id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.13095 | Understanding and Rectifying Safety Perception Distortion in VLMs | [
"cs.CV",
"cs.CL",
"cs.LG"
] | Recent studies reveal that vision-language models (VLMs) become more susceptible to harmful requests and jailbreak attacks after integrating the vision modality, exhibiting greater vulnerability than their text-only LLM backbones. To uncover the root cause of this phenomenon, we conduct an in-depth analysis and identif... | {
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2502.13103 | WeedsGalore: A Multispectral and Multitemporal UAV-based Dataset for
Crop and Weed Segmentation in Agricultural Maize Fields | [
"cs.CV"
] | Weeds are one of the major reasons for crop yield loss but current weeding practices fail to manage weeds in an efficient and targeted manner. Effective weed management is especially important for crops with high worldwide production such as maize, to maximize crop yield for meeting increasing global demands. Advances ... | {
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2502.13105 | Enhanced uncertainty quantification variational autoencoders for the
solution of Bayesian inverse problems | [
"cs.LG",
"cs.NA",
"math.NA"
] | Among other uses, neural networks are a powerful tool for solving deterministic and Bayesian inverse problems in real-time. In the Bayesian framework, variational autoencoders, a specialized type of neural network, enable the estimation of model parameters and their distribution based on observational data allowing to ... | {
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2502.13107 | MatterChat: A Multi-Modal LLM for Material Science | [
"cs.AI",
"cs.LG"
] | Understanding and predicting the properties of inorganic materials is crucial for accelerating advancements in materials science and driving applications in energy, electronics, and beyond. Integrating material structure data with language-based information through multi-modal large language models (LLMs) offers great ... | {
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2502.13108 | Improving Clinical Question Answering with Multi-Task Learning: A Joint
Approach for Answer Extraction and Medical Categorization | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Clinical Question Answering (CQA) plays a crucial role in medical decision-making, enabling physicians to extract relevant information from Electronic Medical Records (EMRs). While transformer-based models such as BERT, BioBERT, and ClinicalBERT have demonstrated state-of-the-art performance in CQA, existing models lac... | {
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2502.13110 | MLPs at the EOC: Dynamics of Feature Learning | [
"cs.LG"
] | Since infinitely wide neural networks in the kernel regime are random feature models, the success of contemporary deep learning lies in the rich regime, where a satisfying theory should explain not only the convergence of gradient descent but the learning of features along the way. Such a theory should also cover pheno... | {
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2502.13112 | Constrained Online Convex Optimization with Polyak Feasibility Steps | [
"cs.LG",
"math.OC"
] | In this work, we study online convex optimization with a fixed constraint function $g : \mathbb{R}^d \rightarrow \mathbb{R}$. Prior work on this problem has shown $O(\sqrt{T})$ regret and cumulative constraint satisfaction $\sum_{t=1}^{T} g(x_t) \leq 0$, while only accessing the constraint value and subgradient at the ... | {
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2502.13114 | The influence of motion features in temporal perception | [
"cs.CL"
] | This paper examines the role of manner-of-motion verbs in shaping subjective temporal perception and emotional resonance. Through four complementary studies, we explore how these verbs influence the conceptualization of time, examining their use in literal and metaphorical (temporal) contexts. Our findings reveal that ... | {
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2502.13115 | Near-Optimal Private Learning in Linear Contextual Bandits | [
"cs.LG",
"cs.AI",
"cs.CR",
"math.ST",
"stat.ML",
"stat.TH"
] | We analyze the problem of private learning in generalized linear contextual bandits. Our approach is based on a novel method of re-weighted regression, yielding an efficient algorithm with regret of order $\sqrt{T}+\frac{1}{\alpha}$ and $\sqrt{T}/\alpha$ in the joint and local model of $\alpha$-privacy, respectively. F... | {
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2502.13117 | Performance Evaluation of Large Language Models in Statistical
Programming | [
"stat.AP",
"cs.AI"
] | The programming capabilities of large language models (LLMs) have revolutionized automatic code generation and opened new avenues for automatic statistical analysis. However, the validity and quality of these generated codes need to be systematically evaluated before they can be widely adopted. Despite their growing pr... | {
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2502.13119 | STEER-ME: Assessing the Microeconomic Reasoning of Large Language Models | [
"cs.CL"
] | How should one judge whether a given large language model (LLM) can reliably perform economic reasoning? Most existing LLM benchmarks focus on specific applications and fail to present the model with a rich variety of economic tasks. A notable exception is Raman et al. [2024], who offer an approach for comprehensively ... | {
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2502.13120 | Adapting Psycholinguistic Research for LLMs: Gender-inclusive Language
in a Coreference Context | [
"cs.CL",
"cs.AI"
] | Gender-inclusive language is often used with the aim of ensuring that all individuals, regardless of gender, can be associated with certain concepts. While psycholinguistic studies have examined its effects in relation to human cognition, it remains unclear how Large Language Models (LLMs) process gender-inclusive lang... | {
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2502.13124 | NaturalReasoning: Reasoning in the Wild with 2.8M Challenging Questions | [
"cs.CL"
