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A Survey of Scaling in Large Language Model Reasoning

arXiv:2504.02181v2 Announce Type: replace Abstract: The rapid advancements in large Language models (LLMs) have significantly enhanced their reasoning capabilities, driven by various strategies such a

A Synchronized Audio-Visual Multi-View Capture System

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DGX agent

arXiv:2603.23089v2 Announce Type: replace Abstract: Multi-view capture systems have been an important tool in research for recording human motion under controlling conditions. Most existing systems ar

safetyarxiv-cs-cv

23 Apr 2026

AdaTracker: Learning Adaptive In-Context Policy for Cross-Embodiment Active Visual Tracking

arXiv:2604.20305v1 Announce Type: new Abstract: Realizing active visual tracking with a single unified model across diverse robots is challenging, as the physical constraints and motion dynamics vary

AI models of unstable flow exhibit hallucination

arXiv:2604.20372v1 Announce Type: cross Abstract: We report the first systematic evidence of hallucination in AI models of fluid dynamics, demonstrated in the canonical problem of hydrodynamically uns

All Languages Matter: Understanding and Mitigating Language Bias in Multilingual RAG

arXiv:2604.20199v1 Announce Type: new Abstract: Multilingual Retrieval-Augmented Generation (mRAG) leverages cross-lingual evidence to ground Large Language Models (LLMs) in global knowledge. However,

Auto-ART: Structured Literature Synthesis and Automated Adversarial Robustness Testing

arXiv:2604.20704v1 Announce Type: cross Abstract: Adversarial robustness evaluation underpins every claim of trustworthy ML deployment, yet the field suffers from fragmented protocols and undetected g

Avoiding Overthinking and Underthinking: Curriculum-Aware Budget Scheduling for LLMs

arXiv:2604.19780v1 Announce Type: new Abstract: Scaling test-time compute via extended reasoning has become a key paradigm for improving the capabilities of large language models (LLMs). However, exis

Best Policy Learning from Trajectory Preference Feedback

arXiv:2501.18873v4 Announce Type: replace Abstract: Reinforcement Learning from Human Feedback (RLHF) has emerged as a powerful approach for aligning generative models, but its reliance on learned rew

Bias in the Tails: How Name-conditioned Evaluative Framing in Resume Summaries Destabilizes LLM-based Hiring

arXiv:2604.19984v1 Announce Type: cross Abstract: Research has documented LLMs' name-based bias in hiring and salary recommendations. In this paper, we instead consider a setting where LLMs generate c

Breaking the Assistant Mold: Modeling Behavioral Variation in LLM Based Procedural Character Generation

arXiv:2601.03396v2 Announce Type: replace Abstract: Procedural content generation has enabled vast virtual worlds through levels, maps, and quests, but large-scale character generation remains underex

CAST: Achieving Stable LLM-based Text Analysis for Data Analytics

arXiv:2602.15861v2 Announce Type: replace-cross Abstract: Text analysis of tabular data relies on two core operations: summarization for corpus-level theme extraction and tagging for row-level labelin

ChipCraftBrain: Validation-First RTL Generation via Multi-Agent Orchestration

arXiv:2604.19856v1 Announce Type: cross Abstract: Large Language Models (LLMs) show promise for generating Register-Transfer Level (RTL) code from natural language specifications, but single-shot gene

CLIP-RD: Relative Distillation for Efficient CLIP Knowledge Distillation

arXiv:2603.25383v3 Announce Type: replace Abstract: CLIP aligns image and text embeddings via contrastive learning and demonstrates strong zero-shot generalization. Its large-scale architecture requir

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment

arXiv:2510.18471v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) excel at code generation by learning from vast code corpora, a fundamental semantic gap remains between the

Cognitive Alignment At No Cost: Inducing Human Attention Biases For Interpretable Vision Transformers

arXiv:2604.20027v1 Announce Type: cross Abstract: For state-of-the-art image understanding, Vision Transformers (ViTs) have become the standard architecture but their processing diverges substantially

CoRe: Joint Optimization with Contrastive Learning for Medical Image Registration

arXiv:2603.23694v2 Announce Type: replace Abstract: Medical image registration is a fundamental task in medical image analysis, enabling the alignment of images from different modalities or time point

