurIPS", "abstract": "Federated learning, a pioneering paradigm, enables collaborative model training without exposing users’ data to central servers. Most existing federated learning systems necessitate uniform model structures across all clients, restricting their practicality. Several methods have emerged to aggregate diverse client models; however, they either lack the ability of personalization, raise privacy and security concerns, need prior knowledge, or ignore the capability and functionality of personalized models. In this paper, we present an innovative approach, named pFedClub, which addresses these challenges. pFedClub introduces personalized federated learning through the substitution of controllable neural network blocks/layers. Initially, pFedClub dissects heterogeneous client models into blocks and organizes them into functional groups on the server. Utilizing the designed CMSR (Controllable Model Searching and Reproduction) algorithm, pFedClub generates a range of personalized candidate models for each client. A model matching technique is then applied to select the optimal personalized model, serving as a teacher model to guide each client’s training process. We conducted extensive experiments across three datasets, examining both IID and non-IID settings. The results demonstrate that pFedClub outperforms baseline approaches, achieving state-of-the-art performance. Moreover, our model insight analysis reveals that pFedClub generates personalized models of reasonable size in a controllable manner, significantly reducing computational costs.", "source": "openreview", "url": "https://openreview.net/forum?id=xW6ga9i4eA", "decision_type": "Poster", "avg_rating": 6.8, "relative_path": "2024/NeurIPS/Poster/6.8_pFedClub_ Controllable Heterogeneous Model Aggregation for Personalized Federated Learning_2024.pdf" }, { "title": "FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference", "authors": [ "Zihan Tan", "Guancheng Wan", "Wenke Huang", "Mang Ye" ], "year": 2024, "venue": "NeurIPS", "abstract": "Personalized Federated Graph Learning (pFGL) facilitates the decentralized training of Graph Neural Networks (GNNs) without compromising privacy while accommodating personalized requirements for non-IID participants. In cross-domain scenarios, structural heterogeneity poses significant challenges for pFGL. Nevertheless, previous pFGL methods incorrectly share non-generic knowledge globally and fail to tailor personalized solutions locally under domain structural shift. We innovatively reveal that the spectral nature of graphs can well reflect inherent domain structural shifts. Correspondingly, our method overcomes it by sharing generic spectral knowledge. Moreover, we indicate the biased message-passing schemes for graph structures and propose the personalized preference module. Combining both strategies, we propose our pFGL framework $\\textbf{FedSSP}$ which $\\textbf{S}$hares generic $\\textbf{S}$pectral knowledge while satisfying graph $\\textbf{P}$references. Furthermore, We perform extensive experiments on cross-dataset and cross-domain settings to demonstrate the superiority of our framework. The code is available at https://github.com/OakleyTan/FedSSP.", "source": "openreview", "url": "https://openreview.net/forum?id=I96GFYalFO", "decision_type": "Poster", "avg_rating": 6.5, "relative_path": "2024/NeurIPS/Poster/6.5_FedSSP_ Federated Graph Learning with Spectral Knowledge and Personalized Preference_2024.pdf" }, { "title": "How to Continually Adapt Text-to-Image Diffusion Models for Flexible Customization?", "authors": [ "Jiahua Dong", "Wenqi Liang", "Hongliu Li", "Duzhen Zhang", "Meng Cao", "Henghui Ding", "Salman Khan", "Fahad Khan" ], "year": 2024, "venue": "NeurIPS", "abstract": "Custom diffusion models (CDMs) have attracted widespread attention due to their astonishing generative ability for personalized concepts. However, most existing CDMs unreasonably assume that personalized concepts are fixed and cannot change over time. Moreover, they heavily suffer from catastrophic forgetting and concept neglect on old personalized concepts when continually learning a series of new concepts. To address these challenges, we propose a novel Concept-Incremental text-to-image Diffusion Model (CIDM), which can resolve catastrophic forgetting and concept neglect to learn new customization tasks in a concept-incremental manner. Specifically, to surmount the catastrophic forgetting of old concepts, we develop a concept consolidation loss and an elastic weight aggregation module. They can explore task-specific and task-shared knowledge during training, and aggregate all low-rank weights of old concepts based on their contributions during inference. Moreover, in order to address concept neglect, we devise a context-controllable synthesis strategy that leverages expressive region features and noise estimation to control the contexts of generated images according to user conditions. Experiments validate that our CIDM surpasses existing custom diffusion models. The source codes are available at https://github.com/JiahuaDong/CIFC.", "source": "openreview", "url": "https://openreview.net/forum?id=O4RCFjVUBJ", "decision_type": "Poster", "avg_rating": 6.5, "relative_path": "2024/NeurIPS/Poster/6.5_How to Continually Adapt Text-to-Image Diffusion Models for Flexible Customization__2024.pdf" }, { "title": "Quantifying and Optimizing Global Faithfulness in Persona-driven Role-playing", "authors": [ "Letian Peng", "Jingbo Shang" ], "year": 2024, "venue": "NeurIPS", "abstract": "Persona-driven role-playing (PRP) aims to build AI characters that can respond to user queries by faithfully sticking with \\emph{all} (factual) statements in persona documents.\nUnfortunately, existing faithfulness criteria for PRP are limited to coarse-grained LLM-based scoring without a clear definition or formulation.\nThis paper presents a pioneering exploration to quantify PRP faithfulness evaluation as a fine-grained and explainable criterion, which also serves as a reliable reference for faithfulness optimization.\nOur criterion first discriminates persona statements into \\emph{active} and \\emph{passive} constraints by identifying the query-statement relevance.\nThen, we incorporate all constraints following the principle that the AI character's response should be (a) entailed by active constraints and (b) not contradicted by passive constraints.\nWe translate this principle mathematically into a novel Active-Passive-Constraint (APC) score, a constraint-wise sum of statement-to-response natural language inference (NLI) scores weighted by constraint-query relevance scores. \nIn practice, we build the APC scoring system by symbolically distilling small NLI and relevance discriminators (300M parameters) from GPT-4 for efficiency, and both show high consistency with GPT-4's discrimination.\nWe validate the quality of the APC score against human evaluation based on example personas with tens of statements, and the results show a high correlation.\nAs the APC score could faithfully reflect the PRP quality, we further leverage it as a reward system in direct preference optimization (DPO) for better AI characters. \nOur experiments offer a fine-grained and explainable comparison between existing PRP techniques, revealing their advantages and limitations.\nWe further find APC-based DPO to be one of the most competitive techniques for sticking with all constraints and can be well incorporated with other techniques.\nWe then extend the scale of the experiments to real persons with hundreds of statements and reach a consistent conclusion. \nFinally, we provide comprehensive analyses and case studies to support the effectiveness of APC and APC-based DPO.", "source": "openreview", "url": "https://openreview.net/forum?id=bzPmjmiaz8", "decision_type": "Poster", "avg_rating": 6.5, "relative_path": "2024/NeurIPS/Poster/6.5_Quantifying and Optimizing Global Faithfulness in Persona-driven Role-playing_2024.pdf" }, { "title": "HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models", "authors": [ "Bernal Jimenez Gutierrez", "Yiheng Shu", "Yu Gu", "Michihiro Yasunaga", "Yu Su" ], "year": 2024, "venue": "NeurIPS", "abstract": "In order to thrive in hostile and ever-changing natural environments, mammalian brains evolved to store large amounts of knowledge about the world and continually integrate new information while avoiding catastrophic forgetting. Despite the impressive accomplishments, large language models (LLMs), even with retrieval-augmented generation (RAG), still struggle to efficiently and effectively integrate a large amount of new experiences after pre-training. In this work, we introduce HippoRAG, a novel retrieval framework inspired by the hippocampal indexing theory of human long-term memory to enable deeper and more efficient knowledge integration over new experiences. HippoRAG synergistically orchestrates LLMs, knowledge graphs, and the Personalized PageRank algorithm to mimic the different roles of neocortex and hippocampus in human memory. We compare HippoRAG with existing RAG methods on multi-hop question answering (QA) and show that our method outperforms the state-of-the-art methods remarkably, by up to 20%. Single-step retrieval with HippoRAG achieves comparable or better performance than iterative retrieval like IRCoT while being 10-20 times cheaper and 6-13 times faster, and integrating HippoRAG into IRCoT brings further substantial gains. Finally, we show that our method can tackle new types of scenarios that are out of reach of existing methods.", "source": "openreview", "url": "https://openreview.net/forum?id=hkujvAPVsg", "decision_type": "Poster", "avg_rating": 6.3, "relative_path": "2024/NeurIPS/Poster/6.3_HippoRAG_ Neurobiologically Inspired Long-Term Memory for Large Language Models_2024.pdf" }, { "title": "Initializing Services in Interactive ML Systems for Diverse Users", "authors": [ "Avinandan Bose", "Mihaela Curmei", "Daniel L. Jiang", "Jamie Heather Morgenstern", "Sarah Dean", "Lillian J. Ratliff", "Maryam Fazel" ], "year": 2024, "venue": "NeurIPS", "abstract": "This paper investigates ML systems serving a group of users, with multiple models/services, each aimed at specializing to a sub-group of users. We consider settings where upon deploying a set of services, users choose the one minimizing their personal losses and the learner iteratively learns by interacting with diverse users. Prior research shows that the outcomes of learning dynamics, which comprise both the services' adjustments and users' service selections, hinge significantly on the initial conditions. However, finding good initial conditions faces two main challenges: (i) \\emph{Bandit feedback:} Typically, data on user preferences are not available before deploying services \nand observing user behavior; (ii) \\emph{Suboptimal local solutions:} The total loss landscape (i.e., the sum of loss functions across all users and services) is not convex and gradient-based algorithms can get stuck in poor local minima.\n\nWe address these challenges with a randomized algorithm to adaptively select a minimal set of users for data collection in order to initialize a set of services. Under mild assumptions on the loss functions, we prove that our initialization leads to a total loss within a factor of the \\textit{globally optimal total loss,with complete user preference data}, and this factor scales logarithmically in the number of services. This result is a generalization of the well-known $k$-means++ guarantee to a broad problem class which is also of independent interest.\nThe theory is complemented by experiments on real as well as semi-synthetic datasets.", "source": "openreview", "url": "https://openreview.net/forum?id=HSJOt2hyDf", "decision_type": "Poster", "avg_rating": 6.3, "relative_path": "2024/NeurIPS/Poster/6.3_Initializing Services in Interactive ML Systems for Diverse Users_2024.pdf" }, { "title": "Personalized Steering of Large Language Models: Versatile Steering Vectors Through Bi-directional Preference Optimization", "authors": [ "Yuanpu Cao", "Tianrong Zhang", "Bochuan Cao", "Ziyi Yin", "Lu Lin", "Fenglong Ma", "Jinghui Chen" ], "year": 2024, "venue": "NeurIPS", "abstract": "Researchers have been studying approaches to steer the behavior of Large Language Models (LLMs) and build personalized LLMs tailored for various applications. While fine-tuning seems to be a direct solution, it requires substantial computational resources and may significantly affect the utility of the original LLM. \nRecent endeavors have introduced more lightweight strategies, focusing on extracting ``steering vectors'' to guide the model's output toward desired behaviors by adjusting activations within specific layers of the LLM's transformer architecture. However, such steering vectors are directly extracted from the activations of human preference data and thus often lead to suboptimal results and occasional failures, especially in alignment-related scenarios.\nIn this work, we propose an innovative approach that could produce more effective steering vectors through bi-directional preference optimization. \nOur method is designed to allow steering vectors to directly influence the generation probability of contrastive human preference data pairs, thereby offering a more precise representation of the target behavior. By carefully adjusting the direction and magnitude of the steering vector, we enabled personalized control over the desired behavior across a spectrum of intensities.\nExtensive experimentation across various open-ended generation tasks, particularly focusing on steering AI personas, has validated the efficacy of our approach. \nMoreover, we comprehensively investigate critical alignment-concerning scenarios, such as managing truthfulness, mitigating hallucination, and addressing jailbreaking attacks alongside their respective defenses. Remarkably, our method can still demonstrate outstanding steering effectiveness across these scenarios. Furthermore, we showcase the transferability of our steering vectors across different models/LoRAs and highlight the synergistic benefits of applying multiple vectors simultaneously. These findings significantly broaden the practicality and versatility of our proposed method.", "source": "openreview", "url": "https://openreview.net/forum?id=7qJFkuZdYo", "decision_type": "Poster", "avg_rating": 6.3, "relative_path": "2024/NeurIPS/Poster/6.3_Personalized Steering of Large Language Models_ Versatile Steering Vectors Through Bi-direction_2024.pdf" }, { "title": "Aligning LLM Agents by Learning Latent Preference from User Edits", "authors": [ "Ge Gao", "Alexey Taymanov", "Eduardo Salinas", "Paul Mineiro", "Dipendra Misra" ], "year": 2024, "venue": "NeurIPS", "abstract": "We study interactive learning of language agents based on user edits made to the agent's output. In a typical setting such as writing assistants, the user interacts with a language agent to generate a response given a context, and may optionally edit the agent response to personalize it based on their latent preference, in addition to improving the correctness. The edit feedback is naturally generated, making it a suitable candidate for improving the agent's alignment with the user's preference, and for reducing the cost of user edits over time. We propose a learning framework, PRELUDE that infers a description of the user's latent preference based on historic edit data and using it to define a prompt policy that drives future response generation. This avoids fine-tuning the agent, which is costly, challenging to scale with the number of users, and may even degrade its performance on other tasks. Furthermore, learning descriptive preference improves interpretability, allowing the user to view and modify the learned preference. However, user preference can be complex and vary based on context, making it challenging to learn. To address this, we propose a simple yet effective algorithm named CIPHER that leverages a large language model (LLM) to infer the user preference for a given context based on user edits. In the future, CIPHER retrieves inferred preferences from the k-closest contexts in the history, and forms an aggregate preference for response generation. We introduce two interactive environments -- summarization and email writing, for evaluation using a GPT-4 simulated user. We compare with algorithms that directly retrieve user edits but do not learn descriptive preference, and algorithms that learn context-agnostic preference. On both tasks, CIPHER outperforms baselines by achieving the lowest edit distance cost. Meanwhile, CIPHER has a lower computational expense, as using learned preference results in a shorter prompt than directly using user edits. Our further analysis reports that the user preference learned by CIPHER shows significant similarity to the ground truth latent preference.", "source": "openreview", "url": "https://openreview.net/forum?id=DlYNGpCuwa", "decision_type": "Poster", "avg_rating": 6.2, "relative_path": "2024/NeurIPS/Poster/6.2_Aligning LLM Agents by Learning Latent Preference from User Edits_2024.pdf" }, { "title": "Amortized Bayesian Experimental Design for Decision-Making", "authors": [ "Daolang Huang", "Yujia Guo", "Luigi Acerbi", "Samuel Kaski" ], "year": 2024, "venue": "NeurIPS", "abstract": "Many critical decisions, such as personalized medical diagnoses and product pricing, are made based on insights gained from designing, observing, and analyzing a series of experiments. This highlights the crucial role of experimental design, which goes beyond merely collecting information on system parameters as in traditional Bayesian experimental design (BED), but also plays a key part in facilitating downstream decision-making. Most recent BED methods use an amortized policy network to rapidly design experiments. However, the information gathered through these methods is suboptimal for down-the-line decision-making, as the experiments are not inherently designed with downstream objectives in mind. In this paper, we present an amortized decision-aware BED framework that prioritizes maximizing downstream decision utility. We introduce a novel architecture, the Transformer Neural Decision Process (TNDP), capable of instantly proposing the next experimental design, whilst inferring the downstream decision, thus effectively amortizing both tasks within a unified workflow. We demonstrate the performance of our method across several tasks, showing that it can deliver informative designs and facilitate accurate decision-making.", "source": "openreview", "url": "https://openreview.net/forum?id=zBG7WogAvm", "decision_type": "Poster", "avg_rating": 6.2, "relative_path": "2024/NeurIPS/Poster/6.2_Amortized Bayesian Experimental Design for Decision-Making_2024.pdf" }, { "title": "Attention boosted Individualized Regression", "authors": [ "Guang Yang", "Yuan Cao", "Long Feng" ], "year": 2024, "venue": "NeurIPS", "abstract": "Different from classical one-model-fits-all strategy, individualized models allow parameters to vary across samples and are gaining popularity in various fields, particularly in personalized medicine. Motivated by medical imaging analysis, this paper introduces a novel individualized modeling framework for matrix-valued data that does not require additional information on sample similarity for the individualized coefficients. Under our framework, the model individualization stems from an optimal internal relation map within the samples themselves. We refer to the proposed method as Attention boosted Individualized Regression, due to its close connections with the self-attention mechanism. Therefore, our approach provides a new interpretation for attention from the perspective of individualized modeling. Comprehensive numerical experiments and real brain MRI analysis using an ADNI dataset demonstrated the superior performance of our model.", "source": "openreview", "url": "https://openreview.net/forum?id=9xoFciqYIU", "decision_type": "Poster", "avg_rating": 6.2, "relative_path": "2024/NeurIPS/Poster/6.2_Attention boosted Individualized Regression_2024.pdf" }, { "title": "Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting", "authors": [ "Qingxiang Liu", "Xu Liu", "Chenghao Liu", "Qingsong Wen", "Yuxuan Liang" ], "year": 2024, "venue": "NeurIPS", "abstract": "Unlike natural language processing and computer vision, the development of Foundation Models (FMs) for time series forecasting is blocked due to data scarcity. \nWhile recent efforts are focused on building such FMs by unlocking the potential of language models (LMs) for time series analysis, dedicated parameters for various downstream forecasting tasks need training, which hinders the common knowledge sharing across domains.\nMoreover, data owners may hesitate to share the access to local data due to privacy concerns and copyright protection, which makes it impossible to simply construct a FM on cross-domain training instances.\nTo address these issues, we propose Time-FFM, a Federated Foundation Model for Time series forecasting by leveraging pretrained LMs.\nSpecifically, we begin by transforming time series into the modality of text tokens.\nTo bootstrap LMs for time series reasoning, we propose a prompt adaption module to determine domain-customized prompts dynamically instead of artificially.\nGiven the data heterogeneity across domains, we design a personalized federated training strategy by learning global encoders and local prediction heads. \nOur comprehensive experiments indicate that Time-FFM outperforms state-of-the-arts and promises effective few-shot and zero-shot forecaster.\nThe code is available at https://github.com/CityMind-Lab/NeurIPS24-Time-FFM/tree/main.", "source": "openreview", "url": "https://openreview.net/forum?id=HS0faHRhWD", "decision_type": "Poster", "avg_rating": 6.2, "relative_path": "2024/NeurIPS/Poster/6.2_Time-FFM_ Towards LM-Empowered Federated Foundation Model for Time Series Forecasting_2024.pdf" }, { "title": "An Autoencoder-Like Nonnegative Matrix Co-Factorization for Improved Student Cognitive Modeling", "authors": [ "Shenbao Yu", "Yinghui Pan", "Yifeng Zeng", "Prashant Doshi", "Guoquan Liu", "Kim-Leng Poh", "Mingwei Lin" ], "year": 2024, "venue": "NeurIPS", "abstract": "Student cognitive modeling (SCM) is a fundamental task in intelligent education, with applications ranging from personalized learning to educational resource allocation. By exploiting students' response logs, SCM aims to predict their exercise performance as well as estimate knowledge proficiency in a subject. Data mining approaches such as matrix factorization can obtain high accuracy in predicting student performance on exercises, but the knowledge proficiency is unknown or poorly estimated. The situation is further exacerbated if only sparse interactions exist between exercises and students (or knowledge concepts). To solve this dilemma, we root monotonicity (a fundamental psychometric theory on educational assessments) in a co-factorization framework and present an autoencoder-like nonnegative matrix co-factorization (AE-NMCF), which improves the accuracy of estimating the student's knowledge proficiency via an encoder-decoder learning pipeline. The resulting estimation problem is nonconvex with nonnegative constraints. We introduce a projected gradient method based on block coordinate descent with Lipschitz constants and guarantee the method's theoretical convergence. Experiments on several real-world data sets demonstrate the efficacy of our approach in terms of both performance prediction accuracy and knowledge estimation ability, when compared with existing student cognitive models.", "source": "openreview", "url": "https://openreview.net/forum?id=8UqyWNsnyA", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2024/NeurIPS/Poster/6.0_An Autoencoder-Like Nonnegative Matrix Co-Factorization for Improved Student Cognitive Modeling_2024.pdf" }, { "title": "Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval", "authors": [ "Haolun Wu", "Ofer Meshi", "Masrour Zoghi", "Fernando Diaz", "Xue Liu", "Craig Boutilier", "MARYAM KARIMZADEHGAN" ], "year": 2024, "venue": "NeurIPS", "abstract": "Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems. Existing user modeling methods, like single-point and multi-point representations, have limitations w.r.t.\\ accuracy, diversity, and adaptability. To overcome these deficiencies, we introduce density-based user representations (DURs), a novel method that leverages Gaussian process regression (GPR) for effective multi-interest recommendation and retrieval. Our approach, GPR4DUR, exploits DURs to capture user interest variability without manual tuning, incorporates uncertainty-awareness, and scales well to large numbers of users. Experiments using real-world offline datasets confirm the adaptability and efficiency of GPR4DUR, while online experiments with simulated users demonstrate its ability to address the exploration-exploitation trade-off by effectively utilizing model uncertainty.", "source": "openreview", "url": "https://openreview.net/forum?id=Px1hQM72iX", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2024/NeurIPS/Poster/6.0_Density-based User Representation using Gaussian Process Regression for Multi-interest Personal_2024.pdf" }, { "title": "Dual-Personalizing Adapter for Federated Foundation Models", "authors": [ "yiyuan yang", "Guodong Long", "Tao Shen", "Jing Jiang", "Michael Blumenstein" ], "year": 2024, "venue": "NeurIPS", "abstract": "Recently, foundation models, particularly large language models (LLMs), have demonstrated an impressive ability to adapt to various tasks by fine-tuning diverse instruction data. Notably, federated foundation models (FedFM) emerge as a privacy preservation method to fine-tune models collaboratively under federated learning (FL) settings by leveraging many distributed datasets with non-IID data. To alleviate communication and computation overhead, parameter-efficient methods are introduced for efficiency, and some research adapted personalization methods to FedFM for better user preferences alignment. However, a critical gap in existing research is the neglect of test-time distribution shifts in real-world applications, and conventional methods for test-time distribution shifts in personalized FL are less effective for FedFM due to their failure to adapt to complex distribution shift scenarios and the requirement to train all parameters. To bridge this gap, we refine the setting in FedFM, termed test-time personalization, which aims to learn personalized federated foundation models on clients while effectively handling test-time distribution shifts simultaneously. To address challenges in this setting, we explore a simple yet effective solution, a Federated Dual-Personalizing Adapter (FedDPA) architecture. By co-working with a foundation model, a global adapter and a local adapter jointly tackle the test-time distribution shifts and client-specific personalization. Additionally, we introduce an instance-wise dynamic weighting mechanism that dynamically integrates the global and local adapters for each test instance during inference, facilitating effective test-time personalization. The effectiveness of the proposed method has been evaluated on benchmark datasets across different NLP tasks.", "source": "openreview", "url": "https://openreview.net/forum?id=nkwPiBSw1f", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2024/NeurIPS/Poster/6.0_Dual-Personalizing Adapter for Federated Foundation Models_2024.pdf" }, { "title": "Dynamic Subgroup Identification in Covariate-adjusted Response-adaptive Randomization Experiments", "authors": [ "Yanping Li", "Jingshen Wang", "Waverly Wei" ], "year": 2024, "venue": "NeurIPS", "abstract": "Identifying subgroups with differential responses to treatment is pivotal in randomized clinical trials, as tailoring treatments to specific subgroups can advance personalized medicine. Upon trial completion, identifying best-performing subgroups–those with the most beneficial treatment effects–is crucial for optimizing resource allocation or mitigating adverse treatment effects. However, traditional clinical trials are not customized for the goal of identifying best-performing subgroups because they typically pre-define subgroups at the beginning of the trial and adhere to a fixed subgroup treatment allocation rule, leading to inefficient use of experimental efforts. While some adaptive experimental strategies exist for the identification of the single best subgroup, they commonly do not enable the identification of the best set of subgroups. To address these challenges, we propose a dynamic subgroup identification covariate-adjusted response-adaptive randomization (CARA) design strategy with the following key features: (i) Our approach is an adaptive experimental strategy that allows the dynamic identification of the best subgroups and the revision of treatment allocation towards the goal of correctly identifying the best subgroups based on collected experimental data. (ii) Our design handles ties between subgroups effectively, merging those with similar treatment effects to maximize experimental efficiency. In the theoretical investigations, we demonstrate that our design has a higher probability of correctly identifying the best set of subgroups compared to conventional designs. Additionally, we prove the statistical validity of our estimator for the best subgroup treatment effect, demonstrating its asymptotic normality and semiparametric efficiency. Finally, we validate our design using synthetic data from a clinical trial on cirrhosis.", "source": "openreview", "url": "https://openreview.net/forum?id=4WIBvL6ZF4", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2024/NeurIPS/Poster/6.0_Dynamic Subgroup Identification in Covariate-adjusted Response-adaptive Randomization Experimen_2024.pdf" }, { "title": "Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data", "authors": [ "Miruna Oprescu", "Nathan Kallus" ], "year": 2024, "venue": "NeurIPS", "abstract": "Accurately predicting conditional average treatment effects (CATEs) is crucial in personalized medicine and digital platform analytics. \nSince the treatments of interest often cannot be directly randomized, observational data is leveraged to learn CATEs, but this approach can incur significant bias from unobserved confounding. One strategy to overcome these limitations is to leverage instrumental variables (IVs) as latent quasi-experiments, such as randomized intent-to-treat assignments or randomized product recommendations. This approach, on the other hand, can suffer from low compliance, i.e., IV weakness. Some subgroups may even exhibit zero compliance, meaning we cannot instrument for their CATEs at all. In this paper, we develop a novel approach to combine IV and observational data to enable reliable CATE estimation in the presence of unobserved confounding in the observational data and low compliance in the IV data, including no compliance for some subgroups. We propose a two-stage framework that first learns \\textit{biased} CATEs from the observational data, and then applies a compliance-weighted correction using IV data, effectively leveraging IV strength variability across covariates. We characterize the convergence rates of our method and validate its effectiveness through a simulation study. Additionally, we demonstrate its utility with real data by analyzing the heterogeneous effects of 401(k) plan participation on wealth.", "source": "openreview", "url": "https://openreview.net/forum?id=c37x7CXZ2Y", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2024/NeurIPS/Poster/6.0_Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data_2024.pdf" }, { "title": "Faster Accelerated First-order Methods for Convex Optimization with Strongly Convex Function Constraints", "authors": [ "Zhenwei Lin", "Qi Deng" ], "year": 2024, "venue": "NeurIPS", "abstract": "In this paper, we introduce faster accelerated primal-dual algorithms for minimizing a convex function subject to strongly convex function constraints. \nPrior to our work, the best complexity bound was $\\mathcal{O}(1/{\\varepsilon})$, regardless of the strong convexity of the constraint function.\nIt is unclear whether the strong convexity assumption can enable even better convergence results. \nTo address this issue, we have developed novel techniques to progressively estimate the strong convexity of the Lagrangian function.\nOur approach, for the first time, effectively leverages the constraint strong convexity, obtaining an improved complexity of $\\mathcal{O}(1/\\sqrt{\\varepsilon})$. This rate matches the complexity lower bound for strongly-convex-concave saddle point optimization and is therefore order-optimal.\nWe show the superior performance of our methods in sparsity-inducing constrained optimization, notably Google's personalized PageRank problem. Furthermore, we show that a restarted version of the proposed methods can effectively identify the optimal solution's sparsity pattern within a finite number of steps, a result that appears to have independent significance.", "source": "openreview", "url": "https://openreview.net/forum?id=pG380vLYRU", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2024/NeurIPS/Poster/6.0_Faster Accelerated First-order Methods for Convex Optimization with Strongly Convex Function Co_2024.pdf" }, { "title": "Faster Diffusion: Rethinking the Role of the Encoder for Diffusion Model Inference", "authors": [ "Senmao Li", "taihang Hu", "Joost van de Weijer", "Fahad Khan", "Tao Liu", "Linxuan Li", "Shiqi Yang", "Yaxing Wang", "Ming-Ming Cheng", "jian Yang" ], "year": 2024, "venue": "NeurIPS", "abstract": "One of the main drawback of diffusion models is the slow inference time for image generation. Among the most successful approaches to addressing this problem are distillation methods. However, these methods require considerable computational resources. In this paper, we take another approach to diffusion model acceleration. We conduct a comprehensive study of the UNet encoder and empirically analyze the encoder features. This provides insights regarding their changes during the inference process. In particular, we find that encoder features change minimally, whereas the decoder features exhibit substantial variations across different time-steps. This insight motivates us to omit encoder computation at certain adjacent time-steps and reuse encoder features of previous time-steps as input to the decoder in multiple time-steps. Importantly, this allows us to perform decoder computation in parallel, further accelerating the denoising process. Additionally, we introduce a prior noise injection method to improve the texture details in the generated image. Besides the standard text-to-image task, we also validate our approach on other tasks: text-to-video, personalized generation and reference-guided generation. Without utilizing any knowledge distillation technique, our approach accelerates both the Stable Diffusion (SD) and DeepFloyd-IF model sampling by 41$\\%$ and 24$\\%$ respectively, and DiT model sampling by 34$\\%$, while maintaining high-quality generation performance. Our code will be publicly released.", "source": "openreview", "url": "https://openreview.net/forum?id=ca2mABGV6p", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2024/NeurIPS/Poster/6.0_Faster Diffusion_ Rethinking the Role of the Encoder for Diffusion Model Inference_2024.pdf" }, { "title": "FedGMKD: An Efficient Prototype Federated Learning Framework through Knowledge Distillation and Discrepancy-Aware Aggregation", "authors": [ "Jianqiao Zhang", "Caifeng Shan", "Jungong Han" ], "year": 2024, "venue": "NeurIPS", "abstract": "Federated Learning (FL) faces significant challenges due to data heterogeneity across distributed clients. To address this, we propose FedGMKD, a novel framework that combines knowledge distillation and differential aggregation for efficient prototype-based personalized FL without the need for public datasets or server-side generative models. FedGMKD introduces Cluster Knowledge Fusion, utilizing Gaussian Mixture Models to generate prototype features and soft predictions on the client side, enabling effective knowledge distillation while preserving data privacy. Additionally, we implement a Discrepancy-Aware Aggregation Technique that weights client contributions based on data quality and quantity, enhancing the global model's generalization across diverse client distributions. Theoretical analysis confirms the convergence of FedGMKD. Extensive experiments on benchmark datasets, including SVHN, CIFAR-10, and CIFAR-100, demonstrate that FedGMKD outperforms state-of-the-art methods, significantly improving both local and global accuracy in non-IID data settings.", "source": "openreview", "url": "https://openreview.net/forum?id=c3OZBJpN7M", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2024/NeurIPS/Poster/6.0_FedGMKD_ An Efficient Prototype Federated Learning Framework through Knowledge Distillation and_2024.pdf" }, { "title": "Federated Model Heterogeneous Matryoshka Representation Learning", "authors": [ "Liping Yi", "Han Yu", "Chao Ren", "Gang Wang", "xiaoguang Liu", "Xiaoxiao Li" ], "year": 2024, "venue": "NeurIPS", "abstract": "Model heterogeneous federated learning (MHeteroFL) enables FL clients to collaboratively train models with heterogeneous structures in a distributed fashion. However, existing MHeteroFL methods rely on training loss to transfer knowledge between the client model and the server model, resulting in limited knowledge exchange. To address this limitation, we propose the **Fed**erated model heterogeneous **M**atryoshka **R**epresentation **L**earning (**FedMRL**) approach for supervised learning tasks. It adds an auxiliary small homogeneous model shared by clients with heterogeneous local models. (1) The generalized and personalized representations extracted by the two models' feature extractors are fused by a personalized lightweight representation projector. This step enables representation fusion to adapt to local data distribution. (2) The fused representation is then used to construct Matryoshka representations with multi-dimensional and multi-granular embedded representations learned by the global homogeneous model header and the local heterogeneous model header. This step facilitates multi-perspective representation learning and improves model learning capability. Theoretical analysis shows that FedMRL achieves a $O(1/T)$ non-convex convergence rate. Extensive experiments on benchmark datasets demonstrate its superior model accuracy with low communication and computational costs compared to seven state-of-the-art baselines. It achieves up to 8.48% and 24.94% accuracy improvement compared with the state-of-the-art and the best same-category baseline, respectively.", "source": "openreview", "url": "https://openreview.net/forum?id=5yboFMpvHf", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2024/NeurIPS/Poster/6.0_Federated Model Heterogeneous Matryoshka Representation Learning_2024.pdf" }, { "title": "HYDRA: Model Factorization Framework for Black-Box LLM Personalization", "authors": [ "Yuchen Zhuang", "Haotian Sun", "Yue Yu", "Rushi Qiang", "Qifan Wang", "Chao Zhang", "Bo Dai" ], "year": 2024, "venue": "NeurIPS", "abstract": "Personalization has emerged as a critical research area in modern intelligent systems, focusing on mining users' behavioral history and adapting to their preferences for delivering tailored experiences. Despite the remarkable few-shot capabilities exhibited by black-box large language models (LLMs), the inherent opacity of their model parameters presents significant challenges in aligning the generated output with individual expectations. Existing solutions have primarily focused on prompt design to incorporate user-specific profiles and behaviors; however, such approaches often struggle to generalize effectively due to their inability to capture shared knowledge among all users. To address these challenges, we propose HYDRA, a model factorization framework that captures both user-specific behavior patterns from historical data and shared general knowledge among all users to deliver personalized generation. In order to capture user-specific behavior patterns, we first train a reranker to prioritize the most useful information from top-retrieved relevant historical records.