] | Scaling reasoning capabilities beyond traditional domains such as math and coding is hindered by the lack of diverse and high-quality questions. To overcome this limitation, we introduce a scalable approach for generating diverse and challenging reasoning questions, accompanied by reference answers. We present NaturalR... | {
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2502.13125 | RuozhiBench: Evaluating LLMs with Logical Fallacies and Misleading
Premises | [
"cs.CL"
] | Recent advances in large language models (LLMs) have shown that they can answer questions requiring complex reasoning. However, their ability to identify and respond to text containing logical fallacies or deliberately misleading premises remains less studied. To address this gap, we introduce RuozhiBench, a bilingual ... | {
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2502.13127 | Facilitating Long Context Understanding via Supervised Chain-of-Thought
Reasoning | [
"cs.CL"
] | Recent advances in Large Language Models (LLMs) have enabled them to process increasingly longer sequences, ranging from 2K to 2M tokens and even beyond. However, simply extending the input sequence length does not necessarily lead to effective long-context understanding. In this study, we integrate Chain-of-Thought (C... | {
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2502.13128 | SongGen: A Single Stage Auto-regressive Transformer for Text-to-Song
Generation | [
"cs.SD",
"cs.AI"
] | Text-to-song generation, the task of creating vocals and accompaniment from textual inputs, poses significant challenges due to domain complexity and data scarcity. Existing approaches often employ multi-stage generation procedures, resulting in cumbersome training and inference pipelines. In this paper, we propose Son... | {
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2502.13129 | Is Noise Conditioning Necessary for Denoising Generative Models? | [
"cs.CV"
] | It is widely believed that noise conditioning is indispensable for denoising diffusion models to work successfully. This work challenges this belief. Motivated by research on blind image denoising, we investigate a variety of denoising-based generative models in the absence of noise conditioning. To our surprise, most ... | {
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2502.13130 | Magma: A Foundation Model for Multimodal AI Agents | [
"cs.CV",
"cs.AI",
"cs.HC",
"cs.LG",
"cs.RO"
] | We present Magma, a foundation model that serves multimodal AI agentic tasks in both the digital and physical worlds. Magma is a significant extension of vision-language (VL) models in that it not only retains the VL understanding ability (verbal intelligence) of the latter, but is also equipped with the ability to pla... | {
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2502.13131 | Rethinking Diverse Human Preference Learning through Principal Component
Analysis | [
"cs.AI",
"cs.CL"
] | Understanding human preferences is crucial for improving foundation models and building personalized AI systems. However, preferences are inherently diverse and complex, making it difficult for traditional reward models to capture their full range. While fine-grained preference data can help, collecting it is expensive... | {
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2502.13132 | Learning to Defer for Causal Discovery with Imperfect Experts | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Integrating expert knowledge, e.g. from large language models, into causal discovery algorithms can be challenging when the knowledge is not guaranteed to be correct. Expert recommendations may contradict data-driven results, and their reliability can vary significantly depending on the domain or specific query. Existi... | {
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2502.13133 | AV-Flow: Transforming Text to Audio-Visual Human-like Interactions | [
"cs.CV"
] | We introduce AV-Flow, an audio-visual generative model that animates photo-realistic 4D talking avatars given only text input. In contrast to prior work that assumes an existing speech signal, we synthesize speech and vision jointly. We demonstrate human-like speech synthesis, synchronized lip motion, lively facial exp... | {
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2502.13134 | RHINO: Learning Real-Time Humanoid-Human-Object Interaction from Human
Demonstrations | [
"cs.RO",
"cs.HC",
"cs.LG"
] | Humanoid robots have shown success in locomotion and manipulation. Despite these basic abilities, humanoids are still required to quickly understand human instructions and react based on human interaction signals to become valuable assistants in human daily life. Unfortunately, most existing works only focus on multi-s... | {
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2502.13135 | Sleepless Nights, Sugary Days: Creating Synthetic Users with Health
Conditions for Realistic Coaching Agent Interactions | [
"cs.LG",
"cs.AI",
"cs.CL"
] | We present an end-to-end framework for generating synthetic users for evaluating interactive agents designed to encourage positive behavior changes, such as in health and lifestyle coaching. The synthetic users are grounded in health and lifestyle conditions, specifically sleep and diabetes management in this study, to... | {
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2502.13137 | Theorem Prover as a Judge for Synthetic Data Generation | [
"cs.AI"
] | The demand for synthetic data in mathematical reasoning has increased due to its potential to enhance the mathematical capabilities of large language models (LLMs). However, ensuring the validity of intermediate reasoning steps remains a significant challenge, affecting data quality. While formal verification via theor... | {
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2502.13138 | AIDE: AI-Driven Exploration in the Space of Code | [
"cs.AI",
"cs.LG"
] | Machine learning, the foundation of modern artificial intelligence, has driven innovations that have fundamentally transformed the world. Yet, behind advancements lies a complex and often tedious process requiring labor and compute intensive iteration and experimentation. Engineers and scientists developing machine lea... | {
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2502.13140 | Towards Quantum Tensor Decomposition in Biomedical Applications | [
"q-bio.QM",
"cs.LG"
