safety

Immiscible Diffusion Policy: Preserving Multimodal Robot Actions through Label-Free Noise Assignment

arXiv:2610.09369v1 Announce Type: cross Abstract: When diffusion policies were first introduced, they were expected to recover multi-modal action distributions. However, we find this expectation does

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Safety

InterView-C: A Synchronized Multimodal Corpus of VR Avatar-Mediated Survey Interviews

DGX agent

arXiv:2610.10145v1 Announce Type: new Abstract: We present InterView-C, a German multimodal corpus of 27 survey interviews conducted entirely in virtual reality, with both interlocutors represented by

safetyarxiv-cs-cl

8 Oct 2026

Is Word Error Rate Enough? Rethinking Privacy Evaluation in Speech with Entity-Aware Metrics

arXiv:2610.08831v1 Announce Type: cross Abstract: As the use of smart devices continues to increase, their potential to capture sensitive speech content raises growing privacy concerns. It is therefor

It Is Not Seeing the Hazard: A Frozen Vision-Language Safety Score Measures Its Caption Bank

arXiv:2610.09517v1 Announce Type: new Abstract: Frozen vision-language models increasingly provide safety signals for reinforcement learning. Their use assumes that similarity to language describing d

Juno: Taming Predictive Latents for Vision-Language-Action Models

arXiv:2610.09940v1 Announce Type: cross Abstract: Joint-embedding predictive architectures (JEPAs) predict masked or future observations in representation space, offering a natural source of predictiv

Kernel Autoresearch for Open-Ended Model Discovery

arXiv:2610.10394v1 Announce Type: new Abstract: Kernels encode the inductive bias of a wide range of machine learning models, yet automated kernel design faces a fundamental dilemma. A fixed grammar o

LACE-CRAFT: Robot Co-Design with Actor Inheritance and Blackboard Collaboration

arXiv:2610.09283v1 Announce Type: new Abstract: Robot co-design couples morphology search with policy learning, yet training every new design from scratch discards acquired control experience. We pres

LASER: Latent Space Adjoint Matching for Support-Constrained Entropy-Regularized Offline RL

arXiv:2610.08989v1 Announce Type: new Abstract: While offline reinforcement learning (RL) enables policy optimization from static datasets without costly online interaction, it remains bottlenecked by

Last week I was called into a meeting with OpenAI’s head of safety and told they no longer trust me. A security guard took my badge and walk…

Last week I was called into a meeting with OpenAI’s head of safety and told they no longer trust me. A security guard took my badge and walked me out of the building. Then I learned my colleagues @bal

safetygary-marcus--x

Learning in the Recurrent State: Gradient Descent with Linear Recurrent Networks

arXiv:2410.11687v4 Announce Type: replace Abstract: In-context learning lets a sequence model adapt to a new task from examples in its input. A prominent line of work shows how self-attention can be c

Many Ways to Succeed: Diversity-Driven RL Fine-Tuning for VLA Generalization

arXiv:2610.09943v1 Announce Type: cross Abstract: Reinforcement learning (RL) fine-tuning improves vision-language-action (VLA) policies through closed-loop experience, yet generalization beyond the f

Marrying Pricing and Advertising with LLMs

arXiv:2610.09985v1 Announce Type: cross Abstract: We study a sequential pricing problem in which a seller jointly posts a price and an advertisement generated by a large language model (LLM). The sell

MCFR: A Mask-Guided Coarse-to-Fine Regression Framework for Robust Multi-Variant Board-to-Board Connector Assembly

arXiv:2610.09327v1 Announce Type: new Abstract: Automated insertion of board-to-board (BTB) connectors in 3C manufacturing requires both high visual accuracy and strong deployment robustness. This pro

MimicX: Policy-in-the-Loop Supervision Refinement for Video-Driven Humanoid Motion Tracking

arXiv:2610.09055v1 Announce Type: new Abstract: Human videos provide rich motion targets for humanoid learning, yet visually plausible references can still produce persistent failures under physics-ba

MIRROR: From Imitation to Internalization in LLM Personalization

arXiv:2610.09795v1 Announce Type: new Abstract: The demand for personalized LLMs is shifting from style imitation toward content quality. We investigate whether self-distillation can bridge this gap i