Coverage, Not Averages: Semantic Stratification for Trustworthy Retrieval Evaluation

arXiv:2604.20763v1 Announce Type: cross Abstract: Retrieval quality is the primary bottleneck for accuracy and robustness in retrieval-augmented generation (RAG). Current evaluation relies on heuristi

CubeDAgger: Interactive Imitation Learning for Dynamic Systems with Efficient yet Low-risk Interaction

arXiv:2505.04897v2 Announce Type: replace-cross Abstract: Interactive imitation learning makes an agent's control policy robust by stepwise supervisions from an expert. The recent algorithms mostly em

Decentralized Machine Learning with Centralized Performance Guarantees via Gibbs Algorithms

arXiv:2604.20492v1 Announce Type: cross Abstract: In this paper, it is shown, for the first time, that centralized performance is achievable in decentralized learning without sharing the local dataset

Distributional Inverse Reinforcement Learning

arXiv:2510.03013v3 Announce Type: replace Abstract: We propose a distributional framework for offline Inverse Reinforcement Learning (IRL) that jointly models uncertainty over reward functions and ful

DRIV-EX: Counterfactual Explanations for Driving LLMs

arXiv:2603.00696v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as reasoning engines in autonomous driving, yet their decision-making remains opaque. We propose

Efficient Reinforcement Learning using Linear Koopman Dynamics for Nonlinear Robotic Systems

arXiv:2604.19980v1 Announce Type: new Abstract: This paper presents a model-based reinforcement learning (RL) framework for optimal closed-loop control of nonlinear robotic systems. The proposed appro

Efficiently Closing Loops in LiDAR-Based SLAM Using Point Cloud Density Maps

arXiv:2501.07399v2 Announce Type: replace Abstract: Consistent maps are key for most autonomous mobile robots, and they often use SLAM approaches to build such maps. Loop closures via place recognitio

EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training

arXiv:2604.20012v1 Announce Type: cross Abstract: Vision-Language-Action Models (VLAs) inherit their visual and linguistic capabilities from Vision-Language Models (VLMs), yet most VLAs are built from

Epistemic Constitutionalism Or: how to avoid coherence bias

arXiv:2601.14295v3 Announce Type: replace Abstract: Large language models increasingly function as artificial reasoners: they evaluate arguments, assign credibility, and express confidence. Yet their

Epistemology gives a Future to Complementarity in Human-AI Interactions

arXiv:2601.09871v2 Announce Type: replace Abstract: Human-AI complementarity is the claim that a human supported by an AI system can outperform either alone in a decision-making process. Since its int

ETac: A Lightweight and Efficient Tactile Simulation Framework for Learning Dexterous Manipulation

arXiv:2604.20295v1 Announce Type: new Abstract: Tactile sensors are increasingly integrated into dexterous robotic manipulators to enhance contact perception. However, learning manipulation policies t

Evaluating Black-Box Vulnerabilities with Wasserstein-Constrained Data Perturbations

arXiv:2603.15867v2 Announce Type: replace Abstract: The growing use of Machine Learning (ML) tools comes with critical challenges, such as limited model explainability. We propose a global explainabil

Explainable Speech Emotion Recognition: Weighted Attribute Fairness to Model Demographic Contributions to Social Bias

arXiv:2604.19763v1 Announce Type: cross Abstract: Speech Emotion Recognition (SER) systems have growing applications in sensitive domains such as mental health and education, where biased predictions

Fairness-Aware Multi-Group Target Detection in Online Discussion

arXiv:2407.11933v4 Announce Type: replace Abstract: Target-group detection is the task of detecting which group(s) a piece of content is ``directed at or about''. Applications include targeted marketi

Faster Fixed-Point Methods for Multichain MDPs

arXiv:2506.20910v2 Announce Type: replace-cross Abstract: We study value-iteration (VI) algorithms for solving general (a.k.a. multichain) Markov decision processes (MDPs) under the average-reward cri

FingerEye: Continuous and Unified Vision-Tactile Sensing for Dexterous Manipulation

arXiv:2604.20689v1 Announce Type: new Abstract: Dexterous robotic manipulation requires comprehensive perception across all phases of interaction: pre-contact, contact initiation, and post-contact. Su

