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a5db7d7ba2a75c80edb0c270b0c4440956e2f8b5d5cf0c1548bacd1b3f5a48d0 | 2026-02-02T00:00:00-05:00 | Comparing and Contrasting DLWP Backbones on Navier-Stokes and Atmospheric Dynamics | arXiv:2407.14129v3 Announce Type: replace Abstract: A large number of Deep Learning Weather Prediction (DLWP) architectures -- based on various backbones, including U-Net, Transformer, Graph Neural Network, and Fourier Neural Operator (FNO) -- have demonstrated their potential at forecasting atmospheric states. However... | https://arxiv.org/abs/2407.14129 | Academic Papers | svg |
1fc8bb7bda512e436d86e5e030ddc498f0eb3db6efe1733e23bebf0c5800de1c | 2026-02-02T00:00:00-05:00 | Are Pose Estimators Ready for the Open World? STAGE: A GenAI Toolkit for Auditing 3D Human Pose Estimators | arXiv:2408.16536v2 Announce Type: replace Abstract: For safety-critical applications, it is crucial to audit 3D human pose estimators before deployment. Will the system break down if the weather or the clothing changes? Is it robust regarding gender and age? To answer these questions and more, we need controlled studie... | https://arxiv.org/abs/2408.16536 | Academic Papers | svg |
e426b02fbdcc7cbd05c312a13b726956e4f988a91d84d63fe3802fcae23e7c0a | 2026-02-02T00:00:00-05:00 | FC-KAN: Function Combinations in Kolmogorov-Arnold Networks | arXiv:2409.01763v4 Announce Type: replace Abstract: In this paper, we introduce FC-KAN, a Kolmogorov-Arnold Network (KAN) that leverages combinations of popular mathematical functions such as B-splines, wavelets, and radial basis functions on low-dimensional data through element-wise operations. We explore several meth... | https://arxiv.org/abs/2409.01763 | Academic Papers | svg |
4d41355a0aa0b2e3646dddf67643174b4913ef83576fb05e5cbd0f7cdb0d7dbb | 2026-02-02T00:00:00-05:00 | Unveiling and Mitigating Bias in Large Language Model Recommendations: A Path to Fairness | arXiv:2409.10825v5 Announce Type: replace Abstract: Large Language Model (LLM)-based recommendation systems excel in delivering comprehensive suggestions by deeply analyzing content and user behavior. However, they often inherit biases from skewed training data, favoring mainstream content while underrepresenting diver... | https://arxiv.org/abs/2409.10825 | Academic Papers | svg |
7d455b36b16a894cbb606473ac7f57291846f6c7529a98092e084b25210c4cca | 2026-02-02T00:00:00-05:00 | Impacts of aspect ratio on task accuracy in parallel coordinates | arXiv:2409.12540v2 Announce Type: replace Abstract: Parallel coordinates plots (PCPs) are a widely used visualization method, particularly for exploratory analysis. Previous studies show that PCPs perform much more poorly for estimating positive correlation than for estimating negative correlation, but it is not clear ... | https://arxiv.org/abs/2409.12540 | Academic Papers | svg |
ea83138d4b4eb8695724affa1f4f5bdcce3834b029d8772a9b635b359bf998e6 | 2026-02-02T00:00:00-05:00 | ARB-LLM: Alternating Refined Binarizations for Large Language Models | arXiv:2410.03129v3 Announce Type: replace Abstract: Large Language Models (LLMs) have greatly pushed forward advancements in natural language processing, yet their high memory and computational demands hinder practical deployment. Binarization, as an effective compression technique, can shrink model weights to just 1 b... | https://arxiv.org/abs/2410.03129 | Academic Papers | svg |
610bda63311f7027744bd4b32a5d43e0df3f4e8e73a12cefa82877643212678e | 2026-02-02T00:00:00-05:00 | Policies for Fair Exchanges of Resources | arXiv:2410.21214v2 Announce Type: replace Abstract: People increasingly use digital platforms to exchange resources in accordance with some policies stating what resources users offer and what they require in return. In this paper, we propose a formal model of these environments, focussing on how users' policies are de... | https://arxiv.org/abs/2410.21214 | Academic Papers | svg |
acf42376bc1111e9b84c5bec0020364124b602667aeed5ab0dc7ff1f4515fc8d | 2026-02-02T00:00:00-05:00 | A spatiotemporal fused network considering electrode spatial topology and time-window transition for MDD detection | arXiv:2411.08521v4 Announce Type: replace Abstract: Recently, researchers have begun to experiment with deep learning-based methods for detecting major depressive disor-der (MDD) using electroencephalogram (EEG) signals in search of a more objective means of diagnosis. However, exist-ing spatiotemporal feature extracti... | https://arxiv.org/abs/2411.08521 | Academic Papers | svg |
5e310f3db9f44ac70b234d954a220c8b4cad5f5b56cb563ec6ce0fff5a873f91 | 2026-02-02T00:00:00-05:00 | Strengthening False Information Propagation Detection: Leveraging SVM and Sophisticated Text Vectorization Techniques in comparison to BERT | arXiv:2411.12703v4 Announce Type: replace Abstract: The rapid spread of misinformation, particularly through online platforms, underscores the urgent need for reliable detection systems. This study explores the utilization of machine learning and natural language processing, specifically Support Vector Machines (SVM) a... | https://arxiv.org/abs/2411.12703 | Academic Papers | svg |
dcd98ddb9141f2441fb55972dd58f1603c44a6a1dc66e80985664f692efb63da | 2026-02-02T00:00:00-05:00 | SilentWood: Private Inference Over Gradient-Boosting Decision Forests | arXiv:2411.15494v2 Announce Type: replace Abstract: Gradient boosting decision forests, used by XGBoost or AdaBoost, offer higher accuracy and lower training times than decision trees for large datasets. Protocols for private inference over decision trees can be used to preserve the privacy of the input data as well as... | https://arxiv.org/abs/2411.15494 | Academic Papers | svg |
1dac54917b3a0faccd652a86643314c765353a44ce12bce43a22a1daf9997918 | 2026-02-02T00:00:00-05:00 | Numerical analysis of a constrained strain energy minimization problem | arXiv:2411.19089v2 Announce Type: replace Abstract: We consider a setting in which an evolving surface is implicitly characterized as the zero level of a level set function. Such an implicit surface does not encode any information about the path of a single point on the evolving surface. In the literature different app... | https://arxiv.org/abs/2411.19089 | Academic Papers | svg |
0efc8e213c3e37ee52d6cfa0420105d1bb4e4cefbe31cadcef52d78653b511a4 | 2026-02-02T00:00:00-05:00 | 2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification | arXiv:2412.00678v3 Announce Type: replace Abstract: Efficiently modeling large 2D contexts is essential for various fields including Giga-Pixel Whole Slide Imaging (WSI) and remote sensing. Transformer-based models offer high parallelism but face challenges due to their quadratic complexity for handling long sequences.... | https://arxiv.org/abs/2412.00678 | Academic Papers | svg |