] | Tensor decomposition has emerged as a powerful framework for feature extraction in multi-modal biomedical data. In this review, we present a comprehensive analysis of tensor decomposition methods such as Tucker, CANDECOMP/PARAFAC, spiked tensor decomposition, etc. and their diverse applications across biomedical domain... | {
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2502.13141 | UniGuardian: A Unified Defense for Detecting Prompt Injection, Backdoor
Attacks and Adversarial Attacks in Large Language Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large Language Models (LLMs) are vulnerable to attacks like prompt injection, backdoor attacks, and adversarial attacks, which manipulate prompts or models to generate harmful outputs. In this paper, departing from traditional deep learning attack paradigms, we explore their intrinsic relationship and collectively term... | {
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2502.13142 | Pre-training Auto-regressive Robotic Models with 4D Representations | [
"cs.RO",
"cs.AI"
] | Foundation models pre-trained on massive unlabeled datasets have revolutionized natural language and computer vision, exhibiting remarkable generalization capabilities, thus highlighting the importance of pre-training. Yet, efforts in robotics have struggled to achieve similar success, limited by either the need for co... | {
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2502.13143 | SoFar: Language-Grounded Orientation Bridges Spatial Reasoning and
Object Manipulation | [
"cs.RO",
"cs.AI",
"cs.CV"
] | Spatial intelligence is a critical component of embodied AI, promoting robots to understand and interact with their environments. While recent advances have enhanced the ability of VLMs to perceive object locations and positional relationships, they still lack the capability to precisely understand object orientations-... | {
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2502.13144 | RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based
Reinforcement Learning | [
"cs.CV",
"cs.RO"
] | Existing end-to-end autonomous driving (AD) algorithms typically follow the Imitation Learning (IL) paradigm, which faces challenges such as causal confusion and the open-loop gap. In this work, we establish a 3DGS-based closed-loop Reinforcement Learning (RL) training paradigm. By leveraging 3DGS techniques, we constr... | {
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2502.13145 | Multimodal Mamba: Decoder-only Multimodal State Space Model via
Quadratic to Linear Distillation | [
"cs.CV"
] | Recent Multimodal Large Language Models (MLLMs) have achieved remarkable performance but face deployment challenges due to their quadratic computational complexity, growing Key-Value cache requirements, and reliance on separate vision encoders. We propose mmMamba, a framework for developing linear-complexity native mul... | {
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2502.13146 | Re-Align: Aligning Vision Language Models via Retrieval-Augmented Direct
Preference Optimization | [
"cs.CV",
"cs.LG"
] | The emergence of large Vision Language Models (VLMs) has broadened the scope and capabilities of single-modal Large Language Models (LLMs) by integrating visual modalities, thereby unlocking transformative cross-modal applications in a variety of real-world scenarios. Despite their impressive performance, VLMs are pron... | {
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2502.13149 | Bi-Fact: A Bidirectional Factorization-based Evaluation of Intent
Extraction from UI Trajectories | [
"cs.AI"
] | Evaluating intent extraction from GUIs demands accurate, fine-grained metrics. This paper introduces Bi-Fact, a novel method that decomposes intents into atomic facts and performs bidirectional comparisons to assess precision and recall. Experiments demonstrate Bi-Fact's superior correlation with human judgments compar... | {
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2502.13160 | Understanding Dynamic Diffusion Process of LLM-based Agents under
Information Asymmetry | [
"cs.MA",
"cs.AI"
] | Large language models have been used to simulate human society using multi-agent systems. Most current social simulation research emphasizes interactive behaviors in fixed environments, ignoring information opacity, relationship variability and diffusion diversity. In this paper, we study the dynamics of information di... | {
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2502.13161 | Noumenal Labs White Paper: How To Build A Brain | [
"q-bio.NC",
"cs.AI"
] | This white paper describes some of the design principles for artificial or machine intelligence that guide efforts at Noumenal Labs. These principles are drawn from both nature and from the means by which we come to represent and understand it. The end goal of research and development in this field should be to design ... | {
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2502.13162 | ShieldLearner: A New Paradigm for Jailbreak Attack Defense in LLMs | [
"cs.CR",
"cs.AI",
"cs.CL"
] | Large Language Models (LLMs) have achieved remarkable success in various domains but remain vulnerable to adversarial jailbreak attacks. Existing prompt-defense strategies, including parameter-modifying and parameter-free approaches, face limitations in adaptability, interpretability, and customization, constraining th... | {
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2502.13164 | Multi-Agent Actor-Critic Generative AI for Query Resolution and Analysis | [
"cs.MA",
"cs.AI"
] | In this paper, we introduce MASQRAD (Multi-Agent Strategic Query Resolution and Diagnostic tool), a transformative framework for query resolution based on the actor-critic model, which utilizes multiple generative AI agents. MASQRAD is excellent at translating imprecise or ambiguous user inquiries into precise and acti... | {
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2502.13165 | HedgeAgents: A Balanced-aware Multi-agent Financial Trading System | [
"cs.MA",
"cs.AI",
"q-fin.TR"
] | As automated trading gains traction in the financial market, algorithmic investment strategies are increasingly prominent. While Large Language Models (LLMs) and Agent-based models exhibit promising potential in real-time market analysis and trading decisions, they still experience a significant -20% loss when confront... | {
