Search: “safety”

EAPO: Enhancing Policy Optimization with On-Demand Expert Assistance

arXiv:2509.23730v2 Announce Type: replace Abstract: Large language models (LLMs) have recently advanced in reasoning when optimized with reinforcement learning (RL) under verifiable rewards. Existing

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Emergent Semantic Representations in World Models through Physical Interaction without Linguistic Supervision

DGX agent

arXiv:2605.28865v1 Announce Type: cross Abstract: What does a world model learn from physical exploration, without any linguistic supervision? We argue the answer is organized by a single principle: t

safetyarxiv-cs-ai

29 May 2026

Entropy-KL Divergence-based Token Masking: A Novel Approach for Selective Fine-tuning of Large Language Models

arXiv:2605.29303v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) followed by reinforcement learning (RL) has become a standard post-training paradigm for large language models. This paradi

EPiC: Efficient Video Camera Control Learning with Precise Anchor-Video Guidance

arXiv:2505.21876v2 Announce Type: replace-cross Abstract: Recent approaches for video generation with camera control often create anchor videos (i.e., rendered videos that approximate desired camera m

Evolutionary Refinement of Generative Graph Topologies: A Hybrid WGAN-GA Approach

arXiv:2605.29161v1 Announce Type: cross Abstract: Generating realistic graph-structured data is challenging due to discrete connectivity, varying graph sizes, and class-specific structural patterns. R

EvoMD-LLM: Learning the Language of Species Evolution in Reactive Molecular Dynamics

arXiv:2605.29394v1 Announce Type: new Abstract: While large language models (LLMs) excel at static scientific reasoning, they struggle to model the temporal structure of dynamic physical processes. We

EvoRubric: Self-Evolving Rubric-Driven RL for Open-Ended Generation

arXiv:2605.29847v1 Announce Type: new Abstract: Reinforcement Learning (RL) has significantly advanced Large Language Models (LLMs) in verifiable domains, but aligning models for open-ended generation

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection

arXiv:2605.30062v1 Announce Type: new Abstract: The development of generative artificial intelligence technologies has propelled the visual realism of synthetic images to an unprecedented level. Altho

Fewer Steps, Better Performance: Efficient Cross-Modal Clip Trimming for Video Moment Retrieval Using Language

arXiv:2605.29793v1 Announce Type: new Abstract: Given an untrimmed video and a sentence query, video moment retrieval using language (VMR) aims to locate a target query-relevant moment. Since the untr

Fisher-Preserving Guidance: Training-Free Manifold Constraints for Safe Diffusion Control

arXiv:2605.29937v1 Announce Type: cross Abstract: Diffusion models are effective for waypoint prediction in visual navigation, but standard sampling and test time guidance can produce unreliable or in

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation

arXiv:2605.29461v1 Announce Type: new Abstract: LLM-conditioned segmentation has recently advanced rapidly by coupling large language models with iterative mask generation frameworks. However, we iden

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality

arXiv:2510.12152v2 Announce Type: replace-cross Abstract: We study the decoupled multi-armed bandit problem, where the learner separately selects one arm for exploration and one, possibly different, a

From Context Shift to Stylistic Collapse: Why Training Objectives Matter More Than Scale

arXiv:2605.28826v1 Announce Type: new Abstract: In modern LLMs, linguistic features function not as stylistic artifacts but as probes of probability mass, allocated under training alignment objectives

Future Forcing: Future-aware Training-free KV Cache Policy for Autoregressive Video Generation

arXiv:2605.30083v1 Announce Type: new Abstract: Autoregressive (AR) video generation has emerged as a promising paradigm for long-horizon video synthesis, where each frame is generated conditioned on

GAP3D: Generative Alignment of VLM Latents to Patch-Level Embeddings for 3D Generation

arXiv:2605.28995v1 Announce Type: new Abstract: Recent approaches integrating vision-language models (VLMs) as prompt encoders for generative model conditioning typically rely on expensive end-to-end

GAPD: Gold-Action Policy Distillation for Agentic Reinforcement Learning in Knowledge Base Question Answering

arXiv:2605.29584v1 Announce Type: new Abstract: Reinforcement learning (RL) is a natural fit for agentic knowledge base question answering (KBQA), where a model must issue executable actions, observe