1e67793dbcfa267f712d3910ab6f124e9f893eb67ba12bc7d4b6686cf99c20bc | 2026-02-02T00:00:00-05:00 | The Narrow Gate: Localized Image-Text Communication in Native Multimodal Models | arXiv:2412.06646v4 Announce Type: replace Abstract: Recent advances in multimodal training have significantly improved the integration of image understanding and generation within a unified model. This study investigates how vision-language models (VLMs) handle image-understanding tasks, focusing on how visual informat... | https://arxiv.org/abs/2412.06646 | Academic Papers | svg |
3ffee32c68e10359e699956e7a039513502fd2c8426a8be1b33ec55a0ab73f63 | 2026-02-02T00:00:00-05:00 | A Library for Learning Neural Operators | arXiv:2412.10354v5 Announce Type: replace Abstract: We present NeuralOperator, an open-source Python library for operator learning. Neural operators generalize neural networks to maps between function spaces instead of finite-dimensional Euclidean spaces. They can be trained and inferenced on input and output functions... | https://arxiv.org/abs/2412.10354 | Academic Papers | svg |
366928823e5df6ecc9a40447dc7cedef350ea4b12fbf7dbd2ab6c9b22e71ac91 | 2026-02-02T00:00:00-05:00 | Softplus Attention with Re-weighting Boosts Length Extrapolation in Large Language Models | arXiv:2501.13428v5 Announce Type: replace Abstract: Large language models have achieved remarkable success in recent years, primarily due to self-attention. However, traditional Softmax attention suffers from numerical instability and reduced performance as the number of inference tokens increases. This work addresses ... | https://arxiv.org/abs/2501.13428 | Academic Papers | svg |
9aee00427a36b56789285fdbedae5b7eeadfcebb400b8ce7ac307b4cb5e8be3d | 2026-02-02T00:00:00-05:00 | Understanding Transformer Optimization via Gradient Heterogeneity | arXiv:2502.00213v3 Announce Type: replace Abstract: Transformers are difficult to optimize with stochastic gradient descent (SGD) and largely rely on adaptive optimizers such as Adam. Despite their empirical success, the reasons behind Adam's superior performance over SGD remain poorly understood. In this study, we ana... | https://arxiv.org/abs/2502.00213 | Academic Papers | svg |
aa1b97a3213c0f09d33ef2dc6074e475967d010b8341431cf7462ed183115e78 | 2026-02-02T00:00:00-05:00 | Sparsity-Guided Multi-Parameter Selection in $\ell_1$-Regularized Models via a Fixed-Point Proximity Approach | arXiv:2502.00655v2 Announce Type: replace Abstract: We study a regularization framework that combines a convex fidelity term with multiple $\ell_1$-based regularizers, each linked to a distinct linear transform. This multi-penalty model enhances flexibility in promoting structured sparsity. We analyze how the choice of... | https://arxiv.org/abs/2502.00655 | Academic Papers | svg |
b7e3ecb1ae23c15d0551db1d75027c4caf0612e44df230fd129a578e5336ccda | 2026-02-02T00:00:00-05:00 | Preprocessing Disks for Convex Hulls, Revisited | arXiv:2502.03633v2 Announce Type: replace Abstract: In the preprocessing framework one is given a set of regions that one is allowed to preprocess to create some auxiliary structure such that when a realization of these regions is given, consisting of one point per region, this auxiliary structure can be used to recons... | https://arxiv.org/abs/2502.03633 | Academic Papers | svg |
172b45ca99c5fd5eac53adaafc6fadf3c9efca6a2412e9c836c69cbc7b08bb12 | 2026-02-02T00:00:00-05:00 | FlashVideo: Flowing Fidelity to Detail for Efficient High-Resolution Video Generation | arXiv:2502.05179v4 Announce Type: replace Abstract: DiT models have achieved great success in text-to-video generation, leveraging their scalability in model capacity and data scale. High content and motion fidelity aligned with text prompts, however, often require large model parameters and a substantial number of fun... | https://arxiv.org/abs/2502.05179 | Academic Papers | svg |
461e749189cb7390d6eaa96cc5fbefe3a2ad3fcae82443ada9d56f1ab909905d | 2026-02-02T00:00:00-05:00 | Causal Imitation Learning under Expert-Observable and Expert-Unobservable Confounding | arXiv:2502.07656v2 Announce Type: replace Abstract: We propose a general framework for causal Imitation Learning (IL) with hidden confounders, which subsumes several existing settings. Our framework accounts for two types of hidden confounders: (a) variables observed by the expert but not by the imitator, and (b) confo... | https://arxiv.org/abs/2502.07656 | Academic Papers | svg |
6e9bd4d7f07dad0d13ff799e28c31727e3265f572af7c14ae567eff16225a916 | 2026-02-02T00:00:00-05:00 | Ambig-SWE: Interactive Agents to Overcome Underspecificity in Software Engineering | arXiv:2502.13069v2 Announce Type: replace Abstract: AI agents are increasingly being deployed to automate tasks, often based on underspecified user instructions. Making unwarranted assumptions to compensate for the missing information and failing to ask clarifying questions can lead to suboptimal outcomes, safety risks... | https://arxiv.org/abs/2502.13069 | Academic Papers | svg |
4391589e1312fc6cf320144504585a8d2fd4227ab3ce56c39b86e1ebdd747311 | 2026-02-02T00:00:00-05:00 | PSDNorm: Test-Time Temporal Normalization for Deep Learning in Sleep Staging | arXiv:2503.04582v3 Announce Type: replace Abstract: Distribution shift poses a significant challenge in machine learning, particularly in biomedical applications using data collected across different subjects, institutions, and recording devices, such as sleep data. While existing normalization layers, BatchNorm, Layer... | https://arxiv.org/abs/2503.04582 | Academic Papers | svg |
7865564a9294fb5b4f15b57873e75d08bb921164cabda713212c1e5280886b84 | 2026-02-02T00:00:00-05:00 | SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion Models | arXiv:2503.07392v4 Announce Type: replace Abstract: Erasing concepts from large-scale text-to-image (T2I) diffusion models has become increasingly crucial due to the growing concerns over copyright infringement, offensive content, and privacy violations. In scalable applications, fine-tuning-based methods are time-cons... | https://arxiv.org/abs/2503.07392 | Academic Papers | svg |
3d10967e69163beaddcb9fb1578e9fc1076db82dbccb33f431c76fbf618e4a6a | 2026-02-02T00:00:00-05:00 | FaVChat: Hierarchical Prompt-Query Guided Facial Video Understanding with Data-Efficient GRPO | arXiv:2503.09158v4 Announce Type: replace Abstract: Existing video large language models (VLLMs) primarily leverage prompt agnostic visual encoders, which extract untargeted facial representations without awareness of the queried information, leading to the loss of task critical cues. To address this challenge, we prop... | https://arxiv.org/abs/2503.09158 | Academic Papers | svg |
963f2c30dd8930e0994a9374d21abb6ffcf15435af077e09ddaf7245f724aea5 | 2026-02-02T00:00:00-05:00 | Ethical AI for Young Digital Citizens: A Call to Action on Privacy Governance | arXiv:2503.11947v4 Announce Type: replace Abstract: The rapid expansion of Artificial Intelligence (AI) in digital platforms used by youth has created significant challenges related to privacy, autonomy, and data protection. While AI-driven personalization offers enhanced user experiences, it often operates without cle... | https://arxiv.org/abs/2503.11947 | Academic Papers | svg |
