Search: “safety”

ChronoSRL: Temporal Geometry for Self-Supervised Reinforcement Learning

arXiv:2609.36238v1 Announce Type: new Abstract: A goal that is close in space can be far away in time. Obstacles, terrain, and the agent's own capabilities determine how long it takes to get there. Ye

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CLeaR: A Unified Framework for Resolving the Leakage-Degradation Dilemma in Style Transfer

DGX agent

arXiv:2609.38136v1 Announce Type: new Abstract: Style transfer aims to render target content in the style of a reference image, but existing methods often suffer from content leakage, where objects, l

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30 Sept 2026

Codebook-Guided Cross-Modal Knowledge Distillation for Structurally Heterogeneous Features

arXiv:2609.37243v1 Announce Type: cross Abstract: Cross-modal knowledge distillation transfers knowledge from a teacher modality to a student modality. Existing feature-level alignment methods typical

CoEM: Empowering Long-Context Reasoning with Commit-on-Evidence Memory

arXiv:2609.36935v1 Announce Type: new Abstract: Long-context reasoning is essential for complex and long-horizon tasks, yet the performance of large language models (LLMs) degrades as context length i

Cognitive Expert Language Models Better Align with the Corresponding Brain Systems

arXiv:2609.36239v1 Announce Type: new Abstract: Large language models (LLMs) can predict human brain activity across a variety of brain regions during natural language comprehension. Typically, howeve

COMiT: Learning Structured Visual Tokens through Sequential Communication

arXiv:2602.20731v2 Announce Type: replace-cross Abstract: Discrete image tokenizers provide a sequential interface for vision and multimodal models, but are typically optimized for reconstruction or c

Commitment Hierarchies under Intent Revision: A Belief-Revision Account of Salvage in Tool-Use Agents

arXiv:2609.37453v1 Announce Type: new Abstract: When a user changes their mind partway through a task, an agent that has already split the task into sub-goals and paid for tool calls must decide, per

Compress to Remember: Learning Compact Memory via On-Policy Distillation for Long Video Generation

arXiv:2609.36364v1 Announce Type: cross Abstract: Standard video generators do not natively compact historical context into reusable memory tokens. As generation continues, the growing history makes i

Constitutional adapters: Inference-time interventions for misalignment and misuse

arXiv:2609.36657v1 Announce Type: cross Abstract: Training models to act in accordance with an explicitly defined set of principles, or 'constitution,' has shown promise as a robust and transparent me

CoRe: Co-Evolving Reward Models for Mitigating Latent Reward Hacking in Video Diffusion Models

arXiv:2609.36245v1 Announce Type: new Abstract: Latent reward models (LRMs) enable efficient alignment of video diffusion models by scoring intermediate states directly in latent space. However, we fi

CoRe-VLA: Preserving Cross-View Coordination in VLAs under Camera Shifts

arXiv:2609.37150v1 Announce Type: new Abstract: VLAs combine pretrained vision-language representations with action generation to enable language-guided control across diverse tasks, becoming a mainst

CorrGRPO: Correlation-Normalized GRPO for Multi-Reward Learning

arXiv:2609.36820v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) is widely used to train reasoning language models, where it computes advantages by centering and normalizing

Counterfactual Video Generation Enables Scalable Humanoid Loco-Manipulation

arXiv:2609.38172v1 Announce Type: cross Abstract: Teaching humanoids loco-manipulation skills, such as carrying diverse objects, via visual imitation is a promising path toward generalist robots. Howe

Credit-Guided Policy Improvement for Test-time Adaptive Vision-Language Navigation

arXiv:2609.37591v1 Announce Type: cross Abstract: Test-time adaptation for vision-language navigation (TTA-VLN) enables pretrained policies to adapt online to unseen environments using only test-time

Cross-Organizational SysML Model Integration: A Survey of Challenges and AI-Supported Tasks

arXiv:2609.37000v1 Announce Type: cross Abstract: Cross-organizational collaboration is widely regarded as a key promise of SysML-based Model-Based Systems Engineering (MBSE), yet practitioners still

CrossBFM: Distilling a Shared Latent Behavior Space Across Humanoid Embodiments

arXiv:2609.38087v1 Announce Type: new Abstract: Behavior Foundation Models (BFMs) give humanoids a promptable policy over a latent behavior space, enabling one single vector to represent a motion to i