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2502.13166 | Large Language Models Can Help Mitigate Barren Plateaus | [
"quant-ph",
"cs.AI",
"cs.CL",
"cs.LG"
] | In the era of noisy intermediate-scale quantum (NISQ) computing, Quantum Neural Networks (QNNs) have emerged as a promising approach for various applications, yet their training is often hindered by barren plateaus (BPs), where gradient variance vanishes exponentially as the model size increases. To address this challe... | {
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2502.13167 | SmartLLM: Smart Contract Auditing using Custom Generative AI | [
"cs.CR",
"cs.AI"
] | Smart contracts are essential to decentralized finance (DeFi) and blockchain ecosystems but are increasingly vulnerable to exploits due to coding errors and complex attack vectors. Traditional static analysis tools and existing vulnerability detection methods often fail to address these challenges comprehensively, lead... | {
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2502.13170 | Unveiling the Magic of Code Reasoning through Hypothesis Decomposition
and Amendment | [
"cs.AI",
"cs.LG"
] | The reasoning abilities are one of the most enigmatic and captivating aspects of large language models (LLMs). Numerous studies are dedicated to exploring and expanding the boundaries of this reasoning capability. However, tasks that embody both reasoning and recall characteristics are often overlooked. In this paper, ... | {
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2502.13171 | Web Phishing Net (WPN): A scalable machine learning approach for
real-time phishing campaign detection | [
"cs.CR",
"cs.AI",
"cs.LG"
] | Phishing is the most prevalent type of cyber-attack today and is recognized as the leading source of data breaches with significant consequences for both individuals and corporations. Web-based phishing attacks are the most frequent with vectors such as social media posts and emails containing links to phishing URLs th... | {
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2502.13172 | Unveiling Privacy Risks in LLM Agent Memory | [
"cs.CR",
"cs.AI"
] | Large Language Model (LLM) agents have become increasingly prevalent across various real-world applications. They enhance decision-making by storing private user-agent interactions in the memory module for demonstrations, introducing new privacy risks for LLM agents. In this work, we systematically investigate the vuln... | {
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2502.13173 | Thinking Preference Optimization | [
"cs.LG",
"cs.AI"
] | Supervised Fine-Tuning (SFT) has been a go-to and effective method for enhancing long chain-of-thought (CoT) reasoning in relatively small LLMs by fine-tuning them with long CoT responses from larger LLMs. To continually improve reasoning abilities, we can either collect new high-quality long CoT reasoning SFT data or ... | {
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2502.13174 | Generative Topology Optimization: Exploring Diverse Solutions in
Structural Design | [
"cs.LG",
"cond-mat.mtrl-sci",
"cs.AI",
"cs.CV"
] | Topology optimization (TO) is a family of computational methods that derive near-optimal geometries from formal problem descriptions. Despite their success, established TO methods are limited to generating single solutions, restricting the exploration of alternative designs. To address this limitation, we introduce Gen... | {
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2502.13175 | Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and
Attacks | [
"cs.CR",
"cs.AI",
"cs.RO"
] | Embodied AI systems, including robots and autonomous vehicles, are increasingly integrated into real-world applications, where they encounter a range of vulnerabilities stemming from both environmental and system-level factors. These vulnerabilities manifest through sensor spoofing, adversarial attacks, and failures in... | {
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2502.13176 | BaKlaVa -- Budgeted Allocation of KV cache for Long-context Inference | [
"cs.LG",
"cs.AI"
] | In Large Language Model (LLM) inference, Key-Value (KV) caches (KV-caches) are essential for reducing time complexity. However, they result in a linear increase in GPU memory as the context length grows. While recent work explores KV-cache eviction and compression policies to reduce memory usage, they often consider un... | {
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2502.13177 | KL Penalty Control via Perturbation for Direct Preference Optimization | [
"cs.LG",
"cs.AI"
] | Direct Preference Optimization (DPO) demonstrates the advantage of aligning a large language model with human preference using only an offline dataset. However, DPO has the limitation that the KL penalty, which prevents excessive deviation from the reference model, is static throughout the training process. Several met... | {
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2502.13178 | Benchmarking Post-Training Quantization in LLMs: Comprehensive Taxonomy,
Unified Evaluation, and Comparative Analysis | [
"cs.LG",
"cs.AI"
] | Post-training Quantization (PTQ) technique has been extensively adopted for large language models (LLMs) compression owing to its efficiency and low resource requirement. However, current research lacks a in-depth analysis of the superior and applicable scenarios of each PTQ strategy. In addition, existing algorithms f... | {
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2502.13179 | PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training
Quantization Methods for Large Language Models | [
"cs.LG",
"cs.AI"
] | Large Language Models (LLMs) suffer severe performance degradation when facing extremely low-bit (sub 2-bit) quantization. Several existing sub 2-bit post-training quantization (PTQ) methods utilize a mix-precision scheme by leveraging an unstructured fine-grained mask to explicitly distinguish salient weights, while w... | {
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} |
2502.13180 | Uncertain Multi-Objective Recommendation via Orthogonal Meta-Learning
Enhanced Bayesian Optimization | [
"cs.LG",
"cs.AI"