f5b272eac908f4811f010ba6fa85943bc3c6dbf1817ac004097c42d2ee9b6fbf | 2026-02-02T00:00:00-05:00 | FactSelfCheck: Fact-Level Black-Box Hallucination Detection for LLMs | arXiv:2503.17229v3 Announce Type: replace Abstract: Large Language Models (LLMs) frequently generate hallucinated content, posing significant challenges for applications where factuality is crucial. While existing hallucination detection methods typically operate at the sentence level or passage level, we propose FactS... | https://arxiv.org/abs/2503.17229 | Academic Papers | svg |
5f726e386fc1061d150fe4f1a9fc1040b366ec612efe750019f242795cc9a731 | 2026-02-02T00:00:00-05:00 | AccidentSim: Generating Vehicle Collision Videos with Physically Realistic Collision Trajectories from Real-World Accident Reports | arXiv:2503.20654v2 Announce Type: replace Abstract: Collecting real-world vehicle accident videos for autonomous driving research is challenging due to their rarity and complexity. While existing driving video generation methods may produce visually realistic videos, they often fail to deliver physically realistic simu... | https://arxiv.org/abs/2503.20654 | Academic Papers | svg |
e9ddf5b8eb1caac3cac3972eb9d2fc12f1281ca5f7bd9f1b141be05e97cf7d43 | 2026-02-02T00:00:00-05:00 | Integrating Fourier Neural Operators with Diffusion Models to improve Spectral Representation of Synthetic Earthquake Ground Motion Response | arXiv:2504.00757v2 Announce Type: replace Abstract: Nuclear reactor buildings must be designed to withstand the dynamic load induced by strong ground motion earthquakes. For this reason, their structural behavior must be assessed in multiple realistic ground shaking scenarios (e.g., the Maximum Credible Earthquake). Ho... | https://arxiv.org/abs/2504.00757 | Academic Papers | svg |
75bf3c82762a8a5ee55929447d813100e6b6ac53e8d324604ba59d139b3f7701 | 2026-02-02T00:00:00-05:00 | Split Federated Learning for Low-Altitude Wireless Networks: Joint Sensing, Communication, Computation, and Control Co-design | arXiv:2504.01443v2 Announce Type: replace Abstract: Unmanned aerial vehicles (UAVs) with integrated sensing, communication, computation and control (ISC3) capabilities have become key enablers of next-generation wireless networks. Federated edge learning (FEL) leverages UAVs as mobile learning agents to collect data, p... | https://arxiv.org/abs/2504.01443 | Academic Papers | svg |
d031cd9155e7fb4a2c3424fd5157d8334a421b393e5e7b870f5284b31139629f | 2026-02-02T00:00:00-05:00 | CaLiV: LiDAR-to-Vehicle Calibration of Arbitrary Sensor Setups | arXiv:2504.01987v3 Announce Type: replace Abstract: In autonomous systems, sensor calibration is essential for safe and efficient navigation in dynamic environments. Accurate calibration is a prerequisite for reliable perception and planning tasks such as object detection and obstacle avoidance. Many existing LiDAR cal... | https://arxiv.org/abs/2504.01987 | Academic Papers | svg |
a132dc8bcd29a63533b87c4603202457b53ab452d436aafcce9ab76d0f4fbf2d | 2026-02-02T00:00:00-05:00 | Decentralized Domain Generalization with Style Sharing: Formal Model and Convergence Analysis | arXiv:2504.06235v4 Announce Type: replace Abstract: Much of federated learning (FL) focuses on settings where local dataset statistics remain the same between training and testing. However, this assumption often does not hold in practice due to distribution shifts, motivating the development of domain generalization (D... | https://arxiv.org/abs/2504.06235 | Academic Papers | svg |
663d715b65b283903fdaa53d6c2ce2b5e1c609f20599193822ed0247d7f381ff | 2026-02-02T00:00:00-05:00 | DeepGreen: Effective LLM-Driven Greenwashing Monitoring System Designed for Empirical Testing -- Evidence from China | arXiv:2504.07733v2 Announce Type: replace Abstract: Motivated by the emerging adoption of Large Language Models (LLMs) in economics and management research, this paper investigates whether LLMs can reliably identify corporate greenwashing narratives and, more importantly, whether and how the greenwashing signals extrac... | https://arxiv.org/abs/2504.07733 | Academic Papers | svg |
f80bfc8a65a35c0e9a5dc625e0fffbec140a93df90b5138b9c510accd9d50e7e | 2026-02-02T00:00:00-05:00 | Location-Oriented Sound Event Localization and Detection with Spatial Mapping and Regression Localization | arXiv:2504.08365v3 Announce Type: replace Abstract: Sound Event Localization and Detection (SELD) combines the Sound Event Detection (SED) with the corresponding Direction Of Arrival (DOA). Recently, adopted event oriented multi-track methods affect the generality in polyphonic environments due to the limitation of the... | https://arxiv.org/abs/2504.08365 | Academic Papers | svg |
91c070c6f228112fddade3a21cb8997b2eadb162e5d581302bc96ec4a7a69bf5 | 2026-02-02T00:00:00-05:00 | Detecting Instruction Fine-tuning Attacks using Influence Function | arXiv:2504.09026v3 Announce Type: replace Abstract: Instruction fine-tuning attacks pose a serious threat to large language models (LLMs) by subtly embedding poisoned examples in fine-tuning datasets, leading to harmful or unintended behaviors in downstream applications. Detecting such attacks is challenging because po... | https://arxiv.org/abs/2504.09026 | Academic Papers | svg |
ddf42165187bffd4242f9d5a6fe117545ec13041252225693941a344bb1585d2 | 2026-02-02T00:00:00-05:00 | Can you map it to English? The Role of Cross-Lingual Alignment in Multilingual Performance of LLMs | arXiv:2504.09378v3 Announce Type: replace Abstract: Large language models (LLMs) can answer prompts in many languages, despite being trained predominantly on English; yet, the mechanisms driving this generalization remain poorly understood. This work asks: How does an LLM's ability to align representations of non-Engli... | https://arxiv.org/abs/2504.09378 | Academic Papers | svg |
bbc8ffe773303b0bddd2250c541cbcbe5e81d0844d2e00ffd191ef385534a50a | 2026-02-02T00:00:00-05:00 | What Matters in Linearizing Language Models? A Comparative Study of Architecture, Scale, and Task Adaptation | arXiv:2504.14366v3 Announce Type: replace Abstract: Linearization has emerged as a strategy for developing efficient language models (LMs). Starting from an existing Transformer-based LM, linearization replaces the attention component with computationally efficient subquadratic \textit{token mixers}. However, as an inc... | https://arxiv.org/abs/2504.14366 | Academic Papers | svg |
22d173bcca6ac6996460a6794617e2c20605c1b6a0a00cd33c245d7689971b08 | 2026-02-02T00:00:00-05:00 | Synthesising Asynchronous Automata from Fair Specifications | arXiv:2504.14623v2 Announce Type: replace Abstract: Asynchronous automata are a model of distributed finite state processes synchronising on shared actions. A celebrated result by Zielonka shows how a deterministic asynchronous automaton (AA) can be synthesised, starting from two inputs: a global specification given as... | https://arxiv.org/abs/2504.14623 | Academic Papers | svg |