\nBy combining the prioritized history with the corresponding query, we train an adapter to align the output with individual user-specific preferences, eliminating the reliance on access to inherent model parameters of black-box LLMs. Both the reranker and the adapter can be decomposed into a base model with multiple user-specific heads, resembling a hydra. The base model maintains shared knowledge across users, while the multiple personal heads capture user-specific preferences. Experimental results demonstrate that \\method outperforms existing state-of-the-art prompt-based methods by an average relative improvement of 9.01% across five diverse personalization tasks in the LaMP benchmark.", "source": "openreview", "url": "https://openreview.net/forum?id=CKgNgKmHYp", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2024/NeurIPS/Poster/6.0_HYDRA_ Model Factorization Framework for Black-Box LLM Personalization_2024.pdf" }, { "title": "Identifying Latent State-Transition Processes for Individualized Reinforcement Learning", "authors": [ "Yuewen Sun", "Biwei Huang", "Yu Yao", "Donghuo Zeng", "Xinshuai Dong", "Songyao Jin", "Boyang Sun", "Roberto Legaspi", "Kazushi Ikeda", "Peter Spirtes", "Kun Zhang" ], "year": 2024, "venue": "NeurIPS", "abstract": "The application of reinforcement learning (RL) involving interactions with individuals has grown significantly in recent years. These interactions, influenced by factors such as personal preferences and physiological differences, causally influence state transitions, ranging from health conditions in healthcare to learning progress in education. As a result, different individuals may exhibit different state-transition processes. Understanding individualized state-transition processes is essential for optimizing individualized policies. In practice, however, identifying these state-transition processes is challenging, as individual-specific factors often remain latent. In this paper, we establish the identifiability of these latent factors and introduce a practical method that effectively learns these processes from observed state-action trajectories. Experiments on various datasets show that the proposed method can effectively identify latent state-transition processes and facilitate the learning of individualized RL policies.", "source": "openreview", "url": "https://openreview.net/forum?id=kREpCQtHdN", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2024/NeurIPS/Poster/6.0_Identifying Latent State-Transition Processes for Individualized Reinforcement Learning_2024.pdf" }, { "title": "Learning Human-like Representations to Enable Learning Human Values", "authors": [ "Andrea Wynn", "Ilia Sucholutsky", "Thomas L. Griffiths" ], "year": 2024, "venue": "NeurIPS", "abstract": "How can we build AI systems that can learn any set of individual human values both quickly and safely, avoiding causing harm or violating societal standards for acceptable behavior during the learning process? We explore the effects of representational alignment between humans and AI agents on learning human values. Making AI systems learn human-like representations of the world has many known benefits, including improving generalization, robustness to domain shifts, and few-shot learning performance. We demonstrate that this kind of representational alignment can also support safely learning and exploring human values in the context of personalization. We begin with a theoretical prediction, show that it applies to learning human morality judgments, then show that our results generalize to ten different aspects of human values -- including ethics, honesty, and fairness -- training AI agents on each set of values in a multi-armed bandit setting, where rewards reflect human value judgments over the chosen action. Using a set of textual action descriptions, we collect value judgments from humans, as well as similarity judgments from both humans and multiple language models, and demonstrate that representational alignment enables both safe exploration and improved generalization when learning human values.", "source": "openreview", "url": "https://openreview.net/forum?id=sQApQMBqiP", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2024/NeurIPS/Poster/6.0_Learning Human-like Representations to Enable Learning Human Values_2024.pdf" }, { "title": "RectifID: Personalizing Rectified Flow with Anchored Classifier Guidance", "authors": [ "Zhicheng Sun", "Zhenhao Yang", "Yang Jin", "Haozhe Chi", "Kun Xu", "Kun Xu", "Liwei Chen", "Hao Jiang", "Yang Song", "Kun Gai", "Yadong MU" ], "year": 2024, "venue": "NeurIPS", "abstract": "Customizing diffusion models to generate identity-preserving images from user-provided reference images is an intriguing new problem. The prevalent approaches typically require training on extensive domain-specific images to achieve identity preservation, which lacks flexibility across different use cases. To address this issue, we exploit classifier guidance, a training-free technique that steers diffusion models using an existing classifier, for personalized image generation. Our study shows that based on a recent rectified flow framework, the major limitation of vanilla classifier guidance in requiring a special classifier can be resolved with a simple fixed-point solution, allowing flexible personalization with off-the-shelf image discriminators. Moreover, its solving procedure proves to be stable when anchored to a reference flow trajectory, with a convergence guarantee. The derived method is implemented on rectified flow with different off-the-shelf image discriminators, delivering advantageous personalization results for human faces, live subjects, and certain objects. Code is available at https://github.com/feifeiobama/RectifID.", "source": "openreview", "url": "https://openreview.net/forum?id=KKrj1vCQaG", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2024/NeurIPS/Poster/6.0_RectifID_ Personalizing Rectified Flow with Anchored Classifier Guidance_2024.pdf" }, { "title": "RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health Interventions", "authors": [ "Easton Knight Huch", "Jieru Shi", "Madeline R Abbott", "Jessica R Golbus", "Alexander Moreno", "Walter H. Dempsey" ], "year": 2024, "venue": "NeurIPS", "abstract": "Mobile health leverages personalized and contextually tailored interventions optimized through bandit and reinforcement learning algorithms. In practice, however, challenges such as participant heterogeneity, nonstationarity, and nonlinear relationships hinder algorithm performance. We propose RoME, a **Ro**bust **M**ixed-**E**ffects contextual bandit algorithm that simultaneously addresses these challenges via (1) modeling the differential reward with user- and time-specific random effects, (2) network cohesion penalties, and (3) debiased machine learning for flexible estimation of baseline rewards. We establish a high-probability regret bound that depends solely on the dimension of the differential-reward model, enabling us to achieve robust regret bounds even when the baseline reward is highly complex. We demonstrate the superior performance of the RoME algorithm in a simulation and two off-policy evaluation studies.", "source": "openreview", "url": "https://openreview.net/forum?id=eKVugi5zr0", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2024/NeurIPS/Poster/6.0_RoME_ A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health Interventions_2024.pdf" }, { "title": "Taming the Long Tail in Human Mobility Prediction", "authors": [ "Xiaohang Xu", "Renhe Jiang", "Chuang Yang", "zipei fan", "Kaoru Sezaki" ], "year": 2024, "venue": "NeurIPS", "abstract": "With the popularity of location-based services, human mobility prediction plays a key role in enhancing personalized navigation, optimizing recommendation systems, and facilitating urban mobility and planning. This involves predicting a user's next POI (point-of-interest) visit using their past visit history. However, the uneven distribution of visitations over time and space, namely the long-tail problem in spatial distribution, makes it difficult for AI models to predict those POIs that are less visited by humans. In light of this issue, we propose the $\\underline{\\bf{Lo}}$ng-$\\underline{\\bf{T}}$ail Adjusted $\\underline{\\bf{Next}}$ POI Prediction (LoTNext) framework for mobility prediction, combining a Long-Tailed Graph Adjustment module to reduce the impact of the long-tailed nodes in the user-POI interaction graph and a novel Long-Tailed Loss Adjustment module to adjust loss by logit score and sample weight adjustment strategy. Also, we employ the auxiliary prediction task to enhance generalization and accuracy. Our experiments with two real-world trajectory datasets demonstrate that LoTNext significantly surpasses existing state-of-the-art works.", "source": "openreview", "url": "https://openreview.net/forum?id=wT2TIfHKp8", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2024/NeurIPS/Poster/6.0_Taming the Long Tail in Human Mobility Prediction_2024.pdf" }, { "title": "Yo'LLaVA: Your Personalized Language and Vision Assistant", "authors": [ "Thao Nguyen", "Haotian Liu", "Yuheng Li", "Mu Cai", "Utkarsh Ojha", "Yong Jae Lee" ], "year": 2024, "venue": "NeurIPS", "abstract": "Large Multimodal Models (LMMs) have shown remarkable capabilities across a variety of tasks (e.g., image captioning, visual question answering).\nWhile broad, their knowledge remains generic (e.g., recognizing a dog), and they are unable to handle personalized subjects (e.g., recognizing a user's pet dog).\n\nHuman reasoning, in contrast, typically operates within the context of specific subjects in our surroundings. For example, one might ask, \"What should I buy for *my dog*'s birthday?\"; as opposed to a generic inquiry about \"What should I buy for *a dog*'s birthday?\".\nSimilarly, when looking at a friend's image, the interest lies in seeing their activities (e.g., \"*my friend* is holding a cat\"), rather than merely observing generic human actions (e.g., \"*a man* is holding a cat\").\n\nIn this paper, we introduce the novel task of personalizing LMMs, so that they can have conversations about a specific subject. We propose Yo'LLaVA, which learns to embed a personalized subject into a set of latent tokens given a handful of example images of the subject. Our qualitative and quantitative analyses reveal that Yo'LLaVA can learn the concept more efficiently using fewer tokens and more effectively encode the visual attributes compared to strong prompting baselines (e.g., LLaVA).", "source": "openreview", "url": "https://openreview.net/forum?id=mjGy8g3pgi", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2024/NeurIPS/Poster/6.0_Yo'LLaVA_ Your Personalized Language and Vision Assistant_2024.pdf" }, { "title": "A Structure-Aware Framework for Learning Device Placements on Computation Graphs", "authors": [ "Shukai Duan", "Heng Ping", "Nikos Kanakaris", "Xiongye Xiao", "Panagiotis Kyriakis", "Nesreen K. Ahmed", "Peiyu Zhang", "Guixiang Ma", "Mihai Capotă", "Shahin Nazarian", "Theodore L. Willke", "Paul Bogdan" ], "year": 2024, "venue": "NeurIPS", "abstract": "Computation graphs are Directed Acyclic Graphs (DAGs) where the nodes correspond to mathematical operations and are used widely as abstractions in optimizations of neural networks. The device placement problem aims to identify optimal allocations of those nodes to a set of (potentially heterogeneous) devices. Existing approaches rely on two types of architectures known as grouper-placer and encoder-placer, respectively. In this work, we bridge the gap between encoder-placer and grouper-placer techniques and propose a novel framework for the task of device placement, relying on smaller computation graphs extracted from the OpenVINO toolkit. The framework consists of five steps, including graph coarsening, node representation learning and policy optimization. It facilitates end-to-end training and takes into account the DAG nature of the computation graphs. We also propose a model variant, inspired by graph parsing networks and complex network analysis, enabling graph representation learning and jointed, personalized graph partitioning, using an unspecified number of groups. To train the entire framework, we use reinforcement learning using the execution time of the placement as a reward. We demonstrate the flexibility and effectiveness of our approach through multiple experiments with three benchmark models, namely Inception-V3, ResNet, and BERT. The robustness of the proposed framework is also highlighted through an ablation study. The suggested placements improve the inference speed for the benchmark models by up to $58.2\\%$ over CPU execution and by up to $60.24\\%$ compared to other commonly used baselines.", "source": "openreview", "url": "https://openreview.net/forum?id=Kzno1r3Xef", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2024/NeurIPS/Poster/5.8_A Structure-Aware Framework for Learning Device Placements on Computation Graphs_2024.pdf" }, { "title": "Direct Consistency Optimization for Robust Customization of Text-to-Image Diffusion models", "authors": [ "Kyungmin Lee", "Sangkyung Kwak", "Kihyuk Sohn", "Jinwoo Shin" ], "year": 2024, "venue": "NeurIPS", "abstract": "Text-to-image (T2I) diffusion models, when fine-tuned on a few personal images, can generate visuals with a high degree of consistency. However, such fine-tuned models are not robust; they often fail to compose with concepts of pretrained model or other fine-tuned models. To address this, we propose a novel fine-tuning objective, dubbed Direct Consistency Optimization, which controls the deviation between fine-tuning and pretrained models to retain the pretrained knowledge during fine-tuning. Through extensive experiments on subject and style customization, we demonstrate that our method positions itself on a superior Pareto frontier between subject (or style) consistency and image-text alignment over all previous baselines; it not only outperforms regular fine-tuning objective in image-text alignment, but also shows higher fidelity to the reference images than the method that fine-tunes with additional prior dataset. More importantly, the models fine-tuned with our method can be merged without interference, allowing us to generate custom subjects in a custom style by composing separately customized subject and style models. Notably, we show that our approach achieves better prompt fidelity and subject fidelity than those post-optimized for merging regular fine-tuned models.", "source": "openreview", "url": "https://openreview.net/forum?id=VazkRbCGxt", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2024/NeurIPS/Poster/5.8_Direct Consistency Optimization for Robust Customization of Text-to-Image Diffusion models_2024.pdf" }, { "title": "FedAvP: Augment Local Data via Shared Policy in Federated Learning", "authors": [ "Minui Hong", "Junhyeog Yun", "Insu Jeon", "Gunhee Kim" ], "year": 2024, "venue": "NeurIPS", "abstract": "Federated Learning (FL) allows multiple clients to collaboratively train models without directly sharing their private data. While various data augmentation techniques have been actively studied in the FL environment, most of these methods share input-level or feature-level data information over communication, posing potential privacy leakage. In response to this challenge, we introduce a federated data augmentation algorithm named FedAvP that shares only the augmentation policies, not the data-related information. \nFor data security and efficient policy search, we interpret the policy loss as a meta update loss in standard FL algorithms and utilize the first-order gradient information to further enhance privacy and reduce communication costs. Moreover, we propose a meta-learning method to search for adaptive personalized policies tailored to heterogeneous clients. Our approach outperforms existing best performing augmentation policy search methods and federated data augmentation methods, in the benchmarks for heterogeneous FL.", "source": "openreview", "url": "https://openreview.net/forum?id=M1PRU0x1Iz", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2024/NeurIPS/Poster/5.8_FedAvP_ Augment Local Data via Shared Policy in Federated Learning_2024.pdf" }, { "title": "IQA-EVAL: Automatic Evaluation of Human-Model Interactive Question Answering", "authors": [ "Ruosen Li", "Ruochen Li", "Barry Wang", "Xinya Du" ], "year": 2024, "venue": "NeurIPS", "abstract": "To evaluate Large Language Models (LLMs) for question answering (QA), traditional methods typically focus on directly assessing the immediate responses generated by the models based on the given question and context. In the common use case of humans seeking AI assistant’s help in finding information, these non-interactive evaluations do not account for the dynamic nature of human-model conversations, and interaction-aware evaluations have shown that accurate models are not necessarily preferred by humans Lee et al. Recent works in human-computer interaction (HCI) have employed human evaluators to conduct interactions and evaluations, but they are often prohibitively expensive and time-consuming to scale. In this work, we introduce an automated evaluation framework IQA-EVAL to Interactive Question Answering Evaluations, more specifically, we introduce LLM-based Evaluation Agent (LEA) that can: (1) simulate human behaviors to generate interactions with IQA models; (2) automatically evaluate the generated interactions. Moreover, we propose assigning personas to LEAs to better simulate groups of real human evaluators. We show that: (1) our evaluation framework with GPT-4 (or Claude) as the backbone model achieves a high correlation with human evaluations on the IQA task; (2) assigning personas to LEA to better represent the crowd further significantly improves correlations. Finally, we use our automated metric to evaluate five recent LLMs with over 1000 questions from complex and ambiguous question answering tasks, which would cost $5k if evaluated by humans.", "source": "openreview", "url": "https://openreview.net/forum?id=MzM99vV5Rx", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2024/NeurIPS/Poster/5.8_IQA-EVAL_ Automatic Evaluation of Human-Model Interactive Question Answering_2024.pdf" }, { "title": "Identity Decoupling for Multi-Subject Personalization of Text-to-Image Models", "authors": [ "Sangwon Jang", "Jaehyeong Jo", "Kimin Lee", "Sung Ju Hwang" ], "year": 2024, "venue": "NeurIPS", "abstract": "Text-to-image diffusion models have shown remarkable success in generating personalized subjects based on a few reference images. However, current methods often fail when generating multiple subjects simultaneously, resulting in mixed\nidentities with combined attributes from different subjects. In this work, we present MuDI, a novel framework that enables multi-subject personalization by effectively decoupling identities from multiple subjects. Our main idea is to utilize segmented subjects generated by a foundation model for segmentation (Segment Anything) for both training and inference, as a form of data augmentation for training and initialization for the generation process. Moreover, we further introduce a new metric to better evaluate the performance of our method on multi-subject personalization. Experimental results show that our MuDI can produce high-quality personalized images without identity mixing, even for highly similar subjects as shown in Figure 1. Specifically, in human evaluation, MuDI obtains twice the success rate for personalizing multiple subjects without identity mixing over existing baselines and is preferred over 70% against the strongest baseline.", "source": "openreview", "url": "https://openreview.net/forum?id=tEEpVPDaRf", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2024/NeurIPS/Poster/5.8_Identity Decoupling for Multi-Subject Personalization of Text-to-Image Models_2024.pdf" }, { "title": "InstructG2I: Synthesizing Images from Multimodal Attributed Graphs", "authors": [ "Bowen Jin", "Ziqi Pang", "Bingjun Guo", "Yu-Xiong Wang", "Jiaxuan You", "Jiawei Han" ], "year": 2024, "venue": "NeurIPS", "abstract": "In this paper, we approach an overlooked yet critical task Graph2Image: generating images from multimodal attributed graphs (MMAGs). This task poses significant challenges due to the explosion in graph size, dependencies among graph entities, and the need for controllability in graph conditions. To address these challenges, we propose a graph context-conditioned diffusion model called InstructG2I. InstructG2I first exploits the graph structure and multimodal information to conduct informative neighbor sampling by combining personalized page rank and re-ranking based on vision-language features. Then, a graph QFormer encoder adaptively encodes the graph nodes into an auxiliary set of graph prompts to guide the denoising process of diffusion. Finally, we propose graph classifier-free guidance, enabling controllable generation by varying the strength of graph guidance and multiple connected edges to a node. Extensive experiments conducted on three datasets from different domains demonstrate the effectiveness and controllability of our approach. The code is available at https://github.com/PeterGriffinJin/InstructG2I.", "source": "openreview", "url": "https://openreview.net/forum?id=zWnW4zqkuM", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2024/NeurIPS/Poster/5.8_InstructG2I_ Synthesizing Images from Multimodal Attributed Graphs_2024.pdf" }, { "title": "Private and Personalized Frequency Estimation in a Federated Setting", "authors": [ "Amrith Setlur", "Vitaly Feldman", "Kunal Talwar" ], "year": 2024, "venue": "NeurIPS", "abstract": "Motivated by the problem of next word prediction on user devices we introduce and study the problem of personalized frequency histogram estimation in a federated setting. In this problem, over some domain, each user observes a number of samples from a distribution which is specific to that user. The goal is to compute for all users a personalized estimate of the user's distribution with error measured in KL divergence. We focus on addressing two central challenges: statistical heterogeneity and protection of user privacy.\nOur approach to the problem relies on discovering and exploiting similar subpopulations of users which are often present and latent in real-world data, while minimizing user privacy leakage at the same time. We first present a non-private clustering-based algorithm for the problem, and give a provably joint differentially private version of it with a private data-dependent initialization scheme. Next, we propose a simple data model which is based on a mixture of Dirichlet distributions, to formally motivate our non-private algorithm and demonstrate some properties of its components. Finally, we provide an extensive empirical evaluation of our private and non-private algorithms under varying levels of statistical and size heterogeneity on the Reddit, StackOverflow, and Amazon Reviews datasets. Our results demonstrate significant improvements over standard and clustering-based baselines, and in particular, they show that it is possible to improve over direct personalization of a single global model.", "source": "openreview", "url": "https://openreview.net/forum?id=0nzKznCjFG", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2024/NeurIPS/Poster/5.8_Private and Personalized Frequency Estimation in a Federated Setting_2024.pdf" }, { "title": "Prompt-Agnostic Adversarial Perturbation for Customized Diffusion Models", "authors": [ "Cong Wan", "Yuhang He", "Xiang Song", "Yihong Gong" ], "year": 2024, "venue": "NeurIPS", "abstract": "Diffusion models have revolutionized customized text-to-image generation, allowing for efficient synthesis of photos from personal data with textual descriptions. However, these advancements bring forth risks including privacy breaches and unauthorized replication of artworks. Previous researches primarily center around using “prompt-specific methods” to generate adversarial examples to protect personal images, yet the effectiveness of existing methods is hindered by constrained adaptability to different prompts.\nIn this paper, we introduce a Prompt-Agnostic Adversarial Perturbation (PAP) method for customized diffusion models. PAP first models the prompt distribution using a Laplace Approximation, and then produces prompt-agnostic perturbations by maximizing a disturbance expectation based on the modeled distribution.\nThis approach effectively tackles the prompt-agnostic attacks, leading to improved defense stability.\nExtensive experiments in face privacy and artistic style protection, demonstrate the superior generalization of our method in comparison to existing techniques.", "source": "openreview", "url": "https://openreview.net/forum?id=oMHpejyGdx", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2024/NeurIPS/Poster/5.8_Prompt-Agnostic Adversarial Perturbation for Customized Diffusion Models_2024.pdf" }, { "title": "Towards Estimating Bounds on the Effect of Policies under Unobserved Confounding", "authors": [ "Alexis Bellot", "Silvia Chiappa" ], "year": 2024, "venue": "NeurIPS", "abstract": "As many practical fields transition to provide personalized decisions, data is increasingly relevant to support the evaluation of candidate plans and policies (e.g., guidelines for the treatment of disease, government directives, etc.). In the machine learning literature, significant efforts have been put into developing machinery to predict the effectiveness of policies efficiently. The challenge is that, in practice, the effectiveness of a candidate policy is not always identifiable, i.e., not uniquely estimable from the combination of the available data and assumptions about the domain at hand (e.g., encoded in a causal graph). In this paper, we develop graphical characterizations and estimation tools to bound the effect of policies given a causal graph and observational data collected in non-identifiable settings. Specifically, our contributions are two-fold: (1) we derive analytical bounds for general probabilistic and conditional policies that are tighter than existing results, (2) we develop an estimation framework to estimate bounds from finite samples, applicable in higher-dimensional spaces and continuously-valued data. We further show that the resulting estimators have favourable statistical properties such as fast convergence and robustness to model misspecification.", "source": "openreview", "url": "https://openreview.net/forum?id=u5enPCwaLt", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2024/NeurIPS/Poster/5.8_Towards Estimating Bounds on the Effect of Policies under Unobserved Confounding_2024.pdf" }, { "title": "Understanding and Improving Adversarial Collaborative Filtering for Robust Recommendation", "authors": [ "Kaike Zhang", "Qi Cao", "Yunfan Wu", "Fei Sun", "Huawei Shen", "Xueqi Cheng" ], "year": 2024, "venue": "NeurIPS", "abstract": "Adversarial Collaborative Filtering (ACF), which typically applies adversarial perturbations at user and item embeddings through adversarial training, is widely recognized as an effective strategy for enhancing the robustness of Collaborative Filtering (CF) recommender systems against poisoning attacks. Besides, numerous studies have empirically shown that ACF can also improve recommendation performance compared to traditional CF. Despite these empirical successes, the theoretical understanding of ACF's effectiveness in terms of both performance and robustness remains unclear. To bridge this gap, in this paper, we first theoretically show that ACF can achieve a lower recommendation error compared to traditional CF with the same training epochs in both clean and poisoned data contexts. Furthermore, by establishing bounds for reductions in recommendation error during ACF's optimization process, we find that applying personalized magnitudes of perturbation for different users based on their embedding scales can further improve ACF's effectiveness. Building on these theoretical understandings, we propose Personalized Magnitude Adversarial Collaborative Filtering (PamaCF). Extensive experiments demonstrate that PamaCF effectively defends against various types of poisoning attacks while significantly enhancing recommendation performance.", "source": "openreview", "url": "https://openreview.net/forum?id=k8AYft5ED1", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2024/NeurIPS/Poster/5.8_Understanding and Improving Adversarial Collaborative Filtering for Robust Recommendation_2024.pdf" }, { "title": "Addressing Hidden Confounding with Heterogeneous Observational Datasets for Recommendation", "authors": [ "Yanghao Xiao", "Haoxuan Li", "Yongqiang Tang", "Wensheng Zhang" ], "year": 2024, "venue": "NeurIPS", "abstract": "The collected data in recommender systems generally suffers selection bias. Considerable works are proposed to address selection bias induced by observed user and item features, but they fail when hidden features (e.g., user age or salary) that affect both user selection mechanism and feedback exist, which is called hidden confounding. To tackle this issue, methods based on sensitivity analysis and leveraging a few randomized controlled trial (RCT) data for model calibration are proposed. However, the former relies on strong assumptions of hidden confounding strength, whereas the latter relies on the expensive RCT data, thereby limiting their applicability in real-world scenarios. In this paper, we propose to employ heterogeneous observational data to address hidden confounding, wherein some data is subject to hidden confounding while the remaining is not. We argue that such setup is more aligned with practical scenarios, especially when some users do not have complete personal information (thus assumed with hidden confounding), while others do have (thus assumed without hidden confounding). To achieve unbiased learning, we propose a novel meta-learning based debiasing method called MetaDebias. This method explicitly models oracle error imputation and hidden confounding bias, and utilizes bi-level optimization for model training. Extensive experiments on three public datasets validate our method achieves state-of-the-art performance in the presence of hidden confounding, regardless of RCT data availability.", "source": "openreview", "url": "https://openreview.net/forum?id=6CFHg7exjY", "decision_type": "Poster", "avg_rating": 5.7, "relative_path": "2024/NeurIPS/Poster/5.7_Addressing Hidden Confounding with Heterogeneous Observational Datasets for Recommendation_2024.pdf" }, { "title": "Reflective Multi-Agent Collaboration based on Large Language Models", "authors": [ "Xiaohe Bo", "Zeyu Zhang", "Quanyu Dai", "Xueyang Feng", "Lei Wang", "Rui Li", "Xu Chen", "Ji-Rong Wen" ], "year": 2024, "venue": "NeurIPS", "abstract": "Benefiting from the powerful language expression and planning capabilities of Large Language Models (LLMs), LLM-based autonomous agents have achieved promising performance in various downstream tasks. Recently, based on the development of single-agent systems, researchers propose to construct LLM-based multi-agent systems to tackle more complicated tasks. In this paper, we propose a novel framework, named COPPER, to enhance the collaborative capabilities of LLM-based agents with the self-reflection mechanism. To improve the quality of reflections, we propose to fine-tune a shared reflector, which automatically tunes the prompts of actor models using our counterfactual PPO mechanism. On the one hand, we propose counterfactual rewards to assess the contribution of a single agent’s reflection within the system, alleviating the credit assignment problem. On the other hand, we propose to train a shared reflector, which enables the reflector to generate personalized reflections according to agent roles, while reducing the computational resource requirements and improving training stability. We conduct experiments on three datasets to evaluate the performance of our model in multi-hop question answering, mathematics, and chess scenarios. Experimental results show that COPPER possesses stronger reflection capabilities and exhibits excellent generalization performance across different actor models.", "source": "openreview", "url": "https://openreview.net/forum?id=wWiAR5mqXq", "decision_type": "Poster", "avg_rating": 5.7, "relative_path": "2024/NeurIPS/Poster/5.7_Reflective Multi-Agent Collaboration based on Large Language Models_2024.pdf" }, { "title": "Differentially Private Graph Diffusion with Applications in Personalized PageRanks", "authors": [ "Rongzhe Wei", "Eli Chien", "Pan Li" ], "year": 2024, "venue": "NeurIPS", "abstract": "Graph diffusion, which iteratively propagates real-valued substances among the graph, is used in numerous graph/network-involved applications. However, releasing diffusion vectors may reveal sensitive linking information in the data such as transaction information in financial network data. However, protecting the privacy of graph data is challenging due to its interconnected nature.\n This work proposes a novel graph diffusion framework with edge-level different privacy guarantees by using noisy diffusion iterates.\n The algorithm injects Laplace noise per diffusion iteration and adopts a degree-based thresholding function to mitigate the high sensitivity induced by low-degree nodes. Our privacy loss analysis is based on Privacy Amplification by Iteration (PABI), which to our best knowledge, is the first effort that analyzes PABI with Laplace noise and provides relevant applications.\n We also introduce a novel $\\infty$-Wasserstein distance tracking method, which tightens the analysis of privacy leakage and makes PABI more applicable in practice. \n We evaluate this framework by applying it to Personalized Pagerank computation for ranking tasks. Experiments on real-world network data demonstrate the superiority of our method under stringent privacy conditions.", "source": "openreview", "url": "https://openreview.net/forum?id=aon7bwYBiq", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2024/NeurIPS/Poster/5.5_Differentially Private Graph Diffusion with Applications in Personalized PageRanks_2024.pdf" }, { "title": "Face2QR: A Unified Framework for Aesthetic, Face-Preserving, and Scannable QR Code Generation", "authors": [ "Xuehao Cui", "Guangyang Wu", "Zhenghao Gan", "Guangtao Zhai", "Xiaohong Liu" ], "year": 2024, "venue": "NeurIPS", "abstract": "Existing methods to generate aesthetic QR codes, such as image and style transfer techniques, tend to compromise either the visual appeal or the scannability of QR codes when they incorporate human face identity. Addressing these imperfections, we present Face2QR—a novel pipeline specifically designed for generating personalized QR codes that harmoniously blend aesthetics, face identity, and scannability. Our pipeline introduces three innovative components. First, the ID-refined QR integration (IDQR) seamlessly intertwines the background styling with face ID, utilizing a unified SD-based framework with control networks. Second, the ID-aware QR ReShuffle (IDRS) effectively rectifies the conflicts between face IDs and QR patterns, rearranging QR modules to maintain the integrity of facial features without compromising scannability. Lastly, the ID-preserved Scannability Enhancement (IDSE) markedly boosts scanning robustness through latent code optimization, striking a delicate balance between face ID, aesthetic quality and QR functionality. In comprehensive experiments, Face2QR demonstrates remarkable performance, outperforming existing approaches, particularly in preserving facial recognition features within custom QR code designs.", "source": "openreview", "url": "https://openreview.net/forum?id=rvBabL7DUu", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2024/NeurIPS/Poster/5.5_Face2QR_ A Unified Framework for Aesthetic, Face-Preserving, and Scannable QR Code Generation_2024.pdf" }, { "title": "Faster Local Solvers for Graph Diffusion Equations", "authors": [ "Jiahe Bai", "Baojian Zhou", "Deqing Yang", "Yanghua Xiao" ], "year": 2024, "venue": "NeurIPS", "abstract": "Efficient computation of graph diffusion equations (GDEs), such as Personalized PageRank, Katz centrality, and the Heat kernel, is crucial for clustering, training neural networks, and many other graph-related problems. Standard iterative methods require accessing the whole graph per iteration, making them time-consuming for large-scale graphs. While existing local solvers approximate diffusion vectors through heuristic local updates, they often operate sequentially and are typically designed for specific diffusion types, limiting their applicability. Given that diffusion vectors are highly localizable, as measured by the participation ratio, this paper introduces a novel framework for approximately solving GDEs using a local diffusion process. This framework reveals the suboptimality of existing local solvers. Furthermore, our approach effectively localizes standard iterative solvers by designing simple and provably sublinear time algorithms. These new local solvers are highly parallelizable, making them well-suited for implementation on GPUs. We demonstrate the effectiveness of our framework in quickly obtaining approximate diffusion vectors, achieving up to a hundred-fold speed improvement, and its applicability to large-scale dynamic graphs. Our framework could also facilitate more efficient local message-passing mechanisms for GNNs.", "source": "openreview", "url": "https://openreview.net/forum?id=3Z0LTDjIM0", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2024/NeurIPS/Poster/5.5_Faster Local Solvers for Graph Diffusion Equations_2024.pdf" }, { "title": "Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients", "authors": [ "Youssef Allouah", "Abdellah El Mrini", "Rachid Guerraoui", "Nirupam Gupta", "Rafael Pinot" ], "year": 2024, "venue": "NeurIPS", "abstract": "Federated learning (FL) is an appealing paradigm that allows a group of machines\n(a.k.a. clients) to learn collectively while keeping their data local. However, due\nto the heterogeneity between the clients’ data distributions, the model obtained\nthrough the use of FL algorithms may perform poorly on some client’s data.