Search: “safety”

Incentivizing High-Quality Human Annotations with Golden Questions

arXiv:2505.19134v2 Announce Type: replace-cross Abstract: Human-annotated data plays a vital role in training large language models (LLMs), such as supervised fine-tuning and human preference alignmen

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Information-Geometric Decomposition of Generalization Error in Unsupervised Learning

DGX agent

arXiv:2604.12340v1 Announce Type: cross Abstract: We decompose the Kullback--Leibler generalization error (GE) -- the expected KL divergence from the data distribution to the trained model -- of unsup

safetyarxiv-cs-lg

15 Apr 2026

InsightFlow: LLM-Driven Synthesis of Patient Narratives for Mental Health into Causal Models

arXiv:2604.12721v1 Announce Type: new Abstract: Clinical case formulation organizes patient symptoms and psychosocial factors into causal models, often using the 5P framework. However, constructing su

Labeled TrustSet Guided: Batch Active Learning with Reinforcement Learning

arXiv:2604.12303v1 Announce Type: new Abstract: Batch active learning (BAL) is a crucial technique for reducing labeling costs and improving data efficiency in training large-scale deep learning model

Learning step-level dynamic soaring in shear flow

arXiv:2604.12413v1 Announce Type: cross Abstract: Dynamic soaring enables sustained flight by extracting energy from wind shear, yet it is commonly understood as a cycle-level maneuver that assumes st

Learning Versatile Humanoid Manipulation with Touch Dreaming

arXiv:2604.13015v1 Announce Type: new Abstract: Humanoid robots promise general-purpose assistance, yet real-world humanoid loco-manipulation remains challenging because it requires whole-body stabili

Lightning OPD: Efficient Post-Training for Large Reasoning Models with Offline On-Policy Distillation

arXiv:2604.13010v1 Announce Type: cross Abstract: On-policy distillation (OPD) has emerged as an efficient post-training paradigm for large language models. However, standard OPD requires a live teach

LiveMoments: Reselected Key Photo Restoration in Live Photos via Reference-guided Diffusion

arXiv:2604.12286v1 Announce Type: new Abstract: Live Photo captures both a high-quality key photo and a short video clip to preserve the precious dynamics around the captured moment. While users may c

Man and machine: artificial intelligence and judicial decision making

arXiv:2603.19042v4 Announce Type: replace Abstract: The integration of artificial intelligence (AI) technologies into judicial decision-making, particularly in pretrial, sentencing, and parole context

Meet Dynamic Individual Preferences: Resolving Conflicting Human Value with Paired Fine-Tuning

arXiv:2604.12479v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have significantly improved the alignment of models with general human preferences. However, a major cha

Models Know Their Shortcuts: Deployment-Time Shortcut Mitigation

arXiv:2604.12277v1 Announce Type: new Abstract: Pretrained language models often rely on superficial features that appear predictive during training yet fail to generalize at test time, a phenomenon k

MODIX: A Training-Free Multimodal Information-Driven Positional Index Scaling for Vision-Language Models

arXiv:2604.12537v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have achieved remarkable progress in multimodal understanding, yet their positional encoding mechanisms remain suboptima

MolMem: Memory-Augmented Agentic Reinforcement Learning for Sample-Efficient Molecular Optimization

arXiv:2604.12237v1 Announce Type: cross Abstract: In drug discovery, molecular optimization aims to iteratively refine a lead compound to improve molecular properties while preserving structural simil

Mutual Information Surprise: Rethinking Unexpectedness in Autonomous Systems

arXiv:2508.17403v3 Announce Type: replace Abstract: A community of researchers appears to think that a machine can be surprised and have introduced various surprise measures, principally the Shannon S

No More Stale Feedback: Co-Evolving Critics for Open-World Agent Learning

arXiv:2601.06794v2 Announce Type: replace Abstract: Critique-guided reinforcement learning (RL) has emerged as a powerful paradigm for training LLM agents by augmenting sparse outcome rewards with nat

Not All Turns Are Equally Hard: Adaptive Thinking Budgets For Efficient Multi-Turn Reasoning

arXiv:2604.05164v2 Announce Type: replace-cross Abstract: As LLM reasoning performance plateau, improving inference-time compute efficiency is crucial to mitigate overthinking and long thinking traces