9c92da79a21ce59592fb8ea6c08ea4a8933f082a1ab18ee7f294f0fa55d89567 | 2026-02-02T00:00:00-05:00 | Analysis and Elimination of Numerical Pressure Dependency in Coupled Stokes-Darcy Problem | arXiv:2504.19116v2 Announce Type: replace Abstract: This paper analyses the classical mixed finite element method (FEM) and a pressure-robust variant with divergence-free reconstruction operators for the coupled Stokes-Darcy problem. Its main contribution is to provide viscosity-explicit a priori error estimates that c... | https://arxiv.org/abs/2504.19116 | Academic Papers | svg |
cac85ba448cbbbd5726f1689c3663ac77420a76ad9565660b73cc3c70715086f | 2026-02-02T00:00:00-05:00 | Vision-Language-Action (VLA) Models: Concepts, Progress, Applications and Challenges | arXiv:2505.04769v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models mark a transformative advancement in artificial intelligence, aiming to unify perception, natural language understanding, and embodied action within a single computational framework. This foundational review presents a comprehensive... | https://arxiv.org/abs/2505.04769 | Academic Papers | svg |
f0a8d50f9367da39f2da13c993449e6a4acb1618d03ce3203e51f1aae59b2abe | 2026-02-02T00:00:00-05:00 | Kalman Filter Enhanced GRPO for Reinforcement Learning-Based Language Model Reasoning | arXiv:2505.07527v4 Announce Type: replace Abstract: The advantage function is a central concept in RL that helps reduce variance in policy gradient estimates. Recently, for language modeling, Group Relative Policy Optimization (GRPO) was proposed to compute the advantage for each output by subtracting the mean reward, ... | https://arxiv.org/abs/2505.07527 | Academic Papers | svg |
8a8e037644ccfa1ff3caa6e27bd1142dfcecac6ab308f75e134330eda5e8ffcd | 2026-02-02T00:00:00-05:00 | Lost in Transmission: When and Why LLMs Fail to Reason Globally | arXiv:2505.08140v5 Announce Type: replace Abstract: Despite their many successes, transformer-based large language models (LLMs) continue to struggle with tasks that require complex reasoning over large parts of their input. We argue that these failures arise due to capacity limits on the accurate flow of information w... | https://arxiv.org/abs/2505.08140 | Academic Papers | svg |
659e4345a64879c575fa0c2a658f05ac98dc590841c01a3ba7143442d7686a44 | 2026-02-02T00:00:00-05:00 | SuperCoder: Assembly Program Superoptimization with Large Language Models | arXiv:2505.11480v3 Announce Type: replace Abstract: Superoptimization is the task of transforming a program into a faster one while preserving its input-output behavior. In this work, we investigate whether large language models (LLMs) can serve as superoptimizers, generating assembly programs that outperform code alre... | https://arxiv.org/abs/2505.11480 | Academic Papers | svg |
6665f9d9928ae40175e1b1feec9c99a3e368a26a5892573046aabf1cc58e1285 | 2026-02-02T00:00:00-05:00 | From Street View to Visibility Network: Mapping Urban Visual Relationships with Vision-Language Models | arXiv:2505.11809v2 Announce Type: replace Abstract: Visibility analysis is one of the fundamental analytics methods in urban planning and landscape research, traditionally conducted through computational simulations based on the Line-of-Sight (LoS) principle. However, when assessing the visibility of named urban object... | https://arxiv.org/abs/2505.11809 | Academic Papers | svg |
1b4a7b9fd5e2a4d8ddee4f30a9c2d25ce17563553e83db748b076b4c66925055 | 2026-02-02T00:00:00-05:00 | SAINT: Attention-Based Policies for Discrete Combinatorial Action Spaces | arXiv:2505.12109v3 Announce Type: replace Abstract: The combinatorial structure of many real-world action spaces leads to exponential growth in the number of possible actions, limiting the effectiveness of conventional reinforcement learning algorithms. Recent approaches for combinatorial action spaces impose factorize... | https://arxiv.org/abs/2505.12109 | Academic Papers | svg |
5078231d9001dd0f518730f22ec56164e0a827f323a3b0176add42a9bf2be0fb | 2026-02-02T00:00:00-05:00 | LightRetriever: A LLM-based Text Retrieval Architecture with Extremely Faster Query Inference | arXiv:2505.12260v5 Announce Type: replace Abstract: Large Language Models (LLMs)-based text retrieval retrieves documents relevant to search queries based on vector similarities. Documents are pre-encoded offline, while queries arrive in real-time, necessitating an efficient online query encoder. Although LLMs signific... | https://arxiv.org/abs/2505.12260 | Academic Papers | svg |
056484facc6ad4c0802828b9dc153b2f1018ac6783f0bfb2543bb536d4687887 | 2026-02-02T00:00:00-05:00 | Language Models That Walk the Talk: A Framework for Formal Fairness Certificates | arXiv:2505.12767v2 Announce Type: replace Abstract: As large language models become integral to high-stakes applications, ensuring their robustness and fairness is critical. Despite their success, large language models remain vulnerable to adversarial attacks, where small perturbations, such as synonym substitutions, c... | https://arxiv.org/abs/2505.12767 | Academic Papers | svg |
6effeadebcc2aa6b53228b633cc6ea378c664ecdcd6ea1e83cea1e464355a169 | 2026-02-02T00:00:00-05:00 | CacheFlow: Fast Human Motion Prediction by Cached Normalizing Flow | arXiv:2505.13140v3 Announce Type: replace Abstract: Many density estimation techniques for 3D human motion prediction require a significant amount of inference time, often exceeding the duration of the predicted time horizon. To address the need for faster density estimation for 3D human motion prediction, we introduce... | https://arxiv.org/abs/2505.13140 | Academic Papers | svg |
6f4255be4c85ef49294ad051e0b9befcbee80807bae7c9cd344978884047a564 | 2026-02-02T00:00:00-05:00 | Policy-Driven World Model Adaptation for Robust Offline Model-based Reinforcement Learning | arXiv:2505.13709v3 Announce Type: replace Abstract: Offline reinforcement learning (RL) offers a powerful paradigm for data-driven control. Compared to model-free approaches, offline model-based RL (MBRL) explicitly learns a world model from a static dataset and uses it as a surrogate simulator, improving data efficien... | https://arxiv.org/abs/2505.13709 | Academic Papers | svg |
c2e101205f472bec862e71012a6ff4564419a427cb55e434404e5baca391b729 | 2026-02-02T00:00:00-05:00 | Warm Up Before You Train: Unlocking General Reasoning in Resource-Constrained Settings | arXiv:2505.13718v3 Announce Type: replace Abstract: Designing effective reasoning-capable LLMs typically requires training using Reinforcement Learning with Verifiable Rewards (RLVR) or distillation with carefully curated Long Chain of Thoughts (CoT), both of which depend heavily on extensive training data. This create... | https://arxiv.org/abs/2505.13718 | Academic Papers | svg |
8aaf5d8d3fa819287e73bd07c27a6c0e6064cac2faae97c40f2417e180cb23e0 | 2026-02-02T00:00:00-05:00 | Mechanistic evaluation of Transformers and state space models | arXiv:2505.15105v3 Announce Type: replace Abstract: State space models (SSMs) for language modelling promise an efficient and performant alternative to quadratic-attention Transformers, yet show variable performance on recalling basic information from the context. While performance on synthetic tasks like Associative R... | https://arxiv.org/abs/2505.15105 | Academic Papers | svg |