\nPersonalization addresses this issue by enabling each client to have a different\nmodel tailored to their own data while simultaneously benefiting from the other\nclients’ data. We consider an FL setting where some clients can be adversarial, and\nwe derive conditions under which full collaboration fails. Specifically, we analyze\nthe generalization performance of an interpolated personalized FL framework in the\npresence of adversarial clients, and we precisely characterize situations when full\ncollaboration performs strictly worse than fine-tuned personalization. Our analysis\ndetermines how much we should scale down the level of collaboration, according\nto data heterogeneity and the tolerable fraction of adversarial clients. We support\nour findings with empirical results on mean estimation and binary classification\nproblems, considering synthetic and benchmark image classification datasets", "source": "openreview", "url": "https://openreview.net/forum?id=WBLPlszJI5", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2024/NeurIPS/Poster/5.5_Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients_2024.pdf" }, { "title": "Idiographic Personality Gaussian Process for Psychological Assessment", "authors": [ "Yehu Chen", "Muchen Xi", "Joshua J. Jackson", "Jacob Montgomery", "Roman Garnett" ], "year": 2024, "venue": "NeurIPS", "abstract": "We develop a novel measurement framework based on Gaussian process coregionalization model to address a long-lasting debate in psychometrics: whether psychological features like personality share a common structure across the population or vary uniquely for individuals. We propose idiographic personality Gaussian process (IPGP), an intermediate model that accommodates both shared trait structure across individuals and \"idiographic\" deviations. IPGP leverages the Gaussian process coregionalization model to conceptualize responses of grouped survey batteries but adjusted to non-Gaussian ordinal data, and exploits stochastic variational inference for latent factor estimation. Using both synthetic data and a novel survey, we show that IPGP improves both prediction of actual responses and estimation of intrapersonal response patterns compared to existing benchmarks. In the survey study, IPGP also identifies unique clusters of personality taxonomies, displaying great potential in advancing individualized approaches to psychological diagnosis.", "source": "openreview", "url": "https://openreview.net/forum?id=Twqa0GFMGX", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2024/NeurIPS/Poster/5.5_Idiographic Personality Gaussian Process for Psychological Assessment_2024.pdf" }, { "title": "MimicTalk: Mimicking a personalized and expressive 3D talking face in minutes", "authors": [ "Zhenhui Ye", "Tianyun Zhong", "Yi Ren", "Ziyue Jiang", "Jiawei Huang", "Rongjie Huang", "Jinglin Liu", "Jinzheng He", "Chen Zhang", "Zehan Wang", "Xize Cheng", "Xiang Yin", "Zhou Zhao" ], "year": 2024, "venue": "NeurIPS", "abstract": "Talking face generation (TFG) aims to animate a target identity's face to create realistic talking videos. Personalized TFG is a variant that emphasizes the perceptual identity similarity of the synthesized result (from the perspective of appearance and talking style). While previous works typically solve this problem by learning an individual neural radiance field (NeRF) for each identity to implicitly store its static and dynamic information, we find it inefficient and non-generalized due to the per-identity-per-training framework and the limited training data. To this end, we propose MimicTalk, the first attempt that exploits the rich knowledge from a NeRF-based person-agnostic generic model for improving the efficiency and robustness of personalized TFG. To be specific, (1) we first come up with a person-agnostic 3D TFG model as the base model and propose to adapt it into a specific identity; (2) we propose a static-dynamic-hybrid adaptation pipeline to help the model learn the personalized static appearance and facial dynamic features; (3) To generate the facial motion of the personalized talking style, we propose an in-context stylized audio-to-motion model that mimics the implicit talking style provided in the reference video without information loss by an explicit style representation. The adaptation process to an unseen identity can be performed in 15 minutes, which is 47 times faster than previous person-dependent methods. Experiments show that our MimicTalk surpasses previous baselines regarding video quality, efficiency, and expressiveness. Video samples are available at https://mimictalk.github.io .", "source": "openreview", "url": "https://openreview.net/forum?id=gjEzL0bamb", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2024/NeurIPS/Poster/5.5_MimicTalk_ Mimicking a personalized and expressive 3D talking face in minutes_2024.pdf" }, { "title": "One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection", "authors": [ "Yiyue Li", "Shaoting Zhang", "Kang Li", "Qicheng Lao" ], "year": 2024, "venue": "NeurIPS", "abstract": "Traditional Anomaly Detection (AD) methods have predominantly relied on unsupervised learning from extensive normal data. Recent AD methods have evolved with the advent of large pre-trained vision-language models, enhancing few-shot anomaly detection capabilities. However, these latest AD methods still exhibit limitations in accuracy improvement. One contributing factor is their direct comparison of a query image's features with those of few-shot normal images. This direct comparison often leads to a loss of precision and complicates the extension of these techniques to more complex domains—an area that remains underexplored in a more refined and comprehensive manner. To address these limitations, we introduce the anomaly personalization method, which performs a personalized one-to-normal transformation of query images using an anomaly-free customized generation model, ensuring close alignment with the normal manifold. Moreover, to further enhance the stability and robustness of prediction results, we propose a triplet contrastive anomaly inference strategy, which incorporates a comprehensive comparison between the query and generated anomaly-free data pool and prompt information. Extensive evaluations across eleven datasets in three domains demonstrate our model's effectiveness compared to the latest AD methods. Additionally, our method has been proven to transfer flexibly to other AD methods, with the generated image data effectively improving the performance of other AD methods.", "source": "openreview", "url": "https://openreview.net/forum?id=tIzW3l2uaN", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2024/NeurIPS/Poster/5.5_One-to-Normal_ Anomaly Personalization for Few-shot Anomaly Detection_2024.pdf" }, { "title": "Private Attribute Inference from Images with Vision-Language Models", "authors": [ "Batuhan Tömekçe", "Mark Vero", "Robin Staab", "Martin Vechev" ], "year": 2024, "venue": "NeurIPS", "abstract": "As large language models (LLMs) become ubiquitous in our daily tasks and digital interactions, associated privacy risks are increasingly in focus. While LLM privacy research has primarily focused on the leakage of model training data, it has recently been shown that LLMs can make accurate privacy-infringing inferences from previously unseen texts. With the rise of vision-language models (VLMs), capable of understanding both images and text, a key question is whether this concern transfers to the previously unexplored domain of benign images posted online. To answer this question, we compile an image dataset with human-annotated labels of the image owner's personal attributes. In order to understand the privacy risks posed by VLMs beyond traditional human attribute recognition, our dataset consists of images where the inferable private attributes do not stem from direct depictions of humans. On this dataset, we evaluate 7 state-of-the-art VLMs, finding that they can infer various personal attributes at up to 77.6% accuracy. Concerningly, we observe that accuracy scales with the general capabilities of the models, implying that future models can be misused as stronger inferential adversaries, establishing an imperative for the development of adequate defenses.", "source": "openreview", "url": "https://openreview.net/forum?id=5MIk4VFn1c", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2024/NeurIPS/Poster/5.5_Private Attribute Inference from Images with Vision-Language Models_2024.pdf" }, { "title": "Provably Efficient Interactive-Grounded Learning with Personalized Reward", "authors": [ "Mengxiao Zhang", "Yuheng Zhang", "Haipeng Luo", "Paul Mineiro" ], "year": 2024, "venue": "NeurIPS", "abstract": "Interactive-Grounded Learning (IGL) [Xie et al., 2021] is a powerful framework in which a learner aims at maximizing unobservable rewards through interacting with an environment and observing reward-dependent feedback on the taken actions.\nTo deal with personalized rewards that are ubiquitous in applications such as recommendation systems, Maghakian et al. [2022] study a version of IGL with context-dependent feedback, but their algorithm does not come with theoretical guarantees. In this work, we consider the same problem and provide the first provably efficient algorithms with sublinear regret under realizability. Our analysis reveals that the step-function estimator of prior work can deviate uncontrollably due to finite-sample effects. Our solution is a novel Lipschitz reward estimator which underestimates the true reward and enjoys favorable generalization performances. Building on this estimator, we propose two algorithms, one based on explore-then-exploit and the other based on inverse-gap weighting. We apply IGL to learning from image feedback and learning from text feedback, which are reward-free settings that arise in practice. Experimental results showcase the importance of using our Lipschitz reward estimator and the overall effectiveness of our algorithms.", "source": "openreview", "url": "https://openreview.net/forum?id=NidGPsP0Qq", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2024/NeurIPS/Poster/5.5_Provably Efficient Interactive-Grounded Learning with Personalized Reward_2024.pdf" }, { "title": "RefDrop: Controllable Consistency in Image or Video Generation via Reference Feature Guidance", "authors": [ "Jiaojiao Fan", "Haotian Xue", "Qinsheng Zhang", "Yongxin Chen" ], "year": 2024, "venue": "NeurIPS", "abstract": "There is a rapidly growing interest in controlling consistency across multiple generated images using diffusion models. Among various methods, recent works have found that simply manipulating attention modules by concatenating features from multiple reference images provides an efficient approach to enhancing consistency without fine-tuning. Despite its popularity and success, few studies have elucidated the underlying mechanisms that contribute to its effectiveness. In this work, we reveal that the popular approach is a linear interpolation of image self-attention and cross-attention between synthesized content and reference features, with a constant rank-1 coefficient. Motivated by this observation, we find that a rank-1 coefficient is not necessary and simplifies the controllable generation mechanism. The resulting algorithm, which we coin as RefDrop, allows users to control the influence of reference context in a direct and precise manner. Besides further enhancing consistency in single-subject image generation, our method also enables more interesting applications, such as the consistent generation of multiple subjects, suppressing specific features to encourage more diverse content, and high-quality personalized video generation by boosting temporal consistency. Even compared with state-of-the-art image-prompt-based generators, such as IP-Adapter, RefDrop is competitive in terms of controllability and quality while avoiding the need to train a separate image encoder for feature injection from reference images, making it a versatile plug-and-play solution for any image or video diffusion model.", "source": "openreview", "url": "https://openreview.net/forum?id=09nyBqSdUz", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2024/NeurIPS/Poster/5.5_RefDrop_ Controllable Consistency in Image or Video Generation via Reference Feature Guidance_2024.pdf" }, { "title": "Robust Reinforcement Learning from Corrupted Human Feedback", "authors": [ "Alexander Bukharin", "Ilgee Hong", "Haoming Jiang", "Zichong Li", "Qingru Zhang", "Zixuan Zhang", "Tuo Zhao" ], "year": 2024, "venue": "NeurIPS", "abstract": "Reinforcement learning from human feedback (RLHF) provides a principled framework for aligning AI systems with human preference data. For various reasons, e.g., personal bias, context ambiguity, lack of training, etc, human annotators may give incorrect or inconsistent preference labels. To tackle this challenge, we propose a robust RLHF approach -- $R^3M$, which models the potentially corrupted preference label as sparse outliers. Accordingly, we formulate the robust reward learning as an $\\ell_1$-regularized maximum likelihood estimation problem. Computationally, we develop an efficient alternating optimization algorithm, which only incurs negligible computational overhead compared with the standard RLHF approach. Theoretically, we prove that under proper regularity conditions, $R^3M$ can consistently learn the underlying reward and identify outliers, provided that the number of outlier labels scales sublinearly with the preference sample size. Furthermore, we remark that $R^3M$ is versatile and can be extended to various preference optimization methods, including direct preference optimization (DPO). Our experiments on robotic control and natural language generation with large language models (LLMs) show that $R^3M$ improves robustness of the reward against several types of perturbations to the preference data.", "source": "openreview", "url": "https://openreview.net/forum?id=cR2QDzdpEv", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2024/NeurIPS/Poster/5.5_Robust Reinforcement Learning from Corrupted Human Feedback_2024.pdf" }, { "title": "Personalized Federated Learning via Feature Distribution Adaptation", "authors": [ "Connor Mclaughlin", "Lili Su" ], "year": 2024, "venue": "NeurIPS", "abstract": "Federated learning (FL) is a distributed learning framework that leverages commonalities between distributed client datasets to train a global model. Under heterogeneous clients, however, FL can fail to produce stable training results. Personalized federated learning (PFL) seeks to address this by learning individual models tailored to each client. One approach is to decompose model training into shared representation learning and personalized classifier training. Nonetheless, previous works struggle to navigate the bias-variance trade-off in classifier learning, relying solely on limited local datasets or introducing costly techniques to improve generalization.\nIn this work, we frame representation learning as a generative modeling task, where representations are trained with a classifier based on the global feature distribution. We then propose an algorithm, pFedFDA, that efficiently generates personalized models by adapting global generative classifiers to their local feature distributions. Through extensive computer vision benchmarks, we demonstrate that our method can adjust to complex distribution shifts with significant improvements over current state-of-the-art in data-scarce settings.", "source": "openreview", "url": "https://openreview.net/forum?id=Wl2optQcng", "decision_type": "Poster", "avg_rating": 5.4, "relative_path": "2024/NeurIPS/Poster/5.4_Personalized Federated Learning via Feature Distribution Adaptation_2024.pdf" }, { "title": "A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings", "authors": [ "Disha Makhija", "Joydeep Ghosh", "Nhat Ho" ], "year": 2024, "venue": "NeurIPS", "abstract": "Federated learning (FL), through its privacy-preserving collaborative learning approach, has significantly empowered decentralized devices. However, constraints in either data and/or computational resources among participating clients introduce several challenges in learning, including the inability to train large model architectures, heightened risks of overfitting, and more. In this work, we present a novel FL framework grounded in Bayesian learning to address these challenges. Our approach involves training personalized Bayesian models at each client tailored to the unique complexities of the clients' datasets and efficiently collaborating across these clients. By leveraging Bayesian neural networks and their uncertainty quantification capabilities, our local training procedure robustly learns from small datasets. And the novel collaboration procedure utilizing priors in the functional (output) space of the networks facilitates collaboration across models of varying sizes, enabling the framework to adapt well in heterogeneous data and computational settings. Furthermore, we present a differentially private version of the algorithm, accompanied by formal differential privacy guarantees that apply without any assumptions on the learning algorithm. Through experiments on popular FL datasets, we demonstrate that our approach outperforms strong baselines in both homogeneous and heterogeneous settings, and under strict privacy constraints.", "source": "openreview", "url": "https://openreview.net/forum?id=hilGwNabqB", "decision_type": "Poster", "avg_rating": 5.2, "relative_path": "2024/NeurIPS/Poster/5.2_A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings_2024.pdf" }, { "title": "DreamSteerer: Enhancing Source Image Conditioned Editability using Personalized Diffusion Models", "authors": [ "Zhengyang Yu", "Zhaoyuan Yang", "Jing Zhang" ], "year": 2024, "venue": "NeurIPS", "abstract": "Recent text-to-image (T2I) personalization methods have shown great premise in teaching a diffusion model user-specified concepts given a few images for reusing the acquired concepts in a novel context. With massive efforts being dedicated to personalized generation, a promising extension is personalized editing, namely to edit an image using personalized concepts, which can provide more precise guidance signal than traditional textual guidance. To address this, one straightforward solution is to incorporate a personalized diffusion model with a text-driven editing framework. However, such solution often shows unsatisfactory editability on the source image. To address this, we propose DreamSteerer, a plug-in method for augmenting existing T2I personalization methods. Specifically, we enhance the source image conditioned editability of a personalized diffusion model via a novel Editability Driven Score Distillation (EDSD) objective. Moreover, we identify a mode trapping issue with EDSD, and propose a mode shifting regularization with spatial feature guided sampling to avoid such issue. We further employ two key modifications on the Delta Denoising Score framework that enable high-fidelity local editing with personalized concepts. Extensive experiments validate that DreamSteerer can significantly improve the editability of several T2I personalization baselines while being computationally efficient.", "source": "openreview", "url": "https://openreview.net/forum?id=UekHycx0lz", "decision_type": "Poster", "avg_rating": 5.2, "relative_path": "2024/NeurIPS/Poster/5.2_DreamSteerer_ Enhancing Source Image Conditioned Editability using Personalized Diffusion Model_2024.pdf" }, { "title": "Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation", "authors": [ "Jiawei Wang", "Renhe Jiang", "Chuang Yang", "Zengqing Wu", "Makoto Onizuka", "Ryosuke Shibasaki", "Noboru Koshizuka", "Chuan Xiao" ], "year": 2024, "venue": "NeurIPS", "abstract": "This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation. LLMs overcome the limitations of previous models by effectively processing semantic data and offering versatility in modeling various tasks. Our approach addresses three research questions: aligning LLMs with real-world urban mobility data, developing reliable activity generation strategies, and exploring LLM applications in urban mobility. The key technical contribution is a novel LLM agent framework that accounts for individual activity patterns and motivations, including a self-consistency approach to align LLMs with real-world activity data and a retrieval-augmented strategy for interpretable activity generation. We evaluate our LLM agent framework and compare it with state-of-the-art personal mobility generation approaches, demonstrating the effectiveness of our approach and its potential applications in urban mobility. Overall, this study marks the pioneering work of designing an LLM agent framework for activity generation based on real-world human activity data, offering a promising tool for urban mobility analysis.", "source": "openreview", "url": "https://openreview.net/forum?id=1iHmhMHNyA", "decision_type": "Poster", "avg_rating": 5.2, "relative_path": "2024/NeurIPS/Poster/5.2_Large Language Models as Urban Residents_ An LLM Agent Framework for Personal Mobility Generati_2024.pdf" }, { "title": "CoBo: Collaborative Learning via Bilevel Optimization", "authors": [ "Diba Hashemi", "Lie He", "Martin Jaggi" ], "year": 2024, "venue": "NeurIPS", "abstract": "Collaborative learning is an important tool to train multiple clients more effectively by enabling communication among clients. Identifying helpful clients, however, presents challenging and often introduces significant overhead. In this paper, we model **client-selection** and **model-training** as two interconnected optimization problems, proposing a novel bilevel optimization problem for collaborative learning.\nWe introduce **CoBo**, a *scalable* and *elastic*, SGD-type alternating optimization algorithm that efficiently addresses these problem with theoretical convergence guarantees. Empirically, **CoBo** achieves superior performance, surpassing popular personalization algorithms by 9.3% in accuracy on a task with high heterogeneity, involving datasets distributed among 80 clients.", "source": "openreview", "url": "https://openreview.net/forum?id=SjQ1iIqpfU", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2024/NeurIPS/Poster/5.0_CoBo_ Collaborative Learning via Bilevel Optimization_2024.pdf" }, { "title": "Disentangled Style Domain for Implicit $z$-Watermark Towards Copyright Protection", "authors": [ "Junqiang Huang", "Zhaojun Guo", "Ge Luo", "Zhenxing Qian", "Sheng Li", "Xinpeng Zhang" ], "year": 2024, "venue": "NeurIPS", "abstract": "Text-to-image models have shown surprising performance in high-quality image generation, while also raising intensified concerns about the unauthorized usage of personal dataset in training and personalized fine-tuning. Recent approaches, embedding watermarks, introducing perturbations, and inserting backdoors into datasets, rely on adding minor information vulnerable to adversarial training, limiting their ability to detect unauthorized data usage. In this paper, we introduce a novel implicit Zero-Watermarking scheme that first utilizes the disentangled style domain to detect unauthorized dataset usage in text-to-image models. Specifically, our approach generates the watermark from the disentangled style domain, enabling self-generalization and mutual exclusivity within the style domain anchored by protected units. The domain achieves the maximum concealed offset of probability distribution through both the injection of identifier $z$ and dynamic contrastive learning, facilitating the structured delineation of dataset copyright boundaries for multiple sources of styles and contents. Additionally, we introduce the concept of watermark distribution to establish a verification mechanism for copyright ownership of hybrid or partial infringements, addressing deficiencies in the traditional mechanism of dataset copyright ownership for AI mimicry. Notably, our method achieves one-sample verification for copyright ownership in AI mimic generations. The code is available at: [https://github.com/Hlufies/ZWatermarking](https://github.com/Hlufies/ZWatermarking)", "source": "openreview", "url": "https://openreview.net/forum?id=4VL5QWQFBV", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2024/NeurIPS/Poster/5.0_Disentangled Style Domain for Implicit $z$-Watermark Towards Copyright Protection_2024.pdf" }, { "title": "Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method", "authors": [ "Bikang Pan", "Wei Huang", "Ye Shi" ], "year": 2024, "venue": "NeurIPS", "abstract": "Integrating pretrained vision-language foundation models like CLIP into federated learning has attracted significant attention for enhancing generalization across diverse tasks. Typically, federated learning of vision-language models employs prompt learning to reduce communication and computational costs, i.e., prompt-based federated learning. However, there is limited theoretical analysis to understand the performance of prompt-based federated learning. In this work, we construct a theoretical analysis framework for prompt-based federated learning via feature learning theory. Specifically, we monitor the evolution of signal learning and noise memorization in prompt-based federated learning, demonstrating that performance can be assessed by the ratio of task-relevant to task-irrelevant coefficients. Furthermore, we draw an analogy between income and risk in portfolio optimization and the task-relevant and task-irrelevant terms in feature learning. Leveraging inspiration from portfolio optimization that combining two independent assets will maintain the income while reducing the risk, we introduce two prompts: global prompt and local prompt to construct a prompt portfolio to balance the generalization and personalization. Consequently, we showed the performance advantage of the prompt portfolio and derived the optimal mixing coefficient. These theoretical claims have been further supported by empirical experiments.", "source": "openreview", "url": "https://openreview.net/forum?id=Y4L8GQXZZO", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2024/NeurIPS/Poster/5.0_Federated Learning from Vision-Language Foundation Models_ Theoretical Analysis and Method_2024.pdf" }, { "title": "Federated Learning over Connected Modes", "authors": [ "Dennis Grinwald", "Philipp Wiesner", "Shinichi Nakajima" ], "year": 2024, "venue": "NeurIPS", "abstract": "Statistical heterogeneity in federated learning poses two major challenges: slow global training due to conflicting gradient signals, and the need of personalization for local distributions. In this work, we tackle both challenges by leveraging recent advances in \\emph{linear mode connectivity} --- identifying a linearly connected low-loss region in the parameter space of neural networks, which we call solution simplex. We propose federated learning over connected modes (\\textsc{Floco}), where clients are assigned local subregions in this simplex based on their gradient signals, and together learn the shared global solution simplex. This allows personalization of the client models to fit their local distributions within the degrees of freedom in the solution simplex and homogenizes the update signals for the global simplex training. Our experiments show that \\textsc{Floco} accelerates the global training process, and significantly improves the local accuracy with minimal computational overhead in cross-silo federated learning settings.", "source": "openreview", "url": "https://openreview.net/forum?id=JL2eMCfDW8", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2024/NeurIPS/Poster/5.0_Federated Learning over Connected Modes_2024.pdf" }, { "title": "Hollowed Net for On-Device Personalization of Text-to-Image Diffusion Models", "authors": [ "Wonguk Cho", "Seokeon Choi", "Debasmit Das", "Matthias Reisser", "Taesup Kim", "Sungrack Yun", "Fatih Porikli" ], "year": 2024, "venue": "NeurIPS", "abstract": "Recent advancements in text-to-image diffusion models have enabled the personalization of these models to generate custom images from textual prompts. This paper presents an efficient LoRA-based personalization approach for on-device subject-driven generation, where pre-trained diffusion models are fine-tuned with user-specific data on resource-constrained devices. Our method, termed Hollowed Net, enhances memory efficiency during fine-tuning by modifying the architecture of a diffusion U-Net to temporarily remove a fraction of its deep layers, creating a hollowed structure. This approach directly addresses on-device memory constraints and substantially reduces GPU memory requirements for training, in contrast to previous methods that primarily focus on minimizing training steps and reducing the number of parameters to update. Additionally, the personalized Hollowed Net can be transferred back into the original U-Net, enabling inference without additional memory overhead. Quantitative and qualitative analyses demonstrate that our approach not only reduces training memory to levels as low as those required for inference but also maintains or improves personalization performance compared to existing methods.", "source": "openreview", "url": "https://openreview.net/forum?id=Pa8jsrdOnU", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2024/NeurIPS/Poster/5.0_Hollowed Net for On-Device Personalization of Text-to-Image Diffusion Models_2024.pdf" }, { "title": "Off-Policy Selection for Initiating Human-Centric Experimental Design", "authors": [ "Ge Gao", "Xi Yang", "Qitong Gao", "Song Ju", "Miroslav Pajic", "Min Chi" ], "year": 2024, "venue": "NeurIPS", "abstract": "In human-centric applications like healthcare and education, the \\textit{heterogeneity} among patients and students necessitates personalized treatments and instructional interventions. While reinforcement learning (RL) has been utilized in those tasks, off-policy selection (OPS) is pivotal to close the loop by offline evaluating and selecting policies without online interactions, yet current OPS methods often overlook the heterogeneity among participants. Our work is centered on resolving a \\textit{pivotal challenge} in human-centric systems (HCSs): \\textbf{\\textit{how to select a policy to deploy when a new participant joining the cohort, without having access to any prior offline data collected over the participant?}} We introduce First-Glance Off-Policy Selection (FPS), a novel approach that systematically addresses participant heterogeneity through sub-group segmentation and tailored OPS criteria to each sub-group. By grouping individuals with similar traits, FPS facilitates personalized policy selection aligned with unique characteristics of each participant or group of participants. FPS is evaluated via two important but challenging applications, intelligent tutoring systems and a healthcare application for sepsis treatment and intervention. FPS presents significant advancement in enhancing learning outcomes of students and in-hospital care outcomes.", "source": "openreview", "url": "https://openreview.net/forum?id=swp3lPDmZe", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2024/NeurIPS/Poster/5.0_Off-Policy Selection for Initiating Human-Centric Experimental Design_2024.pdf" }, { "title": "Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning", "authors": [ "Pouya M. Ghari", "Yanning Shen" ], "year": 2024, "venue": "NeurIPS", "abstract": "Federated learning is renowned for its efficacy in distributed model training, ensuring that users, called clients, retain data privacy by not disclosing their data to the central server that orchestrates collaborations. Most previous work on federated learning assumes that clients possess static batches of training data. However, clients may also need to make real-time predictions on streaming data in non-stationary environments. In such dynamic environments, employing pre-trained models may be inefficient, as they struggle to adapt to the constantly evolving data streams. To address this challenge, clients can fine-tune models online, leveraging their observed data to enhance performance. Despite the potential benefits of client participation in federated online model fine-tuning, existing analyses have not conclusively demonstrated its superiority over local model fine-tuning. To bridge this gap, the present paper develops a novel personalized federated learning algorithm, wherein each client constructs a personalized model by combining a locally fine-tuned model with multiple federated models learned by the server over time. Theoretical analysis and experiments on real datasets corroborate the effectiveness of this approach for real-time predictions and federated model fine-tuning.", "source": "openreview", "url": "https://openreview.net/forum?id=yvUHnBkCzd", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2024/NeurIPS/Poster/5.0_Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-T_2024.pdf" }, { "title": "Where's Waldo: Diffusion Features For Personalized Segmentation and Retrieval", "authors": [ "Dvir Samuel", "Rami Ben-Ari", "Matan Levy", "Nir Darshan", "Gal Chechik" ], "year": 2024, "venue": "NeurIPS", "abstract": "Personalized retrieval and segmentation aim to locate specific instances within a dataset based on an input image and a short description of the reference instance. While supervised methods are effective, they require extensive labeled data for training. Recently, self-supervised foundation models have been introduced to these tasks showing comparable results to supervised methods. However, a significant flaw in these models is evident: they struggle to locate a desired instance when other instances within the same class are presented. In this paper, we explore text-to-image diffusion models for these tasks. Specifically, we propose a novel approach called PDM for Personalized Diffusion Features Matching, that leverages intermediate features of pre-trained text-to-image models for personalization tasks without any additional training. PDM demonstrates superior performance on popular retrieval and segmentation benchmarks, outperforming even supervised methods. We also highlight notable shortcomings in current instance and segmentation datasets and propose new benchmarks for these tasks.", "source": "openreview", "url": "https://openreview.net/forum?id=LGXeIx75sc", "decision_type": "Poster", "avg_rating": 5.0, "relative_path": "2024/NeurIPS/Poster/5.0_Where's Waldo_ Diffusion Features For Personalized Segmentation and Retrieval_2024.pdf" }, { "title": "AttnDreamBooth: Towards Text-Aligned Personalized Text-to-Image Generation", "authors": [ "Lianyu Pang", "Jian Yin", "Baoquan Zhao", "Feize Wu", "Fu Lee Wang", "Qing Li", "Xudong Mao" ], "year": 2024, "venue": "NeurIPS", "abstract": "Recent advances in text-to-image models have enabled high-quality personalized image synthesis based on user-provided concepts with flexible textual control. In this work, we analyze the limitations of two primary techniques in text-to-image personalization: Textual Inversion and DreamBooth. When integrating the learned concept into new prompts, Textual Inversion tends to overfit the concept, while DreamBooth often overlooks it. We attribute these issues to the incorrect learning of the embedding alignment for the concept. To address this, we introduce AttnDreamBooth, a novel approach that separately learns the embedding alignment, the attention map, and the subject identity across different training stages. We also introduce a cross-attention map regularization term to enhance the learning of the attention map. Our method demonstrates significant improvements in identity preservation and text alignment compared to the baseline methods.", "source": "openreview", "url": "https://openreview.net/forum?id=4bINoegDcm", "decision_type": "Poster", "avg_rating": 4.8, "relative_path": "2024/NeurIPS/Poster/4.8_AttnDreamBooth_ Towards Text-Aligned Personalized Text-to-Image Generation_2024.pdf" }, { "title": "MO-DDN: A Coarse-to-Fine Attribute-based Exploration Agent for Multi-Object Demand-driven Navigation", "authors": [ "Hongcheng Wang", "Peiqi Liu", "Wenzhe Cai", "Mingdong Wu", "Zhengyu Qian", "Hao Dong" ], "year": 2024, "venue": "NeurIPS", "abstract": "The process of satisfying daily demands is a fundamental aspect of humans' daily lives. With the advancement of embodied AI, robots are increasingly capable of satisfying human demands. Demand-driven navigation (DDN) is a task in which an agent must locate an object to satisfy a specified demand instruction, such as \"I am thirsty.