] | Recommender systems (RSs) play a crucial role in shaping our digital interactions, influencing how we access and engage with information across various domains. Traditional research has predominantly centered on maximizing recommendation accuracy, often leading to unintended side effects such as echo chambers and const... | {
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2502.13181 | RingFormer: Rethinking Recurrent Transformer with Adaptive Level Signals | [
"cs.LG",
"cs.AI"
] | Transformers have achieved great success in effectively processing sequential data such as text. Their architecture consisting of several attention and feedforward blocks can model relations between elements of a sequence in parallel manner, which makes them very efficient to train and effective in sequence modeling. E... | {
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2502.13182 | Fundus2Globe: Generative AI-Driven 3D Digital Twins for Personalized
Myopia Management | [
"eess.IV",
"cs.CV",
"eess.SP"
] | Myopia, projected to affect 50% population globally by 2050, is a leading cause of vision loss. Eyes with pathological myopia exhibit distinctive shape distributions, which are closely linked to the progression of vision-threatening complications. Recent understanding of eye-shape-based biomarkers requires magnetic res... | {
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2502.13183 | Synthetic generation of 2D data records based on Autoencoders | [
"eess.IV",
"cs.LG"
] | Gas Chromatography coupled with Ion Mobility Spectrometry (GC-IMS) is a dual-separation analytical technique widely used for identifying components in gaseous samples by separating and analysing the arrival times of their constituent species. Data generated by GC-IMS is typically represented as two-dimensional spectra,... | {
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2502.13185 | CondensNet: Enabling stable long-term climate simulations via hybrid
deep learning models with adaptive physical constraints | [
"physics.ao-ph",
"cs.AI",
"cs.LG"
] | Accurate and efficient climate simulations are crucial for understanding Earth's evolving climate. However, current general circulation models (GCMs) face challenges in capturing unresolved physical processes, such as cloud and convection. A common solution is to adopt cloud resolving models, that provide more accurate... | {
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2502.13186 | Model selection for behavioral learning data and applications to
contextual bandits | [
"stat.ML",
"cs.LG"
] | Learning for animals or humans is the process that leads to behaviors better adapted to the environment. This process highly depends on the individual that learns and is usually observed only through the individual's actions. This article presents ways to use this individual behavioral data to find the model that best ... | {
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2502.13187 | A Survey of Sim-to-Real Methods in RL: Progress, Prospects and
Challenges with Foundation Models | [
"cs.LG",
"cs.AI",
"cs.RO"
] | Deep Reinforcement Learning (RL) has been explored and verified to be effective in solving decision-making tasks in various domains, such as robotics, transportation, recommender systems, etc. It learns from the interaction with environments and updates the policy using the collected experience. However, due to the lim... | {
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2502.13188 | Autonomous Vehicles Using Multi-Agent Reinforcement Learning for Routing
Decisions Can Harm Urban Traffic | [
"cs.MA",
"cs.LG",
"cs.RO"
] | Autonomous vehicles (AVs) using Multi-Agent Reinforcement Learning (MARL) for simultaneous route optimization may destabilize traffic environments, with human drivers possibly experiencing longer travel times. We study this interaction by simulating human drivers and AVs. Our experiments with standard MARL algorithms r... | {
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2502.13189 | MoBA: Mixture of Block Attention for Long-Context LLMs | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Scaling the effective context length is essential for advancing large language models (LLMs) toward artificial general intelligence (AGI). However, the quadratic increase in computational complexity inherent in traditional attention mechanisms presents a prohibitive overhead. Existing approaches either impose strongly ... | {
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} |
2502.13190 | Application of machine learning algorithm in temperature field
reconstruction | [
"cs.LG",
"physics.flu-dyn"
] | This study focuses on the stratification patterns and dynamic evolution of reservoir water temperatures, aiming to estimate and reconstruct the temperature field using limited and noisy local measurement data. Due to complex measurement environments and technical limitations, obtaining complete temperature information ... | {
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2502.13191 | On the Privacy Risks of Spiking Neural Networks: A Membership Inference
Analysis | [
"cs.LG",
"cs.AI"
] | Spiking Neural Networks (SNNs) are increasingly explored for their energy efficiency and robustness in real-world applications, yet their privacy risks remain largely unexamined. In this work, we investigate the susceptibility of SNNs to Membership Inference Attacks (MIAs) -- a major privacy threat where an adversary a... | {
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2502.13193 | Private Text Generation by Seeding Large Language Model Prompts | [
"cs.CL"
] | We explore how private synthetic text can be generated by suitably prompting a large language model (LLM). This addresses a challenge for organizations like hospitals, which hold sensitive text data like patient medical records, and wish to share it in order to train machine learning models for medical tasks, while pre... | {
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2502.13194 | Conditional Max-Sum for Asynchronous Multiagent Decision Making | [
"cs.MA",
"cs.AI"
] | In this paper we present a novel approach for multiagent decision making in dynamic environments based on Factor Graphs and the Max-Sum algorithm, considering asynchronous variable reassignments and distributed message-passing among agents. Motivated by the challenging domain of lane-free traffic where automated vehicl... | {