7d214fce3ce42d250886f179aecaad989f0bd974ce4280bc770307bb14663c90 | 2026-02-02T00:00:00-05:00 | Identification of Probabilities of Causation: from Recursive to Closed-Form Bounds | arXiv:2505.15274v3 Announce Type: replace Abstract: Probabilities of causation (PoCs) are fundamental quantities for counterfactual analysis and personalized decision making. However, existing analytical results are largely confined to binary settings. This paper extends PoCs to multi-valued treatments and outcomes by ... | https://arxiv.org/abs/2505.15274 | Academic Papers | svg |
30d67b6fffd36437cdb8db7439a1090341de91ce6f45960b95fd5e1ce974c24c | 2026-02-02T00:00:00-05:00 | Diverse, not Short: A Length-Controlled Data Selection Strategy for Improving Response Diversity of Language Models | arXiv:2505.16245v4 Announce Type: replace Abstract: Diverse language model responses are crucial for creative generation, open-ended tasks, and self-improvement training. We show that common diversity metrics, and even reward models used for preference optimization, systematically bias models toward shorter outputs, li... | https://arxiv.org/abs/2505.16245 | Academic Papers | svg |
cbc9b2a044d4e7692a0bebd2c1c105254bef74f09986ba4c183314aae7437e13 | 2026-02-02T00:00:00-05:00 | An Analysis of Concept Bottleneck Models: Measuring, Understanding, and Mitigating the Impact of Noisy Annotations | arXiv:2505.16705v3 Announce Type: replace Abstract: Concept bottleneck models (CBMs) ensure interpretability by decomposing predictions into human interpretable concepts. Yet the annotations used for training CBMs that enable this transparency are often noisy, and the impact of such corruption is not well understood. I... | https://arxiv.org/abs/2505.16705 | Academic Papers | svg |
d1aea1ffc03d1b459a513eb79b46f710b1c53ee541de0211ce6a8fedefd6edd9 | 2026-02-02T00:00:00-05:00 | NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs | arXiv:2505.17595v3 Announce Type: replace Abstract: Large language models (LLMs) achieve impressive performance across domains but face significant challenges when deployed on consumer-grade GPUs or personal devices such as laptops, due to high memory consumption and inference costs. Post-training quantization (PTQ) of... | https://arxiv.org/abs/2505.17595 | Academic Papers | svg |
c130586f787f418284b1132dc2e0e15b1108ee2286108de6b3db242622367485 | 2026-02-02T00:00:00-05:00 | Rethinking the Sampling Criteria in Reinforcement Learning for LLM Reasoning: A Competence-Difficulty Alignment Perspective | arXiv:2505.17652v3 Announce Type: replace Abstract: Reinforcement learning exhibits potential in enhancing the reasoning abilities of large language models, yet it is hard to scale for the low sample efficiency during the rollout phase. Existing methods attempt to improve efficiency by scheduling problems based on prob... | https://arxiv.org/abs/2505.17652 | Academic Papers | svg |
e235d169859ad2855c311a95fb6d7bb65d630c809451b7d2d5761824f1579259 | 2026-02-02T00:00:00-05:00 | Just as Humans Need Vaccines, So Do Models: Model Immunization to Combat Falsehoods | arXiv:2505.17870v2 Announce Type: replace Abstract: Large language models (LLMs) reproduce misinformation by learning the linguistic patterns that make falsehoods persuasive, such as hedging, false presuppositions, and citation fabrication, rather than merely memorizing false facts. We propose model immunization: super... | https://arxiv.org/abs/2505.17870 | Academic Papers | svg |
c6c3398a7655144dc9c1d8462aa7251f8a4078a422182c4a24c77ca2e94db2c1 | 2026-02-02T00:00:00-05:00 | Reinforcement Learning for Ballbot Navigation in Uneven Terrain | arXiv:2505.18417v2 Announce Type: replace Abstract: Ballbot (i.e. Ball balancing robot) navigation usually relies on methods rooted in control theory (CT), and works that apply Reinforcement learning (RL) to the problem remain rare while generally being limited to specific subtasks (e.g. balance recovery). Unlike CT ba... | https://arxiv.org/abs/2505.18417 | Academic Papers | svg |
95239865ea110cbf6bd90df4af1db7574167f34895ff6333aa4704f500881123 | 2026-02-02T00:00:00-05:00 | Surrogate Signals from Format and Length: Reinforcement Learning for Solving Mathematical Problems without Ground Truth Answers | arXiv:2505.19439v5 Announce Type: replace Abstract: Large Language Models (LLMs) have achieved remarkable success in natural language processing tasks, with Reinforcement Learning (RL) playing a key role in adapting them to specific applications. In mathematical problem solving, however, the reliance on ground truth an... | https://arxiv.org/abs/2505.19439 | Academic Papers | svg |
d501a1041cd3ad2dc2ff704cf7e46725854fab511835d86a17d6a8b39874e4a1 | 2026-02-02T00:00:00-05:00 | Model Agnostic Differentially Private Causal Inference | arXiv:2505.19589v3 Announce Type: replace Abstract: Estimating causal effects from observational data is essential in fields such as medicine, economics and social sciences, where privacy concerns are paramount. We propose a general, model-agnostic framework for differentially private estimation of average treatment ef... | https://arxiv.org/abs/2505.19589 | Academic Papers | svg |
7bf46c7e762859a900a6b4d711e9792f40510c05f587da5aa438bc19e7a4e4ef | 2026-02-02T00:00:00-05:00 | Spatially-Adaptive Gradient Re-parameterization for 3D Large Kernel Optimization | arXiv:2505.19603v2 Announce Type: replace Abstract: Large kernel convolutions offer a scalable alternative to vision transformers for high-resolution 3D volumetric analysis, yet naively increasing kernel size often leads to optimization instability. Motivated by the spatial bias inherent in effective receptive fields (... | https://arxiv.org/abs/2505.19603 | Academic Papers | svg |
18648ffadfa5e99b4a630521a8fd5285148400c40e4c2511a483203f8f7f7235 | 2026-02-02T00:00:00-05:00 | VScan: Rethinking Visual Token Reduction for Efficient Large Vision-Language Models | arXiv:2505.22654v3 Announce Type: replace Abstract: Recent Large Vision-Language Models (LVLMs) have advanced multi-modal understanding by incorporating finer-grained visual perception and encoding. However, such methods incur significant computational costs due to longer visual token sequences, posing challenges for r... | https://arxiv.org/abs/2505.22654 | Academic Papers | svg |
0bb009d3f04637983db354468af0c86901431d2dcf59c4b1b65838b43d9e859f | 2026-02-02T00:00:00-05:00 | Genomic-Informed Heterogeneous Graph Learning for Spatiotemporal Avian Influenza Outbreak Forecasting | arXiv:2505.22692v5 Announce Type: replace Abstract: Accurate forecasting of Avian Influenza Virus (AIV) outbreaks within wild bird populations necessitates models that account for complex, multi-scale transmission patterns driven by diverse factors. While conventional spatiotemporal epidemic models are robust for human... | https://arxiv.org/abs/2505.22692 | Academic Papers | svg |