\" The previous study typically assumes that each demand instruction requires only one object to be fulfilled and does not consider individual preferences. However, the realistic human demand may involve multiple objects. In this paper, we introduce the Multi-object Demand-driven Navigation (MO-DDN) benchmark, which addresses these nuanced aspects, including multi-object search and personal preferences, thus making the MO-DDN task more reflective of real-life scenarios compared to DDN. Building upon previous work, we employ the concept of ``attribute'' to tackle this new task. However, instead of solely relying on attribute features in an end-to-end manner like DDN, we propose a modular method that involves constructing a coarse-to-fine attribute-based exploration agent (C2FAgent). Our experimental results illustrate that this coarse-to-fine exploration strategy capitalizes on the advantages of attributes at various decision-making levels, resulting in superior performance compared to baseline methods. Code and video can be found at https://sites.google.com/view/moddn.", "source": "openreview", "url": "https://openreview.net/forum?id=MzTdZhMjeC", "decision_type": "Poster", "avg_rating": 4.8, "relative_path": "2024/NeurIPS/Poster/4.8_MO-DDN_ A Coarse-to-Fine Attribute-based Exploration Agent for Multi-Object Demand-driven Navig_2024.pdf" }, { "title": "On Softmax Direct Preference Optimization for Recommendation", "authors": [ "Yuxin Chen", "Junfei Tan", "An Zhang", "Zhengyi Yang", "Leheng Sheng", "Enzhi Zhang", "Xiang Wang", "Tat-Seng Chua" ], "year": 2024, "venue": "NeurIPS", "abstract": "Recommender systems aim to predict personalized rankings based on user preference data. With the rise of Language Models (LMs), LM-based recommenders have been widely explored due to their extensive world knowledge and powerful reasoning abilities. Most of the LM-based recommenders convert historical interactions into language prompts, pairing with a positive item as the target response and fine-tuning LM with a language modeling loss. However, the current objective fails to fully leverage preference data and is not optimized for personalized ranking tasks, which hinders the performance of LM-based recommenders. Inspired by the current advancement of Direct Preference Optimization (DPO) in human preference alignment and the success of softmax loss in recommendations, we propose Softmax-DPO (\\textbf{S-DPO}) to instill ranking information into the LM to help LM-based recommenders distinguish preferred items from negatives, rather than solely focusing on positives. Specifically, we incorporate multiple negatives in user preference data and devise an alternative version of DPO loss tailored for LM-based recommenders, which is extended from the traditional full-ranking Plackett-Luce (PL) model to partial rankings and connected to softmax sampling strategies. Theoretically, we bridge S-DPO with the softmax loss over negative sampling and find that it has an inherent benefit of mining hard negatives, which assures its exceptional capabilities in recommendation tasks. Empirically, extensive experiments conducted on three real-world datasets demonstrate the superiority of S-DPO to effectively model user preference and further boost recommendation performance while providing better rewards for preferred items. Our codes are available at https://github.com/chenyuxin1999/S-DPO.", "source": "openreview", "url": "https://openreview.net/forum?id=qp5VbGTaM0", "decision_type": "Poster", "avg_rating": 4.8, "relative_path": "2024/NeurIPS/Poster/4.8_On Softmax Direct Preference Optimization for Recommendation_2024.pdf" }, { "title": "ReF-LDM: A Latent Diffusion Model for Reference-based Face Image Restoration", "authors": [ "Chi-Wei Hsiao", "Yu-Lun Liu", "Cheng-Kun Yang", "Sheng-Po Kuo", "Kevin Jou", "Chia-Ping Chen" ], "year": 2024, "venue": "NeurIPS", "abstract": "While recent works on blind face image restoration have successfully produced impressive high-quality (HQ) images with abundant details from low-quality (LQ) input images, the generated content may not accurately reflect the real appearance of a person. To address this problem, incorporating well-shot personal images as additional reference inputs may be a promising strategy. Inspired by the recent success of the Latent Diffusion Model (LDM) in image generation, we propose ReF-LDM—an adaptation of LDM designed to generate HQ face images conditioned on one LQ image and multiple HQ reference images. Our LDM-based model incorporates an effective and efficient mechanism, CacheKV, for conditioning on reference images. Additionally, we design a timestep-scaled identity loss, enabling LDM to focus on learning the discriminating features of human faces. Lastly, we construct FFHQ-ref, a dataset consisting of 20,406 high-quality (HQ) face images with corresponding reference images, which can serve as both training and evaluation data for reference-based face restoration models.", "source": "openreview", "url": "https://openreview.net/forum?id=QY4SpBhQZI", "decision_type": "Poster", "avg_rating": 4.8, "relative_path": "2024/NeurIPS/Poster/4.8_ReF-LDM_ A Latent Diffusion Model for Reference-based Face Image Restoration_2024.pdf" }, { "title": "FineStyle: Fine-grained Controllable Style Personalization for Text-to-image Models", "authors": [ "Gong Zhang", "Kihyuk Sohn", "Meera Hahn", "Humphrey Shi", "Irfan Essa" ], "year": 2024, "venue": "NeurIPS", "abstract": "Few-shot fine-tuning of text-to-image (T2I) generation models enables people to create unique images in their own style using natural languages without requiring extensive prompt engineering. However, fine-tuning with only a handful, as little as one, of image-text paired data prevents fine-grained control of style attributes at generation. In this paper, we present FineStyle, a few-shot fine-tuning method that allows enhanced controllability for style personalized text-to-image generation. To overcome the lack of training data for fine-tuning, we propose a novel concept-oriented data scaling that amplifies the number of image-text pair, each of which focuses on different concepts (e.g., objects) in the style reference image. We also identify the benefit of parameter-efficient adapter tuning of key and value kernels of cross-attention layers. Extensive experiments show the effectiveness of FineStyle at following fine-grained text prompts and delivering visual quality faithful to the specified style, measured by CLIP scores and human raters.", "source": "openreview", "url": "https://openreview.net/forum?id=1SmXUGzrH8", "decision_type": "Poster", "avg_rating": 4.7, "relative_path": "2024/NeurIPS/Poster/4.7_FineStyle_ Fine-grained Controllable Style Personalization for Text-to-image Models_2024.pdf" }, { "title": "SocraticLM: Exploring Socratic Personalized Teaching with Large Language Models", "authors": [ "Jiayu Liu", "Zhenya Huang", "Tong Xiao", "Jing Sha", "Jinze Wu", "Qi Liu", "Shijin Wang", "Enhong Chen" ], "year": 2024, "venue": "NeurIPS", "abstract": "Large language models (LLMs) are considered a crucial technology for advancing intelligent education since they exhibit the potential for an in-depth understanding of teaching scenarios and providing students with personalized guidance. Nonetheless, current LLM-based application in personalized teaching predominantly follows a \"Question-Answering\" paradigm, where students are passively provided with answers and explanations. In this paper, we propose SocraticLM, which achieves a Socratic \"Thought-Provoking\" teaching paradigm that fulfills the role of a real classroom teacher in actively engaging students in the thought process required for genuine problem-solving mastery. To build SocraticLM, we first propose a novel \"Dean-Teacher-Student\" multi-agent pipeline to construct a new dataset, SocraTeach, which contains $35$K meticulously crafted Socratic-style multi-round (equivalent to $208$K single-round) teaching dialogues grounded in fundamental mathematical problems. Our dataset simulates authentic teaching scenarios, interacting with six representative types of simulated students with different cognitive states, and strengthening four crucial teaching abilities. SocraticLM is then fine-tuned on SocraTeach with three strategies balancing its teaching and reasoning abilities. Moreover, we contribute a comprehensive evaluation system encompassing five pedagogical dimensions for assessing the teaching quality of LLMs. Extensive experiments verify that SocraticLM achieves significant improvements in the teaching performance, outperforming GPT4 by more than 12\\%. Our dataset and code is available at https://github.com/Ljyustc/SocraticLM.", "source": "openreview", "url": "https://openreview.net/forum?id=qkoZgJhxsA", "decision_type": "Spotlight", "avg_rating": 7.2, "relative_path": "2024/NeurIPS/Spotlight/7.2_SocraticLM_ Exploring Socratic Personalized Teaching with Large Language Models_2024.pdf" }, { "title": "Who's asking? User personas and the mechanics of latent misalignment", "authors": [ "Asma Ghandeharioun", "Ann Yuan", "Marius Guerard", "Emily Reif", "Michael A. Lepori", "Lucas Dixon" ], "year": 2024, "venue": "NeurIPS", "abstract": "Studies show that safety-tuned models may nevertheless divulge harmful information. In this work, we show that whether they do so depends significantly on who they are talking to, which we refer to as *user persona*. In fact, we find manipulating user persona to be more effective for eliciting harmful content than certain more direct attempts to control model refusal. We study both natural language prompting and activation steering as intervention methods and show that activation steering is significantly more effective at bypassing safety filters.\nWe shed light on the mechanics of this phenomenon by showing that even when model generations are safe, harmful content can persist in hidden representations and can be extracted by decoding from earlier layers. We also show we can predict a persona’s effect on refusal given only the geometry of its steering vector. Finally, we show that certain user personas induce the model to form more charitable interpretations of otherwise dangerous queries.", "source": "openreview", "url": "https://openreview.net/forum?id=eSes1Mic9d", "decision_type": "Spotlight", "avg_rating": 7.2, "relative_path": "2024/NeurIPS/Spotlight/7.2_Who's asking_ User personas and the mechanics of latent misalignment_2024.pdf" }, { "title": "Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts", "authors": [ "Sukwon Yun", "Inyoung Choi", "Jie Peng", "Yangfan Wu", "Jingxuan Bao", "Qiyiwen Zhang", "Jiayi Xin", "Qi Long", "Tianlong Chen" ], "year": 2024, "venue": "NeurIPS", "abstract": "Multimodal learning has gained increasing importance across various fields, offering the ability to integrate data from diverse sources such as images, text, and personalized records, which are frequently observed in medical domains. However, in scenarios where some modalities are missing, many existing frameworks struggle to accommodate arbitrary modality combinations, often relying heavily on a single modality or complete data. This oversight of potential modality combinations limits their applicability in real-world situations. To address this challenge, we propose Flex-MoE (Flexible Mixture-of-Experts), a new framework designed to flexibly incorporate arbitrary modality combinations while maintaining robustness to missing data. The core idea of Flex-MoE is to first address missing modalities using a new missing modality bank that integrates observed modality combinations with the corresponding missing ones. This is followed by a uniquely designed Sparse MoE framework. Specifically, Flex-MoE first trains experts using samples with all modalities to inject generalized knowledge through the generalized router ($\\mathcal{G}$-Router). The $\\mathcal{S}$-Router then specializes in handling fewer modality combinations by assigning the top-1 gate to the expert corresponding to the observed modality combination. We evaluate Flex-MoE on the ADNI dataset, which encompasses four modalities in the Alzheimer's Disease domain, as well as on the MIMIC-IV dataset. The results demonstrate the effectiveness of Flex-MoE, highlighting its ability to model arbitrary modality combinations in diverse missing modality scenarios. Code is available at: \\url{https://github.com/UNITES-Lab/flex-moe}.", "source": "openreview", "url": "https://openreview.net/forum?id=ihEHCbqZEx", "decision_type": "Spotlight", "avg_rating": 6.3, "relative_path": "2024/NeurIPS/Spotlight/6.3_Flex-MoE_ Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts_2024.pdf" }, { "title": "Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning", "authors": [ "Sriyash Poddar", "Yanming Wan", "Hamish Ivison", "Abhishek Gupta", "Natasha Jaques" ], "year": 2024, "venue": "NeurIPS", "abstract": "Reinforcement Learning from Human Feedback (RLHF) is a powerful paradigm for aligning foundation models to human values and preferences. However, current RLHF techniques cannot account for the naturally occurring differences in individual human preferences across a diverse population. When these differences arise, traditional RLHF frameworks simply average over them, leading to inaccurate rewards and poor performance for individual subgroups. To address the need for pluralistic alignment, we develop a class of multimodal RLHF methods. Our proposed techniques are based on a latent variable formulation - inferring a novel user-specific latent and learning reward models and policies conditioned on this latent without additional user-specific data. While conceptually simple, we show that in practice, this reward modeling requires careful algorithmic considerations around model architecture and reward scaling. To empirically validate our proposed technique, we first show that it can provide a way to combat underspecification in simulated control problems, inferring and optimizing user-specific reward functions. Next, we conduct experiments on pluralistic language datasets representing diverse user preferences and demonstrate improved reward function accuracy. We additionally show the benefits of this probabilistic framework in terms of measuring uncertainty, and actively learning user preferences. This work enables learning from diverse populations of users with divergent preferences, an important challenge that naturally occurs in problems from robot learning to foundation model alignment.", "source": "openreview", "url": "https://openreview.net/forum?id=gRG6SzbW9p", "decision_type": "Spotlight", "avg_rating": 5.5, "relative_path": "2024/NeurIPS/Spotlight/5.5_Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning_2024.pdf" }, { "title": "Adaptive Test-Time Personalization for Federated Learning", "authors": [ "Wenxuan Bao", "Tianxin Wei", "Haohan Wang", "Jingrui He" ], "year": 2023, "venue": "NeurIPS", "abstract": "Personalized federated learning algorithms have shown promising results in adapting models to various distribution shifts. However, most of these methods require labeled data on testing clients for personalization, which is usually unavailable in real-world scenarios. In this paper, we introduce a novel setting called test-time personalized federated learning (TTPFL), where clients locally adapt a global model in an unsupervised way without relying on any labeled data during test-time. While traditional test-time adaptation (TTA) can be used in this scenario, most of them inherently assume training data come from a single domain, while they come from multiple clients (source domains) with different distributions. Overlooking these domain interrelationships can result in suboptimal generalization. Moreover, most TTA algorithms are designed for a specific kind of distribution shift and lack the flexibility to handle multiple kinds of distribution shifts in FL. In this paper, we find that this lack of flexibility partially results from their pre-defining which modules to adapt in the model. To tackle this challenge, we propose a novel algorithm called ATP to adaptively learns the adaptation rates for each module in the model from distribution shifts among source domains. Theoretical analysis proves the strong generalization of ATP. Extensive experiments demonstrate its superiority in handling various distribution shifts including label shift, image corruptions, and domain shift, outperforming existing TTA methods across multiple datasets and model architectures. Our code is available at https://github.com/baowenxuan/ATP.", "source": "openreview", "url": "https://openreview.net/forum?id=rbw9xCU6Ci", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Adaptive Test-Time Personalization for Federated Learning_2023.pdf" }, { "title": "An Empirical Study Towards Prompt-Tuning for Graph Contrastive Pre-Training in Recommendations", "authors": [ "Haoran Yang", "Xiangyu Zhao", "Yicong Li", "Hongxu Chen", "Guandong Xu" ], "year": 2023, "venue": "NeurIPS", "abstract": "Graph contrastive learning (GCL) has emerged as a potent technology for numerous graph learning tasks. It has been successfully applied to real-world recommender systems, where the contrastive loss and the downstream recommendation objectives are always combined to form the overall objective function. Such a strategy is inconsistent with the original GCL paradigm, where graph embeddings are pre-trained without involving downstream training objectives. In this paper, we innovatively propose a prompt-enhanced framework for GCL-based recommender systems, namely CPTPP, which can fully leverage the advantages of the original GCL protocol through prompt tuning. Specifically, we first summarise user profiles in graph recommender systems to automatically generate personalized user prompts. These prompts will then be combined with pre-trained user embeddings to conduct prompt-tuning in downstream tasks, thereby narrowing the distinct targets between pre-training and downstream tasks. Extensive experiments on three benchmark datasets validate the effectiveness of CPTPP against state-of-the-art baselines. A further visualization experiment demonstrates that user embeddings generated by CPTPP have a more uniform distribution, indicating a better capacity to model the diversity of user preferences.\nThe implementation code is available online to ease reproducibility: https://anonymous.4open.science/r/CPTPP-F8F4", "source": "openreview", "url": "https://openreview.net/forum?id=XyAP8ScqLV", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_An Empirical Study Towards Prompt-Tuning for Graph Contrastive Pre-Training in Recommendations_2023.pdf" }, { "title": "An Iterative Self-Learning Framework for Medical Domain Generalization", "authors": [ "Zhenbang Wu", "Huaxiu Yao", "David Liebovitz", "Jimeng Sun" ], "year": 2023, "venue": "NeurIPS", "abstract": "Deep learning models have been widely used to assist doctors with clinical decision-making. However, these models often encounter a significant performance drop when applied to data that differs from the distribution they were trained on. This challenge is known as the domain shift problem. Existing domain generalization algorithms attempt to address this problem by assuming the availability of domain IDs and training a single model to handle all domains. However, in healthcare settings, patients can be classified into numerous latent domains, where the actual domain categorizations are unknown. Furthermore, each patient domain exhibits distinct clinical characteristics, making it sub-optimal to train a single model for all domains. To overcome these limitations, we propose SLGD, a self-learning framework that iteratively discovers decoupled domains and trains personalized classifiers for each decoupled domain. We evaluate the generalizability of SLGD across spatial and temporal data distribution shifts on two real-world public EHR datasets: eICU and MIMIC-IV. Our results show that SLGD achieves up to 11% improvement in the AUPRC score over the best baseline.", "source": "openreview", "url": "https://openreview.net/forum?id=PHKkBbuJWM", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_An Iterative Self-Learning Framework for Medical Domain Generalization_2023.pdf" }, { "title": "Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization", "authors": [ "Adel Javanmard", "Vahab Mirrokni" ], "year": 2023, "venue": "NeurIPS", "abstract": "While personalized recommendations systems have become increasingly popular, ensuring user data protection remains a top concern in the development of these learning systems. A common approach to enhancing privacy involves training models using anonymous data rather than individual data. In this paper, we explore a natural technique called \"look-alike clustering\", which involves replacing sensitive features of individuals with the cluster's average values. We provide a precise analysis of how training models using anonymous cluster centers affects their generalization capabilities. We focus on an asymptotic regime where the size of the training set grows in proportion to the features dimension. Our analysis is based on the Convex Gaussian Minimax Theorem (CGMT) and allows us to theoretically understand the role of different model components on the generalization error. In addition, we demonstrate that in certain high-dimensional regimes, training over anonymous cluster centers acts as a regularization and improves generalization error of the trained models. Finally, we corroborate our asymptotic theory with finite-sample numerical experiments where we observe a perfect match when the sample size is only of order of a few hundreds.", "source": "openreview", "url": "https://openreview.net/forum?id=WfsWy59bX2", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Anonymous Learning via Look-Alike Clustering_ A Precise Analysis of Model Generalization_2023.pdf" }, { "title": "Can Language Models Teach? Teacher Explanations Improve Student Performance via Personalization", "authors": [ "Swarnadeep Saha", "Peter Hase", "Mohit Bansal" ], "year": 2023, "venue": "NeurIPS", "abstract": "A hallmark property of explainable AI models is the ability to teach other agents, communicating knowledge of how to perform a task. While Large Language Models (LLMs) perform complex reasoning by generating explanations for their predictions, it is unclear whether they also make good teachers for weaker agents. To address this, we consider a student-teacher framework between two LLM agents and study if, when, and how the teacher should intervene with natural language explanations to improve the student’s performance. Since communication is expensive, we define a budget such that the teacher only communicates explanations for a fraction of the data, after which the student should perform well on its own. We decompose the teaching problem along four axes: (1) if teacher’s test time in- tervention improve student predictions, (2) when it is worth explaining a data point, (3) how the teacher should personalize explanations to better teach the student, and (4) if teacher explanations also improve student performance on future unexplained data. We first show that teacher LLMs can indeed intervene on student reasoning to improve their performance. Next, inspired by the Theory of Mind abilities of effective teachers, we propose building two few-shot mental models of the student. The first model defines an Intervention Function that simulates the utility of an intervention, allowing the teacher to intervene when this utility is the highest and improving student performance at lower budgets. The second model enables the teacher to personalize explanations for a particular student and outperform unpersonalized teachers. We also demonstrate that in multi-turn interactions, teacher explanations generalize and learning from explained data improves student performance on future unexplained data. Finally, we also verify that misaligned teachers can lower student performance to random chance by intentionally misleading them.", "source": "openreview", "url": "https://openreview.net/forum?id=IacxcFpvWQ", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Can Language Models Teach_ Teacher Explanations Improve Student Performance via Personalization_2023.pdf" }, { "title": "Contextual Gaussian Process Bandits with Neural Networks", "authors": [ "Haoting Zhang", "Jinghai He", "Rhonda Righter", "Zuo-Jun Shen", "Zeyu Zheng" ], "year": 2023, "venue": "NeurIPS", "abstract": "Contextual decision-making problems have witnessed extensive applications in various fields such as online content recommendation, personalized healthcare, and autonomous vehicles, where a core practical challenge is to select a suitable surrogate model for capturing unknown complicated reward functions. It is often the case that both high approximation accuracy and explicit uncertainty quantification are desired. In this work, we propose a neural network-accompanied Gaussian process (NN-AGP) model, which leverages neural networks to approximate the unknown and potentially complicated reward function regarding the contextual variable, and maintains a Gaussian process surrogate model with respect to the decision variable. Our model is shown to outperform existing approaches by offering better approximation accuracy thanks to the use of neural networks and possessing explicit uncertainty quantification from the Gaussian process. We also analyze the maximum information gain of the NN-AGP model and prove regret bounds for the corresponding algorithms. Moreover, we conduct experiments on both synthetic and practical problems, illustrating the effectiveness of our approach.", "source": "openreview", "url": "https://openreview.net/forum?id=eNhW9UnlGG", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Contextual Gaussian Process Bandits with Neural Networks_2023.pdf" }, { "title": "Contextual Stochastic Bilevel Optimization", "authors": [ "Yifan Hu", "Jie Wang", "Yao Xie", "Andreas Krause", "Daniel Kuhn" ], "year": 2023, "venue": "NeurIPS", "abstract": "We introduce contextual stochastic bilevel optimization (CSBO) -- a stochastic bilevel optimization framework with the lower-level problem minimizing an expectation conditioned on some contextual information and the upper-level decision variable. This framework extends classical stochastic bilevel optimization when the lower-level decision maker responds optimally not only to the decision of the upper-level decision maker but also to some side information and when there are multiple or even infinite many followers. It captures important applications such as meta-learning, personalized federated learning, end-to-end learning, and Wasserstein distributionally robust optimization with side information (WDRO-SI). Due to the presence of contextual information, existing single-loop methods for classical stochastic bilevel optimization are unable to converge. To overcome this challenge, we introduce an efficient double-loop gradient method based on the Multilevel Monte-Carlo (MLMC) technique and establish its sample and computational complexities. When specialized to stochastic nonconvex optimization, our method matches existing lower bounds. For meta-learning, the complexity of our method does not depend on the number of tasks. Numerical experiments further validate our theoretical results.", "source": "openreview", "url": "https://openreview.net/forum?id=SHBksHKutP", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Contextual Stochastic Bilevel Optimization_2023.pdf" }, { "title": "Cookie Consent Has Disparate Impact on Estimation Accuracy", "authors": [ "Erik Miehling", "Rahul Nair", "Elizabeth M. Daly", "Karthikeyan Natesan Ramamurthy", "Robert Nelson Redmond" ], "year": 2023, "venue": "NeurIPS", "abstract": "Cookies are designed to enable more accurate identification and tracking of user behavior, in turn allowing for more personalized ads and better performing ad campaigns. Given the additional information that is recorded, questions related to privacy and fairness naturally arise. How does a user's consent decision influence how much the system can learn about their demographic and tastes? Is the impact of a user's consent decision on the recommender system's ability to learn about their latent attributes uniform across demographics? We investigate these questions in the context of an engagement-driven recommender system using simulation. We empirically demonstrate that when consent rates exhibit demographic-dependence, user consent has a disparate impact on the recommender agent's ability to estimate users' latent attributes. In particular, we find that when consent rates are demographic-dependent, a user disagreeing to share their cookie may counter-intuitively cause the recommender agent to know more about the user than if the user agreed to share their cookie. Furthermore, the gap in base consent rates across demographics serves as an amplifier: users from the lower consent rate demographic who agree to cookie sharing generally experience higher estimation errors than the same users from the higher consent rate demographic, and conversely for users who choose to disagree to cookie sharing, with these differences increasing in consent rate gap. We discuss the need for new notions of fairness that encourage consistency between a user's privacy decisions and the system's ability to estimate their latent attributes.", "source": "openreview", "url": "https://openreview.net/forum?id=dFtpRphNb3", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Cookie Consent Has Disparate Impact on Estimation Accuracy_2023.pdf" }, { "title": "Data Minimization at Inference Time", "authors": [ "Cuong Tran", "Ferdinando Fioretto" ], "year": 2023, "venue": "NeurIPS", "abstract": "In high-stakes domains such as legal, banking, hiring, and healthcare, learning models frequently rely on sensitive user information for inference, necessitating the complete set of features. This not only poses significant privacy risks for individuals but also demands substantial human effort from organizations to verify information accuracy. \nThis study asks whether it is necessary to use all input features for accurate predictions at inference time. The paper demonstrates that, in a personalized setting, individuals may only need to disclose a small subset of features without compromising decision-making accuracy. The paper also provides an efficient sequential algorithm to determine the appropriate attributes for each individual to provide. Evaluations across various learning tasks show that individuals can potentially report as little as 10\\% of their information while maintaining the same accuracy level as a model that employs the full set of user information.", "source": "openreview", "url": "https://openreview.net/forum?id=cZS5X3PLOR", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Data Minimization at Inference Time_2023.pdf" }, { "title": "Demo2Code: From Summarizing Demonstrations to Synthesizing Code via Extended Chain-of-Thought", "authors": [ "Huaxiaoyue Wang", "Gonzalo Gonzalez-Pumariega", "Yash Sharma", "Sanjiban Choudhury" ], "year": 2023, "venue": "NeurIPS", "abstract": "Language instructions and demonstrations are two natural ways for users to teach robots personalized tasks. Recent progress in Large Language Models (LLMs) has shown impressive performance in translating language instructions into code for robotic tasks. However, translating demonstrations into task code continues to be a challenge due to the length and complexity of both demonstrations and code, making learning a direct mapping intractable. This paper presents Demo2Code, a novel framework that generates robot task code from demonstrations via an extended chain-of-thought and defines a common latent specification to connect the two. Our framework employs a robust two-stage process: (1) a recursive summarization technique that condenses demonstrations into concise specifications, and (2) a code synthesis approach that expands each function recursively from the generated specifications. We conduct extensive evaluation on various robot task benchmarks, including a novel game benchmark Robotouille, designed to simulate diverse cooking tasks in a kitchen environment.", "source": "openreview", "url": "https://openreview.net/forum?id=ftPoVcm821", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Demo2Code_ From Summarizing Demonstrations to Synthesizing Code via Extended Chain-of-Thought_2023.pdf" }, { "title": "DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model", "authors": [ "Yuanshao Zhu", "Yongchao Ye", "Shiyao Zhang", "Xiangyu Zhao", "James Yu" ], "year": 2023, "venue": "NeurIPS", "abstract": "Pervasive integration of GPS-enabled devices and data acquisition technologies has led to an exponential increase in GPS trajectory data, fostering advancements in spatial-temporal data mining research. Nonetheless, GPS trajectories contain personal geolocation information, rendering serious privacy concerns when working with raw data. A promising approach to address this issue is trajectory generation, which involves replacing original data with generated, privacy-free alternatives. Despite the potential of trajectory generation, the complex nature of human behavior and its inherent stochastic characteristics pose challenges in generating high-quality trajectories. \nIn this work, we propose a spatial-temporal diffusion probabilistic model for trajectory generation (DiffTraj). This model effectively combines the generative abilities of diffusion models with the spatial-temporal features derived from real trajectories. The core idea is to reconstruct and synthesize geographic trajectories from white noise through a reverse trajectory denoising process. Furthermore, we propose a Trajectory UNet (Traj-UNet) deep neural network to embed conditional information and accurately estimate noise levels during the reverse process. Experiments on two real-world datasets show that DiffTraj can be intuitively applied to generate high-fidelity trajectories while retaining the original distributions. Moreover, the generated results can support downstream trajectory analysis tasks and significantly outperform other methods in terms of geo-distribution evaluations.", "source": "openreview", "url": "https://openreview.net/forum?id=ykMdzevPkJ", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_DiffTraj_ Generating GPS Trajectory with Diffusion Probabilistic Model_2023.pdf" }, { "title": "Distributed Personalized Empirical Risk Minimization", "authors": [ "Yuyang Deng", "Mohammad Mahdi Kamani", "Pouria Mahdavinia", "Mehrdad Mahdavi" ], "year": 2023, "venue": "NeurIPS", "abstract": "This paper advocates a new paradigm Personalized Empirical Risk Minimization (PERM) to facilitate learning from heterogeneous data sources without imposing stringent constraints on computational resources shared by participating devices. In PERM, we aim at learning a distinct model for each client by personalizing the aggregation of local empirical losses by effectively estimating the statistical discrepancy among data distributions, which entails optimal statistical accuracy for all local distributions and overcomes the data heterogeneity issue. To learn personalized models at scale, we propose a distributed algorithm that replaces the standard model averaging with model shuffling to simultaneously optimize \nPERM objectives for all devices. This also allows to learn distinct model architectures (e.g., neural networks with different number of parameters) for different clients, thus confining to underlying memory and compute resources of individual clients. We rigorously analyze the convergence of proposed algorithm and conduct experiments that corroborates the effectiveness of proposed paradigm.", "source": "openreview", "url": "https://openreview.net/forum?id=KoQgA0coZ9", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Distributed Personalized Empirical Risk Minimization_2023.pdf" }, { "title": "Dynamic Personalized Federated Learning with Adaptive Differential Privacy", "authors": [ "Xiyuan Yang", "Wenke Huang", "Mang Ye" ], "year": 2023, "venue": "NeurIPS", "abstract": "Personalized federated learning with differential privacy has been considered a feasible solution to address non-IID distribution of data and privacy leakage risks. However, current personalized federated learning methods suffer from inflexible personalization and convergence difficulties due to two main factors: 1) Firstly, we observe that the prevailing personalization methods mainly achieve this by personalizing a fixed portion of the model, which lacks flexibility. 