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2502.13195 | Linguistic Generalizations are not Rules: Impacts on Evaluation of LMs | [
"cs.CL"
] | Linguistic evaluations of how well LMs generalize to produce or understand novel text often implicitly take for granted that natural languages are generated by symbolic rules. Grammaticality is thought to be determined by whether or not sentences obey such rules. Interpretation is believed to be compositionally generat... | {
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2502.13196 | GS-QA: Comprehensive Quality Assessment Benchmark for Gaussian Splatting
View Synthesis | [
"cs.MM",
"cs.CV"
] | Gaussian Splatting (GS) offers a promising alternative to Neural Radiance Fields (NeRF) for real-time 3D scene rendering. Using a set of 3D Gaussians to represent complex geometry and appearance, GS achieves faster rendering times and reduced memory consumption compared to the neural network approach used in NeRF. Howe... | {
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2502.13198 | Enhancing Machine Learning Performance through Intelligent Data Quality
Assessment: An Unsupervised Data-centric Framework | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Poor data quality limits the advantageous power of Machine Learning (ML) and weakens high-performing ML software systems. Nowadays, data are more prone to the risk of poor quality due to their increasing volume and complexity. Therefore, tedious and time-consuming work goes into data preparation and improvement before ... | {
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2502.13199 | The Role of GitHub Copilot on Software Development: A Perspec-tive on
Productivity, Security, Best Practices and Future Directions | [
"cs.SE",
"cs.AI"
] | GitHub Copilot is transforming software development by automating tasks and boosting productivity through AI-driven code generation. In this paper, we con-duct a literature survey to synthesize insights on Copilot's impact on productivity and security. We review academic journal databases, industry reports, and officia... | {
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2502.13200 | Learning To Explore With Predictive World Model Via Self-Supervised
Learning | [
"cs.LG",
"cs.AI"
] | Autonomous artificial agents must be able to learn behaviors in complex environments without humans to design tasks and rewards. Designing these functions for each environment is not feasible, thus, motivating the development of intrinsic reward functions. In this paper, we propose using several cognitive elements that... | {
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2502.13207 | Thinking Outside the (Gray) Box: A Context-Based Score for Assessing
Value and Originality in Neural Text Generation | [
"cs.CL",
"cs.AI",
"cs.CY",
"cs.LG"
] | Despite the increasing use of large language models for creative tasks, their outputs often lack diversity. Common solutions, such as sampling at higher temperatures, can compromise the quality of the results. Drawing on information theory, we propose a context-based score to quantitatively evaluate value and originali... | {
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2502.13220 | The impact of conformer quality on learned representations of molecular
conformer ensembles | [
"cs.LG",
"physics.chem-ph"
] | Training machine learning models to predict properties of molecular conformer ensembles is an increasingly popular strategy to accelerate the conformational analysis of drug-like small molecules, reactive organic substrates, and homogeneous catalysts. For high-throughput analyses especially, trained surrogate models ca... | {
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2502.13221 | Two Tickets are Better than One: Fair and Accurate Hiring Under
Strategic LLM Manipulations | [
"cs.LG",
"cs.AI",
"cs.CY",
"cs.GT"
] | In an era of increasingly capable foundation models, job seekers are turning to generative AI tools to enhance their application materials. However, unequal access to and knowledge about generative AI tools can harm both employers and candidates by reducing the accuracy of hiring decisions and giving some candidates an... | {
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2502.13228 | Conformal Prediction as Bayesian Quadrature | [
"cs.LG",
"cs.AI",
"stat.ML"
] | As machine learning-based prediction systems are increasingly used in high-stakes situations, it is important to understand how such predictive models will perform upon deployment. Distribution-free uncertainty quantification techniques such as conformal prediction provide guarantees about the loss black-box models wil... | {
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2502.13233 | SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question
Answering? | [
"cs.CL",
"cs.AI",
"cs.IR",
"cs.IT",
"math.IT"
] | Large Language Models (LLMs) have shown remarkable capabilities in general domains but often struggle with tasks requiring specialized knowledge. Conventional Retrieval-Augmented Generation (RAG) techniques typically retrieve external information from static knowledge bases, which can be outdated or incomplete, missing... | {
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2502.13234 | MotionMatcher: Motion Customization of Text-to-Video Diffusion Models
via Motion Feature Matching | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Text-to-video (T2V) diffusion models have shown promising capabilities in synthesizing realistic videos from input text prompts. However, the input text description alone provides limited control over the precise objects movements and camera framing. In this work, we tackle the motion customization problem, where a ref... | {
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2502.13243 | Learning the Universe: Learning to Optimize Cosmic Initial Conditions
with Non-Differentiable Structure Formation Models | [
"astro-ph.CO",
"astro-ph.GA",
"cs.LG"
] | Making the most of next-generation galaxy clustering surveys requires overcoming challenges in complex, non-linear modelling to access the significant amount of information at smaller cosmological scales. Field-level inference has provided a unique opportunity beyond summary statistics to use all of the information of ... | {