Offline-Online Reinforcement Learning for Linear Mixture MDPs

arXiv:2604.11994v1 Announce Type: new Abstract: We study offline-online reinforcement learning in linear mixture Markov decision processes (MDPs) under environment shift. In the offline phase, data ar

PAINT: Partner-Agnostic Intent-Aware Cooperative Transport with Legged Robots

arXiv:2604.12852v1 Announce Type: new Abstract: Collaborative transport requires robots to infer partner intent through physical interaction while maintaining stable loco-manipulation. This becomes pa

Perception-Aware Policy Optimization for Multimodal Reasoning

arXiv:2507.06448v5 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has proven to be a highly effective strategy for endowing Large Language Models (LLMs) with ro

PR-MaGIC: Prompt Refinement Via Mask Decoder Gradient Flow For In-Context Segmentation

arXiv:2604.12113v1 Announce Type: cross Abstract: Visual Foundation Models (VFMs) such as the Segment Anything Model (SAM) have significantly advanced broad use of image segmentation. However, SAM and

Progress-Think: Semantic Progress Reasoning for Vision-Language Navigation

arXiv:2511.17097v2 Announce Type: replace Abstract: Vision-Language Navigation requires agents to act coherently over long horizons by understanding not only local visual context but also how far they

PubSwap: Public-Data Off-Policy Coordination for Federated RLVR

arXiv:2604.12160v1 Announce Type: new Abstract: Reasoning post-training with reinforcement learning from verifiable rewards (RLVR) is typically studied in centralized settings, yet many realistic appl

Redefining Quality Criteria and Distance-Aware Score Modeling for Image Editing Assessment

arXiv:2604.12175v1 Announce Type: new Abstract: Recent advances in image editing have heightened the need for reliable Image Editing Quality Assessment (IEQA). Unlike traditional methods, IEQA require

Relaxing Anchor-Frame Dominance for Mitigating Hallucinations in Video Large Language Models

arXiv:2604.12582v1 Announce Type: new Abstract: Recent Video Large Language Models (Video-LLMs) have demonstrated strong capability in video understanding, yet they still suffer from hallucinations. E

Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe

arXiv:2604.13016v1 Announce Type: cross Abstract: On-policy distillation (OPD) has become a core technique in the post-training of large language models, yet its training dynamics remain poorly unders

Retrieval as a Decision: Training-Free Adaptive Gating for Efficient RAG

arXiv:2511.09803v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) improves factuality but retrieving for every query often hurts quality while inflating tokens and latency. We p

SAM3-I: Segment Anything with Instructions

arXiv:2512.04585v3 Announce Type: replace Abstract: Segment Anything Model 3 (SAM3) advances open-vocabulary segmentation through promptable concept segmentation, enabling users to segment all instanc

Scaffold-Conditioned Preference Triplets for Controllable Molecular Optimization with Large Language Models

arXiv:2604.12350v1 Announce Type: cross Abstract: Molecular property optimization is central to drug discovery, yet many deep learning methods rely on black-box scoring and offer limited control over

Scalable and General Whole-Body Control for Cross-Humanoid Locomotion

arXiv:2602.05791v2 Announce Type: replace Abstract: Learning-based whole-body controllers have become a key driver for humanoid robots, yet most existing approaches require robot-specific training. In

Schema-Adaptive Tabular Representation Learning with LLMs for Generalizable Multimodal Clinical Reasoning

arXiv:2604.11835v1 Announce Type: cross Abstract: Machine learning for tabular data remains constrained by poor schema generalization, a challenge rooted in the lack of semantic understanding of struc

Self-Distillation Zero: Self-Revision Turns Binary Rewards into Dense Supervision

arXiv:2604.12002v1 Announce Type: new Abstract: Current post-training methods in verifiable settings fall into two categories. Reinforcement learning (RLVR) relies on binary rewards, which are broadly

Simulation as Supervision: Mechanistic Pretraining for Scientific Discovery

arXiv:2507.08977v4 Announce Type: replace-cross Abstract: Scientific modeling faces a tradeoff between the interpretability of mechanistic theory and the predictive power of machine learning. While ex

SOAR: Self-Correction for Optimal Alignment and Refinement in Diffusion Models

arXiv:2604.12617v1 Announce Type: cross Abstract: The post-training pipeline for diffusion models currently has two stages: supervised fine-tuning (SFT) on curated data and reinforcement learning (RL)