b38c282488edf21b36144860edf41a3b9bd08ab2c6f504e99c4c503b6c6676f9 | 2026-02-02T00:00:00-05:00 | Learning Hierarchical Sparse Transform Coding for 3DGS Compression | arXiv:2505.22908v3 Announce Type: replace Abstract: Current 3DGS compression methods largely forego the neural analysis-synthesis transform, which is a crucial component in learned signal compression systems. As a result, redundancy removal is left solely to the entropy coder, overburdening the entropy coding module an... | https://arxiv.org/abs/2505.22908 | Academic Papers | svg |
d202d14fad836ef103451be16083b43e167bda6a87b4e0db0d170fa028b0a467 | 2026-02-02T00:00:00-05:00 | Studying the Soupability of Documents in State Space Models | arXiv:2505.24033v2 Announce Type: replace Abstract: We investigate whether hidden states from Structured State Space Models (SSMs) can be merged post hoc to support downstream reasoning. Inspired by model souping, we study document souping, a strategy where documents are encoded independently, and their representations... | https://arxiv.org/abs/2505.24033 | Academic Papers | svg |
17f492302d0af5fed6cba780b7488a00dca060c895e51902b22b786c38b29eed | 2026-02-02T00:00:00-05:00 | Framing Political Bias in Multilingual LLMs Across Pakistani Languages | arXiv:2506.00068v3 Announce Type: replace Abstract: Large Language Models (LLMs) increasingly shape public discourse, yet most evaluations of political and economic bias have focused on high-resource, Western languages and contexts. This leaves critical blind spots in low-resource, multilingual regions such as Pakistan... | https://arxiv.org/abs/2506.00068 | Academic Papers | svg |
d17f81ed0dff3b1ae76667a725e3300eb56106a0c80b927acade2b4b1e81a925 | 2026-02-02T00:00:00-05:00 | Unlearning's Blind Spots: Over-Unlearning and Prototypical Relearning Attack | arXiv:2506.01318v3 Announce Type: replace Abstract: Machine unlearning (MU) aims to expunge a designated forget set from a trained model without costly retraining, yet the existing techniques overlook two critical blind spots: "over-unlearning" that deteriorates retained data near the forget set, and post-hoc "relearni... | https://arxiv.org/abs/2506.01318 | Academic Papers | svg |
1c6a08510ee25fb05c506f020af00b3c427a1f1520abb9d4a3b4415a5843198a | 2026-02-02T00:00:00-05:00 | A Continual Offline Reinforcement Learning Benchmark for Navigation Tasks | arXiv:2506.02883v2 Announce Type: replace Abstract: Autonomous agents operating in domains such as robotics or video game simulations must adapt to changing tasks without forgetting about the previous ones. This process called Continual Reinforcement Learning poses non-trivial difficulties, from preventing catastrophic... | https://arxiv.org/abs/2506.02883 | Academic Papers | svg |
685543a08af532da228fb59efe2ff928432da5ebd044bb443a64bd0e25325b9a | 2026-02-02T00:00:00-05:00 | PPO in the Fisher-Rao geometry | arXiv:2506.03757v2 Announce Type: replace Abstract: Proximal Policy Optimization (PPO) is widely used in reinforcement learning due to its strong empirical performance, yet it lacks formal guarantees for policy improvement and convergence. PPO's clipped surrogate objective is motivated by a lower bound on linearization... | https://arxiv.org/abs/2506.03757 | Academic Papers | svg |
3a6dba726274a1cde85436549864548c16d02009fc3ef24eed64d082b1c63a9d | 2026-02-02T00:00:00-05:00 | Zero-Shot Open-Schema Entity Structure Discovery | arXiv:2506.04458v2 Announce Type: replace Abstract: Entity structure extraction, which aims to extract entities and their associated attribute-value structures from text, is an essential task for text understanding and knowledge graph construction. Existing methods based on large language models (LLMs) typically rely h... | https://arxiv.org/abs/2506.04458 | Academic Papers | svg |
881cba546352d50d862b61ac99e9a3995751643a5a0972287b94300b4f7bad31 | 2026-02-02T00:00:00-05:00 | Are LLMs Stable Formal Logic Translators in Logical Reasoning Across Linguistically Diversified Texts? | arXiv:2506.04575v3 Announce Type: replace Abstract: Logical reasoning with large language models (LLMs) has received growing attention. One mainstream approach translates natural language into formal logic and then applies symbolic solvers for deduction. While effective in many tasks, these LLM-based translators often ... | https://arxiv.org/abs/2506.04575 | Academic Papers | svg |
13fdf553b4bc890dd1eb33b965d66bbff46a3ddc13798f191851411b2746b115 | 2026-02-02T00:00:00-05:00 | Influence Functions for Edge Edits in Non-Convex Graph Neural Networks | arXiv:2506.04694v2 Announce Type: replace Abstract: Understanding how individual edges influence the behavior of graph neural networks (GNNs) is essential for improving their interpretability and robustness. Graph influence functions have emerged as promising tools to efficiently estimate the effects of edge deletions ... | https://arxiv.org/abs/2506.04694 | Academic Papers | svg |
f74c4414b2be99af1f787d8a80dbfefe59e957d39d4a56fde3c81c50dd705034 | 2026-02-02T00:00:00-05:00 | Quasiparticle Interference Kernel Extraction with Variational Autoencoders via Latent Alignment | arXiv:2506.05325v2 Announce Type: replace Abstract: Quasiparticle interference (QPI) imaging is a powerful tool for probing electronic structures in quantum materials, but extracting the single-scatterer QPI pattern (i.e., the kernel) from a multi-scatterer image remains a fundamentally ill-posed inverse problem, becau... | https://arxiv.org/abs/2506.05325 | Academic Papers | svg |
712297208d49f197c286e5ac6571a5506e5915ca5a7fb60f962ca3f4a3b1b8be | 2026-02-02T00:00:00-05:00 | Ravan: Multi-Head Low-Rank Adaptation for Federated Fine-Tuning | arXiv:2506.05568v2 Announce Type: replace Abstract: Large language models (LLMs) have not yet effectively leveraged the vast amounts of edge-device data, and federated learning (FL) offers a promising paradigm to collaboratively fine-tune LLMs without transferring private edge data to the cloud. To operate within the c... | https://arxiv.org/abs/2506.05568 | Academic Papers | svg |
3c310237a7855b6a90a0ed9bdfecaf5afe2914ef606e90150628940383dd171c | 2026-02-02T00:00:00-05:00 | Antithetic Noise in Diffusion Models | arXiv:2506.06185v2 Announce Type: replace Abstract: We systematically study antithetic initial noise in diffusion models, discovering that pairing each noise sample with its negation consistently produces strong negative correlation. This universal phenomenon holds across datasets, model architectures, conditional and ... | https://arxiv.org/abs/2506.06185 | Academic Papers | svg |
b460be1e5bc846328783a236556251963baea269fca58a594e65d7c6b2d44949 | 2026-02-02T00:00:00-05:00 | Tokenization Multiplicity Leads to Arbitrary Price Variation in LLM-as-a-service | arXiv:2506.06446v2 Announce Type: replace Abstract: Providers of LLM-as-a-service have predominantly adopted a simple pricing model: users pay a fixed price per token. Consequently, one may think that the price two different users would pay for the same output string under the same input prompt is the same. In our work... | https://arxiv.org/abs/2506.06446 | Academic Papers | svg |