GASS: Geometry-Aware Spherical Sampling for Disentangled Diversity Enhancement in Text-to-Image Generation

arXiv:2602.17200v2 Announce Type: replace Abstract: Despite high semantic alignment, modern text-to-image (T2I) generative models still struggle to synthesize diverse images from a given prompt. In th

Gaze2Act: Gaze-Conditioned Vision-Language-Action Policies for Interactive Robot Manipulation

arXiv:2605.30282v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have recently shown strong potential for robot learning by following language instructions. However, in practice, la

safetyarxiv-cs-ro

GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models

arXiv:2605.29398v1 Announce Type: cross Abstract: Reinforcement learning (RL) can be used to improve the policy (denoiser) of diffusion large language models (dLLMs), while being hindered by the intra

Genetically Aligned Patient Representations Improve Hematological Diagnosis

arXiv:2605.29980v1 Announce Type: cross Abstract: Multimodal alignment of histopathology encoders with transcriptomic and genomic data has been shown to significantly improve performance in downstream

Geometry-Guided Modeling of Foundation Features Enables Generalizable Object Shape Deformation Learning

arXiv:2605.29661v1 Announce Type: new Abstract: Monocular 3D shape recovery is fundamental to geometric understanding, yet achieving robust generalization across arbitrary viewpoints and unseen object

Grammar-Aware Literate Generative Mathematical Programming with Compiler-in-the-Loop

arXiv:2601.17670v2 Announce Type: replace-cross Abstract: Mathematical programming is widely employed across various sectors - such as logistics, energy, and workforce planning - to model and solve in

Graph-Enhanced Policy Optimization in LLM Agent Training

arXiv:2510.26270v2 Announce Type: replace Abstract: Multi-step LLM agents in interactive environments represent a crucial step toward long-horizon decision-making. To train such agents, group-based re

GrepSeek: Training Search Agents for Direct Corpus Interaction

arXiv:2605.29307v1 Announce Type: cross Abstract: Large Language Model (LLM) search agents have shown strong promise for knowledge-intensive language tasks through multiple rounds of reasoning and inf

Grounded 3D-Aware Spatial Vision-Language Modeling

arXiv:2605.30307v1 Announce Type: new Abstract: We present GR3D, a spatial vision language model equipped with three complementary grounding capabilities--explicit 2D grounding, implicit 2D grounding,

GRPO is Secretly a Process Reward Model

arXiv:2509.21154v4 Announce Type: replace-cross Abstract: Process reward models (PRMs) allow for fine-grained credit assignment in reinforcement learning (RL), and seemingly contrast with outcome rewa

GRUFF: LLM Pronoun Fidelity, Reasoning, and Biases in German

arXiv:2605.30214v1 Announce Type: new Abstract: Third-person singular pronouns have long been used to study stereotypical biases in language models and to test their abilities to reason about referenc

Guidance Contrastive Token Credit Assignment for Discrete Policy Optimization

arXiv:2605.29198v1 Announce Type: new Abstract: Group-advantage-based reinforcement learning methods, such as GRPO and DAPO, have demonstrated strong performance across diverse domains, including math

Harnessing non-adversarial robustness in large language models

arXiv:2605.29816v1 Announce Type: new Abstract: The work presents an approach for addressing the challenge of robustness in Large Language Models (LLMs) to alterations and potential errors caused by s

How's it going? Reinforcement learning in language models recruits a functional welfare axis

arXiv:2605.30232v1 Announce Type: cross Abstract: How does reinforcement learning shape a language model's internal representations? We present evidence that RL recruits a pre-existing representation

HPO: Hysteretic Policy Optimization for Stable and Efficient Training under Sparse-Reward Regime

arXiv:2605.30201v1 Announce Type: cross Abstract: We investigate a narrow but common failure mode of GRPO-style reinforcement learning in the context of sparse verifiable rewards: early updates contai