Mitigating Accent-Language Confusion in Self-Supervised Speech Representations for Language Identification

arXiv:2610.09486v1 Announce Type: cross Abstract: Spoken language identification (LID) aims to recognize the target language regardless of accent. In practice, however, LID models fine-tuned from self

MOTIP2: Spatial Priors for End-to-End Multi-Object Tracking

arXiv:2610.10391v1 Announce Type: new Abstract: End-to-end multi-object trackers have narrowed the gap with classical tracking-by-detection on association-difficult benchmarks. Yet they still make spa

Multi-Objective Aligned Small Language Model Framework for SUD Patient Dialogue Generation

arXiv:2610.09209v1 Announce Type: new Abstract: Substance Use Disorder (SUD) counseling requires patient responses that reflect underlying cognitive states such as beliefs, coping strategies, and read

MUNITE: Unified Multimodal Latent Inference for Any-to-Any Multimodal Generation

arXiv:2610.09866v1 Announce Type: new Abstract: We introduce MUNITE, a latent-variable framework for flexible any-to-any multimodal generation that treats encoding and latent generation as the same in

On KL-Regularized Policy Optimization

arXiv:2610.08963v1 Announce Type: cross Abstract: Asynchronous reinforcement learning (RL) for large language model (LLM) agents trains one policy on trajectories generated by another: rollouts come f

On-Policy Distillation Teaches New Skills but Not New Knowledge

arXiv:2610.09639v1 Announce Type: new Abstract: On-policy distillation (OPD) strengthens language-model reasoning, yet whether students acquire new factual knowledge or compositional skill for multi-s

One-Slide Calibration of Pathology Foundation Models

arXiv:2610.08944v1 Announce Type: new Abstract: Scanner variation changes how pathology foundation models represent the same tissue. We introduce SlideRuler, which uses regions within a slide as inter

OnlineQAT: On-Policy Distillation for Ultra-Low-Bit Large Language Models

arXiv:2610.09346v1 Announce Type: new Abstract: Quantization-aware training (QAT) can recover much of the accuracy lost when large language models are compressed below four bits. Existing re- covery s

ORCA: Hunting Compositional Failures in Text-to-Image Diffusion

arXiv:2610.09841v1 Announce Type: new Abstract: Text-to-image diffusion models fail predictably on compositional prompts: attributes bind to the wrong objects, spatial relations invert, and multi-obje

Pathwise Information Certificates for Decentralized Adaptive Sensing

arXiv:2610.10362v1 Announce Type: cross Abstract: We study decentralized adaptive sensing, where multiple agents choose measurements from evolving local beliefs while exchanging information over a com

Persistence Paradox in Dynamic Science: Evidence from the Deep Learning Revolution

arXiv:2506.22729v3 Announce Type: replace-cross Abstract: Persistence is often regarded as a virtue in science. In this paper, however, we challenge this conventional view by highlighting its contextu

Personalize at Test Time: Learning User Preferences for Image Generation

arXiv:2610.09015v1 Announce Type: new Abstract: Diffusion models can generate high-quality images, yet aligning their outputs with individual user preferences remains challenging. A key bottleneck is

PhysEvo: Astra Can Act, Let It

arXiv:2610.08995v1 Announce Type: new Abstract: Astra can act, yet reliable manipulation depends on the system through which it observes and controls the world. We introduce PhysEvo, a framework for p

Point It, Strike It: Direction-Conditioned Dynamic Manipulation of Deformable Linear Objects

arXiv:2610.09573v1 Announce Type: new Abstract: Goal-conditioned dynamic manipulation of deformable linear objects has mainly specified goals as positions for a rope tip to reach. Many tasks, however,

Policy Learning with Weak Signals

arXiv:2610.10167v1 Announce Type: cross Abstract: Policy learning in digital experimentation faces three challenges: weak signal-to-noise ratios, rich covariate spaces, and massive data volumes. We fo

Predicted Futures Are Not Enough: Learning Executable Goals for Robot Manipulation

arXiv:2610.09309v1 Announce Type: new Abstract: Generative world models provide rich predictions of how manipulation scenes may evolve toward task objectives, yet those futures do not directly expose