2) Moreover, we further demonstrate that the default gradient calculation is sensitive to the widely-used clipping operations in differential privacy, resulting in difficulties in convergence. Considering that Fisher information values can serve as an effective measure for estimating the information content of parameters by reflecting the model sensitivity to parameters, we aim to leverage this property to address the aforementioned challenges. In this paper, we propose a novel federated learning method with Dynamic Fisher Personalization and Adaptive Constraint (FedDPA) to handle these challenges. Firstly, by using layer-wise Fisher information to measure the information content of local parameters, we retain local parameters with high Fisher values during the personalization process, which are considered informative, simultaneously prevent these parameters from noise perturbation. Secondly, we introduce an adaptive approach by applying differential constraint strategies to personalized parameters and shared parameters identified in the previous for better convergence. Our method boosts performance through flexible personalization while mitigating the slow convergence caused by clipping operations. Experimental results on CIFAR-10, FEMNIST and SVHN dataset demonstrate the effectiveness of our approach in achieving better performance and robustness against clipping, under personalized federated learning with differential privacy.", "source": "openreview", "url": "https://openreview.net/forum?id=RteNLuc8D9", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Dynamic Personalized Federated Learning with Adaptive Differential Privacy_2023.pdf" }, { "title": "Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback", "authors": [ "Han Shao", "Lee Cohen", "Avrim Blum", "Yishay Mansour", "Aadirupa Saha", "Matthew Walter" ], "year": 2023, "venue": "NeurIPS", "abstract": "In this work, we propose a multi-objective decision making framework that accommodates different user preferences over objectives, where preferences are learned via policy comparisons. Our model consists of a known Markov decision process with a vector-valued reward function, with each user having an unknown preference vector that expresses the relative importance of each objective. The goal is to efficiently compute a near-optimal policy for a given user. We consider two user feedback models. We first address the case where a user is provided with two policies and returns their preferred policy as feedback. We then move to a different user feedback model, where a user is instead provided with two small weighted sets of representative trajectories and selects the preferred one. In both cases, we suggest an algorithm that finds a nearly optimal policy for the user using a number of comparison queries that scales quasilinearly in the number of objectives.", "source": "openreview", "url": "https://openreview.net/forum?id=PYASzxr2OP", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative_2023.pdf" }, { "title": "Eliminating Domain Bias for Federated Learning in Representation Space", "authors": [ "Jianqing Zhang", "Yang Hua", "Jian Cao", "Hao Wang", "Tao Song", "Zhengui XUE", "Ruhui Ma", "Haibing Guan" ], "year": 2023, "venue": "NeurIPS", "abstract": "Recently, federated learning (FL) is popular for its privacy-preserving and collaborative learning abilities. However, under statistically heterogeneous scenarios, we observe that biased data domains on clients cause a representation bias phenomenon and further degenerate generic representations during local training, i.e., the representation degeneration phenomenon. To address these issues, we propose a general framework Domain Bias Eliminator (DBE) for FL. Our theoretical analysis reveals that DBE can promote bi-directional knowledge transfer between server and client, as it reduces the domain discrepancy between server and client in representation space. Besides, extensive experiments on four datasets show that DBE can greatly improve existing FL methods in both generalization and personalization abilities. The DBE-equipped FL method can outperform ten state-of-the-art personalized FL methods by a large margin. Our code is public at https://github.com/TsingZ0/DBE.", "source": "openreview", "url": "https://openreview.net/forum?id=nO5i1XdUS0", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Eliminating Domain Bias for Federated Learning in Representation Space_2023.pdf" }, { "title": "Emergent and Predictable Memorization in Large Language Models", "authors": [ "Stella Biderman", "USVSN Sai Prashanth", "Lintang Sutawika", "Hailey Schoelkopf", "Quentin Gregory Anthony", "Shivanshu Purohit", "Edward Raff" ], "year": 2023, "venue": "NeurIPS", "abstract": "Memorization, or the tendency of large language models (LLMs) to output entire sequences from their training data verbatim, is a key concern for deploying language models. In particular, it is vital to minimize a model's memorization of sensitive datapoints such as those containing personal identifiable information (PII). The prevalence of such undesirable memorization can pose issues for model trainers, and may even require discarding an otherwise functional model. We therefore seek to predict which sequences will be memorized before a large model's full train-time by extrapolating the memorization behavior of lower-compute trial runs. We measure memorization in the Pythia model suite and plot scaling laws for forecasting memorization, allowing us to provide equi-compute recommendations to maximize the reliability (recall) of such predictions. We additionally provide further novel discoveries on the distribution of memorization scores across models and data. We release all code and data necessary to reproduce the results in this paper at https://github.com/EleutherAI/pythia.", "source": "openreview", "url": "https://openreview.net/forum?id=Iq0DvhB4Kf", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Emergent and Predictable Memorization in Large Language Models_2023.pdf" }, { "title": "Face Reconstruction from Facial Templates by Learning Latent Space of a Generator Network", "authors": [ "Hatef Otroshi Shahreza", "Sébastien Marcel" ], "year": 2023, "venue": "NeurIPS", "abstract": "In this paper, we focus on the template inversion attack against face recognition systems and propose a new method to reconstruct face images from facial templates. Within a generative adversarial network (GAN)-based framework, we learn a mapping from facial templates to the intermediate latent space of a pre-trained face generation network, from which we can generate high-resolution realistic reconstructed face images. We show that our proposed method can be applied in whitebox and blackbox attacks against face recognition systems. Furthermore, we evaluate the transferability of our attack when the adversary uses the reconstructed face image to impersonate the underlying subject in an attack against another face recognition system. Considering the adversary's knowledge and the target face recognition system, we define five different attacks and evaluate the vulnerability of state-of-the-art face recognition systems. Our experiments show that our proposed method achieves high success attack rates in whitebox and blackbox scenarios. Furthermore, the reconstructed face images are transferable and can be used to enter target face recognition systems with a different feature extractor model. We also explore important areas in the reconstructed face images that can fool the target face recognition system.", "source": "openreview", "url": "https://openreview.net/forum?id=hI6EPhq70A", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Face Reconstruction from Facial Templates by Learning Latent Space of a Generator Network_2023.pdf" }, { "title": "Fed-CO$_{2}$: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning", "authors": [ "Zhongyi Cai", "Ye Shi", "Wei Huang", "Jingya Wang" ], "year": 2023, "venue": "NeurIPS", "abstract": "Federated Learning (FL) has emerged as a promising distributed learning paradigm that enables multiple clients to learn a global model collaboratively without sharing their private data. However, the effectiveness of FL is highly dependent on the quality of the data that is being used for training. In particular, data heterogeneity issues, such as label distribution skew and feature skew, can significantly impact the performance of FL. Previous studies in FL have primarily focused on addressing label distribution skew data heterogeneity, while only a few recent works have made initial progress in tackling feature skew issues. Notably, these two forms of data heterogeneity have been studied separately and have not been well explored within a unified FL framework. To address this gap, we propose Fed-CO$_2$, a universal FL framework that handles both label distribution skew and feature skew within a Cooperation mechanism between the Online and Offline models. Specifically, the online model learns general knowledge that is shared among all clients, while the offline model is trained locally to learn the specialized knowledge of each individual client. To further enhance model cooperation in the presence of feature shifts, we design an intra-client knowledge transfer mechanism that reinforces mutual learning between the online and offline models, and an inter-client knowledge transfer mechanism to increase the models’ domain generalization ability. Extensive experiments show that our Fed-CO$_2$ outperforms a wide range of existing personalized federated learning algorithms in terms of handling label distribution skew and feature skew, both individually and collectively. The empirical results are supported by our convergence analyses in a simplified setting.", "source": "openreview", "url": "https://openreview.net/forum?id=dEDdRWunxU", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Fed-CO$_{2}$_ Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federat_2023.pdf" }, { "title": "FedL2P: Federated Learning to Personalize", "authors": [ "Royson Lee", "Minyoung Kim", "Da Li", "Xinchi Qiu", "Timothy Hospedales", "Ferenc Huszár", "Nicholas Donald Lane" ], "year": 2023, "venue": "NeurIPS", "abstract": "Federated learning (FL) research has made progress in developing algorithms for distributed learning of global models, as well as algorithms for local personalization of those common models to the specifics of each client’s local data distribution. However, different FL problems may require different personalization strategies, and it may not even be possible to define an effective one-size-fits-all personalization strategy for all clients: Depending on how similar each client’s optimal predictor is to that of the global model, different personalization strategies may be preferred. In this paper, we consider the federated meta-learning problem of learning personalization strategies. Specifically, we consider meta-nets that induce the batch-norm and learning rate parameters for each client given local data statistics. By learning these meta-nets through FL, we allow the whole FL network to collaborate in learning a customized personalization strategy for each client. Empirical results show that this framework improves on a range of standard hand-crafted personalization baselines in both label and feature shift situations.", "source": "openreview", "url": "https://openreview.net/forum?id=FM81CI68Iz", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_FedL2P_ Federated Learning to Personalize_2023.pdf" }, { "title": "Federated Learning via Meta-Variational Dropout", "authors": [ "Insu Jeon", "Minui Hong", "Junhyeog Yun", "Gunhee Kim" ], "year": 2023, "venue": "NeurIPS", "abstract": "Federated Learning (FL) aims to train a global inference model from remotely distributed clients, gaining popularity due to its benefit of improving data privacy. However, traditional FL often faces challenges in practical applications, including model overfitting and divergent local models due to limited and non-IID data among clients. To address these issues, we introduce a novel Bayesian meta-learning approach called meta-variational dropout (MetaVD). MetaVD learns to predict client-dependent dropout rates via a shared hypernetwork, enabling effective model personalization of FL algorithms in limited non-IID data settings. We also emphasize the posterior adaptation view of meta-learning and the posterior aggregation view of Bayesian FL via the conditional dropout posterior. We conducted extensive experiments on various sparse and non-IID FL datasets. MetaVD demonstrated excellent classification accuracy and uncertainty calibration performance, especially for out-of-distribution (OOD) clients. MetaVD compresses the local model parameters needed for each client, mitigating model overfitting and reducing communication costs. Code is available at https://github.com/insujeon/MetaVD.", "source": "openreview", "url": "https://openreview.net/forum?id=VNyKBipt91", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Federated Learning via Meta-Variational Dropout_2023.pdf" }, { "title": "Federated Learning with Bilateral Curation for Partially Class-Disjoint Data", "authors": [ "Ziqing Fan", "Ruipeng Zhang", "Jiangchao Yao", "Bo Han", "Ya Zhang", "Yanfeng Wang" ], "year": 2023, "venue": "NeurIPS", "abstract": "Partially class-disjoint data (PCDD), a common yet under-explored data formation where each client contributes a part of classes (instead of all classes) of samples, severely challenges the performance of federated algorithms. Without full classes, the local objective will contradict the global objective, yielding the angle collapse problem for locally missing classes and the space waste problem for locally existing classes. As far as we know, none of the existing methods can intrinsically mitigate PCDD challenges to achieve holistic improvement in the bilateral views (both global view and local view) of federated learning. To address this dilemma, we are inspired by the strong generalization of simplex Equiangular Tight Frame (ETF) on the imbalanced data, and propose a novel approach called FedGELA where the classifier is globally fixed as a simplex ETF while locally adapted to the personal distributions. Globally, FedGELA provides fair and equal discrimination for all classes and avoids inaccurate updates of the classifier, while locally it utilizes the space of locally missing classes for locally existing classes. We conduct extensive experiments on a range of datasets to demonstrate that our FedGELA achieves promising performance (averaged improvement of 3.9% to FedAvg and 1.5% to best baselines) and provide both local and global convergence guarantees.", "source": "openreview", "url": "https://openreview.net/forum?id=wwmKVO8bsR", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Federated Learning with Bilateral Curation for Partially Class-Disjoint Data_2023.pdf" }, { "title": "Flow: Per-instance Personalized Federated Learning", "authors": [ "Kunjal Panchal", "Sunav Choudhary", "Nisarg Parikh", "Lijun Zhang", "Hui Guan" ], "year": 2023, "venue": "NeurIPS", "abstract": "Federated learning (FL) suffers from data heterogeneity, where the diverse data distributions across clients make it challenging to train a single global model effectively. Existing personalization approaches aim to address the data heterogeneity issue by creating a personalized model for each client from the global model that fits their local data distribution. However, these personalized models may achieve lower accuracy than the global model in some clients, resulting in limited performance improvement compared to that without personalization. To overcome this limitation, we propose a per-instance personalization FL algorithm Flow. Flow creates dynamic personalized models that are adaptive not only to each client’s data distributions but also to each client’s data instances. The personalized model allows each instance to dynamically determine whether it prefers the local parameters or its global counterpart to make correct predictions, thereby improving clients’\naccuracy. We provide theoretical analysis on the convergence of Flow and empirically demonstrate the superiority of Flow in improving clients’ accuracy compared to state-of-the-art personalization approaches on both vision and language-based tasks.", "source": "openreview", "url": "https://openreview.net/forum?id=BI031mw7iS", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Flow_ Per-instance Personalized Federated Learning_2023.pdf" }, { "title": "Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses", "authors": [ "Jacob Granley", "Tristan Fauvel", "Matthew Chalk", "Michael Beyeler" ], "year": 2023, "venue": "NeurIPS", "abstract": "Neuroprostheses show potential in restoring lost sensory function and enhancing human capabilities, but the sensations produced by current devices often seem unnatural or distorted. Exact placement of implants and differences in individual perception lead to significant variations in stimulus response, making personalized stimulus optimization a key challenge. Bayesian optimization could be used\nto optimize patient-specific stimulation parameters with limited noisy observations, but is not feasible for high-dimensional stimuli. Alternatively, deep learning models can optimize stimulus encoding strategies, but typically assume perfect knowledge of patient-specific variations. Here we propose a novel, practically feasible approach that overcomes both of these fundamental limitations. First, a deep encoder network is trained to produce optimal stimuli for any individual patient by inverting a forward model mapping electrical stimuli to visual percepts. Second, a preferential Bayesian optimization strategy utilizes this encoder to learn the optimal patient-specific parameters for a new patient, using a minimal number of pairwise comparisons between candidate stimuli. We demonstrate the viability of this approach on a novel, state-of-the-art visual prosthesis model. Our approach quickly learns a personalized stimulus encoder and leads to dramatic improvements in the quality of restored vision, outperforming existing encoding strategies. Further, this approach is robust to noisy patient feedback and misspecifications in the underlying forward model. Overall, our results suggest that combining the strengths of deep learning and Bayesian optimization could significantly improve the perceptual experience of patients fitted with visual prostheses and may prove a viable solution for a range of neuroprosthetic technologies", "source": "openreview", "url": "https://openreview.net/forum?id=ZED5wdGous", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses_2023.pdf" }, { "title": "IBA: Towards Irreversible Backdoor Attacks in Federated Learning", "authors": [ "Dung Thuy Nguyen", "Tuan Minh Nguyen", "Anh Tuan Tran", "Khoa D Doan", "KOK SENG WONG" ], "year": 2023, "venue": "NeurIPS", "abstract": "Federated learning (FL) is a distributed learning approach that enables machine learning models to be trained on decentralized data without compromising end devices' personal, potentially sensitive data. However, the distributed nature and uninvestigated data intuitively introduce new security vulnerabilities, including backdoor attacks. In this scenario, an adversary implants backdoor functionality into the global model during training, which can be activated to cause the desired misbehaviors for any input with a specific adversarial pattern. Despite having remarkable success in triggering and distorting model behavior, prior backdoor attacks in FL often hold impractical assumptions, limited imperceptibility, and durability. Specifically, the adversary needs to control a sufficiently large fraction of clients or know the data distribution of other honest clients. In many cases, the trigger inserted is often visually apparent, and the backdoor effect is quickly diluted if the adversary is removed from the training process. To address these limitations, we propose a novel backdoor attack framework in FL, the Irreversible Backdoor Attack (IBA), that jointly learns the optimal and visually stealthy trigger and then gradually implants the backdoor into a global model. This approach allows the adversary to execute a backdoor attack that can evade both human and machine inspections. Additionally, we enhance the efficiency and durability of the proposed attack by selectively poisoning the model's parameters that are least likely updated by the main task's learning process and constraining the poisoned model update to the vicinity of the global model. Finally, we evaluate the proposed attack framework on several benchmark datasets, including MNIST, CIFAR-10, and Tiny ImageNet, and achieved high success rates while simultaneously bypassing existing backdoor defenses and achieving a more durable backdoor effect compared to other backdoor attacks. Overall, IBA offers a more effective, stealthy, and durable approach to backdoor attacks in FL. The code associated with this paper is available on [GitHub](https://github.com/sail-research/iba).", "source": "openreview", "url": "https://openreview.net/forum?id=cemEOP8YoC", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_IBA_ Towards Irreversible Backdoor Attacks in Federated Learning_2023.pdf" }, { "title": "Inserting Anybody in Diffusion Models via Celeb Basis", "authors": [ "Ge Yuan", "Xiaodong Cun", "Yong Zhang", "Maomao Li", "Chenyang Qi", "Xintao Wang", "Ying Shan", "Huicheng Zheng" ], "year": 2023, "venue": "NeurIPS", "abstract": "Exquisite demand exists for customizing the pretrained large text-to-image model, $e.g.$ Stable Diffusion, to generate innovative concepts, such as the users themselves. However, the newly-added concept from previous customization methods often shows weaker combination abilities than the original ones even given several images during training. We thus propose a new personalization method that allows for the seamless integration of a unique individual into the pre-trained diffusion model using just $one\\ facial\\ photograph$ and only $1024\\ learnable\\ parameters$ under $3\\ minutes$. So we can effortlessly generate stunning images of this person in any pose or position, interacting with anyone and doing anything imaginable from text prompts. To achieve this, we first analyze and build a well-defined celeb basis from the embedding space of the pre-trained large text encoder. Then, given one facial photo as the target identity, we generate its own embedding by optimizing the weight of this basis and locking all other parameters. Empowered by the proposed celeb basis, the new identity in our customized model showcases a better concept combination ability than previous personalization methods. Besides, our model can also learn several new identities at once and interact with each other where the previous customization model fails to. Project page is at: http://celeb-basis.github.io. Code is at: https://github.com/ygtxr1997/CelebBasis.", "source": "openreview", "url": "https://openreview.net/forum?id=OGQWZ3p0Zn", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Inserting Anybody in Diffusion Models via Celeb Basis_2023.pdf" }, { "title": "Is This Loss Informative? Faster Text-to-Image Customization by Tracking Objective Dynamics", "authors": [ "Anton Voronov", "Mikhail Khoroshikh", "Artem Babenko", "Max Ryabinin" ], "year": 2023, "venue": "NeurIPS", "abstract": "Text-to-image generation models represent the next step of evolution in image synthesis, offering a natural way to achieve flexible yet fine-grained control over the result.\nOne emerging area of research is the fast adaptation of large text-to-image models to smaller datasets or new visual concepts.\nHowever, many efficient methods of adaptation have a long training time, which limits their practical applications, slows down experiments, and spends excessive GPU resources.\nIn this work, we study the training dynamics of popular text-to-image personalization methods (such as Textual Inversion or DreamBooth), aiming to speed them up.\nWe observe that most concepts are learned at early stages and do not improve in quality later, but standard training convergence metrics fail to indicate that.\nInstead, we propose a simple drop-in early stopping criterion that only requires computing the regular training objective on a fixed set of inputs for all training iterations.\nOur experiments on Stable Diffusion for 48 different concepts and three personalization methods demonstrate the competitive performance of our approach, which makes adaptation up to 8 times faster with no significant drops in quality.", "source": "openreview", "url": "https://openreview.net/forum?id=Xs6Xwc0Glj", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Is This Loss Informative_ Faster Text-to-Image Customization by Tracking Objective Dynamics_2023.pdf" }, { "title": "Large-Scale Distributed Learning via Private On-Device LSH", "authors": [ "Tahseen Rabbani", "Marco Bornstein", "Furong Huang" ], "year": 2023, "venue": "NeurIPS", "abstract": "Locality-sensitive hashing (LSH) based frameworks have been used efficiently to select weight vectors in a dense hidden layer with high cosine similarity to an input, enabling dynamic pruning. \n While this type of scheme has been shown to improve computational training efficiency, existing algorithms require repeated randomized projection of the full layer weight, which is impractical for computational- and memory-constrained devices. \n In a distributed setting, deferring LSH analysis to a centralized host is (i) slow if the device cluster is large and (ii) requires access to input data which is forbidden in a federated context. \n Using a new family of hash functions, we develop the first private, personalized, and memory-efficient on-device LSH framework.\nOur framework enables privacy and personalization by allowing each device to generate hash tables, without the help of a central host, using device-specific hashing hyper-parameters (e.g., number of hash tables or hash length).\nHash tables are generated with a compressed set of the full weights, and can be serially generated and discarded if the process is memory-intensive.\nThis allows devices to avoid maintaining (i) the fully-sized model and (ii) large amounts of hash tables in local memory for LSH analysis. We prove several statistical and sensitivity properties of our hash functions, and experimentally demonstrate that our framework is competitive in training large scale recommender networks compared to other LSH frameworks which assume unrestricted on-device capacity.", "source": "openreview", "url": "https://openreview.net/forum?id=dpdbbN7AKr", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Large-Scale Distributed Learning via Private On-Device LSH_2023.pdf" }, { "title": "LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference", "authors": [ "Hongwu Peng", "Ran Ran", "Yukui Luo", "Jiahui Zhao", "Shaoyi Huang", "Kiran Thorat", "Tong Geng", "Chenghong Wang", "Xiaolin Xu", "Wujie Wen", "Caiwen Ding" ], "year": 2023, "venue": "NeurIPS", "abstract": "The growth of Graph Convolution Network (GCN) model sizes has revolutionized numerous applications, surpassing human performance in areas such as personal healthcare and financial systems. The deployment of GCNs in the cloud raises privacy concerns due to potential adversarial attacks on client data. To address security concerns, Privacy-Preserving Machine Learning (PPML) using Homomorphic Encryption (HE) secures sensitive client data. However, it introduces substantial computational overhead in practical applications. To tackle those challenges, we present LinGCN, a framework designed to reduce multiplication depth and optimize the performance of HE based GCN inference. LinGCN is structured around three key elements: (1) A differentiable structural linearization algorithm, complemented by a parameterized discrete indicator function, co-trained with model weights to meet the optimization goal. This strategy promotes fine-grained node-level non-linear location selection, resulting in a model with minimized multiplication depth. (2) A compact node-wise polynomial replacement policy with a second-order trainable activation function, steered towards superior convergence by a two-level distillation approach from an all-ReLU based teacher model. (3) an enhanced HE solution that enables finer-grained operator fusion for node-wise activation functions, further reducing multiplication level consumption in HE-based inference. Our experiments on the NTU-XVIEW skeleton joint dataset reveal that LinGCN excels in latency, accuracy, and scalability for homomorphically encrypted inference, outperforming solutions such as CryptoGCN. Remarkably, LinGCN achieves a 14.2× latency speedup relative to CryptoGCN, while preserving an inference accuracy of ~75\\% and notably reducing multiplication depth. Additionally, LinGCN proves scalable for larger models, delivering a substantial 85.78\\% accuracy with 6371s latency, a 10.47\\% accuracy improvement over CryptoGCN.", "source": "openreview", "url": "https://openreview.net/forum?id=5loV5tVzsY", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_LinGCN_ Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inferen_2023.pdf" }, { "title": "Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM", "authors": [ "Ziba Parsons", "Fei Dou", "Houyi Du", "Zheng Song", "Jin Lu" ], "year": 2023, "venue": "NeurIPS", "abstract": "This paper explores the challenges of implementing Federated Learning (FL) in practical scenarios featuring isolated nodes with data heterogeneity, which can only be connected to the server through wireless links in an infrastructure-less environment. To overcome these challenges, we propose a novel mobilizing personalized FL approach, which aims to facilitate mobility and resilience. Specifically, we develop a novel optimization algorithm called Random Walk Stochastic Alternating Direction Method of Multipliers (RWSADMM). RWSADMM capitalizes on the server's random movement toward clients and formulates local proximity among their adjacent clients based on hard inequality constraints rather than requiring consensus updates or introducing bias via regularization methods. To mitigate the computational burden on the clients, an efficient stochastic solver of the approximated optimization problem is designed in RWSADMM, which provably converges to the stationary point almost surely in expectation. Our theoretical and empirical results demonstrate the provable fast convergence and substantial accuracy improvements achieved by RWSADMM compared to baseline methods, along with its benefits of reduced communication costs and enhanced scalability.", "source": "openreview", "url": "https://openreview.net/forum?id=EcmqyXekuP", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environment_2023.pdf" }, { "title": "Noether Embedding: Efficient Learning of Temporal Regularities", "authors": [ "Chi Gao", "Zidong Zhou", "Luping Shi" ], "year": 2023, "venue": "NeurIPS", "abstract": "Learning to detect and encode temporal regularities (TRs) in events is a prerequisite for human-like intelligence. These regularities should be formed from limited event samples and stored as easily retrievable representations. Existing event embeddings, however, cannot effectively decode TR validity with well-trained vectors, let alone satisfy the efficiency requirements. We develop Noether Embedding (NE) as the first efficient TR learner with event embeddings. Specifically, NE possesses the intrinsic time-translation symmetries of TRs indicated as conserved local energies in the embedding space. This structural bias reduces the calculation of each TR validity to embedding each event sample, enabling NE to achieve data-efficient TR formation insensitive to sample size and time-efficient TR retrieval in constant time complexity. To comprehensively evaluate the TR learning capability of embedding models, we define complementary tasks of TR detection and TR query, formulate their evaluation metrics, and assess embeddings on classic ICEWS14, ICEWS18, and GDELT datasets. Our experiments demonstrate that NE consistently achieves about double the F1 scores for detecting valid TRs compared to classic embeddings, and it provides over ten times higher confidence scores for querying TR intervals. Additionally, we showcase NE's potential applications in social event prediction, personal decision-making, and memory-constrained scenarios.", "source": "openreview", "url": "https://openreview.net/forum?id=27CRbwewyb", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Noether Embedding_ Efficient Learning of Temporal Regularities_2023.pdf" }, { "title": "P-Flow: A Fast and Data-Efficient Zero-Shot TTS through Speech Prompting", "authors": [ "Sungwon Kim", "Kevin J. Shih", "Rohan Badlani", "Joao Felipe Santos", "Evelina Bakhturina", "Mikyas T. Desta", "Rafael Valle", "Sungroh Yoon", "Bryan Catanzaro" ], "year": 2023, "venue": "NeurIPS", "abstract": "While recent large-scale neural codec language models have shown significant improvement in zero-shot TTS by training on thousands of hours of data, they suffer from drawbacks such as a lack of robustness, slow sampling speed similar to previous autoregressive TTS methods, and reliance on pre-trained neural codec representations. Our work proposes P-Flow, a fast and data-efficient zero-shot TTS model that uses speech prompts for speaker adaptation. P-Flow comprises a speech-prompted text encoder for speaker adaptation and a flow matching generative decoder for high-quality and fast speech synthesis. Our speech-prompted text encoder uses speech prompts and text input to generate speaker-conditional text representation. The flow matching generative decoder uses the speaker-conditional output to synthesize high-quality personalized speech significantly faster than in real-time. Unlike the neural codec language models, we specifically train P-Flow on LibriTTS dataset using a continuous mel-representation. Through our training method using continuous speech prompts, P-Flow matches the speaker similarity performance of the large-scale zero-shot TTS models with two orders of magnitude less training data and has more than 20$\\times$ faster sampling speed. Our results show that P-Flow has better pronunciation and is preferred in human likeness and speaker similarity to its recent state-of-the-art counterparts, thus defining P-Flow as an attractive and desirable alternative. We provide audio samples on our demo page: [https://research.nvidia.com/labs/adlr/projects/pflow](https://research.nvidia.com/labs/adlr/projects/pflow)", "source": "openreview", "url": "https://openreview.net/forum?id=zNA7u7wtIN", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_P-Flow_ A Fast and Data-Efficient Zero-Shot TTS through Speech Prompting_2023.pdf" }, { "title": "PHOTOSWAP: Personalized Subject Swapping in Images", "authors": [ "Jing Gu", "Yilin Wang", "Nanxuan Zhao", "Tsu-Jui Fu", "Wei Xiong", "Qing Liu", "Zhifei Zhang", "HE Zhang", "Jianming Zhang", "HyunJoon Jung", "Xin Eric Wang" ], "year": 2023, "venue": "NeurIPS", "abstract": "In an era where images and visual content dominate our digital landscape, the ability to manipulate and personalize these images has become a necessity.\nEnvision seamlessly substituting a tabby cat lounging on a sunlit window sill in a photograph with your own playful puppy, all while preserving the original charm and composition of the image. \nWe present \\emph{Photoswap}, a novel approach that enables this immersive image editing experience through personalized subject swapping in existing images.\n\\emph{Photoswap} first learns the visual concept of the subject from reference images and then swaps it into the target image using pre-trained diffusion models in a training-free manner. We establish that a well-conceptualized visual subject can be seamlessly transferred to any image with appropriate self-attention and cross-attention manipulation, maintaining the pose of the swapped subject and the overall coherence of the image. \nComprehensive experiments underscore the efficacy and controllability of \\emph{Photoswap} in personalized subject swapping. Furthermore, \\emph{Photoswap} significantly outperforms baseline methods in human ratings across subject swapping, background preservation, and overall quality, revealing its vast application potential, from entertainment to professional editing.", "source": "openreview", "url": "https://openreview.net/forum?id=qqcIM8NiiB", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_PHOTOSWAP_ Personalized Subject Swapping in Images_2023.pdf" }, { "title": "PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning.", "authors": [ "Mingjia Shi", "Yuhao Zhou", "Kai Wang", "Huaizheng Zhang", "Shudong Huang", "Qing Ye", "Jiancheng Lv" ], "year": 2023, "venue": "NeurIPS", "abstract": "Classical federated learning (FL) enables training machine learning models without sharing data for privacy preservation, but heterogeneous data characteristic degrades the performance of the localized model. Personalized FL (PFL) addresses this by synthesizing personalized models from a global model via training on local data. Such a global model may overlook the specific information that the clients have been sampled. In this paper, we propose a novel scheme to inject personalized prior knowledge into the global model in each client, which attempts to mitigate the introduced incomplete information problem in PFL. At the heart of our proposed approach is a framework, the $\\textit{PFL with Bregman Divergence}$ (pFedBreD), decoupling the personalized prior from the local objective function regularized by Bregman divergence for greater adaptability in personalized scenarios. We also relax the mirror descent (RMD) to extract the prior explicitly to provide optional strategies. Additionally, our pFedBreD is backed up by a convergence analysis. Sufficient experiments demonstrate that our method reaches the $\\textit{state-of-the-art}$ performances on 5 datasets and outperforms other methods by up to 3.5% across 8 benchmarks. Extensive analyses verify the robustness and necessity of proposed designs. The code will be made public.", "source": "openreview", "url": "https://openreview.net/forum?id=kuxu4lCRr5", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_PRIOR_ Personalized Prior for Reactivating the Information Overlooked in Federated Learning._2023.pdf" }, { "title": "Personalized Dictionary Learning for Heterogeneous Datasets", "authors": [ "Geyu Liang", "Naichen Shi", "Raed Al Kontar", "Salar Fattahi" ], "year": 2023, "venue": "NeurIPS", "abstract": "We introduce a relevant yet challenging problem named Personalized Dictionary Learning (PerDL), where the goal is to learn sparse linear representations from heterogeneous datasets that share some commonality. In PerDL, we model each dataset's shared and unique features as global and local dictionaries. Challenges for PerDL not only are inherited from classical dictionary learning(DL), but also arise due to the unknown nature of the shared and unique features. In this paper, we rigorously formulate this problem and provide conditions under which the global and local dictionaries can be provably disentangled. Under these conditions, we provide a meta-algorithm called Personalized Matching and Averaging (PerMA) that can recover both global and local dictionaries from heterogeneous datasets. PerMA is highly efficient; it converges to the ground truth at a linear rate under suitable conditions. Moreover, it automatically borrows strength from strong learners to improve the prediction of weak learners. As a general framework for extracting global and local dictionaries, we show the application of PerDL in different learning tasks, such as training with imbalanced datasets and video surveillance.", "source": "openreview", "url": "https://openreview.net/forum?id=xw6Szwu4xz", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Personalized Dictionary Learning for Heterogeneous Datasets_2023.pdf" }, { "title": "REFINE: A Fine-Grained Medication Recommendation System Using Deep Learning and Personalized Drug Interaction Modeling", "authors": [ "Suman Bhoi", "Mong-Li Lee", "Wynne Hsu", "Ngiap Chuan Tan" ], "year": 2023, "venue": "NeurIPS", "abstract": "Patients with co-morbidities often require multiple medications to manage their conditions. However, existing medication recommendation systems only offer class-level medications and regard all interactions among drugs to have the same level of severity. This limits their ability to provide personalized and safe recommendations tailored to individual needs. In this work, we introduce a deep learning-based fine-grained medication recommendation system called REFINE, which is designed to improve treatment outcomes and minimize adverse drug interactions. In order to better characterize patients’ health conditions, we model the trend in medication dosage titrations and lab test responses, and adapt the vision transformer to obtain effective patient representations. We also model drug interaction severity levels as weighted graphs to learn safe drug combinations and design a balanced loss function to avoid overly conservative recommendations and miss medications that might be needed for certain conditions. Extensive experiments on two real-world datasets show that REFINE outperforms state-of-the-art techniques.", "source": "openreview", "url": "https://openreview.net/forum?id=GsCTjmYe5v", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_REFINE_ A Fine-Grained Medication Recommendation System Using Deep Learning and Personalized Dr_2023.pdf" }, { "title": "Reliable Off-Policy Learning for Dosage Combinations", "authors": [ "Jonas Schweisthal", "Dennis Frauen", "Valentyn Melnychuk", "Stefan Feuerriegel" ], "year": 2023, "venue": "NeurIPS", "abstract": "Decision-making in personalized medicine such as cancer therapy or critical care must often make choices for dosage combinations, i.e., multiple continuous treatments. Existing work for this task has modeled the effect of multiple treatments independently, while estimating the joint effect has received little attention but comes with non-trivial challenges. In this paper, we propose a novel method for reliable off-policy learning for dosage combinations. Our method proceeds along three steps: (1) We develop a tailored neural network that estimates the individualized dose-response function while accounting for the joint effect of multiple dependent dosages. (2) We estimate the generalized propensity score using conditional normalizing flows in order to detect regions with limited overlap in the shared covariate-treatment space. (3) We present a gradient-based learning algorithm to find the optimal, individualized dosage combinations. Here, we ensure reliable estimation of the policy value by avoiding regions with limited overlap. We finally perform an extensive evaluation of our method to show its effectiveness. To the best of our knowledge, ours is the first work to provide a method for reliable off-policy learning for optimal dosage combinations.", "source": "openreview", "url": "https://openreview.net/forum?id=muVKSb8gi5", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Reliable Off-Policy Learning for Dosage Combinations_2023.pdf" }, { "title": "Spectral Co-Distillation for Personalized Federated Learning", "authors": [ "Zihan Chen", "Howard Hao Yang", "Tony Quek", "Kai Fong Ernest Chong" ], "year": 2023, "venue": "NeurIPS", "abstract": "Personalized federated learning (PFL) has been widely investigated to address the challenge of data heterogeneity, especially when a single generic model is inadequate in satisfying the diverse performance requirements of local clients simultaneously. Existing PFL methods are inherently based on the idea that the relations between the generic global and personalized local models are captured by the similarity of model weights. Such a similarity is primarily based on either partitioning the model architecture into generic versus personalized components or modeling client relationships via model weights. To better capture similar (yet distinct) generic versus personalized model representations, we propose $\\textit{spectral distillation}$, a novel distillation method based on model spectrum information. Building upon spectral distillation, we also introduce a co-distillation framework that establishes a two-way bridge between generic and personalized model training. Moreover, to utilize the local idle time in conventional PFL, we propose a wait-free local training protocol. Through extensive experiments on multiple datasets over diverse heterogeneous data settings, we demonstrate the outperformance and efficacy of our proposed spectral co-distillation method, as well as our wait-free training protocol.", "source": "openreview", "url": "https://openreview.net/forum?id=RqjQL08UFc", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Spectral Co-Distillation for Personalized Federated Learning_2023.pdf" }, { "title": "Strategic Classification under Unknown Personalized Manipulation", "authors": [ "Han Shao", "Avrim Blum", "Omar Montasser" ], "year": 2023, "venue": "NeurIPS", "abstract": "We study the fundamental mistake bound and sample complexity in the strategic classification, where agents can strategically manipulate their feature vector up to an extent in order to be predicted as positive. For example, given a classifier determining college admission, student candidates may try to take easier classes to improve their GPA, retake SAT and change schools in an effort to fool the classifier. *Ball manipulations* are a widely studied class of manipulations in the literature, where agents can modify their feature vector within a bounded radius ball. Unlike most prior work, our work consider manipulations to be *personalized*, meaning that agents can have different levels of manipulation abilities (e.g., varying radii for ball manipulations), and *unknown* to the learner.