Search: “safety”

FACES: Fine-grained Gated Semantic Alignment for Multimodal Emotional Support Conversation

arXiv:2610.20051v1 Announce Type: new Abstract: In recent years, with advances in multimodal interaction technologies and the growing demand for intelligent emotional support, Multimodal Emotional Sup

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Safety

FARL: A Fully Automated Pipeline of RL Interface and Solver Design

DGX agent

arXiv:2610.15691v1 Announce Type: new Abstract: Existing reinforcement learning (RL) largely focuses on optimizing learning algorithms within a pre-defined task formulation, while applying RL to new t

safetyarxiv-cs-ai

15 Oct 2026

FireGraphNet: Sub-daily wildfire spread prediction with directed graph neural networks

arXiv:2610.19191v1 Announce Type: new Abstract: Wildfire decisions are made in hours, yet most learned spread models predict daily on targets including burned area. We propose FireGraphNet which formu

Focus, Align, and Diffuse: Time-Series-Aware Keyframe Diffusion for Cardiac Dynamic Synthesis from Sparse Observations

arXiv:2610.19050v1 Announce Type: cross Abstract: Dense cardiac dynamics are essential for assessing cardiac function, yet in practice clinicians often observe only extremely sparse measurements. Howe

safetyarxiv-cs-cv

FOMI: A Force-Feedback Manipulation Interface for Teaching Contact-Rich Manipulation

arXiv:2610.19839v1 Announce Type: new Abstract: Robot-free collection with handheld interfaces enables scalable acquisition of human demonstrations for policy learning. However, existing handheld inte

FoveaVLA: Foveation Actions Replace the Wrist Camera in VLAs

arXiv:2610.20064v1 Announce Type: new Abstract: We present FoveaVLA, a Vision-Language-Action (VLA) model that replaces the wrist camera with learned foveation actions. Wrist cameras provide close-up

From Human Corrections to Learned Segmentations: A Critical Mathematical Review of Information, Learning, and Human Effort

arXiv:2610.20396v1 Announce Type: new Abstract: When does a human correction become useful learning, and what establishes better segmentation at matched human effort? This critical narrative review fo

Fully sequential Gaussian process regression for preference learning

arXiv:2610.19391v1 Announce Type: new Abstract: We study continuous global optimization problems in which the objective function cannot be directly evaluated; rather, we observe only which of two sele

Function-Space Temporal Self-Distillation for Policy Optimization

arXiv:2610.19687v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) improves reasoning in language models, but scalar outcomes provide coarse supervision over respo

GDEPO: Group Dual-dynamic and Equal-right Advantage Policy Optimization for Sample-Efficient Reinforcement Learning

arXiv:2601.06795v4 Announce Type: replace Abstract: Automated Theorem Proving (ATP) is a fundamental AI challenge, requiring machine-verifiable proofs in formal languages like Lean to evaluate reasoni

Geometric Alignment without Functional Equivalence: A Layer-wise Analysis of the Speech-Text Modality Gap

arXiv:2603.01502v2 Announce Type: replace Abstract: End-to-end speech large language models (LLMs) often answer the same question less accurately from speech than from text, even when the spoken conte

GS-PRM: Gaussian Visual Roadmap for Image-Goal Navigation

arXiv:2610.19541v1 Announce Type: new Abstract: Image-goal navigation requires a global planner that connects visually specified endpoints while providing observations that a local policy can follow.

GUIDE: Computationally Augmented Consensus for Trustworthy Clinical Practice Guidelines

arXiv:2610.16108v1 Announce Type: new Abstract: Maintaining up-to-date clinical practice guidelines (CPGs) is essential for evidence-based medicine, but rapid evidence growth and complex expert consen

Harness Policy Distillation: Aligning Agent Policies with Runtime Semantics

arXiv:2610.20261v1 Announce Type: new Abstract: Deploying a general-purpose model as an agent means running it inside a product-specific runtime harness that decides which actions are executable, how

Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance

arXiv:2610.20607v1 Announce Type: cross Abstract: Mobile Manipulation (MoMa) is a cornerstone of autonomous robotic systems, yet real-world fine-tuning remains a formidable challenge because of high-d

HiFun: a Hierarchical Framework for Efficient Functional Dexterous Manipulation Learning

arXiv:2610.19518v1 Announce Type: new Abstract: While multi-fingered dexterous robot hands have the potential to provide human-level dexterity, using them for functional manipulation remains difficult