Diffusion Policy Improvement with Proposal-Conditioned Refinement Flows

arXiv:2609.36812v1 Announce Type: cross Abstract: Diffusion and flow policies can model complex behaviors in offline reinforcement learning (RL). However, penalizing their KL divergence from the behav

Direct Experience World-Model Optimization: Learning the World Beyond Action Imitation

arXiv:2609.37398v1 Announce Type: new Abstract: World-Action Models (WAMs) couple action generation with predictions of how physical interactions unfold. However, current post-deployment learning para

Disentangling Spurious Correlations in Vision-Language-Action Models via Predicting Domain-Invariant Latent Lookahead

arXiv:2609.37165v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models remain brittle under visual distribution shifts, often relying on spurious correlations tied to domain-specific fact

DispFlow-GS: Displacement Flow Supervision with Motion Disentangling for Monocular Deformable 3D Gaussian Splatting

arXiv:2609.36940v1 Announce Type: new Abstract: Accurate dynamic scene reconstruction is important for robotic perception, where temporally consistent representations of dynamic environments are essen

Distilled Reinforcement Learning for LLM Post-training

arXiv:2607.17247v2 Announce Type: replace-cross Abstract: Large language model (LLM) post-training is essential for improving reasoning, adaptation, and alignment. Existing methods mainly follow two p

Distilling What Matters: Confidence-Aware Selective Distillation for Large Language Models

arXiv:2609.36734v1 Announce Type: new Abstract: Knowledge Distillation (KD) trains a smaller-capacity student model to imitate a larger-capacity teacher model by matching output distributions, implici

Distinguish or Homogenize: Last-Chance Policy Identification and Risk-Budgeted Recovery under Irreversible Resource Depletion

arXiv:2609.36741v1 Announce Type: new Abstract: Under irreversible resource depletion, an agent can spend resources to distinguish among latent fault models, or to change the system state so that the

Dr. OPD: Learning What to Follow for Optimal On-Policy Distillation of Large Language Models

arXiv:2609.38025v1 Announce Type: cross Abstract: On-policy distillation (OPD) trains a student on its own generated responses using dense, token-level supervision from a stronger teacher. Vanilla OPD

DROM: A Language-Guided Diffusion Framework for Multi-Skill Robotic Manipulation

arXiv:2609.37348v1 Announce Type: new Abstract: Learning robust manipulation policies for diverse, long-horizon tasks from limited demonstrations remains a fundamental challenge in robotics. We presen

DSPO: Diversity-aware Subjective Policy Optimization for Robust Emotional Reasoning

arXiv:2609.36775v1 Announce Type: new Abstract: Reinforcement Learning has significantly advanced the complex reasoning capabilities of MLLMs. However, prevailing RL algorithms suffer a severe failure

Dual-Channel Robust Group-Relative Policy Optimization via Advantage and Sequence-Weight Estimation

arXiv:2609.36944v1 Announce Type: new Abstract: Group-relative policy optimization relies on reward-derived advantages and sequence-level likelihood weights, both of which can be sensitive to localize

Dyad: Extending Large Language Models with Native Typed Decision-Making

arXiv:2609.36116v1 Announce Type: new Abstract: We study how to build more capable general-purpose agents by extending large language models (LLMs) with native typed decision-making. We introduce Dyad

Dynamic Optimizations of LLM Ensembles with Two-Stage Reinforcement Learning Agents

arXiv:2502.04492v3 Announce Type: replace Abstract: The advancement of LLMs and their accessibility have triggered renewed interest in multi-agent reinforcement learning as robust and adaptive framewo

EasyPPO: Stabilizing the Critic Is Key

arXiv:2609.36802v1 Announce Type: new Abstract: A key strength of Proximal Policy Optimization (PPO) is its learned critic, which uses historical trajectories collected during reinforcement learning t

Efficient Offline Learning of Ranking Policies via Top-k Policy Decomposition

arXiv:2609.36740v1 Announce Type: new Abstract: Many recommender systems such as for e-commerce and news platforms aim to provide users with rankings they are likely to interact with. Off-Policy Learn

EgoAlign: Bridging the Human-Humanoid Gap for Long-Range Loco-Manipulation

arXiv:2609.38046v1 Announce Type: new Abstract: Egocentric human demonstrations offer an accessible source of task experience, but differences in body scale and controller response, together with miss