\n\nWe formalize the learning problem in an interaction model where the learner first deploys a classifier and the agent manipulates the feature vector within their manipulation set to game the deployed classifier. We investigate various scenarios in terms of the information available to the learner during the interaction, such as observing the original feature vector before or after deployment, observing the manipulated feature vector, or not seeing either the original or the manipulated feature vector. We begin by providing online mistake bounds and PAC sample complexity in these scenarios for ball manipulations. We also explore non-ball manipulations and show that, even in the simplest scenario where both the original and the manipulated feature vectors are revealed, the mistake bounds and sample complexity are lower bounded by $\\Omega(|\\mathcal H|)$ when the target function belongs to a known class $\\mathcal H$.", "source": "openreview", "url": "https://openreview.net/forum?id=6cJKcIxPck", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Strategic Classification under Unknown Personalized Manipulation_2023.pdf" }, { "title": "Supply-Side Equilibria in Recommender Systems", "authors": [ "Meena Jagadeesan", "Nikhil Garg", "Jacob Steinhardt" ], "year": 2023, "venue": "NeurIPS", "abstract": "Algorithmic recommender systems such as Spotify and Netflix affect not only consumer behavior but also *producer incentives*. Producers seek to create content that will be shown by the recommendation algorithm, which can impact both the diversity and quality of their content. In this work, we investigate the resulting supply-side equilibria in personalized content recommender systems. We model the decisions of producers as choosing *multi-dimensional* content vectors and users as having *heterogenous* preferences, which contrasts with classical low-dimensional models. Multi-dimensionality and heterogeneity creates the potential for *specialization*, where different producers create different types of content at equilibrium. Using a duality argument, we derive necessary and sufficient conditions for whether specialization occurs. Then, we characterize the distribution of content at equilibrium in concrete settings with two populations of users. Lastly, we show that specialization can enable producers to achieve *positive profit at equilibrium*, which means that specialization can reduce the competitiveness of the marketplace. At a conceptual level, our analysis of supply-side competition takes a step towards elucidating how personalized recommendations shape the marketplace of digital goods.", "source": "openreview", "url": "https://openreview.net/forum?id=eqyhjLG5Nr", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Supply-Side Equilibria in Recommender Systems_2023.pdf" }, { "title": "Towards Personalized Federated Learning via Heterogeneous Model Reassembly", "authors": [ "Jiaqi Wang", "Xingyi Yang", "Suhan Cui", "Liwei Che", "Lingjuan Lyu", "Dongkuan Xu", "Fenglong Ma" ], "year": 2023, "venue": "NeurIPS", "abstract": "This paper focuses on addressing the practical yet challenging problem of model heterogeneity in federated learning, where clients possess models with different network structures. To track this problem, we propose a novel framework called pFedHR, which leverages heterogeneous model reassembly to achieve personalized federated learning. In particular, we approach the problem of heterogeneous model personalization as a model-matching optimization task on the server side. Moreover, pFedHR automatically and dynamically generates informative and diverse personalized candidates with minimal human intervention. Furthermore, our proposed heterogeneous model reassembly technique mitigates the adverse impact introduced by using public data with different distributions from the client data to a certain extent. Experimental results demonstrate that pFedHR outperforms baselines on three datasets under both IID and Non-IID settings. Additionally, pFedHR effectively reduces the adverse impact of using different public data and dynamically generates diverse personalized models in an automated manner.", "source": "openreview", "url": "https://openreview.net/forum?id=zpVCITHknd", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Towards Personalized Federated Learning via Heterogeneous Model Reassembly_2023.pdf" }, { "title": "Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization", "authors": [ "Yan Sun", "Li Shen", "Dacheng Tao" ], "year": 2023, "venue": "NeurIPS", "abstract": "Federated learning (FL) is a distributed paradigm that coordinates massive local clients to collaboratively train a global model via stage-wise local training processes on the heterogeneous dataset.\n Previous works have implicitly studied that FL suffers from the \"client-drift\" problem, which is caused by the inconsistent optimum across local clients. However, till now it still lacks solid theoretical analysis to explain the impact of this local inconsistency. \n To alleviate the negative impact of the \"client drift\" and explore its substance in FL, in this paper, we first design an efficient FL algorithm FedInit, which allows employing the personalized relaxed initialization state at the beginning of each local training stage. Specifically, FedInit initializes the local state by moving away from the current global state towards the reverse direction of the latest local state. This relaxed initialization helps to revise the local divergence and enhance the local consistency level.\n Moreover, to further understand how inconsistency disrupts performance in FL, we introduce the excess risk analysis and study the divergence term to investigate the test error of the proposed FedInit method. Our studies show that on the non-convex objectives, optimization error is not sensitive to this local inconsistency, while it mainly affects the generalization error bound in FedInit. \n Extensive experiments are conducted to validate this conclusion. Our proposed FedInit could achieve state-of-the-art (SOTA) results compared to several advanced benchmarks without any additional costs. Meanwhile, stage-wise relaxed initialization could also be incorporated into the current advanced algorithms to achieve higher performance in the FL paradigm.", "source": "openreview", "url": "https://openreview.net/forum?id=ylPX5D7It7", "decision_type": "Poster", "avg_rating": null, "relative_path": "2023/NeurIPS/Poster/x_Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization_2023.pdf" }, { "title": "Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processes", "authors": [ "Zheng Wang", "Shikai Fang", "Shibo Li", "Shandian Zhe" ], "year": 2023, "venue": "NeurIPS", "abstract": "Tensor decomposition is an important tool for multiway data analysis. In practice, the data is often sparse yet associated with rich temporal information. Existing methods, however, often under-use the time information and ignore the structural knowledge within the sparsely observed tensor entries. To overcome these limitations and to better capture the underlying temporal structure, we propose Dynamic EMbedIngs fOr dynamic Tensor dEcomposition (DEMOTE). We develop a neural diffusion-reaction process to estimate dynamic embeddings for the entities in each tensor mode. Specifically, based on the observed tensor entries, we build a multi-partite graph to encode the correlation between the entities. We construct a graph diffusion process to co-evolve the embedding trajectories of the correlated entities and use a neural network to construct a reaction process for each individual entity. In this way, our model can capture both the commonalities and personalities during the evolution of the embeddings for different entities. We then use a neural network to model the entry value as a nonlinear function of the embedding trajectories. For model estimation, we combine ODE solvers to develop a stochastic mini-batch learning algorithm. We propose a stratified sampling method to balance the cost of processing each mini-batch so as to improve the overall efficiency. We show the advantage of our approach in both simulation studies and real-world applications. The code is available at https://github.com/wzhut/Dynamic-Tensor-Decomposition-via-Neural-Diffusion-Reaction-Processes.", "source": "openreview", "url": "https://openreview.net/forum?id=LGqIAn2OaZ", "decision_type": "Spotlight", "avg_rating": null, "relative_path": "2023/NeurIPS/Spotlight/x_Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processes_2023.pdf" }, { "title": "Evaluating and Inducing Personality in Pre-trained Language Models", "authors": [ "Guangyuan Jiang", "Manjie Xu", "Song-Chun Zhu", "Wenjuan Han", "Chi Zhang", "Yixin Zhu" ], "year": 2023, "venue": "NeurIPS", "abstract": "Standardized and quantified evaluation of machine behaviors is a crux of understanding LLMs. In this study, we draw inspiration from psychometric studies by leveraging human personality theory as a tool for studying machine behaviors. Originating as a philosophical quest for human behaviors, the study of personality delves into how individuals differ in thinking, feeling, and behaving. Toward building and understanding human-like social machines, we are motivated to ask: Can we assess machine behaviors by leveraging human psychometric tests in a **principled** and **quantitative** manner? If so, can we induce a specific personality in LLMs? To answer these questions, we introduce the Machine Personality Inventory (MPI) tool for studying machine behaviors; MPI follows standardized\npersonality tests, built upon the Big Five Personality Factors (Big Five) theory and personality assessment inventories. By systematically evaluating LLMs with MPI, we provide the first piece of evidence demonstrating the efficacy of MPI in studying LLMs behaviors. We further devise a Personality Prompting (P$^2$) method to induce LLMs with specific personalities in a **controllable** way, capable of producing diverse and verifiable behaviors. We hope this work sheds light on future studies by adopting personality as the essential indicator for various downstream tasks, and could further motivate research into equally intriguing human-like machine behaviors.", "source": "openreview", "url": "https://openreview.net/forum?id=I9xE1Jsjfx", "decision_type": "Spotlight", "avg_rating": null, "relative_path": "2023/NeurIPS/Spotlight/x_Evaluating and Inducing Personality in Pre-trained Language Models_2023.pdf" }, { "title": "In-Context Impersonation Reveals Large Language Models' Strengths and Biases", "authors": [ "Leonard Salewski", "Stephan Alaniz", "Isabel Rio-Torto", "Eric Schulz", "Zeynep Akata" ], "year": 2023, "venue": "NeurIPS", "abstract": "In everyday conversations, humans can take on different roles and adapt their vocabulary to their chosen roles. We explore whether LLMs can take on, that is impersonate, different roles when they generate text in-context. We ask LLMs to assume different personas before solving vision and language tasks. We do this by prefixing the prompt with a persona that is associated either with a social identity or domain expertise. In a multi-armed bandit task, we find that LLMs pretending to be children of different ages recover human-like developmental stages of exploration. In a language-based reasoning task, we find that LLMs impersonating domain experts perform better than LLMs impersonating non-domain experts. Finally, we test whether LLMs' impersonations are complementary to visual information when describing different categories. We find that impersonation can improve performance: an LLM prompted to be a bird expert describes birds better than one prompted to be a car expert. However, impersonation can also uncover LLMs' biases: an LLM prompted to be a man describes cars better than one prompted to be a woman. These findings demonstrate that LLMs are capable of taking on diverse roles and that this in-context impersonation can be used to uncover their strengths and hidden biases. Our code is available at https://github.com/ExplainableML/in-context-impersonation.", "source": "openreview", "url": "https://openreview.net/forum?id=CbsJ53LdKc", "decision_type": "Spotlight", "avg_rating": null, "relative_path": "2023/NeurIPS/Spotlight/x_In-Context Impersonation Reveals Large Language Models' Strengths and Biases_2023.pdf" }, { "title": "Mitigating the Popularity Bias of Graph Collaborative Filtering: A Dimensional Collapse Perspective", "authors": [ "Yifei Zhang", "Hao Zhu", "Yankai Chen", "Zixing Song", "Piotr Koniusz", "Irwin King" ], "year": 2023, "venue": "NeurIPS", "abstract": "Graph-based Collaborative Filtering (GCF) is widely used in personalized recommendation systems. However, GCF suffers from a fundamental problem where features tend to occupy the embedding space inefficiently (by spanning only a low-dimensional subspace). Such an effect is characterized in GCF by the embedding space being dominated by a few of popular items with the user embeddings highly concentrated around them. This enhances the so-called Matthew effect of the popularity bias where popular items are highly recommend whereas remaining items are ignored. In this paper, we analyze the above effect in GCF and reveal that the simplified graph convolution operation (typically used in GCF) shrinks the singular space of the feature matrix. As typical approaches (i.e., optimizing the uniformity term) fail to prevent the embedding space degradation, we propose a decorrelation-enhanced GCF objective that promotes feature diversity by leveraging the so-called principle of redundancy reduction in embeddings. However, unlike conventional methods that use the Euclidean geometry to relax hard constraints for decorrelation, we exploit non-Euclidean geometry. Such a choice helps maintain the range space of the matrix and obtain small condition number, which prevents the embedding space degradation. Our method outperforms contrastive-based GCF models on several benchmark datasets and improves the performance for unpopular items.", "source": "openreview", "url": "https://openreview.net/forum?id=MvCq52yt9Y", "decision_type": "Spotlight", "avg_rating": null, "relative_path": "2023/NeurIPS/Spotlight/x_Mitigating the Popularity Bias of Graph Collaborative Filtering_ A Dimensional Collapse Perspec_2023.pdf" }, { "title": "Participatory Personalization in Classification", "authors": [ "Hailey Joren", "Chirag Nagpal", "Katherine A Heller", "Berk Ustun" ], "year": 2023, "venue": "NeurIPS", "abstract": "Machine learning models are often personalized based on information that is protected, sensitive, self-reported, or costly to acquire. These models use information about people, but do not facilitate nor inform their *consent*. Individuals cannot opt out of reporting information that a model needs to personalize their predictions nor tell if they benefit from personalization in the first place. We introduce a new family of prediction models, called participatory systems, that let individuals opt into personalization at prediction time. We present a model-agnostic algorithm to learn participatory systems for supervised learning tasks where models are personalized with categorical group attributes. We conduct a comprehensive empirical study of participatory systems in clinical prediction tasks, comparing them to common approaches for personalization and imputation. Our results show that participatory systems can facilitate and inform consent in a way that improves performance and privacy across all groups who report personal data.", "source": "openreview", "url": "https://openreview.net/forum?id=Bj1QSgiBPP", "decision_type": "Spotlight", "avg_rating": null, "relative_path": "2023/NeurIPS/Spotlight/x_Participatory Personalization in Classification_2023.pdf" }, { "title": "ProPILE: Probing Privacy Leakage in Large Language Models", "authors": [ "Siwon Kim", "Sangdoo Yun", "Hwaran Lee", "Martin Gubri", "Sungroh Yoon", "Seong Joon Oh" ], "year": 2023, "venue": "NeurIPS", "abstract": "The rapid advancement and widespread use of large language models (LLMs) have raised significant concerns regarding the potential leakage of personally identifiable information (PII). These models are often trained on vast quantities of web-collected data, which may inadvertently include sensitive personal data. This paper presents ProPILE, a novel probing tool designed to empower data subjects, or the owners of the PII, with awareness of potential PII leakage in LLM-based services. ProPILE lets data subjects formulate prompts based on their own PII to evaluate the level of privacy intrusion in LLMs. We demonstrate its application on the OPT-1.3B model trained on the publicly available Pile dataset. We show how hypothetical data subjects may assess the likelihood of their PII being included in the Pile dataset being revealed. ProPILE can also be leveraged by LLM service providers to effectively evaluate their own levels of PII leakage with more powerful prompts specifically tuned for their in-house models. This tool represents a pioneering step towards empowering the data subjects for their awareness and control over their own data on the web.", "source": "openreview", "url": "https://openreview.net/forum?id=QkLpGxUboF", "decision_type": "Spotlight", "avg_rating": null, "relative_path": "2023/NeurIPS/Spotlight/x_ProPILE_ Probing Privacy Leakage in Large Language Models_2023.pdf" }, { "title": "Zero-shot causal learning", "authors": [ "Hamed Nilforoshan", "Michael Moor", "Yusuf H Roohani", "Yining Chen", "Anja Šurina", "Michihiro Yasunaga", "Sara Oblak", "Jure Leskovec" ], "year": 2023, "venue": "NeurIPS", "abstract": "Predicting how different interventions will causally affect a specific individual is important in a variety of domains such as personalized medicine, public policy, and online marketing. There are a large number of methods to predict the effect of an existing intervention based on historical data from individuals who received it. \nHowever, in many settings it is important to predict the effects of novel interventions (e.g., a newly invented drug), which these methods do not address.\nHere, we consider zero-shot causal learning: predicting the personalized effects of a novel intervention. We propose CaML, a causal meta-learning framework which formulates the personalized prediction of each intervention's effect as a task. CaML trains a single meta-model across thousands of tasks, each constructed by sampling an intervention, its recipients, and its nonrecipients. By leveraging both intervention information (e.g., a drug's attributes) and individual features (e.g., a patient's history), CaML is able to predict the personalized effects of novel interventions that do not exist at the time of training. Experimental results on real world datasets in large-scale medical claims and cell-line perturbations demonstrate the effectiveness of our approach. Most strikingly, CaML's zero-shot predictions outperform even strong baselines trained directly on data from the test interventions.", "source": "openreview", "url": "https://openreview.net/forum?id=BfQJrIiOZC", "decision_type": "Spotlight", "avg_rating": null, "relative_path": "2023/NeurIPS/Spotlight/x_Zero-shot causal learning_2023.pdf" }, { "title": "A Multi-persona Framework for Argument Quality Assessment", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.acl-long.593/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_A Multi-persona Framework for Argument Quality Assessment_2025.pdf" }, { "title": "Comparison-based Active Preference Learning for Multi-dimensional Personalization", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.acl-long.1590/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_Comparison-based Active Preference Learning for Multi-dimensional Personalization_2025.pdf" }, { "title": "Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-acl.517/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented G_2025.pdf" }, { "title": "Exploring Persona Sentiment Sensitivity in Personalized Dialogue Generation", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.acl-long.900/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_Exploring Persona Sentiment Sensitivity in Personalized Dialogue Generation_2025.pdf" }, { "title": "In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.acl-long.413/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_In Prospect and Retrospect_ Reflective Memory Management for Long-term Personalized Dialogue Ag_2025.pdf" }, { "title": "Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational History", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.conll-1.4/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_Interpersonal Memory Matters_ A New Task for Proactive Dialogue Utilizing Conversational Histor_2025.pdf" }, { "title": "Meetalk: Retrieval-Augmented and Adaptively Personalized Meeting Summarization with Knowledge Learning from User Corrections", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.knowllm-1.9/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_Meetalk_ Retrieval-Augmented and Adaptively Personalized Meeting Summarization with Knowledge L_2025.pdf" }, { "title": "Mixture-of-Personas Language Models for Population Simulation", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-acl.1271/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_Mixture-of-Personas Language Models for Population Simulation_2025.pdf" }, { "title": "My Words Imply Your Opinion: Reader Agent-Based Propagation Enhancement for Personalized Implicit Emotion Analysis", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.acl-long.787/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_My Words Imply Your Opinion_ Reader Agent-Based Propagation Enhancement for Personalized Implic_2025.pdf" }, { "title": "Persona Dynamics: Unveiling the Impact of Persona Traits on Agents in Text-Based Games", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.acl-long.1515/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_Persona Dynamics_ Unveiling the Impact of Persona Traits on Agents in Text-Based Games_2025.pdf" }, { "title": "Persona-judge: Personalized Alignment of Large Language Models via Token-level Self-judgment", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-acl.260/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_Persona-judge_ Personalized Alignment of Large Language Models via Token-level Self-judgment_2025.pdf" }, { "title": "Personality-Guided Code Generation Using Large Language Models", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.acl-long.54/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_Personality-Guided Code Generation Using Large Language Models_2025.pdf" }, { "title": "Personalized Generation In Large Model Era: A Survey", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.acl-long.1201/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_Personalized Generation In Large Model Era_ A Survey_2025.pdf" }, { "title": "Personalized Text Generation with Contrastive Activation Steering", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.acl-long.353/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_Personalized Text Generation with Contrastive Activation Steering_2025.pdf" }, { "title": "Principled Content Selection to Generate Diverse and Personalized Multi-Document Summaries", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.acl-long.1445/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_Principled Content Selection to Generate Diverse and Personalized Multi-Document Summaries_2025.pdf" }, { "title": "Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-acl.959/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training_2025.pdf" }, { "title": "Prompt-based Personality Profiling: Reinforcement Learning for Relevance Filtering", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.realm-1.1/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_Prompt-based Personality Profiling_ Reinforcement Learning for Relevance Filtering_2025.pdf" }, { "title": "Recursive Question Understanding for Complex Question Answering over Heterogeneous Personal Data", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-acl.939/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_Recursive Question Understanding for Complex Question Answering over Heterogeneous Personal Dat_2025.pdf" }, { "title": "Rehearse With User: Personalized Opinion Summarization via Role-Playing based on Large Language Models", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-acl.787/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_Rehearse With User_ Personalized Opinion Summarization via Role-Playing based on Large Language_2025.pdf" }, { "title": "Scholar Inbox: Personalized Paper Recommendations for Scientists", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.acl-demo.30/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_Scholar Inbox_ Personalized Paper Recommendations for Scientists_2025.pdf" }, { "title": "Spotting Out-of-Character Behavior: Atomic-Level Evaluation of Persona Fidelity in Open-Ended Generation", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-acl.1349/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_Spotting Out-of-Character Behavior_ Atomic-Level Evaluation of Persona Fidelity in Open-Ended G_2025.pdf" }, { "title": "Stereotype or Personalization? User Identity Biases Chatbot Recommendations", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-acl.1254/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_Stereotype or Personalization_ User Identity Biases Chatbot Recommendations_2025.pdf" }, { "title": "Tree-of-Debate: Multi-Persona Debate Trees Elicit Critical Thinking for Scientific Comparative Analysis", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.acl-long.1422/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_Tree-of-Debate_ Multi-Persona Debate Trees Elicit Critical Thinking for Scientific Comparative _2025.pdf" }, { "title": "Wanted: Personalised Bias Warnings for Gender Bias in Language Models", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.gebnlp-1.13/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_Wanted_ Personalised Bias Warnings for Gender Bias in Language Models_2025.pdf" }, { "title": "When Harry Meets Superman: The Role of The Interlocutor in Persona-Based Dialogue Generation", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.acl-long.879/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_When Harry Meets Superman_ The Role of The Interlocutor in Persona-Based Dialogue Generation_2025.pdf" }, { "title": "Whose Boat Does it Float? Improving Personalization in Preference Tuning via Inferred User Personas", "authors": [], "year": 2025, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.acl-long.168/", "decision_type": null, "avg_rating": null, "relative_path": "2025/ACL/Other/x_Whose Boat Does it Float_ Improving Personalization in Preference Tuning via Inferred User Pers_2025.pdf" }, { "title": "Can LLM \"Self-report\"?: Evaluating the Validity of Self-report Scales in Measuring Personality Design in LLM-based Chatbots", "authors": [ "Huiqi Zou", "Pengda Wang", "Zihan Yan", "Tianjun Sun", "Ziang Xiao" ], "year": 2025, "venue": "COLM", "abstract": "A chatbot’s personality design is key to interaction quality. As chatbots evolved from rule-based systems to those powered by large language models (LLMs), evaluating the effectiveness of their personality design has become increasingly complex, particularly due to the open-ended nature of interactions. A recent and widely adopted method for assessing the personality design of LLM-based chatbots is the use of self-report questionnaires. These questionnaires, often borrowed from established human personality inventories, ask the chatbot to rate itself on various personality traits. Can LLM-based chatbots meaningfully \"self-report\" their personality? We created 500 chatbots with distinct personality designs and evaluated the validity of their self-report personality scores by examining human perceptions formed during interactions with these chatbots. Our findings indicate that the chatbot's answers on human personality scales exhibit weak correlations with both human-perceived personality traits and the overall interaction quality. These findings raise concerns about both the criterion validity and the predictive validity of self-report methods in this context. Further analysis revealed the role of task context and interaction in the chatbot's personality design assessment. We further discuss design implications for creating more contextualized and interactive evaluation.", "source": "openreview", "url": "https://openreview.net/forum?id=xqIwK9mNkj", "decision_type": "Poster", "avg_rating": 8.0, "relative_path": "2025/COLM/Poster/8.0_Can LLM _Self-report___ Evaluating the Validity of Self-report Scales in Measuring Personality _2025.pdf" }, { "title": "CUPID: Evaluating Personalized and Contextualized Alignment of LLMs from Interactions", "authors": [ "Tae Soo Kim", "Yoonjoo Lee", "Yoonah Park", "Jiho Kim", "Young-Ho Kim", "Juho Kim" ], "year": 2025, "venue": "COLM", "abstract": "Personalization of Large Language Models (LLMs) often assumes users hold static preferences that reflect globally in all tasks. In reality, humans hold dynamic preferences that change depending on the context. As users interact with an LLM in various contexts, they naturally reveal their contextual preferences, which a model must infer and apply in future contexts to ensure alignment. To assess this, we introduce 🏹 CUPID, a benchmark of 756 human-curated interaction session histories between users and LLM-based chat assistants. In each interaction session, the user provides a request in a specific context and expresses their preference through multi-turn feedback. Given a new user request and prior interaction sessions, our benchmark assesses whether LLMs can infer the preference relevant to this request and generate a response that satisfies this preference. With CUPID, we evaluated 10 open and proprietary LLMs, revealing that state-of-the-art LLMs struggle to infer preferences from multi-turn interactions and fail to discern what previous context is relevant to a new request—under 50% precision and 65% recall. Our work highlights the need to advance LLM capabilities for more contextually personalized interactions and proposes CUPID as a resource to drive these improvements.", "source": "openreview", "url": "https://openreview.net/forum?id=JMxRn7orEk", "decision_type": "Poster", "avg_rating": 7.3, "relative_path": "2025/COLM/Poster/7.3_CUPID_ Evaluating Personalized and Contextualized Alignment of LLMs from Interactions_2025.pdf" }, { "title": "LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models", "authors": [ "Minqian Liu", "Zhiyang Xu", "Xinyi Zhang", "Heajun An", "Sarvech Qadir", "Qi Zhang", "Pamela J. Wisniewski", "Jin-Hee Cho", "Sang Won Lee", "Ruoxi Jia", "Lifu Huang" ], "year": 2025, "venue": "COLM", "abstract": "Recent advancements in Large Language Models (LLMs) have enabled them to approach human-level persuasion capabilities. However, such potential also raises concerns about the safety risks of LLM-driven persuasion, particularly their potential for unethical influence through manipulation, deception, exploitation of vulnerabilities, and many other harmful tactics. In this work, we present a systematic investigation of LLM persuasion safety through two critical aspects: (1) whether LLMs appropriately reject unethical persuasion tasks and avoid unethical strategies during execution, including cases where the initial persuasion goal appears ethically neutral, and (2) how influencing factors like personality traits and external pressures affect their behavior. To this end, we introduce PersuSafety, the first comprehensive framework for the assessment of persuasion safety which consists of three stages, i.e., persuasion scene creation, persuasive conversation simulation, and persuasion safety assessment. PersuSafety covers 6 diverse unethical persuasion topics and 15 common unethical strategies. Through extensive experiments across 8 widely used LLMs, we observe significant safety concerns in most LLMs, including failing to identify harmful persuasion tasks and leveraging various unethical persuasion strategies. Our study calls for more attention to improve safety alignment in progressive and goal-driven conversations such as persuasion.", "source": "openreview", "url": "https://openreview.net/forum?id=TMB9SKqit9", "decision_type": "Poster", "avg_rating": 7.2, "relative_path": "2025/COLM/Poster/7.2_LLM Can be a Dangerous Persuader_ Empirical Study of Persuasion Safety in Large Language Models_2025.pdf" }, { "title": "Deep Binding of Language Model Virtual Personas: a Study on Approximating Political Partisan Misperceptions", "authors": [ "Minwoo Kang", "Suhong Moon", "Seung Hyeong Lee", "Ayush Raj", "Joseph Suh", "David Chan" ], "year": 2025, "venue": "COLM", "abstract": "Large language models (LLMs) are increasingly capable of simulating human behavior, offering cost-effective ways to estimate user responses during the early phases of survey design. While previous studies have examined whether models can reflect individual opinions or attitudes, we argue that a higher-order binding of virtual personas requires successfully approximating not only the opinions of a user as an identified member of a group, but also the nuanced ways in which that user perceives and evaluates those outside the group. In particular, faithfully simulating how humans perceive different social groups is critical for applying LLMs to various political science studies, including timely topics on polarization dynamics, inter-group conflict, and democratic backsliding. To this end, we propose a novel methodology for constructing virtual personas with synthetic user \"backstories\" generated as extended, multi-turn interview transcripts. Our generated backstories are longer, rich in detail, and consistent in authentically describing a singular individual, compared to previous methods. We show that virtual personas conditioned on our backstories closely replicate human response distributions (up to an 87% improvement as measured by Wasserstein Distance) and produce effect sizes that closely match those observed in the original studies.