Purifying Backdoored Large Vision-Language Models by Removing Hijacked Directions

arXiv:2610.09941v1 Announce Type: new Abstract: Large vision-language models (LVLMs) are increasingly deployed in safety-critical applications, yet they remain vulnerable to backdoor attacks. Defendin

Q-Learning with Scalar Adjoint Matching

arXiv:2610.10437v1 Announce Type: new Abstract: Flow policies capture rich and diverse action distributions, and fine-tuning them with off-policy RL to improve beyond the demonstrations has drawn grow

RELATE: An Evaluation Framework for measuring Relational Orientation of Large Language Models

arXiv:2610.09569v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for emotional support, raising concern that sustained use may draw users away from their real-world r

Rephrase Before You Act: Characterizing and Mitigating Language Sensitivity in Vision-Language-Action Models

arXiv:2610.10526v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) are strikingly sensitive to instruction phrasing and do not inherit the language robustness of the vision-languag

Revisiting Explainable AI through Model-Independent Concept Dictionaries

arXiv:2610.10301v1 Announce Type: new Abstract: Modern applications of AI rely on increasingly complex models. Explainable AI (XAI) has emerged as a set of techniques aimed at improving model transpar

RFPO: Rectified Flow Policy Optimization for Embodied Control

arXiv:2610.10453v1 Announce Type: new Abstract: Flow-based policies provide an expressive framework for continuous robot control, but their iterative ODE integration incurs substantial inference cost.

RoboPace: Contact-Aware Time-Optimal Retiming for Action-Chunk Policies

arXiv:2610.09696v1 Announce Type: new Abstract: Robot manipulation data collection has been shifting from teleoperation toward robot-free demonstrations, through interfaces such as the Universal Manip

RoboPrompt: Intuitive Robot Policy Steering with Sparse Human Input

arXiv:2610.10534v1 Announce Type: new Abstract: End-to-end robot policies trained through imitation learning remain constrained by limited data diversity, making reliable zero-shot deployment in real-

RoboRender: Robot-Oriented Video Generation for Visual Sim-to-Real Transfer

arXiv:2610.09254v1 Announce Type: new Abstract: Simulation enables large-scale, low-cost robot data generation, but policies trained in simulation often fail to transfer to the real world due to the s

RollVerify: Bridging Efficiency and Accuracy in Long-Tail Rollout Reinforcement Learning

arXiv:2610.09914v1 Announce Type: new Abstract: Reinforcement learning is crucial for improving large language models' reasoning and generalization. It relies on massive rollouts whose lengths become

Rubix: Global Correspondence-Free Point Set Alignment through Assignment Geometry

arXiv:2610.10408v1 Announce Type: new Abstract: Procrustes-Wasserstein alignment jointly estimates a matching and rotation without supplied correspondences, but alternating minimization can stop at su

Safe Meta-Policy Design with Risk Control

arXiv:2610.10393v1 Announce Type: cross Abstract: Models can be retrained as new data arrive, but deploying every new version risks replacing a good policy with a worse one. We study how to plan polic

Safe on Average, Unsafe in the Tail: When Is the Episodic-Cost Tail Controllable?

arXiv:2610.09508v1 Announce Type: new Abstract: Safe reinforcement learning seeks policies that maximize return while satisfying constraints on cumulative cost. Most methods impose these constraints o

SAPD: Step-Aligned Privileged Distillation

arXiv:2610.09665v1 Announce Type: new Abstract: On-policy post-training can improve large language models by learning from their own trajectories, but requires costly rollout generation. We ask whethe

SAPS: Shared Autonomy for Policy Steering by Blending Teleoperation with a Pretrained VLA

arXiv:2606.15568v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models perform well in generalist robot manipulation but remain brittle to out-of-distribution perturbations, while ful

Scalable Logistic Gaussian Process Density Regression with Kinetic Langevin Sampling

arXiv:2610.09591v1 Announce Type: cross Abstract: Conditional density estimation targets the full distribution of a response given covariates, as required, for example, for per-galaxy photometric reds

SearchWorld: Spatial Value-Grounded Imagination for UAV Object Search via World Models

arXiv:2610.09335v1 Announce Type: cross Abstract: Autonomous unmanned aerial vehicle (UAV) object search involves a closed loop of perception, decision-making, and action under partial observability.