EgoHumanoid-V2: Human-to-Humanoid Transfer of Coordinated Whole-Body Skills for Loco-Manipulation

arXiv:2609.37181v1 Announce Type: cross Abstract: Human demonstrations capture diverse scenes and rich whole-body skills without requiring robot teleoperation. Prior work on egocentric transfer has em

EGSD: Event-Grounded Self-Distillation for Streaming Video Understanding

arXiv:2609.36803v1 Announce Type: new Abstract: Real-time video understanding requires incrementally maintaining a memory of streaming content, and optimizing this requires dense process signals. On-P

Emergent Tonal Structure in Learned Chord Embeddings and Its Relation to Tonal Tension

arXiv:2609.36460v1 Announce Type: cross Abstract: Several tonal pitch spaces and computational models have been proposed to analyze tonal structure in Western tonal music, many of them grounded in pri

Encore: Few-Shot Agentic Discovery of Manipulation Strategies

arXiv:2609.37359v1 Announce Type: cross Abstract: Coding agents can now write, run, and debug programs with little human help. Robot tasks, however, are usually specified by a sentence that leaves out

End-to-End Self-Supervised RGB-T Tracking without Modality Misleading

arXiv:2609.37162v1 Announce Type: new Abstract: RGB-T object tracking leverages the complementary characteristics of visible and thermal infrared modalities to improve robustness under adverse conditi

ER-JEPA: Experience Replay Improves Joint-Embedding Predictive Learning in Language Models

arXiv:2609.36952v1 Announce Type: cross Abstract: Large language models (LLMs) excel at token-level generation but may learn undesirable abstract semantics and lack comprehensive perception. LLM-JEPA

Evaluating Alignment of Behavioral Dispositions in LLMs

arXiv:2602.11328v2 Announce Type: replace Abstract: As people turn to LLMs for social advice, understanding their behavior in such contexts becomes essential. In this work, we focus on behavioral disp

Evaluating Bounded Autonomy in Regulated Agentic AI: A Diagnostic Harness with Constitutional Rewards, Escalation Labels, and Runtime Governance

arXiv:2609.37501v1 Announce Type: cross Abstract: We propose RegLLM, a diagnostic harness for bounded autonomy in regulated agentic workflows. It instruments six trustworthiness signals: citation vali

Event-based Scene Synthesis via Inter-Frame Residual Alignment

arXiv:2512.17323v2 Announce Type: replace Abstract: Event-based scene synthesis reconstructs target RGB frames from sparse image observations and asynchronous event streams, encompassing both video fr

Factorized Scheduling Principle: Learning Interpretable and Transferable Policies via Structured Additive Functions

arXiv:2609.36578v1 Announce Type: cross Abstract: Scheduling problems arise from repeatedly selecting one item from a set of candidates based on their states. These problems often reduce to assigning

Fair Policy Optimization in Major-Minor Weakly Coupled Markov Decision Processes

arXiv:2609.36174v1 Announce Type: new Abstract: We consider fair resource allocation in sequential decision-making environments modeled as major-minor weakly coupled Markov decision processes (M2WCMDP

FairDiff: Mitigating the Self-Reinforcing Matthew Effect in Diffusion Recommender Models

arXiv:2609.36671v1 Announce Type: new Abstract: While the 'Matthew Effect' and filter bubbles are widely recognized outcome-level biases in recommender systems, we reveal that Diffusion Recommender Mo

Feedback-Calibrated Protein Optimization with Batch-Aligned Tail Arbitration

arXiv:2609.37808v1 Announce Type: new Abstract: Protein optimization aims to discover high-fitness sequences under a limited experimental budget. Existing machine-learning methods use task-specific pr

FineART: Fine-grained Annotated Robotic Trajectory Dataset and Vision-Language-Action Model for Bimanual Manipulation

arXiv:2609.36416v1 Announce Type: cross Abstract: Robots operating in real-world environments must execute complex, multi-step bimanual tasks over long horizons rather than single, isolated actions. C

FineSID: Scalable and Efficient Semantic Identifier Learning for Generative Recommendation

arXiv:2609.36670v1 Announce Type: new Abstract: A critical prerequisite of generative recommendation is designing semantic identifiers (SIDs) that are both scalable to large item sets and efficiently

Finite-Sample Theory for Fitted Q-Iteration When Actions Are Functions

arXiv:2609.36390v1 Announce Type: cross Abstract: Offline reinforcement learning seeks optimal decision rules from previously collected data. In some applications, a decision can be an entire function