\nAltogether, our work extends the applicability of LLMs beyond estimating individual self-opinions, enabling their use in a broader range of human studies.", "source": "openreview", "url": "https://openreview.net/forum?id=zHdSCtNmM4", "decision_type": "Poster", "avg_rating": 7.0, "relative_path": "2025/COLM/Poster/7.0_Deep Binding of Language Model Virtual Personas_ a Study on Approximating Political Partisan Mi_2025.pdf" }, { "title": "Epistemic Alignment: A Mediating Framework for User-LLM Knowledge Delivery", "authors": [ "Nicholas Clark", "Hua Shen", "Bill Howe", "Tanu Mitra" ], "year": 2025, "venue": "COLM", "abstract": "Large Language Models (LLMs) increasingly serve as tools for knowledge acquisition, yet users cannot effectively specify how they want information presented. When users request that LLMs \"cite reputable sources,\" \"express appropriate uncertainty,\" or \"include multiple perspectives,\" they discover that current interfaces provide no structured way to articulate these preferences. The result is prompt sharing folklore: community-specific copied prompts passed through trust relationships rather than based on measured efficacy. We propose the Epistemic Alignment Framework, a set of ten challenges in knowledge transmission derived from the philosophical literature of epistemology, concerning issues such as uncertainty expression, evidence quality assessment, and calibration of testimonial reliance. The framework serves as a structured intermediary between user needs and system capabilities, creating a common vocabulary to bridge the gap between what users want and what systems deliver. Through a thematic analysis of custom prompts and personalization strategies shared on online communities where these issues are actively discussed, we find users develop elaborate workarounds to address each of the challenges. We then apply our framework to two prominent model providers, OpenAI and Anthropic, through structured content analysis of their documented policies and product features. Our analysis shows that while these providers have partially addressed the challenges we identified, they fail to establish adequate mechanisms for specifying epistemic preferences, lack transparency about how preferences are implemented, and offer no verification tools to confirm whether preferences were followed. For AI developers, the Epistemic Alignment Framework offers concrete guidance for supporting diverse approaches to knowledge; for users, it works toward information delivery that aligns with their specific needs rather than defaulting to one-size-fits-all approaches.", "source": "openreview", "url": "https://openreview.net/forum?id=Orvjm9UqH2", "decision_type": "Poster", "avg_rating": 7.0, "relative_path": "2025/COLM/Poster/7.0_Epistemic Alignment_ A Mediating Framework for User-LLM Knowledge Delivery_2025.pdf" }, { "title": "IMPersona: Evaluating Individual Level LLM Impersonation", "authors": [ "Quan Shi", "Carlos E Jimenez", "Stephen Dong", "Brian Seo", "Caden Yao", "Adam Kelch", "Karthik R Narasimhan" ], "year": 2025, "venue": "COLM", "abstract": "As language models achieve increasingly human-like capabilities in conversational text generation, a critical question emerges: to what extent can these systems simulate the characteristics of specific individuals? To evaluate this, we introduce IMPersona, a framework for evaluating LMs at impersonating specific individuals' writing style and personal knowledge. Using supervised fine-tuning and a hierarchical memory-inspired retrieval system, we demonstrate that even modestly sized open-source models, such as Llama-3.1-8B-Instruct, can achieve impersonation abilities at concerning levels. In blind conversation experiments, participants (mis)identified our fine-tuned models with memory integration as human in \\textbf{44.44\\%} of interactions, compared to just \\textbf{25.00\\%} for the best prompting-based approach. We analyze these results to propose detection methods and defense strategies against such impersonation attempts. Our findings raise important questions about both the potential applications and risks of personalized language models, particularly regarding privacy, security, and the ethical deployment of such technologies in real-world contexts.", "source": "openreview", "url": "https://openreview.net/forum?id=7qhBXq0NLN", "decision_type": "Poster", "avg_rating": 7.0, "relative_path": "2025/COLM/Poster/7.0_IMPersona_ Evaluating Individual Level LLM Impersonation_2025.pdf" }, { "title": "Values in the Wild: Discovering and Mapping Values in Real-World Language Model Interactions", "authors": [ "Saffron Huang", "Esin DURMUS", "Kunal Handa", "Miles McCain", "Alex Tamkin", "Michael Stern", "Jerry Hong", "Deep Ganguli" ], "year": 2025, "venue": "COLM", "abstract": "AI assistants interact with millions of real users everyday, imparting normative judgments that can have significant personal and societal impact—but little is known about what values guide these interactions in practice. To address this, we develop a method to empirically analyze values expressed in hundreds of thousands of real-world conversations with Claude models. We empirically discover and taxonomize 3,308 AI values, and study how model values and responses depend on context. We find that Claude expresses many professional and intellectual values, and typically supports prosocial human values while resisting values like \"moral nihilism.\" While some values appear consistently (e.g. \"professionalism\"), most are highly context-dependent—\"harm prevention\" emerges when the model resists users, \"historical accuracy\" when discussing controversial events, \"healthy boundaries\" in relationship advice, and \"human agency\" in technology ethics discussions. By providing the first large-scale empirical mapping of AI values in deployment, this work creates a foundation for more grounded evaluation and design of values in increasingly influential AI systems.", "source": "openreview", "url": "https://openreview.net/forum?id=zJHZJClG1Z", "decision_type": "Poster", "avg_rating": 7.0, "relative_path": "2025/COLM/Poster/7.0_Values in the Wild_ Discovering and Mapping Values in Real-World Language Model Interactions_2025.pdf" }, { "title": "LeakAgent: RL-based Red-teaming Agent for LLM Privacy Leakage", "authors": [ "Yuzhou Nie", "Zhun Wang", "Ye Yu", "Xian Wu", "Xuandong Zhao", "Nathaniel D. Bastian", "Wenbo Guo", "Dawn Song" ], "year": 2025, "venue": "COLM", "abstract": "Recent studies have discovered that large language models (LLM) may be ``fooled'' to output private information, including training data, system prompts, and personally identifiable information, under carefully crafted adversarial prompts. Existing red-teaming approaches for privacy leakage either rely on manual efforts or focus solely on system prompt extraction, making them ineffective for severe risks of training data leakage.\nWe propose LeakAgent, a novel black-box red-teaming framework for LLM privacy leakage. Our framework trains an open-source LLM through reinforcement learning as the attack agent to generate adversarial prompts for both training data extraction and system prompt extraction.\nTo achieve this, we propose a novel reward function to provide effective and fine-grained rewards and design novel mechanisms to balance exploration and exploitation during learning and enhance the diversity of adversarial prompts. Through extensive evaluations, we first show that LeakAgent significantly outperforms existing rule-based approaches in training data extraction and automated methods in system prompt leakage. We also demonstrate the effectiveness of LeakAgent in extracting system prompts from real-world applications in OpenAI's GPT Store. We further demonstrate LeakAgent's effectiveness in evading the existing guardrail defense and its helpfulness in enabling better safety alignment.\nFinally, we validate our customized designs through a detailed ablation study.\nWe release our code here \\url{https://github.com/rucnyz/LeakAgent}.", "source": "openreview", "url": "https://openreview.net/forum?id=WIfns41MAb", "decision_type": "Poster", "avg_rating": 6.8, "relative_path": "2025/COLM/Poster/6.8_LeakAgent_ RL-based Red-teaming Agent for LLM Privacy Leakage_2025.pdf" }, { "title": "PrefPalette: Personalized Preference Modeling with Latent Attributes", "authors": [ "Shuyue Stella Li", "Melanie Sclar", "Hunter Lang", "Ansong Ni", "Jacqueline He", "Puxin Xu", "Andrew Cohen", "Chan Young Park", "Yulia Tsvetkov", "Asli Celikyilmaz" ], "year": 2025, "venue": "COLM", "abstract": "Personalizing AI systems requires understanding not just what users prefer, but the reasons that underlie those preferences—yet current preference models typically treat human judgment as a black box. We introduce PrefPalette, a framework that decomposes preferences into attribute dimensions and tailors its preference prediction to distinct social community values in a human-interpretable way. PrefPalette operationalizes a cognitive science principle known as multi-attribute decision making in two ways: (1) a scalable counterfactual attribute synthesis step that involves generating synthetic training data to isolate for individual attribute effects (e.g., formality, humor, cultural values), and (2) attention-based preference modeling that learns how different social communities dynamically weight these attributes. This approach moves beyond aggregate preference modeling to capture the diverse evaluation frameworks that drive human judgment. When evaluated on 45 social communities from the online platform Reddit, PrefPalette outperforms GPT-4o by 46.6% in average prediction accuracy. Beyond raw predictive improvements, PrefPalette also shed light on intuitive, community-specific profiles: scholarly communities prioritize verbosity and stimulation, conflict-oriented communities value sarcasm and directness, and support-based communities emphasize empathy. By modeling the attribute-mediated structure of human judgment, PrefPalette delivers both superior preference modeling\nand transparent, interpretable insights, and serves as a first step toward\nmore trustworthy, value-aware personalized applications.", "source": "openreview", "url": "https://openreview.net/forum?id=p4ujQsKmPV", "decision_type": "Poster", "avg_rating": 6.8, "relative_path": "2025/COLM/Poster/6.8_PrefPalette_ Personalized Preference Modeling with Latent Attributes_2025.pdf" }, { "title": "HIPPO-VIDEO : Simulating Watch Histories with Large Language Models for History-Driven Video Highlighting", "authors": [ "Jeongeun Lee", "Youngjae Yu", "Dongha Lee" ], "year": 2025, "venue": "COLM", "abstract": "The exponential growth of video content has made personalized video highlighting an essential task, as user preferences are highly variable and complex. Existing video datasets, however, often lack personalization, relying on isolated videos or simple text queries that fail to capture the intricacies of user behavior. In this work, we introduce HIPPO-VIDEO, a novel dataset for personalized video highlighting, created using an LLM-based user simulator to generate realistic watch histories reflecting diverse user preferences. The dataset includes 2,040 (watch history, saliency score) pairs, covering 20,400 videos across 170 semantic categories. To validate our dataset, we propose HiPHer, a method that leverages these personalized watch histories to predict preference-conditioned segment-wise saliency scores. \nThrough extensive experiments, we demonstrate that our method outperforms existing generic and query-based approaches, showcasing its potential for highly user-centric video highlighting in real-world scenarios. The code is publicly available at https://anonymous.4open.science/r/HIPPO-4EEE/README.md.", "source": "openreview", "url": "https://openreview.net/forum?id=Q6TCkggzQ2", "decision_type": "Poster", "avg_rating": 6.7, "relative_path": "2025/COLM/Poster/6.7_HIPPO-VIDEO _ Simulating Watch Histories with Large Language Models for History-Driven Video Hi_2025.pdf" }, { "title": "Know Me, Respond to Me: Benchmarking LLMs for Dynamic User Profiling and Personalized Responses at Scale", "authors": [ "Bowen Jiang", "Zhuoqun Hao", "Young Min Cho", "Bryan Li", "Yuan Yuan", "Sihao Chen", "Lyle Ungar", "Camillo Jose Taylor", "Dan Roth" ], "year": 2025, "venue": "COLM", "abstract": "Large Language Models (LLMs) have emerged as personalized assistants for users across a wide range of tasks – from offering writing support to delivering tailored recommendations or consultations. Over time, the interaction history between a user and an LLM can provide extensive information about an individual’s traits and preferences. However, open questions remain on how well LLMs today can effectively leverage such history to (1) internalize the user’s inherent traits and preferences, (2) track how the user profiling and preferences evolve over time, and (3) generate personalized responses accordingly in new scenarios.\n\nIn this work, we introduce the PERSONAMEM benchmark. PERSONAMEM features curated user profiles with over 180 simulated user-LLM interaction histories, each containing up to 60 sessions of multi-turn conversations across 15 real-world tasks that require personalization. Given an in-situ user query at a specific time point, we evaluate LLM chatbots’ ability to identify the most suitable response according to the current state of the user’s profile. We observe that current LLMs still struggle to recognize the dynamic evolution in users’ profiles over time through direct prompting approaches. As a consequence, LLMs often fail to deliver responses that align with users’ current situations and preferences, with frontier models such as GPT-4.5, or Gemini-2.0 achieving only around 50% overall accuracy, suggesting room for improvement. We hope that PERSONAMEM, along with the user profile and conversation simulation pipeline, can facilitate future research in the development of truly user-aware chatbots.", "source": "openreview", "url": "https://openreview.net/forum?id=6ox8XZGOqP", "decision_type": "Poster", "avg_rating": 6.7, "relative_path": "2025/COLM/Poster/6.7_Know Me, Respond to Me_ Benchmarking LLMs for Dynamic User Profiling and Personalized Responses_2025.pdf" }, { "title": "VisualTrap: A Stealthy Backdoor Attack on GUI Agents via Visual Grounding Manipulation", "authors": [ "Ziang Ye", "Yang Zhang", "Wentao Shi", "Xiaoyu You", "Fuli Feng", "Tat-Seng Chua" ], "year": 2025, "venue": "COLM", "abstract": "Graphical User Interface (GUI) agents powered by Large Vision-Language Models (LVLMs) have emerged as a revolutionary approach to automating human-machine interactions, capable of autonomously operating personal devices (e.g., mobile phones) or applications within the device to perform complex real-world tasks in a human-like manner. However, their close integration with personal devices raises significant security concerns, with many threats, including backdoor attacks, remaining largely unexplored. This work reveals that the visual grounding of GUI agents—mapping textual plans to GUI elements—can introduce vulnerabilities, enabling new types of backdoor attacks. With backdoor attack targeting visual grounding, \nthe agent’s behavior can be compromised even when given correct task-solving plans. \nTo validate this vulnerability, we propose \\textit{VisualTrap}, a method that can hijack the grounding by misleading the agent to locate textual plans to trigger locations instead of the intended targets. VisualTrap uses the common method of injecting poisoned data for attacks, and does so during the pre-training of visual grounding \\textcolor{black}{to ensure practical feasibility of attacking.} \nEmpirical results show that VisualTrap can effectively hijack visual grounding with as little as 5\\% poisoned data and highly stealthy visual triggers (invisible to the human eye); and the attack can be generalized to downstream tasks, even after clean fine-tuning. Moreover, the injected trigger can remain effective across different GUI environments, \\textit{e.g.,} being trained on mobile/web and generalizing to desktop environments.\nThese findings underscore the urgent need for further research on backdoor attack risks in GUI agents.", "source": "openreview", "url": "https://openreview.net/forum?id=7HPuAkgdVm", "decision_type": "Poster", "avg_rating": 6.7, "relative_path": "2025/COLM/Poster/6.7_VisualTrap_ A Stealthy Backdoor Attack on GUI Agents via Visual Grounding Manipulation_2025.pdf" }, { "title": "LoRe: Personalizing LLMs via Low-Rank Reward Modeling", "authors": [ "Avinandan Bose", "Zhihan Xiong", "Yuejie Chi", "Simon Shaolei Du", "Lin Xiao", "Maryam Fazel" ], "year": 2025, "venue": "COLM", "abstract": "Personalizing large language models (LLMs) to accommodate diverse user preferences is essential for enhancing alignment and user satisfaction. Traditional reinforcement learning from human feedback (RLHF) approaches often rely on monolithic value representations, limiting their ability to adapt to individual preferences. We introduce a novel framework that leverages low-rank preference modeling to efficiently learn and generalize user-specific reward functions. By representing reward functions in a low-dimensional subspace and modeling individual preferences as weighted combinations of shared basis functions, our approach avoids rigid user categorization while enabling scalability and few-shot adaptation. We validate our method on multiple preference datasets, demonstrating superior generalization to unseen users and improved accuracy in preference prediction tasks.", "source": "openreview", "url": "https://openreview.net/forum?id=bYu4DOqRY8", "decision_type": "Poster", "avg_rating": 6.5, "relative_path": "2025/COLM/Poster/6.5_LoRe_ Personalizing LLMs via Low-Rank Reward Modeling_2025.pdf" }, { "title": "PersonaEval: Are LLM Evaluators Human Enough to Judge Role-Play?", "authors": [ "Lingfeng Zhou", "Jialing Zhang", "Jin Gao", "Mohan Jiang", "Dequan Wang" ], "year": 2025, "venue": "COLM", "abstract": "Current role-play studies often rely on unvalidated LLM-as-a-judge paradigms, which may fail to reflect how humans perceive role fidelity. A key prerequisite for human-aligned evaluation is role identification, the ability to recognize who is speaking based on dialogue context. We argue that any meaningful judgment of role-playing quality (how well a character is played) fundamentally depends on first correctly attributing words and actions to the correct persona (who is speaking). We present PersonaEval, the first benchmark designed to test whether LLM evaluators can reliably identify human roles. PersonaEval uses human-authored dialogues from novels, scripts, and video transcripts, challenging models to determine the correct persona according to the conversation context. Our experiments, including a human study, show that even the best-performing LLMs reach only around 69% accuracy, well below the level needed for reliable evaluation. In contrast, human participants perform near ceiling with 90.8% accuracy, highlighting that current LLM evaluators are still not human enough to effectively judge role-play scenarios. To better understand this gap, we examine training-time adaptation and test-time compute, suggesting that reliable evaluation requires more than task-specific tuning, but depends on strong, human-like reasoning abilities in LLM evaluators. We release our benchmark at https://github.com/maple-zhou/PersonaEval.", "source": "openreview", "url": "https://openreview.net/forum?id=drdrFhKYjP", "decision_type": "Poster", "avg_rating": 6.5, "relative_path": "2025/COLM/Poster/6.5_PersonaEval_ Are LLM Evaluators Human Enough to Judge Role-Play__2025.pdf" }, { "title": "OpinioRAG: Towards Generating User-Centric Opinion Highlights from Large-scale Online Reviews", "authors": [ "Mir Tafseer Nayeem", "Davood Rafiei" ], "year": 2025, "venue": "COLM", "abstract": "We study the problem of opinion highlights generation from large volumes of user reviews, often exceeding thousands per entity, where existing methods either fail to scale or produce generic, one-size-fits-all summaries that overlook personalized needs. To tackle this, we introduce OpinioRAG, a scalable, training-free framework that combines RAG-based evidence retrieval with LLMs to efficiently produce tailored summaries. Additionally, we propose novel reference-free verification metrics designed for sentiment-rich domains, where accurately capturing opinions and sentiment alignment is essential. These metrics offer a fine-grained, context-sensitive assessment of factual consistency. To facilitate evaluation, we contribute the first large-scale dataset of long-form user reviews, comprising entities with over a thousand reviews each, paired with unbiased expert summaries and manually annotated queries. Through extensive experiments, we identify key challenges, provide actionable insights into improving systems, pave the way for future research, and position OpinioRAG as a robust framework for generating accurate, relevant, and structured summaries at scale.", "source": "openreview", "url": "https://openreview.net/forum?id=R94bCTckhV", "decision_type": "Poster", "avg_rating": 6.3, "relative_path": "2025/COLM/Poster/6.3_OpinioRAG_ Towards Generating User-Centric Opinion Highlights from Large-scale Online Reviews_2025.pdf" }, { "title": "Probing then Editing Response Personality of Large Language Models", "authors": [ "Tianjie Ju", "Zhenyu Shao", "Bowen Wang", "Yujia Chen", "Zhuosheng Zhang", "Hao Fei", "Mong-Li Lee", "Wynne Hsu", "Sufeng Duan", "Gongshen Liu" ], "year": 2025, "venue": "COLM", "abstract": "Large Language Models (LLMs) have demonstrated promising capabilities to generate responses that simulate consistent personality traits. \nDespite the major attempts to analyze personality expression through output-based evaluations, little is known about how such traits are internally encoded within LLM parameters. In this paper, we introduce a layer-wise probing framework to systematically investigate the layer-wise capability of LLMs in simulating personality for responding. We conduct probing experiments on 11 open-source LLMs over the PersonalityEdit benchmark and find that LLMs predominantly simulate personality for responding in their middle and upper layers, with instruction-tuned models demonstrating a slightly clearer separation of personality traits. Furthermore, by interpreting the trained probing hyperplane as a layer-wise boundary for each personality category, we propose a layer-wise perturbation method to edit the personality expressed by LLMs during inference. Our results show that even when the prompt explicitly specifies a particular personality, our method can still successfully alter the response personality of LLMs. Interestingly, the difficulty of converting between certain personality traits varies substantially, which aligns with the representational distances in our probing experiments. Finally, we conduct a comprehensive MMLU benchmark evaluation and time overhead analysis, demonstrating that our proposed personality editing method incurs only minimal degradation in general capabilities while maintaining low training costs and acceptable inference latency. Our code is publicly available at https://github.com/universe-sky/probing-then-editing-personality.", "source": "openreview", "url": "https://openreview.net/forum?id=z9SbcYYP0M", "decision_type": "Poster", "avg_rating": 6.2, "relative_path": "2025/COLM/Poster/6.2_Probing then Editing Response Personality of Large Language Models_2025.pdf" }, { "title": "A Survey on Personalized and Pluralistic Preference Alignment in Large Language Models", "authors": [ "Zhouhang Xie", "Junda Wu", "Yiran Shen", "Raghav Jain", "Yu Xia", "Xintong Li", "Aaron Chang", "Ryan A. Rossi", "Tong Yu", "Sachin Kumar", "Bodhisattwa Prasad Majumder", "Jingbo Shang", "Prithviraj Ammanabrolu", "Julian McAuley" ], "year": 2025, "venue": "COLM", "abstract": "Personalized preference alignment for large language models (LLMs), the process of tailoring LLMs to individual users' preferences, is an emerging research direction spanning the area of NLP and personalization. In this survey, we present an analysis of works on personalized alignment and modeling for LLMs. We introduce a taxonomy of preference alignment techniques, including training time, inference time, and heuristic-driven methods. We provide analysis and discussion on the strengths and limitations of each group of techniques and then cover evaluation, benchmarks, as well as open problems in the field.", "source": "openreview", "url": "https://openreview.net/forum?id=lSWOMjonL7", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2025/COLM/Poster/6.0_A Survey on Personalized and Pluralistic Preference Alignment in Large Language Models_2025.pdf" }, { "title": "Beyond Blanket Masking: Examining Granularity for Privacy Protection in Images Captured by Blind and Low Vision Users", "authors": [ "Jeffri Murrugarra-Llerena", "Haoran Niu", "K. Suzanne Barber", "Hal Daumé III", "Yang Trista Cao", "Paola Cascante-Bonilla" ], "year": 2025, "venue": "COLM", "abstract": "As visual assistant systems powered by visual language models (VLMs) become more prevalent, concerns over user privacy have grown, particularly for blind and low vision users who may unknowingly capture personal private information in their images. Existing privacy protection methods rely on coarse-grained segmentation, which uniformly masks entire private objects, often at the cost of usability. In this work, we propose FiG-Priv, a fine-grained privacy protection framework that selectively masks only high-risk private information while preserving low-risk information. Our approach integrates fine-grained segmentation with a data-driven risk scoring mechanism. By leveraging a more nuanced understanding of privacy risk, our method enables more effective protection without unnecessarily restricting users’ access to critical information. We evaluate our framework using the BIV-Priv-Seg dataset and show that FiG-Priv preserves +26% of image content, enhancing the ability of VLMs to provide useful responses by 11% and identify the image content by 45%, while ensuring privacy protection.", "source": "openreview", "url": "https://openreview.net/forum?id=hLjoekkPiJ", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2025/COLM/Poster/6.0_Beyond Blanket Masking_ Examining Granularity for Privacy Protection in Images Captured by Blin_2025.pdf" }, { "title": "Language Model Personalization via Reward Factorization", "authors": [ "Idan Shenfeld", "Felix Faltings", "Pulkit Agrawal", "Aldo Pacchiano" ], "year": 2025, "venue": "COLM", "abstract": "Modern large language models (LLMs) are optimized for human-aligned responses using Reinforcement Learning from Human Feedback (RLHF). However, existing RLHF approaches assume a universal preference model and fail to account for individual user preferences, limiting their effectiveness in personalized applications. We introduce a framework that extends RLHF to enable user personalization by leveraging the assumption that user preferences lie in a low-dimensional space. Instead of training a separate model per user, we represent user-specific rewards as a linear combination of base reward functions. Using only 10 user responses, our method can infer user-specific rewards and align LLM outputs accordingly. We validate our approach through experiments with both synthetic and real users, demonstrating significant personalization achieved by our method. In human evaluations, our method achieves a 67% win rate over default GPT-4o responses.", "source": "openreview", "url": "https://openreview.net/forum?id=E7Tu5yjqXw", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2025/COLM/Poster/6.0_Language Model Personalization via Reward Factorization_2025.pdf" }, { "title": "Yourbench: Dynamic Evaluation Set Generation with LLMs", "authors": [ "Sumuk Shashidhar", "Clémentine Fourrier", "Alina Lozovskaya", "Thomas Wolf", "Gokhan Tur", "Dilek Hakkani-Tür" ], "year": 2025, "venue": "COLM", "abstract": "Large language models (LLMs) have rapidly outpaced traditional evaluation methodologies, with static benchmarks suffering from saturation, contamination, and domain-specificity limitations while human evaluation remains prohibitively expensive. We present YourBench, an open-source framework that transforms this evaluation paradigm by enabling automated generation of reliable, contamination-free benchmarks directly from user-provided documents without human annotation. To validate our approach, we successfully reproduce the challenging MMLU-Pro benchmark across 86 models spanning 400M to 405B parameters, achieving remarkable Pearson correlations of 0.91-0.99 while generating entirely novel questions for under $15 per model. This demonstrates that dynamically generated evaluations can match the discriminative power of expert-curated benchmarks while eliminating contamination risks. YourBench enables researchers to create domain-specific benchmarks in minutes rather than months. We demonstrate applications in agriculture, personalized education, and RAG training that were previously infeasible. By releasing the YourBench library, Tempora-0325 dataset, 150K+ generated QA pairs, and all evaluation traces, we provide the community with a practical solution to the challenge of keeping pace with rapidly evolving model capabilities.", "source": "openreview", "url": "https://openreview.net/forum?id=bkWERVKzuP", "decision_type": "Poster", "avg_rating": 5.5, "relative_path": "2025/COLM/Poster/5.5_Yourbench_ Dynamic Evaluation Set Generation with LLMs_2025.pdf" }, { "title": "A Generative Framework for Personalized Sticker Retrieval", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-emnlp.753/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_A Generative Framework for Personalized Sticker Retrieval_2025.pdf" }, { "title": "Beyond Demographics: Enhancing Cultural Value Survey Simulation with Multi-Stage Personality-Driven Cognitive Reasoning", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.emnlp-main.928/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Beyond Demographics_ Enhancing Cultural Value Survey Simulation with Multi-Stage Personality-Dr_2025.pdf" }, { "title": "Beyond Self-Reports: Multi-Observer Agents for Personality Assessment in Large Language Models", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-emnlp.1150/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Beyond Self-Reports_ Multi-Observer Agents for Personality Assessment in Large Language Models_2025.pdf" }, { "title": "Can Large Language Models Personalize Dialogues to Generational Styles?", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-emnlp.5/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Can Large Language Models Personalize Dialogues to Generational Styles__2025.pdf" }, { "title": "Character is Destiny: Can Persona-assigned Language Models Make Personal Choices?", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-emnlp.813/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Character is Destiny_ Can Persona-assigned Language Models Make Personal Choices__2025.pdf" }, { "title": "Dissecting Persona-Driven Reasoning in Language Models via Activation Patching", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-emnlp.1335/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Dissecting Persona-Driven Reasoning in Language Models via Activation Patching_2025.pdf" }, { "title": "Drift: Decoding-time Personalized Alignments with Implicit User Preferences", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-emnlp.324/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Drift_ Decoding-time Personalized Alignments with Implicit User Preferences_2025.pdf" }, { "title": "Evaluating Conversational Agents with Persona-driven User Simulations based on Large Language Models: A Sales Bot Case Study", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.emnlp-industry.16/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Evaluating Conversational Agents with Persona-driven User Simulations based on Large Language M_2025.pdf" }, { "title": "Exploring Chain-of-Thought Reasoning for Steerable Pluralistic Alignment", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.emnlp-main.1301/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Exploring Chain-of-Thought Reasoning for Steerable Pluralistic Alignment_2025.pdf" }, { "title": "Formalizing Style in Personal Narratives", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.emnlp-main.371/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Formalizing Style in Personal Narratives_2025.pdf" }, { "title": "From Generic Empathy to Personalized Emotional Support: A Self-Evolution Framework for User Preference Alignment", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-emnlp.1024/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_From Generic Empathy to Personalized Emotional Support_ A Self-Evolution Framework for User Pre_2025.pdf" }, { "title": "Improving Language Model Personas via Rationalization with Psychological Scaffolds", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-emnlp.1187/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Improving Language Model Personas via Rationalization with Psychological Scaffolds_2025.pdf" }, { "title": "Machine Unlearning of Personally Identifiable Information in Large Language Models", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.nllp-1.6/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Machine Unlearning of Personally Identifiable Information in Large Language Models_2025.pdf" }, { "title": "Measuring Lexical Diversity of Synthetic Data Generated through Fine-Grained Persona Prompting", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-emnlp.1146/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Measuring Lexical Diversity of Synthetic Data Generated through Fine-Grained Persona Prompting_2025.pdf" }, { "title": "No for Some, Yes for Others: Persona Prompts and Other Sources of False Refusal in Language Models", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.winlp-main.39/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_No for Some, Yes for Others_ Persona Prompts and Other Sources of False Refusal in Language Mod_2025.pdf" }, { "title": "Personality Vector: Modulating Personality of Large Language Models by Model Merging", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.emnlp-main.1253/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Personality Vector_ Modulating Personality of Large Language Models by Model Merging_2025.pdf" }, { "title": "Personalization up to a Point: Why Personalized Content Moderation Needs Boundaries, and How We Can Enforce Them", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.emnlp-main.1726/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Personalization up to a Point_ Why Personalized Content Moderation Needs Boundaries, and How We_2025.pdf" }, { "title": "Personalized Language Models via Privacy-Preserving Evolutionary Model Merging", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.emnlp-main.1747/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Personalized Language Models via Privacy-Preserving Evolutionary Model Merging_2025.pdf" }, { "title": "Personalized Question Answering with User Profile Generation and Compression", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-emnlp.255/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Personalized Question Answering with User Profile Generation and Compression_2025.pdf" }, { "title": "Personalized open world plan generation for safety-critical human centered autonomous systems: A case study on Artificial Pancreas", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-emnlp.1219/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Personalized open world plan generation for safety-critical human centered autonomous systems_ _2025.pdf" }, { "title": "Pluralistic Alignment for Healthcare: A Role-Driven Framework", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.emnlp-main.1596/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Pluralistic Alignment for Healthcare_ A Role-Driven Framework_2025.pdf" }, { "title": "Post Persona Alignment for Multi-Session Dialogue Generation", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-emnlp.1098/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Post Persona Alignment for Multi-Session Dialogue Generation_2025.pdf" }, { "title": "Pre-Storage Reasoning for Episodic Memory: Shifting Inference Burden to Memory for Personalized Dialogue", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-emnlp.1204/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Pre-Storage Reasoning for Episodic Memory_ Shifting Inference Burden to Memory for Personalized_2025.pdf" }, { "title": "Principled Personas: Defining and Measuring the Intended Effects of Persona Prompting on Task Performance", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.emnlp-main.1364/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Principled Personas_ Defining and Measuring the Intended Effects of Persona Prompting on Task P_2025.pdf" }, { "title": "Re:Member: Emotional Question Generation from Personal Memories", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.hcinlp-1.13/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Re_Member_ Emotional Question Generation from Personal Memories_2025.pdf" }, { "title": "Rethinking Personality Assessment from Human-Agent Dialogues: Fewer Rounds May Be Better Than More", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-emnlp.287/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Rethinking Personality Assessment from Human-Agent Dialogues_ Fewer Rounds May Be Better Than M_2025.pdf" }, { "title": "Similarity = Value? Consultation Value-Assessment and Alignment for Personalized Search", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.emnlp-main.498/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Similarity = Value_ Consultation Value-Assessment and Alignment for Personalized Search_2025.pdf" }, { "title": "Synthetic Socratic Debates: Examining Persona Effects on Moral Decision and Persuasion Dynamics", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.emnlp-main.831/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Synthetic Socratic Debates_ Examining Persona Effects on Moral Decision and Persuasion Dynamics_2025.pdf" }, { "title": "The Prompt Makes the Person(a): A Systematic Evaluation of Sociodemographic Persona Prompting for Large Language Models", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-emnlp.1261/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_The Prompt Makes the Person(a)_ A Systematic Evaluation of Sociodemographic Persona Prompting f_2025.pdf" }, { "title": "Toward Multi-Session Personalized Conversation: A Large-Scale Dataset and Hierarchical Tree Framework for Implicit Reasoning", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.emnlp-main.580/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Toward Multi-Session Personalized Conversation_ A Large-Scale Dataset and Hierarchical Tree Fra_2025.pdf" }, { "title": "Towards Personalized Conversational Sales Agents: Contextual User Profiling for Strategic Action", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-emnlp.275/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Towards Personalized Conversational Sales Agents_ Contextual User Profiling for Strategic Actio_2025.pdf" }, { "title": "Towards Transferable Personality Representation Learning based on Triplet Comparisons and Its Applications", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.emnlp-main.509/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Towards Transferable Personality Representation Learning based on Triplet Comparisons and Its A_2025.pdf" }, { "title": "We Argue to Agree: Towards Personality-Driven Argumentation-Based Negotiation Dialogue Systems for Tourism", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-emnlp.1390/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_We Argue to Agree_ Towards Personality-Driven Argumentation-Based Negotiation Dialogue Systems _2025.pdf" }, { "title": "When Personalization Meets Reality: A Multi-Faceted Analysis of Personalized Preference Learning", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-emnlp.916/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_When Personalization Meets Reality_ A Multi-Faceted Analysis of Personalized Preference Learnin_2025.pdf" }, { "title": "Words Like Knives: Backstory-Personalized Modeling and Detection of Violent Communication", "authors": [], "year": 2025, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.emnlp-main.586/", "decision_type": null, "avg_rating": null, "relative_path": "2025/EMNLP/Other/x_Words Like Knives_ Backstory-Personalized Modeling and Detection of Violent Communication_2025.pdf" }, { "title": "Dialogue Language Model with Large-Scale Persona Data Engineering", "authors": [], "year": 2025, "venue": "NAACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.naacl-industry.71/", "decision_type": null, "avg_rating": null, "relative_path": "2025/NAACL/Other/x_Dialogue Language Model with Large-Scale Persona Data Engineering_2025.pdf" }, { "title": "Embedded Personalities: Word Embeddings and the “Big Five” Personality Model", "authors": [], "year": 2025, "venue": "NAACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.latechclfl-1.18/", "decision_type": null, "avg_rating": null, "relative_path": "2025/NAACL/Other/x_Embedded Personalities_ Word Embeddings and the “Big Five” Personality Model_2025.pdf" }, { "title": "Exploring Safety-Utility Trade-Offs in Personalized