FluSplat: Sparse-View 3D Editing without Test-Time Optimization

arXiv:2604.20038v1 Announce Type: new Abstract: Recent advances in text-guided image editing and 3D Gaussian Splatting (3DGS) have enabled high-quality 3D scene manipulation. However, existing pipelin

Frictionless Love: Associations Between AI Companion Roles and Behavioral Addiction

arXiv:2604.20011v1 Announce Type: cross Abstract: AI companion chatbots increasingly shape how people seek social and emotional connection, sometimes substituting for relationships with romantic partn

FurnSet: Exploiting Repeats for 3D Scene Reconstruction

arXiv:2604.20093v1 Announce Type: new Abstract: Single-view 3D scene reconstruction involves inferring both object geometry and spatial layout. Existing methods typically reconstruct objects independe

Generative Flow Networks for Model Adaptation in Digital Twins of Natural Systems

arXiv:2604.20707v1 Announce Type: new Abstract: Digital twins of natural systems must remain aligned with physical systems that evolve over time, are only partially observed, and are typically modeled

GRPO-VPS: Enhancing Group Relative Policy Optimization with Verifiable Process Supervision for Effective Reasoning

arXiv:2604.20659v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has advanced the reasoning capabilities of Large Language Models (LLMs) by leveraging direct out

Hidden Reliability Risks in Large Language Models: Systematic Identification of Precision-Induced Output Disagreements

arXiv:2604.19790v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed under diverse numerical precision configurations, including standard floating-point formats (e.g.

Hybrid Latent Reasoning with Decoupled Policy Optimization

arXiv:2604.20328v1 Announce Type: new Abstract: Chain-of-Thought (CoT) reasoning significantly elevates the complex problem-solving capabilities of multimodal large language models (MLLMs). However, a

Hybrid Policy Distillation for LLMs

arXiv:2604.20244v1 Announce Type: cross Abstract: Knowledge distillation (KD) is a powerful paradigm for compressing large language models (LLMs), whose effectiveness depends on intertwined choices of

i-WiViG: Interpretable Window Vision GNN

arXiv:2503.08321v2 Announce Type: replace Abstract: Vision graph neural networks have emerged as a popular approach for modeling the global and spatial context for image recognition. However, a signif

Kalman Filter Enhanced GRPO for Reinforcement Learning-Based Language Model Reasoning

arXiv:2505.07527v5 Announce Type: replace Abstract: The advantage function is a central concept in RL that helps reduce variance in policy gradient estimates. For language modeling, Group Relative Pol

Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented Generation

arXiv:2601.14896v2 Announce Type: replace Abstract: Multilingual retrieval-augmented generation (MRAG) requires models to effectively acquire and integrate beneficial external knowledge from multiling

Learning to count small and clustered objects with application to bacterial colonies

arXiv:2604.20030v1 Announce Type: new Abstract: Automated bacterial colony counting from images is an important technique to obtain data required for the development of vaccines and antibiotics. Howev

Lever: Inference-Time Policy Reuse under Support Constraints

arXiv:2604.20174v1 Announce Type: new Abstract: Reinforcement learning (RL) policies are typically trained for fixed objectives, making reuse difficult when task requirements change. We study inferenc

LLM Agents Predict Social Media Reactions but Do Not Outperform Text Classifiers: Benchmarking Simulation Accuracy Using 120K+ Personas of 1511 Humans

arXiv:2604.19787v1 Announce Type: cross Abstract: Social media platforms mediate how billions form opinions and engage with public discourse. As autonomous AI agents increasingly participate in these

MATT-Diff: Multimodal Active Target Tracking by Diffusion Policy

arXiv:2511.11931v2 Announce Type: replace Abstract: This paper proposes MATT-Diff: Multimodal Active Target Tracking by Diffusion Policy, a control policy for active multi-target tracking using a mobi

Membership Inference for Contrastive Pre-training Models with Text-only PII Queries

arXiv:2603.14222v2 Announce Type: replace-cross Abstract: Contrastive pretraining models such as CLIP and CLAP, serve as the ubiquitous perceptual backbones for modern multimodal large models, yet the