Improving CLIP Adaptation by Breaking Tail Alignment for Source-Free Cross-Domain Few-Shot Learning

arXiv:2605.29776v1 Announce Type: new Abstract: Vision-Language Models (VLMs) such as CLIP demonstrate strong zero-shot generalization, but their performance significantly degrades in cross-domain sce

In-Context Reward Adaptation for Robust Preference Modeling

arXiv:2605.30323v1 Announce Type: cross Abstract: Reinforcement Learning from Human Feedback (RLHF) typically relies on static reward models to align Large Language Models with human preferences. Howe

Inferring Code Correctness from Specification

arXiv:2605.29822v1 Announce Type: cross Abstract: Large language models (LLMs) have become integral to modern software development, enabling automated code generation at scale. However, validating the

Information-Directed Offline-to-Online Reinforcement Learning

arXiv:2605.29405v1 Announce Type: new Abstract: Decision-making from offline datasets typically warm-starts a policy or score model from fixed offline data and then refines it with limited online inte

KGEdit: Ambiguity-Aware Knowledge Graphs for Training-Free Precise Video Generation and Editing

arXiv:2605.29509v1 Announce Type: new Abstract: In recent years, training-free video generation has progressed remarkably. However, when handling complex textual instructions, existing methods still s

Learning A Simulation-based Visual Policy for Real-world Peg In Unseen Holes

arXiv:2205.04297v2 Announce Type: replace-cross Abstract: This paper proposes a learning-based visual peg-in-hole that enables training with several shapes in simulation, and adapting to arbitrary uns

Learning to Choose: An Empowerment-Guided Multi-Agent System with semantic communication for Adaptive Method Selection

arXiv:2605.30042v1 Announce Type: new Abstract: Automating scientific computing workflows requires more than generating executable code: autonomous systems must also select appropriate computational s

Less Is More: Elevating RAG via Performance-Driven Context Compression

arXiv:2508.19282v4 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm for improving the timeliness of knowledge updates and the factual acc

LLM-Guided Future Hypotheses for Horizon-Aware Exploration in Multi-Step Robot Manipulation

arXiv:2605.29864v1 Announce Type: new Abstract: Multi-step robot manipulation requires acting under uncertainty about how the scene will evolve, making exploration and policy adaptation challenging. W

safetyarxiv-cs-ro

LoMo: Local Modality Substitution for Deeper Vision-Language Fusion

arXiv:2605.30265v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have achieved substantial progress across a wide range of understanding and reasoning tasks, driven by large-scale image

Looking Beyond Text: Reducing Language bias in Large Vision-Language Models via Multimodal Dual-Attention and Soft-Image Guidance

arXiv:2411.14279v2 Announce Type: replace-cross Abstract: Large vision-language models (LVLMs) have achieved impressive results in various vision-language tasks. However, despite showing promising per

MARS Policy: Multimodality Only When It Matters

arXiv:2605.29766v1 Announce Type: new Abstract: Imitation learning has become a cornerstone for solving complex robotic manipulation tasks. In particular, multimodality, which enables robots to captur

safetyarxiv-cs-ro

Mask the Target: A Plug-and-Play Regularizer Against LoRA Forgetting

arXiv:2605.29498v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has become one of the most widely used fine-tuning mechanisms for adapting large language models to new domains, tasks, and u

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species

arXiv:2601.03729v2 Announce Type: replace Abstract: Fine-grained recognition of marine organisms is important for ecological research, biodiversity monitoring, habitat conservation, and evidence-based

Mean-Field Diffuser: Scaling Offline MARL to Thousands of Agents

arXiv:2605.30190v1 Announce Type: new Abstract: Diffusion-based planning has achieved strong results in single-agent offline reinforcement learning, yet scaling to many-agent systems remains intractab

MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality

arXiv:2605.29212v1 Announce Type: new Abstract: Image quality in modern imaging systems emerges from the coupled effects of the sensor, optics, and computational reconstruction. Ultra-thin metalenses

MIC: Maximizing Informational Capacity in Adaptive Representations via Isotropic Subspace Alignment

arXiv:2605.29987v1 Announce Type: cross Abstract: Although multi-scales representation learning enables elastic-dimension embeddings, nested subspaces often suffer from dimensional redundancy and spec