Language Models", "authors": [], "year": 2025, "venue": "NAACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.naacl-long.565/", "decision_type": null, "avg_rating": null, "relative_path": "2025/NAACL/Other/x_Exploring Safety-Utility Trade-Offs in Personalized Language Models_2025.pdf" }, { "title": "Masks and Mimicry: Strategic Obfuscation and Impersonation Attacks on Authorship Verification", "authors": [], "year": 2025, "venue": "NAACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.nlp4dh-1.10/", "decision_type": null, "avg_rating": null, "relative_path": "2025/NAACL/Other/x_Masks and Mimicry_ Strategic Obfuscation and Impersonation Attacks on Authorship Verification_2025.pdf" }, { "title": "Personalized Help for Optimizing Low-Skilled Users’ Strategy", "authors": [], "year": 2025, "venue": "NAACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.naacl-short.6/", "decision_type": null, "avg_rating": null, "relative_path": "2025/NAACL/Other/x_Personalized Help for Optimizing Low-Skilled Users’ Strategy_2025.pdf" }, { "title": "Tuning-Free Personalized Alignment via Trial-Error-Explain In-Context Learning", "authors": [], "year": 2025, "venue": "NAACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2025.findings-naacl.326/", "decision_type": null, "avg_rating": null, "relative_path": "2025/NAACL/Other/x_Tuning-Free Personalized Alignment via Trial-Error-Explain In-Context Learning_2025.pdf" }, { "title": "A Graph per Persona: Reasoning about Subjective Natural Language Descriptions", "authors": [], "year": 2024, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.findings-acl.115/", "decision_type": null, "avg_rating": null, "relative_path": "2024/ACL/Other/x_A Graph per Persona_ Reasoning about Subjective Natural Language Descriptions_2024.pdf" }, { "title": "Chamain: Harmonizing Character Persona Integrity with Domain-Adaptive Knowledge in Dialogue Generation", "authors": [], "year": 2024, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.nlp4convai-1.7/", "decision_type": null, "avg_rating": null, "relative_path": "2024/ACL/Other/x_Chamain_ Harmonizing Character Persona Integrity with Domain-Adaptive Knowledge in Dialogue Gen_2024.pdf" }, { "title": "Evaluating Large Language Model Biases in Persona-Steered Generation", "authors": [], "year": 2024, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.findings-acl.586/", "decision_type": null, "avg_rating": null, "relative_path": "2024/ACL/Other/x_Evaluating Large Language Model Biases in Persona-Steered Generation_2024.pdf" }, { "title": "Faithful Persona-based Conversational Dataset Generation with Large Language Models", "authors": [], "year": 2024, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.findings-acl.904/", "decision_type": null, "avg_rating": null, "relative_path": "2024/ACL/Other/x_Faithful Persona-based Conversational Dataset Generation with Large Language Models_2024.pdf" }, { "title": "On The Persona-based Summarization of Domain-Specific Documents", "authors": [], "year": 2024, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.findings-acl.849/", "decision_type": null, "avg_rating": null, "relative_path": "2024/ACL/Other/x_On The Persona-based Summarization of Domain-Specific Documents_2024.pdf" }, { "title": "P4: Plug-and-Play Discrete Prompting for Large Language Models Personalization", "authors": [], "year": 2024, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.findings-acl.541/", "decision_type": null, "avg_rating": null, "relative_path": "2024/ACL/Other/x_P4_ Plug-and-Play Discrete Prompting for Large Language Models Personalization_2024.pdf" }, { "title": "Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset", "authors": [], "year": 2024, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.findings-acl.65/", "decision_type": null, "avg_rating": null, "relative_path": "2024/ACL/Other/x_Pearl_ A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset_2024.pdf" }, { "title": "Personalized Topic Selection Model for Topic-Grounded Dialogue", "authors": [], "year": 2024, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.findings-acl.429/", "decision_type": null, "avg_rating": null, "relative_path": "2024/ACL/Other/x_Personalized Topic Selection Model for Topic-Grounded Dialogue_2024.pdf" }, { "title": "Measuring and Controlling Instruction (In)Stability in Language Model Dialogs", "authors": [ "Kenneth Li", "Tianle Liu", "Naomi Bashkansky", "David Bau", "Fernanda Viégas", "Hanspeter Pfister", "Martin Wattenberg" ], "year": 2024, "venue": "COLM", "abstract": "System-prompting is a standard tool for customizing language-model chatbots, enabling them to follow a specific instruction. An implicit assumption in the use of system prompts is that they will be _stable_, so the chatbot will continue to generate text according to the stipulated instructions for the duration of a conversation. We propose a quantitative benchmark to test this assumption, evaluating instruction stability via self-chats between two instructed chatbots. Testing popular models like LLaMA2-chat-70B and GPT-3.5, we reveal a significant _instruction drift_ within eight rounds of conversations. An empirical and theoretical analysis of this phenomenon suggests the transformer attention mechanism plays a role, due to _attention decay_ over long exchanges. To combat attention decay and instruction drift, we propose a lightweight method called split-softmax, which compares favorably against two strong baselines. \nCode: [https://github.com/likenneth/persona_drift](https://github.com/likenneth/persona_drift).", "source": "openreview", "url": "https://openreview.net/forum?id=60a1SAtH4e", "decision_type": "Poster", "avg_rating": 7.7, "relative_path": "2024/COLM/Poster/7.7_Measuring and Controlling Instruction (In)Stability in Language Model Dialogs_2024.pdf" }, { "title": "Evaluating Cultural Adaptability of a Large Language Model via Simulation of Synthetic Personas", "authors": [ "Louis Kwok", "Michal Bravansky", "Lewis Griffin" ], "year": 2024, "venue": "COLM", "abstract": "The success of Large Language Models (LLMs) in multicultural environments hinges on their ability to understand users' diverse cultural backgrounds. We measure this capability by having an LLM simulate human profiles representing various nationalities within the scope of a questionnaire-style psychological experiment. Specifically, we employ GPT-3.5 to reproduce reactions to persuasive news articles of 7,286 participants from 15 countries; comparing the results with a dataset of real participants sharing the same demographic traits. Our analysis shows that specifying a person's country of residence improves GPT-3.5's alignment with their responses. In contrast, using native language prompting introduces shifts that significantly reduce overall alignment, with some languages particularly impairing performance. These findings suggest that while direct nationality information enhances the model's cultural adaptability, native language cues do not reliably improve simulation fidelity and can detract from the model's effectiveness.", "source": "openreview", "url": "https://openreview.net/forum?id=S4ZOkV1AHl", "decision_type": "Poster", "avg_rating": 7.5, "relative_path": "2024/COLM/Poster/7.5_Evaluating Cultural Adaptability of a Large Language Model via Simulation of Synthetic Personas_2024.pdf" }, { "title": "STaR-GATE: Teaching Language Models to Ask Clarifying Questions", "authors": [ "Chinmaya Andukuri", "Jan-Philipp Fränken", "Tobias Gerstenberg", "Noah Goodman" ], "year": 2024, "venue": "COLM", "abstract": "When prompting language models to complete a task, users often leave important aspects unsaid. While asking questions could resolve this ambiguity (GATE; Li et al., 2023), models often struggle to ask good questions. We explore a language model's ability to self-improve (STaR; Zelikman et al., 2022) by rewarding the model for generating useful questions—a simple method we dub STaR-GATE. We generate a synthetic dataset of 25,500 unique persona-task prompts to simulate conversations between a pretrained language model—the $\\texttt{Questioner}$—and a $\\texttt{Roleplayer}$ whose preferences are unknown to the $\\texttt{Questioner}$. By asking questions, the $\\texttt{Questioner}$ elicits preferences from the $\\texttt{Roleplayer}$. The $\\texttt{Questioner}$ is iteratively finetuned on questions that increase the probability of high-quality responses to the task, which are generated by an $\\texttt{Oracle}$ with access to the $\\texttt{Roleplayer}$'s latent preferences. After two iterations of self-improvement, the $\\texttt{Questioner}$ asks better questions, allowing it to generate responses that are preferred over responses from the initial model on $\\textbf{72}$% of tasks. Our results indicate that teaching a language model to ask better questions leads to better personalized responses.", "source": "openreview", "url": "https://openreview.net/forum?id=CrzAj0kZjR", "decision_type": "Poster", "avg_rating": 7.0, "relative_path": "2024/COLM/Poster/7.0_STaR-GATE_ Teaching Language Models to Ask Clarifying Questions_2024.pdf" }, { "title": "Trust No Bot: Discovering Personal Disclosures in Human-LLM Conversations in the Wild", "authors": [ "Niloofar Mireshghallah", "Maria Antoniak", "Yash More", "Yejin Choi", "Golnoosh Farnadi" ], "year": 2024, "venue": "COLM", "abstract": "Measuring personal disclosures made in human-chatbot interactions can provide a better understanding of users’ AI literacy and facilitate privacy research for large language models (LLMs). We run an extensive, fine-grained analysis on the personal disclosures made by real users to commercial GPT models, investigating the leakage of personally identifiable and sensitive information. To understand the contexts in which users disclose to chatbots, we develop a taxonomy of tasks and sensitive topics, based on qualitative and quantitative analysis of naturally occurring conversations. We discuss these potential privacy harms and observe that: (1) personally identifiable information (PII) appears in unexpected contexts such as in translation or code editing (48% and 16% of the time, respectively) and (2) PII detection alone is insufficient to capture the sensitive topics that are common in human-chatbot interactions, such as detailed sexual preferences or specific drug use habits. We believe that these high disclosure rates are of significant importance for researchers and data curators, and we call for the design of appropriate nudging mechanisms to help users moderate their interactions.", "source": "openreview", "url": "https://openreview.net/forum?id=tIpWtMYkzU", "decision_type": "Poster", "avg_rating": 6.5, "relative_path": "2024/COLM/Poster/6.5_Trust No Bot_ Discovering Personal Disclosures in Human-LLM Conversations in the Wild_2024.pdf" }, { "title": "SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design", "authors": [ "Carl Edwards", "Aakanksha Naik", "Tushar Khot", "Martin D. Burke", "Heng Ji", "Tom Hope" ], "year": 2024, "venue": "COLM", "abstract": "Predicting synergistic drug combinations can help accelerate discovery of cancer treatments, particularly therapies personalized to a patient's specific tumor via biopsied cells. In this paper, we propose a novel setting and models for *in-context drug synergy learning*. We are given a small \"personalized dataset\" of 10-20 drug synergy relationships in the context of specific cancer cell targets. Our goal is to predict additional drug synergy relationships in that context. Inspired by recent work that pre-trains a GPT language model (LM) to \"in-context learn\" common function classes, we devise novel pre-training schemes that enable a GPT model to in-context learn \"drug synergy functions\". Our model---which does not use any textual corpora, molecular fingerprints, protein interaction or any other domain-specific knowledge--- is able to achieve competitive results. We further integrate our in-context approach with a genetic algorithm to optimize model prompts and select synergy candidates to test after conducting a patient biopsy. Finally, we explore a novel task of inverse drug design which can potentially enable the design of drugs that synergize specifically to target a given patient's \"personalized dataset'\". Our findings could have an important impact on precision cancer medicine, and also raise intriguing questions on non-textual pre-training for LMs.", "source": "openreview", "url": "https://openreview.net/forum?id=Aaz6R4Tlwv", "decision_type": "Poster", "avg_rating": 6.3, "relative_path": "2024/COLM/Poster/6.3_SynerGPT_ In-Context Learning for Personalized Drug Synergy Prediction and Drug Design_2024.pdf" }, { "title": "CA-LoRA: Adapting Existing LoRA for Compressed LLMs to Enable Efficient Multi-Tasking on Personal Devices", "authors": [ "Weilin Zhao", "Yuxiang Huang", "Xu Han", "Zhiyuan Liu", "Zhengyan Zhang", "Kuai Li", "Chen Chen", "TAO YANG", "Maosong Sun" ], "year": 2024, "venue": "COLM", "abstract": "Recently, there has been a demand to deploy Large Language Models (LLMs) on personal devices such as laptops and smartphones. These LLMs have different model variants when handling different tasks. However, personal devices have limited resources and require reduced storage overhead. To address this, there are two key methods available: the first is model compression, which compresses LLMs into smaller sizes; the second is LoRA, which can transfer an LLM to other tasks with very few parameters, avoiding the storage of multiple model variants in multi-task scenarios by only preserving LoRAs.\nHowever, our experiments show that directly combining these two methods yields sub-optimal performance. Considering that the open-source community has already contributed many LoRAs to LLMs, we propose to adapt these existing LoRAs from the LLMs to their compressed version and introduce a Compression-Aware LoRA (CA-LoRA) framework.\nWe incorporate knowledge inheritance and recovery strategies to recover the lost knowledge caused by model compression.\nExperiment results demonstrate that CA-LoRA outperforms the vanilla LoRA methods applied to a compressed LLM and achieves comparable performance to the non-compressed LLM with existing LoRA modules. The source code of CA-LoRA is available at https://github.com/thunlp/CA-LoRA.", "source": "openreview", "url": "https://openreview.net/forum?id=kpf7UbnSAm", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2024/COLM/Poster/6.0_CA-LoRA_ Adapting Existing LoRA for Compressed LLMs to Enable Efficient Multi-Tasking on Person_2024.pdf" }, { "title": "Large Language Model is not a (Multilingual) Compositional Relation Reasoner", "authors": [ "Jinman Zhao", "Xueyan Zhang" ], "year": 2024, "venue": "COLM", "abstract": "We present a comprehensive evaluation of large language models' \ncapability to reason compositional relations through \na benchmark encompassing 1,800 test cases in both English and Chinese, \ncovering six distinct categories of composition relations: \nPositional, Comparative, Personal, Mathematical, Identity, and Other. \nWe expand our assessment to the multilingual realm by including translations of the benchmark suite into \nJapanese, French, and Korean.\nOur Multilingual Composition Relation (MCR) benchmark\naims at investigating the robustness and adaptability of LLMs in handling compositional relation reasoning across diverse linguistic contexts.", "source": "openreview", "url": "https://openreview.net/forum?id=wLQ3I0F1oj", "decision_type": "Poster", "avg_rating": 6.0, "relative_path": "2024/COLM/Poster/6.0_Large Language Model is not a (Multilingual) Compositional Relation Reasoner_2024.pdf" }, { "title": "Personalized Collaborative Fine-Tuning for On-Device Large Language Models", "authors": [ "Nicolas Wagner", "Dongyang Fan", "Martin Jaggi" ], "year": 2024, "venue": "COLM", "abstract": "We explore on-device collaborative fine-tuning of large language models under limited local data availability. We introduce three distinct dynamic collaborator selection schemes, allowing trust-weighted personalized update aggregation: model-similarity-based, prediction-similarity-based and validation-performance-based. To minimize communication overhead, we integrate Low-Rank Adaptation (LoRA) and only exchange LoRA model updates. Our protocols, driven by prediction and performance metrics, surpass both FedAvg and local fine-tuning methods, which is particularly evident in realistic distributed scenarios with more diverse local data distributions. The results underscore the effectiveness of our approach in addressing heterogeneity and scarcity of the local datasets.", "source": "openreview", "url": "https://openreview.net/forum?id=bwo3GVsgOv", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2024/COLM/Poster/5.8_Personalized Collaborative Fine-Tuning for On-Device Large Language Models_2024.pdf" }, { "title": "Yes, no, maybe? Revisiting language models' response stability under paraphrasing for the assessment of political leaning", "authors": [ "Patrick Haller", "Jannis Vamvas", "Lena Ann Jäger" ], "year": 2024, "venue": "COLM", "abstract": "An increasing number of studies are aimed at uncovering characteristics such as personality traits or political leanings of language models (LMs), using questionnaires developed for human respondents. From this previous body of work, it is evident that models are highly sensitive to prompt design, including the phrasing of questions and statements, as well as the format of the expected response (e.g., forced choice, vs open-ended). These sensitivities then often lead to inconsistent responses. However, most studies assess response stability on a small scale with low statistical power e.g., using less than ten paraphrases of the same question.\n\nIn this work, we investigate the stability of responses to binary forced-choice questions using a large number of paraphrases. Specifically, we probe both masked language models (MLMs) and left-to-right generative language models (GLMs) on the political compass test, assessing response validity (i.e., the proportion of valid responses to a prompt) and response stability (i.e., the variability under paraphrasing) across 500 paraphrases of each statement. This large-scale assessment allows us to approximate the underlying distribution of model responses more precisely, both in terms of the overall stability of a model under paraphrasing as well as the stability of specific items (i.e., the intended meaning of a question). In addition, to investigate whether there are structural biases that drive model responses into a certain direction, we test the association between different word- and sentence-level features, and the models' responses.\n\nWe find that while all MLMs exhibit a high degree of response validity, GLMs do not consistently produce valid responses when assessed via forced choice. In terms of response stability, we show that even models that exhibit high overall stability scores flip their responses given certain paraphrases. Crucially, even within-model, response stability can vary considerably between items. We also find that models tend to agree more with statements that show high positive sentiment scores.\n\nBased on our results, we argue that human-centered questionnaires might not be appropriate in the context of probing LMs as both their response validity and stability differ considerably between items. Moreover, although stability metrics represent useful descriptions of model properties, it should be emphasized that even for models exhibiting fairly high stability, specific paraphrases can lead to substantially different model responses.", "source": "openreview", "url": "https://openreview.net/forum?id=7xUtka9ck9", "decision_type": "Poster", "avg_rating": 5.8, "relative_path": "2024/COLM/Poster/5.8_Yes, no, maybe_ Revisiting language models' response stability under paraphrasing for the asses_2024.pdf" }, { "title": "Active Listening: Personalized Question Generation in Open-Domain Social Conversation with User Model Based Prompting", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.findings-emnlp.826/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_Active Listening_ Personalized Question Generation in Open-Domain Social Conversation with User_2024.pdf" }, { "title": "Capturing Minds, Not Just Words: Enhancing Role-Playing Language Models with Personality-Indicative Data", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.findings-emnlp.853/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_Capturing Minds, Not Just Words_ Enhancing Role-Playing Language Models with Personality-Indica_2024.pdf" }, { "title": "Crafting Personalized Agents through Retrieval-Augmented Generation on Editable Memory Graphs", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.emnlp-main.281/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_Crafting Personalized Agents through Retrieval-Augmented Generation on Editable Memory Graphs_2024.pdf" }, { "title": "Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.emnlp-main.372/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning_2024.pdf" }, { "title": "From Pixels to Personas: Investigating and Modeling Self-Anthropomorphism in Human-Robot Dialogues", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.findings-emnlp.567/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_From Pixels to Personas_ Investigating and Modeling Self-Anthropomorphism in Human-Robot Dialog_2024.pdf" }, { "title": "Guided Profile Generation Improves Personalization with Large Language Models", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.findings-emnlp.231/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_Guided Profile Generation Improves Personalization with Large Language Models_2024.pdf" }, { "title": "How Personality Traits Influence Negotiation Outcomes? A Simulation based on Large Language Models", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.findings-emnlp.605/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_How Personality Traits Influence Negotiation Outcomes_ A Simulation based on Large Language Mod_2024.pdf" }, { "title": "Implicit Personalization in Language Models: A Systematic Study", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.findings-emnlp.717/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_Implicit Personalization in Language Models_ A Systematic Study_2024.pdf" }, { "title": "Investigating the Personality Consistency in Quantized Role-Playing Dialogue Agents", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.emnlp-industry.19/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_Investigating the Personality Consistency in Quantized Role-Playing Dialogue Agents_2024.pdf" }, { "title": "Kiss up, Kick down: Exploring Behavioral Changes in Multi-modal Large Language Models with Assigned Visual Personas", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.emnlp-main.609/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_Kiss up, Kick down_ Exploring Behavioral Changes in Multi-modal Large Language Models with Assi_2024.pdf" }, { "title": "Learning Dynamic Multi-attribute Interest for Personalized Product Search", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.findings-emnlp.168/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_Learning Dynamic Multi-attribute Interest for Personalized Product Search_2024.pdf" }, { "title": "Learning Personalized Alignment for Evaluating Open-ended Text Generation", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.emnlp-main.737/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_Learning Personalized Alignment for Evaluating Open-ended Text Generation_2024.pdf" }, { "title": "Low-Resource Machine Translation through the Lens of Personalized Federated Learning", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.findings-emnlp.514/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_Low-Resource Machine Translation through the Lens of Personalized Federated Learning_2024.pdf" }, { "title": "Pearl: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.customnlp4u-1.16/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_Pearl_ Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retriev_2024.pdf" }, { "title": "Personal Large Language Model Agents: A Case Study on Tailored Travel Planning", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.emnlp-industry.37/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_Personal Large Language Model Agents_ A Case Study on Tailored Travel Planning_2024.pdf" }, { "title": "Personality-aware Student Simulation for Conversational Intelligent Tutoring Systems", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.emnlp-main.37/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_Personality-aware Student Simulation for Conversational Intelligent Tutoring Systems_2024.pdf" }, { "title": "Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Efforts", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.emnlp-main.371/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_Personalized Pieces_ Efficient Personalized Large Language Models through Collaborative Efforts_2024.pdf" }, { "title": "Personalized Video Comment Generation", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.findings-emnlp.979/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_Personalized Video Comment Generation_2024.pdf" }, { "title": "Personas as a Way to Model Truthfulness in Language Models", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.emnlp-main.364/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_Personas as a Way to Model Truthfulness in Language Models_2024.pdf" }, { "title": "Revealing Personality Traits: A New Benchmark Dataset for Explainable Personality Recognition on Dialogues", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.emnlp-main.1115/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_Revealing Personality Traits_ A New Benchmark Dataset for Explainable Personality Recognition o_2024.pdf" }, { "title": "Stark: Social Long-Term Multi-Modal Conversation with Persona Commonsense Knowledge", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.findings-emnlp.708/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_Stark_ Social Long-Term Multi-Modal Conversation with Persona Commonsense Knowledge_2024.pdf" }, { "title": "Virtual Personas for Language Models via an Anthology of Backstories", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.emnlp-main.1110/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_Virtual Personas for Language Models via an Anthology of Backstories_2024.pdf" }, { "title": "When ”A Helpful Assistant” Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.findings-emnlp.888/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_When ”A Helpful Assistant” Is Not Really Helpful_ Personas in System Prompts Do Not Improve Per_2024.pdf" }, { "title": "“In-Dialogues We Learn”: Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning", "authors": [], "year": 2024, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.emnlp-main.581/", "decision_type": null, "avg_rating": null, "relative_path": "2024/EMNLP/Other/x_“In-Dialogues We Learn”_ Towards Personalized Dialogue Without Pre-defined Profiles through In-_2024.pdf" }, { "title": "Context Aggregation with Topic-focused Summarization for Personalized Medical Dialogue Generation", "authors": [], "year": 2024, "venue": "NAACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.clinicalnlp-1.27/", "decision_type": null, "avg_rating": null, "relative_path": "2024/NAACL/Other/x_Context Aggregation with Topic-focused Summarization for Personalized Medical Dialogue Generati_2024.pdf" }, { "title": "Error Tracing in Programming: A Path to Personalised Feedback", "authors": [], "year": 2024, "venue": "NAACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.bea-1.27/", "decision_type": null, "avg_rating": null, "relative_path": "2024/NAACL/Other/x_Error Tracing in Programming_ A Path to Personalised Feedback_2024.pdf" }, { "title": "Harnessing Personalization Methods to Identify and Predict Unreliable Information Spreader Behavior", "authors": [], "year": 2024, "venue": "NAACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.woah-1.11/", "decision_type": null, "avg_rating": null, "relative_path": "2024/NAACL/Other/x_Harnessing Personalization Methods to Identify and Predict Unreliable Information Spreader Beha_2024.pdf" }, { "title": "Personalized Federated Learning for Text Classification with Gradient-Free Prompt Tuning", "authors": [], "year": 2024, "venue": "NAACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.findings-naacl.286/", "decision_type": null, "avg_rating": null, "relative_path": "2024/NAACL/Other/x_Personalized Federated Learning for Text Classification with Gradient-Free Prompt Tuning_2024.pdf" }, { "title": "Personalized Jargon Identification for Enhanced Interdisciplinary Communication", "authors": [], "year": 2024, "venue": "NAACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.naacl-long.255/", "decision_type": null, "avg_rating": null, "relative_path": "2024/NAACL/Other/x_Personalized Jargon Identification for Enhanced Interdisciplinary Communication_2024.pdf" }, { "title": "Personalized Review Recommendation based on Implicit dimension mining", "authors": [], "year": 2024, "venue": "NAACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.naacl-short.8/", "decision_type": null, "avg_rating": null, "relative_path": "2024/NAACL/Other/x_Personalized Review Recommendation based on Implicit dimension mining_2024.pdf" }, { "title": "The steerability of large language models toward data-driven personas", "authors": [], "year": 2024, "venue": "NAACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.naacl-long.405/", "decision_type": null, "avg_rating": null, "relative_path": "2024/NAACL/Other/x_The steerability of large language models toward data-driven personas_2024.pdf" }, { "title": "Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration", "authors": [], "year": 2024, "venue": "NAACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.naacl-long.15/", "decision_type": null, "avg_rating": null, "relative_path": "2024/NAACL/Other/x_Unleashing the Emergent Cognitive Synergy in Large Language Models_ A Task-Solving Agent throug_2024.pdf" }, { "title": "You don’t need a personality test to know these models are unreliable: Assessing the Reliability of Large Language Models on Psychometric Instruments", "authors": [], "year": 2024, "venue": "NAACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2024.naacl-long.295/", "decision_type": null, "avg_rating": null, "relative_path": "2024/NAACL/Other/x_You don’t need a personality test to know these models are unreliable_ Assessing the Reliabilit_2024.pdf" }, { "title": "Adaptive and Personalized Exercise Generation for Online Language Learning", "authors": [], "year": 2023, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2023.acl-long.567/", "decision_type": null, "avg_rating": null, "relative_path": "2023/ACL/Other/x_Adaptive and Personalized Exercise Generation for Online Language Learning_2023.pdf" }, { "title": "An Annotated Dataset for Explainable Interpersonal Risk Factors of Mental Disturbance in Social Media Posts", "authors": [], "year": 2023, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2023.findings-acl.757/", "decision_type": null, "avg_rating": null, "relative_path": "2023/ACL/Other/x_An Annotated Dataset for Explainable Interpersonal Risk Factors of Mental Disturbance in Social_2023.pdf" }, { "title": "Detecting Personal Information in Training Corpora: an Analysis", "authors": [], "year": 2023, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2023.trustnlp-1.18/", "decision_type": null, "avg_rating": null, "relative_path": "2023/ACL/Other/x_Detecting Personal Information in Training Corpora_ an Analysis_2023.pdf" }, { "title": "Distinguishing Address vs. Reference Mentions of Personal Names in Text", "authors": [], "year": 2023, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2023.findings-acl.425/", "decision_type": null, "avg_rating": null, "relative_path": "2023/ACL/Other/x_Distinguishing Address vs. Reference Mentions of Personal Names in Text_2023.pdf" }, { "title": "Domain Transfer for Empathy, Distress, and Personality Prediction", "authors": [], "year": 2023, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2023.wassa-1.50/", "decision_type": null, "avg_rating": null, "relative_path": "2023/ACL/Other/x_Domain Transfer for Empathy, Distress, and Personality Prediction_2023.pdf" }, { "title": "Enhancing Personalized Dialogue Generation with Contrastive Latent Variables: Combining Sparse and Dense Persona", "authors": [], "year": 2023, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2023.acl-long.299/", "decision_type": null, "avg_rating": null, "relative_path": "2023/ACL/Other/x_Enhancing Personalized Dialogue Generation with Contrastive Latent Variables_ Combining Sparse _2023.pdf" }, { "title": "Explainable Recommendation with Personalized Review Retrieval and Aspect Learning", "authors": [], "year": 2023, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2023.acl-long.4/", "decision_type": null, "avg_rating": null, "relative_path": "2023/ACL/Other/x_Explainable Recommendation with Personalized Review Retrieval and Aspect Learning_2023.pdf" }, { "title": "Exploiting Rich Textual User-Product Context for Improving Personalized Sentiment Analysis", "authors": [], "year": 2023, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2023.findings-acl.92/", "decision_type": null, "avg_rating": null, "relative_path": "2023/ACL/Other/x_Exploiting Rich Textual User-Product Context for Improving Personalized Sentiment Analysis_2023.pdf" }, { "title": "Learning to Predict Persona Information for Dialogue Personalization without Explicit Persona Description", "authors": [], "year": 2023, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2023.findings-acl.186/", "decision_type": null, "avg_rating": null, "relative_path": "2023/ACL/Other/x_Learning to Predict Persona Information for Dialogue Personalization without Explicit Persona D_2023.pdf" }, { "title": "Make Text Unlearnable: Exploiting Effective Patterns to Protect Personal Data", "authors": [], "year": 2023, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2023.trustnlp-1.22/", "decision_type": null, "avg_rating": null, "relative_path": "2023/ACL/Other/x_Make Text Unlearnable_ Exploiting Effective Patterns to Protect Personal Data_2023.pdf" }, { "title": "Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models", "authors": [], "year": 2023, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2023.acl-long.84/", "decision_type": null, "avg_rating": null, "relative_path": "2023/ACL/Other/x_Marked Personas_ Using Natural Language Prompts to Measure Stereotypes in Language Models_2023.pdf" }, { "title": "Measuring the Effect of Influential Messages on Varying Personas", "authors": [], "year": 2023, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2023.acl-short.48/", "decision_type": null, "avg_rating": null, "relative_path": "2023/ACL/Other/x_Measuring the Effect of Influential Messages on Varying Personas_2023.pdf" }, { "title": "Multimodal Persona Based Generation of Comic Dialogs", "authors": [], "year": 2023, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2023.acl-long.791/", "decision_type": null, "avg_rating": null, "relative_path": "2023/ACL/Other/x_Multimodal Persona Based Generation of Comic Dialogs_2023.pdf" }, { "title": "On Text-based Personality Computing: Challenges and Future Directions", "authors": [], "year": 2023, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2023.findings-acl.691/", "decision_type": null, "avg_rating": null, "relative_path": "2023/ACL/Other/x_On Text-based Personality Computing_ Challenges and Future Directions_2023.pdf" }, { "title": "Personality Understanding of Fictional Characters during Book Reading", "authors": [], "year": 2023, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2023.acl-long.826/", "decision_type": null, "avg_rating": null, "relative_path": "2023/ACL/Other/x_Personality Understanding of Fictional Characters during Book Reading_2023.pdf" }, { "title": "Pre-trained Personalized Review Summarization with Effective Salience Estimation", "authors": [], "year": 2023, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2023.findings-acl.684/", "decision_type": null, "avg_rating": null, "relative_path": "2023/ACL/Other/x_Pre-trained Personalized Review Summarization with Effective Salience Estimation_2023.pdf" }, { "title": "Sequential Path Signature Networks for Personalised Longitudinal Language Modeling", "authors": [], "year": 2023, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2023.findings-acl.310/", "decision_type": null, "avg_rating": null, "relative_path": "2023/ACL/Other/x_Sequential Path Signature Networks for Personalised Longitudinal Language Modeling_2023.pdf" }, { "title": "Towards Robust Personalized Dialogue Generation via Order-Insensitive Representation Regularization", "authors": [], "year": 2023, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2023.findings-acl.462/", "decision_type": null, "avg_rating": null, "relative_path": "2023/ACL/Other/x_Towards Robust Personalized Dialogue Generation via Order-Insensitive Representation Regulariza_2023.pdf" }, { "title": "Towards Zero-Shot Persona Dialogue Generation with In-Context Learning", "authors": [], "year": 2023, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2023.findings-acl.90/", "decision_type": null, "avg_rating": null, "relative_path": "2023/ACL/Other/x_Towards Zero-Shot Persona Dialogue Generation with In-Context Learning_2023.pdf" }, { "title": "What to Fuse and How to Fuse: Exploring Emotion and Personality Fusion Strategies for Explainable Mental Disorder Detection", "authors": [], "year": 2023, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2023.findings-acl.568/", "decision_type": null, "avg_rating": null, "relative_path": "2023/ACL/Other/x_What to Fuse and How to Fuse_ Exploring Emotion and Personality Fusion Strategies for Explainab_2023.pdf" }, { "title": "What, When, and How to Ground: Designing User Persona-Aware Conversational Agents for Engaging Dialogue", "authors": [], "year": 2023, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2023.acl-industry.68/", "decision_type": null, "avg_rating": null, "relative_path": "2023/ACL/Other/x_What, When, and How to Ground_ Designing User Persona-Aware Conversational Agents for Engaging _2023.pdf" }, { "title": "You Are What You Read: Inferring Personality From Consumed Textual Content", "authors": [], "year": 2023, "venue": "ACL", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2023.wassa-1.4/", "decision_type": null, "avg_rating": null, "relative_path": "2023/ACL/Other/x_You Are What You Read_ Inferring Personality From Consumed Textual Content_2023.pdf" }, { "title": "Accuracy is not enough: Evaluating Personalization in Summarizers", "authors": [], "year": 2023, "venue": "EMNLP", "abstract": null, "source": "acl_anthology", "url": "https://aclanthology.org/2023.findings-emnlp.169/", 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