Hindsight-Privileged RLVR: Single-Rollout Token Credit Without Group Sampling

arXiv:2610.20497v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) trains language models from checked task outcomes, but assigning a terminal reward to individual r

Historical-Feature Residual Adaptation for Sequential Screening

arXiv:2610.19315v1 Announce Type: new Abstract: Sequential screening policies must use acquired feedback effectively under a fixed experiment budget. We study acquisition-time residual adaptation: a s

Human Demonstrations as Post-Training Data for Any Robot Body: Retargeting the Object, Not the Hand

arXiv:2610.20351v1 Announce Type: new Abstract: Human demonstrations are today used largely for pretraining vision-language-action models, with little ability to use the same data for target-embodimen

Hyperspherical Contrastive Representations Unlock Planning and Exploration

arXiv:2610.20409v1 Announce Type: new Abstract: We develop a self-supervised reinforcement learning algorithm that learns to build towers of up to eight cubes from scratch, without prior data, rewards

Imaging-anchored Multiomics in Cardiovascular Disease: Integrating Cardiac Imaging, Bulk, Single-cell, and Spatial Transcriptomics

arXiv:2601.07871v2 Announce Type: replace-cross Abstract: Cardiovascular disease arises from interactions between inherited risk, molecular programmes, and tissue-scale remodelling that are observed c

safetyarxiv-cs-cv

Imagining Goals with Geometric Horizon Models for Policy Generalization

arXiv:2610.19129v1 Announce Type: new Abstract: Training from offline data has allowed for substantial progress in domains such as robotics, leading to general-purpose policies that can be easily appl

Inverse Reasoning Models for Efficient Post-Training and Verification from Answer-Only Data

arXiv:2610.18878v1 Announce Type: new Abstract: Post-training reasoning LLMs often requires making a tradeoff between costly supervision and compute inefficiency: supervised finetuning typically requi

Investigating Behavior Cloning from Few Demonstrations for Autonomous Driving based on Bird's-Eye View in Simulated Cities

arXiv:2610.20203v1 Announce Type: cross Abstract: This paper investigates the use of Behavior Cloning (BC) for autonomous driving from a bird's-eye view (BEV) perspective in simulated urban environmen

Lag: A Mean-Field Optimal Policy for Online Assignment with Reusable Resources

arXiv:2610.19038v1 Announce Type: new Abstract: We study online task assignment with reusable resources. This problem arises in peer reviewer assignment, skill-based call routing, rideshare matching,

Large-Step Last-Iterate Convergence of Random-Reshuffling Stochastic Gradient Descent

arXiv:2610.19444v1 Announce Type: cross Abstract: Random reshuffling stochastic gradient descent (RR-SGD) is widely used for finite-sum optimization in machine learning. For convex L-smooth components

Learning Across Temporal Graph Representation Silos

arXiv:2610.19576v1 Announce Type: new Abstract: Temporal graph data are recorded in different representations, but whether these differences also determine the scope of learned knowledge reuse remains

Learning Adaptive Deliberation Control for Reliable Recognition with Multi-Agent Language Models

arXiv:2610.17840v1 Announce Type: new Abstract: Multi-agent deliberation can improve recognition by criticizing and revising candidate answers, but fixed controls cannot respond to changes in disagree

Learning after Execution with Action-Specific Credit

arXiv:2610.14573v1 Announce Type: new Abstract: Execution is often used as terminal feedback: it scores a generated program, but the resulting state is not used for another learning action. Learning a

Learning in Strategic Queuing with Geometric Recharge: From Bandits to Systems

arXiv:2610.19179v1 Announce Type: cross Abstract: We study packet routing in queuing systems where queues act as selfish, myopic agents that use no-regret learning algorithms to select servers with un

Learning Navigation from Multimodal Demonstrations with Geometric Candidate Modes

arXiv:2610.20241v1 Announce Type: new Abstract: Offline navigation demonstrations are multimodal, because one observation often allows several valid actions, such as passing an obstacle on either side

Learning Perception to Action Rules through Reinforcement Learning for Theory-of-Mind Reasoning

arXiv:2610.19796v1 Announce Type: cross Abstract: In Theory-of-Mind (ToM) reasoning, large language models may correctly infer the agent's mental state but fail to use that inference when predicting o