49ef63083026dc2f1211d5e52ea3e228a49a6b2e36c93ce2ac67166780ee6f47 | 2026-02-02T00:00:00-05:00 | Video Unlearning via Low-Rank Refusal Vector | arXiv:2506.07891v2 Announce Type: replace Abstract: Video generative models achieve high-quality synthesis from natural-language prompts by leveraging large-scale web data. However, this training paradigm inherently exposes them to unsafe biases and harmful concepts, introducing the risk of generating undesirable or il... | https://arxiv.org/abs/2506.07891 | Academic Papers | svg |
ece8dac800daeecb92259eecca2008ec3fd16c4350278ccc28b58a4d36f2b913 | 2026-02-02T00:00:00-05:00 | Diffusion Models under Alternative Noise: Simplified Analysis and Sensitivity | arXiv:2506.08337v2 Announce Type: replace Abstract: Diffusion models, typically formulated as discretizations of stochastic differential equations (SDEs), have achieved state-of-the-art performance in generative tasks. However, their theoretical analysis often involves complex proofs. In this work, we present a simplif... | https://arxiv.org/abs/2506.08337 | Academic Papers | svg |
b9677aafd04e678b8e94636cf4bf68839f2948dcbeeff78216497b51d537294c | 2026-02-02T00:00:00-05:00 | BNMusic: Blending Environmental Noises into Personalized Music | arXiv:2506.10754v3 Announce Type: replace 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... | https://arxiv.org/abs/2506.10754 | Academic Papers | svg |
5cd3791378093f0f0d6c3efd2690ece717c810a9375f096b8c92a81f0a9220a8 | 2026-02-02T00:00:00-05:00 | Synthetic Socratic Debates: Examining Persona Effects on Moral Decision and Persuasion Dynamics | arXiv:2506.12657v2 Announce Type: replace Abstract: As large language models (LLMs) are increasingly used in morally sensitive domains, it is crucial to understand how persona traits affect their moral reasoning and persuasive behavior. We present the first large-scale study of multi-dimensional persona effects in AI-A... | https://arxiv.org/abs/2506.12657 | Academic Papers | svg |
afad45563d44c33f9339a4b7ee5a2f3d8dd04bcaa29841ac64727173c94de9af | 2026-02-02T00:00:00-05:00 | SuperPoint-SLAM3: Augmenting ORB-SLAM3 with Deep Features, Adaptive NMS, and Learning-Based Loop Closure | arXiv:2506.13089v2 Announce Type: replace Abstract: Visual simultaneous localization and mapping (SLAM) must remain accurate under extreme viewpoint, scale and illumination variations. The widely adopted ORB-SLAM3 falters in these regimes because it relies on hand-crafted ORB keypoints. We introduce SuperPoint-SLAM3, a... | https://arxiv.org/abs/2506.13089 | Academic Papers | svg |
1de373f709b93ad3b58d9e41d72e916f95d264d3736de29364b6ce00eabb0f2e | 2026-02-02T00:00:00-05:00 | Direct Reasoning Optimization: Constrained RL with Token-Level Dense Reward and Rubric-Gated Constraints for Open-ended Tasks | arXiv:2506.13351v2 Announce Type: replace Abstract: RL training of LLMs on open-ended tasks is challenging due to the lack of direct verifiability. In this paper, we frame such training as constrained RL that (i) optimizes a token-level dense Reasoning Reflection Reward (R3) aligned with reasoning quality, and (ii) enf... | https://arxiv.org/abs/2506.13351 | Academic Papers | svg |
754942013a8d1a615ca062011c00f137a12d1cbcfb50c056a3759e05fa8e51a8 | 2026-02-02T00:00:00-05:00 | A hybrid isogeometric and finite element method: NURBS-enhanced finite element method for hexahedral meshes (NEFEM-HEX) | arXiv:2506.13694v3 Announce Type: replace Abstract: In this paper, we present a NURBS-enhanced finite element method that integrates the NURBS-based boundary representation of a geometric domain into a standard finite element framework for hexahedral meshes. We decompose an open, bounded, convex three-dimensional domai... | https://arxiv.org/abs/2506.13694 | Academic Papers | svg |
a5689389ee080ac521d58860c051144c8b3ea8308fabb7c31313e476dc0e6cd5 | 2026-02-02T00:00:00-05:00 | Stretching Beyond the Obvious: A Gradient-Free Framework to Unveil the Hidden Landscape of Visual Invariance | arXiv:2506.17040v2 Announce Type: replace Abstract: Uncovering which feature combinations are encoded by visual units is critical to understanding how images are transformed into representations that support recognition. While existing feature visualization approaches typically infer a unit's most exciting images, this... | https://arxiv.org/abs/2506.17040 | Academic Papers | svg |
460b66e3d27fa88b734b8cc45d9e5054f2386b9f1b5cb6413b2be2ece7c2a60f | 2026-02-02T00:00:00-05:00 | Offline Goal-Conditioned Reinforcement Learning with Projective Quasimetric Planning | arXiv:2506.18847v3 Announce Type: replace Abstract: Offline Goal-Conditioned Reinforcement Learning seeks to train agents to reach specified goals from previously collected trajectories. Scaling that promises to long-horizon tasks remains challenging, notably due to compounding value-estimation errors. Principled geome... | https://arxiv.org/abs/2506.18847 | Academic Papers | svg |
598614ea8964ee46f740575cb806d17efe726714b9cd291989b54a66a43f65ff | 2026-02-02T00:00:00-05:00 | The lightning method for the heat equation | arXiv:2506.22576v3 Announce Type: replace Abstract: This paper introduces a new method for solving the planar heat equation based on the Lightning Method. The lightning method is a recent development in the numerical solution of linear PDEs which expresses solutions using sums of polynomials and rational functions, or ... | https://arxiv.org/abs/2506.22576 | Academic Papers | svg |
a6e26840ce564bdc21a1ac1d5b5598a3f00bf90b13936fb904577717f48f24f6 | 2026-02-02T00:00:00-05:00 | On the rank weight hierarchy of $M$-codes | arXiv:2507.00609v3 Announce Type: replace Abstract: We study the rank weight hierarchy of linear codes which are stable under a linear endomorphism defined over the base field, in particular when the endomorphism is cyclic. In this last case, we give a necessary and sufficient condition for such a code to have first ra... | https://arxiv.org/abs/2507.00609 | Academic Papers | svg |
eec535dbdaf4ca6d1feb8f2732f2a8eb51c7112b7329635102c8a761914000bf | 2026-02-02T00:00:00-05:00 | SAFER: Probing Safety in Reward Models with Sparse Autoencoder | arXiv:2507.00665v3 Announce Type: replace Abstract: Reinforcement learning from human feedback (RLHF) is a key paradigm for aligning large language models (LLMs) with human values, yet the reward models at its core remain largely opaque. In this work, we present Sparse Autoencoder For Enhanced Reward model (\textbf{SAF... | https://arxiv.org/abs/2507.00665 | Academic Papers | svg |
3e1d002f4e70533dc3374b8a3efe5d3571fb0064befe5ade57ab2d996a818397 | 2026-02-02T00:00:00-05:00 | DiffusionLight-Turbo: Accelerated Light Probes for Free via Single-Pass Chrome Ball Inpainting | arXiv:2507.01305v2 Announce Type: replace Abstract: We introduce a simple yet effective technique for estimating lighting from a single low-dynamic-range (LDR) image by reframing the task as a chrome ball inpainting problem. This approach leverages a pre-trained diffusion model, Stable Diffusion XL, to overcome the gen... | https://arxiv.org/abs/2507.01305 | Academic Papers | svg |