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2502.13245 | Range Retrieval with Graph-Based Indices | [
"cs.IR"
] | Retrieving points based on proximity in a high-dimensional vector space is a crucial step in information retrieval applications. The approximate nearest neighbor search (ANNS) problem, which identifies the $k$ nearest neighbors for a query (approximately, since exactly is hard), has been extensively studied in recent y... | {
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2502.13246 | When People are Floods: Analyzing Dehumanizing Metaphors in Immigration
Discourse with Large Language Models | [
"cs.CL",
"cs.CY"
] | Metaphor, discussing one concept in terms of another, is abundant in politics and can shape how people understand important issues. We develop a computational approach to measure metaphorical language, focusing on immigration discourse on social media. Grounded in qualitative social science research, we identify seven ... | {
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2502.13247 | Grounding LLM Reasoning with Knowledge Graphs | [
"cs.CL"
] | Knowledge Graphs (KGs) are valuable tools for representing relationships between entities in a structured format. Traditionally, these knowledge bases are queried to extract specific information. However, question-answering (QA) over such KGs poses a challenge due to the intrinsic complexity of natural language compare... | {
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2502.13248 | Communication Strategy on Macro-and-Micro Traffic State in Cooperative
Deep Reinforcement Learning for Regional Traffic Signal Control | [
"cs.MA",
"cs.AI",
"cs.LG"
] | Adaptive Traffic Signal Control (ATSC) has become a popular research topic in intelligent transportation systems. Regional Traffic Signal Control (RTSC) using the Multi-agent Deep Reinforcement Learning (MADRL) technique has become a promising approach for ATSC due to its ability to achieve the optimum trade-off betwee... | {
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2502.13249 | Evidence of Replica Symmetry Breaking under the Nishimori conditions in
epidemic inference on graphs | [
"cond-mat.dis-nn",
"cond-mat.stat-mech",
"cs.IT",
"cs.LG",
"math.IT",
"physics.soc-ph"
] | In Bayesian inference, computing the posterior distribution from the data is typically a non-trivial problem, which usually requires approximations such as mean-field approaches or numerical methods, like the Monte Carlo Markov Chain. Being a high-dimensional distribution over a set of correlated variables, the posteri... | {
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2502.13251 | Neural Attention Search | [
"cs.CL",
"cs.AI"
] | We present Neural Attention Search (NAtS), a framework that automatically evaluates the importance of each token within a sequence and determines if the corresponding token can be dropped after several steps. This approach can efficiently reduce the KV cache sizes required by transformer-based models during inference a... | {
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2502.13252 | Multilingual Language Model Pretraining using Machine-translated Data | [
"cs.CL"
] | High-resource languages such as English, enables the pretraining of high-quality large language models (LLMs). The same can not be said for most other languages as LLMs still underperform for non-English languages, likely due to a gap in the quality and diversity of the available multilingual pretraining corpora. In th... | {
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2502.13255 | PCB Renewal: Iterative Reuse of PCB Substrates for Sustainable
Electronic Making | [
"cs.HC",
"cs.CY",
"cs.RO"
] | PCB (printed circuit board) substrates are often single-use, leading to material waste in electronics making. We introduce PCB Renewal, a novel technique that "erases" and "reconfigures" PCB traces by selectively depositing conductive epoxy onto outdated areas, transforming isolated paths into conductive planes that su... | {
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2502.13256 | A Survey of Anomaly Detection in Cyber-Physical Systems | [
"cs.CR",
"cs.AI"
] | In our increasingly interconnected world, Cyber-Physical Systems (CPS) play a crucial role in industries like healthcare, transportation, and manufacturing by combining physical processes with computing power. These systems, however, face many challenges, especially regarding security and system faults. Anomalies in CP... | {
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2502.13257 | Random Forest Autoencoders for Guided Representation Learning | [
"cs.LG"
] | Decades of research have produced robust methods for unsupervised data visualization, yet supervised visualization$\unicode{x2013}$where expert labels guide representations$\unicode{x2013}$remains underexplored, as most supervised approaches prioritize classification over visualization. Recently, RF-PHATE, a diffusion-... | {
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2502.13259 | HumT DumT: Measuring and controlling human-like language in LLMs | [
"cs.CL",
"cs.AI",
"cs.CY"
] | Should LLMs generate language that makes them seem human? Human-like language might improve user experience, but might also lead to overreliance and stereotyping. Assessing these potential impacts requires a systematic way to measure human-like tone in LLM outputs. We introduce HumT and SocioT, metrics for human-like t... | {
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2502.13260 | Stepwise Perplexity-Guided Refinement for Efficient Chain-of-Thought
Reasoning in Large Language Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Chain-of-Thought (CoT) reasoning, which breaks down complex tasks into intermediate reasoning steps, has significantly enhanced the performance of large language models (LLMs) on challenging tasks. However, the detailed reasoning process in CoT often incurs long generation times and high computational costs, partly due... | {
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2502.13263 | Spectral method for low-dose Poisson and Bernoulli phase retrieval | [
"cs.IT",
"math.IT",
"math.PR"
] | We consider the problem of phaseless reconstruction from measurements with Poisson or Bernoulli distributed noise. This is of particular interest in biological imaging experiments where a low dose of radiation has to be used to mitigate potential damage of the specimen, resulting in low observed particle counts. We der... | {