StableSketcher: Enhancing Diffusion Model for Pixel-based Sketch Generation via Visual Question Answering Feedback

arXiv:2510.20093v2 Announce Type: replace-cross Abstract: Although recent advancements in diffusion models have significantly enriched the quality of generated images, challenges remain in synthesizin

Task Alignment: A simple and effective proxy for model merging in computer vision

arXiv:2604.12935v1 Announce Type: new Abstract: Efficiently merging several models fine-tuned for different tasks, but stemming from the same pretrained base model, is of great practical interest. Des

Teaching LLMs Human-Like Editing of Inappropriate Argumentation via Reinforcement Learning

arXiv:2604.12770v1 Announce Type: new Abstract: Editing human-written text has become a standard use case of large language models (LLMs), for example, to make one's arguments more appropriate for a d

The role of System 1 and System 2 semantic memory structure in human and LLM biases

arXiv:2604.12816v1 Announce Type: new Abstract: Implicit biases in both humans and large language models (LLMs) pose significant societal risks. Dual process theories propose that biases arise primari

The Stackelberg Speaker: Optimizing Persuasive Communication in Social Deduction Games

arXiv:2510.09087v2 Announce Type: replace Abstract: Large language model (LLM) agents have shown remarkable progress in social deduction games (SDGs). However, existing approaches primarily focus on i

Thinking Sparks!: Emergent Attention Heads in Reasoning Models During Post Training

arXiv:2509.25758v2 Announce Type: replace Abstract: The remarkable capabilities of modern large reasoning models are largely unlocked through post-training techniques such as supervised fine-tuning (S

Token-Level Policy Optimization: Linking Group-Level Rewards to Token-Level Aggregation via Sequence-Level Likelihood

arXiv:2604.12736v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) has significantly advanced the reasoning ability of large language models (LLMs), particularly in their mathem

Towards Generalized Certified Robustness with Multi-Norm Training

arXiv:2410.03000v3 Announce Type: replace Abstract: Existing certified training methods can only train models to be robust against a certain perturbation type (e.g. l_infty or l_2). However, an l_inft

Towards Platonic Representation for Table Reasoning: A Foundation for Permutation-Invariant Retrieval

arXiv:2604.12133v1 Announce Type: new Abstract: Historical approaches to Table Representation Learning (TRL) have largely adopted the sequential paradigms of Natural Language Processing (NLP). We argu

WebChain: A Large-Scale Human-Annotated Dataset of Real-World Web Interaction Traces

arXiv:2603.05295v3 Announce Type: replace Abstract: We introduce WebChain, the largest open-source dataset of human-annotated trajectories on real-world websites, designed to accelerate reproducible r

Whole-Body Mobile Manipulation using Offline Reinforcement Learning on Sub-optimal Controllers

arXiv:2604.12509v1 Announce Type: cross Abstract: Mobile Manipulation (MoMa) of articulated objects, such as opening doors, drawers, and cupboards, demands simultaneous, whole-body coordination betwee

WiseOWL: A Methodology for Evaluating Ontological Descriptiveness and Semantic Correctness for Ontology Reuse and Ontology Recommendations

arXiv:2604.12025v1 Announce Type: new Abstract: The Semantic Web standardizes concept meaning for humans and machines, enabling machine-operable content and consistent interpretation that improves adv

XRZero-G0: Pushing the Frontier of Dexterous Robotic Manipulation with Interfaces, Quality and Ratios

arXiv:2604.13001v1 Announce Type: new Abstract: The acquisition of high-quality, action-aligned demonstration data remains a fundamental bottleneck in scaling foundation models for dexterous robot man

3D Multi-View Stylization with Pose-Free Correspondences Matching for Robust 3D Geometry Preservation

arXiv:2604.09639v1 Announce Type: new Abstract: Artistic style transfer is well studied for images and videos, but extending it to multi-view 3D scenes remains difficult because stylization can disrup

14 Apr 2026

A Comparative Theoretical Analysis of Entropy Control Methods in Reinforcement Learning

arXiv:2604.09676v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a key approach for enhancing reasoning in large language models (LLMs), yet scalable training is often hindered