Learning Perceptual Geometry of Natural Odor Mixtures from LLM-Derived Descriptors

arXiv:2610.17164v1 Announce Type: new Abstract: The relationship between odor chemistry and perception remains incompletely understood. Many existing studies focus on the perception of single molecule

Lifted State Policy Optimization: Internalizing Self-Correction through Transition Geometry

arXiv:2610.20350v1 Announce Type: cross Abstract: Preference optimization ranks final answers and trajectory RL maximizes aggregate returns, yet neither credits the local change that turns a flawed re

Long-Horizon RL Needs Muon

arXiv:2610.20256v1 Announce Type: new Abstract: Long-horizon agents must coordinate reasoning, tool use, and environment feedback over extended interactions. Learning from sparse outcome rewards requi

Lost in Translation: Why Lower Bias Scores Don't Mean Fairer Models

arXiv:2610.20479v1 Announce Type: new Abstract: Bias audits of multilingual language models usually compare a model's average stereotype score in English with its average in another language and read

MAEM: Mitigating Recency Bias via a Replay-Free, Capacity-Controlled Dynamic Mixture of Experts for Class-Incremental Graph Learning

arXiv:2610.20238v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) excel on graph-structured data but suffer catastrophic forgetting when classes arrive sequentially. In class-incremental gr

Maximum-Entropy Exploration with Future State-Action Visitation Measures

arXiv:2603.18965v2 Announce Type: replace Abstract: Maximum entropy reinforcement learning motivates agents to explore states and actions to maximize the entropy of some distribution, typically by pro

MemDuet: Learning Dual-Process Memory for Long-Context Reasoning

arXiv:2610.19928v1 Announce Type: new Abstract: Reasoning over infinitely long documents is becoming a reality with recurrent memory agents. However, increasing context length exacerbates the trade-of

MIPS: Mitigating Mode Collapse in Reinforcement Learning for Large Outcome Spaces

arXiv:2610.19457v1 Announce Type: new Abstract: Expected-reward maximization in reinforcement learning concentrates probability mass on a few outcomes, even in tasks that require many distinct, high-q

MMLA-RSI: Recursive Self-Improvement through RTT and RTMA

arXiv:2610.20294v1 Announce Type: new Abstract: An agent can improve its behavior within an unresolved problem by changing how it acts and which experience it retains. We propose MMLA-RSI, a recursive

Model What Matters: Decision-Guided Selective World Modeling for Language Agents

arXiv:2610.19642v1 Announce Type: cross Abstract: Reinforcement learning provides a direct mechanism for optimizing task objectives in large language model-driven agents, but task rewards are typicall

Multi-Sensitive Distributional Fairness: Joint Wasserstein Audits and Shapley Allocation

arXiv:2610.19252v1 Announce Type: cross Abstract: Fairness audits with several sensitive attributes are often performed one coordinate at a time. This can miss purely intersectional disparities: a sco

Neos: Learning to Interleave Search and Visual Reasoning

arXiv:2610.17043v1 Announce Type: new Abstract: Recent multimodal deep search agents have advanced tool use and visual content retrieval, yet they still struggle to convert intermediate visual evidenc

NePPO: Near-Potential Policy Optimization for General-Sum Multi-Agent Reinforcement Learning

arXiv:2603.06977v3 Announce Type: replace Abstract: Multi-agent reinforcement learning (MARL) is increasingly used to design learning-enabled agents that interact in shared environments. However, trai

Neural Supervised Clustering for Learning Individualized Treatment Rules with Many Treatments

arXiv:2610.19104v1 Announce Type: cross Abstract: Learning an individualized treatment rule (ITR) with many treatments requires estimating treatment-specific effects from limited observations per arm.

Noisy-Label Calibration with Multiple Unreliable Labels

arXiv:2610.19543v1 Announce Type: cross Abstract: In medical annotation and crowdsourcing, multiple annotators assign labels to each example, but these labels may be incorrect. Treating such noisy lab

Not All Criteria Matter: Importance-Aware Criteria Weighting for Rubric-Based LLM Evaluation

arXiv:2610.19915v1 Announce Type: new Abstract: Rubric-based evaluation assesses the responses of large language models (LLMs) by aggregating the verification results of explicit criteria, yet not all