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2502.13266 | A Machine Learning Approach That Beats Large Rubik's Cubes | [
"cs.LG",
"cs.DM"
] | The paper proposes a novel machine learning-based approach to the pathfinding problem on extremely large graphs. This method leverages diffusion distance estimation via a neural network and uses beam search for pathfinding. We demonstrate its efficiency by finding solutions for 4x4x4 and 5x5x5 Rubik's cubes with unprec... | {
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2502.13267 | BeforeIT.jl: High-Performance Agent-Based Macroeconomics Made Easy | [
"cs.MA",
"cs.CE",
"econ.GN",
"q-fin.EC"
] | BeforeIT is an open-source software for building and simulating state-of-the-art macroeconomic agent-based models (macro ABMs) based on the recently introduced macro ABM developed in [1] and here referred to as the base model. Written in Julia, it combines extraordinary computational efficiency with user-friendliness a... | {
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} |
2502.13268 | Talking About the Assumption in the Room | [
"cs.HC",
"cs.LG"
] | The reference to assumptions in how practitioners use or interact with machine learning (ML) systems is ubiquitous in HCI and responsible ML discourse. However, what remains unclear from prior works is the conceptualization of assumptions and how practitioners identify and handle assumptions throughout their workflows.... | {
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} |
2502.13270 | REALTALK: A 21-Day Real-World Dataset for Long-Term Conversation | [
"cs.CL"
] | Long-term, open-domain dialogue capabilities are essential for chatbots aiming to recall past interactions and demonstrate emotional intelligence (EI). Yet, most existing research relies on synthetic, LLM-generated data, leaving open questions about real-world conversational patterns. To address this gap, we introduce ... | {
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} |
2502.13277 | HyperGCL: Multi-Modal Graph Contrastive Learning via Learnable
Hypergraph Views | [
"cs.LG",
"cs.AI"
] | Recent advancements in Graph Contrastive Learning (GCL) have demonstrated remarkable effectiveness in improving graph representations. However, relying on predefined augmentations (e.g., node dropping, edge perturbation, attribute masking) may result in the loss of task-relevant information and a lack of adaptability t... | {
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} |
2502.13278 | Performance Evaluation of Sentiment Analysis on Text and Emoji Data
Using End-to-End, Transfer Learning, Distributed and Explainable AI Models | [
"cs.CL",
"cs.AI"
] | Emojis are being frequently used in todays digital world to express from simple to complex thoughts more than ever before. Hence, they are also being used in sentiment analysis and targeted marketing campaigns. In this work, we performed sentiment analysis of Tweets as well as on emoji dataset from the Kaggle. Since tw... | {
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} |
2502.13280 | Value Gradient Sampler: Sampling as Sequential Decision Making | [
"cs.LG"
] | We propose the Value Gradient Sampler (VGS), a trainable sampler based on the interpretation of sampling as discrete-time sequential decision-making. VGS generates samples from a given unnormalized density (i.e., energy) by drifting and diffusing randomly initialized particles. In VGS, finding the optimal drift is equi... | {
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} |
2502.13283 | Benefits of Early Stopping in Gradient Descent for Overparameterized
Logistic Regression | [
"cs.LG",
"stat.ML"
] | In overparameterized logistic regression, gradient descent (GD) iterates diverge in norm while converging in direction to the maximum $\ell_2$-margin solution -- a phenomenon known as the implicit bias of GD. This work investigates additional regularization effects induced by early stopping in well-specified high-dimen... | {
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} |
2502.13285 | Task Shift: From Classification to Regression in Overparameterized
Linear Models | [
"stat.ML",
"cs.LG"
] | Modern machine learning methods have recently demonstrated remarkable capability to generalize under task shift, where latent knowledge is transferred to a different, often more difficult, task under a similar data distribution. We investigate this phenomenon in an overparameterized linear regression setting where the ... | {
"Other": 0,
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} |
2502.13286 | BoundPlanner: A convex-set-based approach to bounded manipulator
trajectory planning | [
"cs.RO"
] | Online trajectory planning enables robot manipulators to react quickly to changing environments or tasks. Many robot trajectory planners exist for known environments but are often too slow for online computations. Current methods in online trajectory planning do not find suitable trajectories in challenging scenarios t... | {
"Other": 0,
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} |
2502.13287 | Breaking the bonds of generative artificial intelligence by minimizing
the maximum entropy | [
"cs.LG",
"cond-mat.stat-mech",
"cs.IT",
"math.IT"
] | The emergence of generative artificial intelligence (GenAI), comprising large language models, text-to-image generators, and AI algorithms for medical drug and material design, had a transformative impact on society. However, despite an initial exponential growth surpassing Moore's law, progress is now plateauing, sugg... | {
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} |
2502.13289 | Multiple Distribution Shift -- Aerial (MDS-A): A Dataset for Test-Time
Error Detection and Model Adaptation | [
"cs.LG"
] | Machine learning models assume that training and test samples are drawn from the same distribution. As such, significant differences between training and test distributions often lead to degradations in performance. We introduce Multiple Distribution Shift -- Aerial (MDS-A) -- a collection of inter-related datasets of ... | {
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} |
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