2994e8be15692e4680c82ea8cf3c261a9edc96a593aacf600498f43b52f9de6a | 2026-02-02T00:00:00-05:00 | Hybrid Approach to Directed Fuzzing | arXiv:2507.04855v2 Announce Type: replace Abstract: Program analysis and automated testing have recently become an essential part of SSDLC. Directed greybox fuzzing is one of the most popular automated testing methods that focuses on error detection in predefined code regions. However, it still lacks ability to overcom... | https://arxiv.org/abs/2507.04855 | Academic Papers | svg |
088a27d2da84b0e89edf46918669e304db2f80fce9ccbe56ce67251a34ee4f41 | 2026-02-02T00:00:00-05:00 | Spattack: Subgroup Poisoning Attacks on Federated Recommender Systems | arXiv:2507.06258v2 Announce Type: replace Abstract: Federated recommender systems (FedRec) have emerged as a promising approach to provide personalized recommendations while protecting user privacy. However, recent studies have shown their vulnerability to poisoning attacks, where malicious clients inject crafted gradi... | https://arxiv.org/abs/2507.06258 | Academic Papers | svg |
ab89184d8a37832305ef906fbc29555e26316523151e8868e877863383528fee | 2026-02-02T00:00:00-05:00 | Online Navigation Refinement: Achieving Lane-Level Guidance by Associating Standard-Definition and Online Perception Maps | arXiv:2507.07487v5 Announce Type: replace Abstract: Lane-level navigation is critical for geographic information systems and navigation-based tasks, offering finer-grained guidance than road-level navigation by standard definition (SD) maps. However, it currently relies on expansive global HD maps that cannot adapt to ... | https://arxiv.org/abs/2507.07487 | Academic Papers | svg |
9f50bb36a2ab7664befe0b1fd43994a4541ea09ed9a8531e4a4f8151df63d203 | 2026-02-02T00:00:00-05:00 | FloorplanQA: A Benchmark for Spatial Reasoning in LLMs using Structured Representations | arXiv:2507.07644v3 Announce Type: replace Abstract: We introduce FloorplanQA, a diagnostic benchmark for evaluating spatial reasoning in large-language models (LLMs). FloorplanQA is grounded in structured representations of indoor scenes, such as (e.g., kitchens, living rooms, bedrooms, bathrooms, and others), encoded ... | https://arxiv.org/abs/2507.07644 | Academic Papers | svg |
b3b337b55eee4c46f7d6b5c06a2b5578806af836ada6aa01e0f561dc854de048 | 2026-02-02T00:00:00-05:00 | BlindSight: Harnessing Sparsity for Efficient Vision-Language Models | arXiv:2507.09071v3 Announce Type: replace Abstract: Large vision-language models (VLMs) enable joint processing of text and images. However, incorporating vision data significantly increases the prompt length, resulting in a longer time to first token (TTFT). This bottleneck can be alleviated by leveraging the inherent... | https://arxiv.org/abs/2507.09071 | Academic Papers | svg |
96682fa24a38d53d6f7f4202516633e01746fbcd59182e94d7ddc24734b6280e | 2026-02-02T00:00:00-05:00 | A Pre-training Framework for Relational Data with Information-theoretic Principles | arXiv:2507.09837v2 Announce Type: replace Abstract: Relational databases underpin critical infrastructure across a wide range of domains, yet the design of generalizable pre-training strategies for learning from relational databases remains an open challenge due to task heterogeneity. Specifically, there exist many pos... | https://arxiv.org/abs/2507.09837 | Academic Papers | svg |
38073effd1880ac8fdd5037130791fc07dc76e019fa3a8b1063c4931836d111c | 2026-02-02T00:00:00-05:00 | EquiContact: A Hierarchical SE(3) Vision-to-Force Equivariant Policy for Spatially Generalizable Contact-rich Tasks | arXiv:2507.10961v4 Announce Type: replace Abstract: This paper presents a framework for learning vision-based robotic policies for contact-rich manipulation tasks that generalize spatially across task configurations. We focus on achieving robust spatial generalization of the policy for the peg-in-hole (PiH) task traine... | https://arxiv.org/abs/2507.10961 | Academic Papers | svg |
bc4255f370758c006c822fa53869e51de1ab78eab66c46b1c29ad38d786af523 | 2026-02-02T00:00:00-05:00 | Foundation Models for Logistics: Toward Certifiable, Conversational Planning Interfaces | arXiv:2507.11352v2 Announce Type: replace Abstract: Logistics operators, from battlefield coordinators re-routing airlifts ahead of a storm to warehouse managers juggling late trucks, need to make mission-critical decisions. Prevailing methods for logistics planning such as integer programming yield plans that satisfy ... | https://arxiv.org/abs/2507.11352 | Academic Papers | svg |
03ee7cc96c9a12c0867986b62b745808bbf8821b43a14ae68483245be83940d6 | 2026-02-02T00:00:00-05:00 | MetaLint: Generalizable Idiomatic Code Quality Analysis through Instruction-Following and Easy-to-Hard Generalization | arXiv:2507.11687v3 Announce Type: replace Abstract: Large Language Models excel at code generation but struggle with code quality analysis, where best practices evolve and cannot be fully captured by static training data. We introduce MetaLint, a training framework that treats code quality analysis as detecting best pr... | https://arxiv.org/abs/2507.11687 | Academic Papers | svg |
77b1873152a2744ee567f6c932332b50d9cdfa74b70d3ad84ec1a4097ee5b388 | 2026-02-02T00:00:00-05:00 | PICACO: Pluralistic In-Context Value Alignment of LLMs via Total Correlation Optimization | arXiv:2507.16679v2 Announce Type: replace Abstract: In-Context Learning has shown great potential for aligning Large Language Models (LLMs) with human values, helping reduce harmful outputs and accommodate diverse preferences without costly post-training, known as In-Context Alignment (ICA). However, LLMs' comprehensio... | https://arxiv.org/abs/2507.16679 | Academic Papers | svg |
2d782fcaa2f8a9e8c233f6584289f46946733006c15df152f9451a8556f720e4 | 2026-02-02T00:00:00-05:00 | A Zero-overhead Flow for Security Closure | arXiv:2507.17385v2 Announce Type: replace Abstract: In the traditional Application-Specific Integrated Circuit (ASIC) design flow, the concept of timing closure implies to reach convergence during physical synthesis such that, under a given area and power budget, the design works at the targeted frequency. However, sec... | https://arxiv.org/abs/2507.17385 | Academic Papers | svg |
778fea846a1c42a1c6b013bd9f995401e2020e4db0d91a29305c9721cc910c67 | 2026-02-02T00:00:00-05:00 | Debating Truth: Debate-driven Claim Verification with Multiple Large Language Model Agents | arXiv:2507.19090v3 Announce Type: replace Abstract: State-of-the-art single-agent claim verification methods struggle with complex claims that require nuanced analysis of multifaceted evidence. Inspired by real-world professional fact-checkers, we propose \textbf{DebateCV}, the first debate-driven claim verification fr... | https://arxiv.org/abs/2507.19090 | Academic Papers | svg |
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