safety

GRAM: Correcting Frozen Time-Series Foundation Models via Graph-Retrieved Amplitude Memory

arXiv:2610.04827v1 Announce Type: new Abstract: Time-series foundation models (TSFMs) enable zero-shot forecasting through large-scale cross-domain pretraining, while retrieval augmentation further im

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Groupwise Distortion Guarantees for Preference-Based Alignment

DGX agent

arXiv:2610.05450v1 Announce Type: new Abstract: Preference-based alignment methods such as reinforcement learning from human feedback (RLHF) and Nash learning from human feedback (NLHF) aggregate pair

safetyarxiv-cs-lg

6 Oct 2026

GUIDE: Reinforcement Learning for Behavioral Action Support in Type 1 Diabetes

arXiv:2604.00385v2 Announce Type: replace Abstract: Type 1 diabetes (T1D) management requires continuous adjustment of insulin and lifestyle behaviors to maintain blood glucose within a safe target ra

Harmful Content Generation in Text-to-Image Models: Capabilities and Moderation Limitations

arXiv:2610.06503v1 Announce Type: new Abstract: Text-to-image generative models can produce highly realistic imagery but also raise concerns about harmful misuse. While safety mechanisms exist, system

Hierarchical Reinforcement Learning for Collision-Free Locomotion of an Underactuated Biped

arXiv:2610.05855v1 Announce Type: new Abstract: A bipedal robot cannot deviate from its path to avoid an obstacle without disturbing its balance, and this coupling is most severe on underactuated plat

Hierarchical Time-aware Bootstrapping for Off-Policy Subgoal Value Learning

arXiv:2610.05446v1 Announce Type: new Abstract: Off-policy hierarchical reinforcement learning must estimate the values of high-level decisions while the low-level policy changes. HIRO adapts replay d

How Should Teachers Be Prepared? RL on Student-Induced States for On-Policy Distillation

arXiv:2610.04950v1 Announce Type: new Abstract: On-policy distillation (OPD) improves the reasoning capabilities of small language models through token-level teacher supervision on student-generated t

Human Behavior-Informed Crash Scenario Generation with Real-World Crash Priors for Autonomous Vehicle Safety Evaluation

arXiv:2610.04366v1 Announce Type: new Abstract: Reliable safety evaluation of autonomous vehicles (AVs) is essential to improving road safety, yet it depends critically on realistic simulation of rare

Humanoid Rickshaw Pulling: Whole-Body Locomotion under Coupled Wheeled Loads

arXiv:2610.04238v1 Announce Type: cross Abstract: Humanoid robots could transport payloads substantially heavier than themselves by pulling passive wheeled vehicles instead of carrying the load. This

I-BFM: Reward-Conditioned Robust Humanoid Interaction via Unsupervised Reinforcement Learning

arXiv:2610.06129v1 Announce Type: new Abstract: Behavioral foundation models (BFMs) have recently shown that a single humanoid policy can support diverse whole-body control, but extending such general

ilde{O}(sqrt{T}) Regret and Polylogarithmic Constraint Violation for COCO

arXiv:2610.03983v1 Announce Type: new Abstract: We study constrained online convex optimization with adversarial convex losses and constraints (mathsf{COCO}). At each round (tin[T]), a learner selects

Incentive Alignment in Online Experimentation

arXiv:2610.05922v1 Announce Type: cross Abstract: Evaluating the causal effect of new features is a central goal for online platforms. While recent literature addresses limited testing traffic via cen

Inspect Robots: Evaluating the Capabilities and Safety of Embodied AI

arXiv:2610.06306v1 Announce Type: new Abstract: General purpose language models are increasingly able to control robotic hardware. Understanding the capabilities and safety of these models when embodi

InterMimicGen: Scaling Humanoid Loco-Manipulation through Self-Evolving Motion Imitation

arXiv:2610.06850v1 Announce Type: cross Abstract: Captured human-object interactions provide rich supervision for humanoid loco-manipulation, but they are sparse, heterogeneous, and not directly execu

Investors pattern matching on old movies is going to lead to a recession. There is no network effect here and no reason to think that large …

Investors pattern matching on old movies is going to lead to a recession. There is no network effect here and no reason to think that large language models will be anything other than a brutal commodi

safetygary-marcus--x

Kandinsky 6.0 Video: Foundation Models for Synchronized Video and Audio Generation

arXiv:2610.05608v1 Announce Type: new Abstract: We present Kandinsky 6.0 Video, a family of foundation diffusion models for synchronized text-to-audio-video generation, comprising Kandinsky 6.0 Video

Latent Safety Filters: When a Lossy Encoder Admits a Transferable Certificate

arXiv:2610.04297v1 Announce Type: cross Abstract: Latent safety filters certify safety on a learned low-dimensional representation of the state, enabling constraints that resist analytic description.

Learning Coordinated Visuomotor Box-Pushing from Solo Demonstrations

arXiv:2610.05677v1 Announce Type: new Abstract: Multi-robot imitation learning, particularly in settings where visuomotor policies are deployed in a communication-free, onboard decentralised style, re

Learning Pareto Stationary Fronts via Single-Pass Backpropagation

arXiv:2610.06397v1 Announce Type: new Abstract: We propose MOSEL (Multi-Objective Stackelberg Efficient Learning), a framework for a posteriori multi-objective optimization (MOO) in deep neural networ

Learning to Read the Contextual Tokens in Diffusion Transformers

arXiv:2610.06844v1 Announce Type: new Abstract: Multimodal Diffusion Transformers (MM-DiTs) jointly process visual and textual representations throughout generation. These models repeatedly update the

Learning What to Imitate: Entropy-Aware Distribution Mixing

arXiv:2610.06671v1 Announce Type: new Abstract: Small language models are often post-trained as students on reasoning traces from stronger teacher models to efficiently learn new skills. However, toke

Learning without Overwriting: A Theory of Self-Distillation and Supervised Fine-Tuning in Continual Reasoning

arXiv:2610.05200v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) of large language models (LLMs) has demonstrated the ability to improve reasoning capabilities while preserving previ

Least Restrictive Hyperplane Control Barrier Functions

arXiv:2510.18643v3 Announce Type: replace Abstract: Control Barrier Functions (CBFs) can provide provable safety guarantees for dynamic systems. However, finding a valid CBF for a system of interest i

LeAVJEPA: A Minimalist Architecture for Audio-Visual Self-Supervised Learning

arXiv:2610.06226v1 Announce Type: cross Abstract: Prior audio-visual self-supervised learning methods rely on mechanisms such as EMA target encoders, prediction heads, reconstruction decoders, and con

Lie Rarely, Lie Big: Stealthy Insider Attacks on LLM Robot Teams

arXiv:2610.04744v1 Announce Type: new Abstract: When a team of robots delegates planning and mutual trust to LLM agents, a single compromised robot can corrupt the shared outcome. We study this threat

Local2Mesh: Spatially Localized Contour-to-Mesh for Left Ventricular Reconstruction from Sparse 2D Cardiac MRI

arXiv:2610.06052v1 Announce Type: new Abstract: Three-dimensional (3D) left ventricular (LV) reconstruction from sparse cardiac magnetic resonance (CMR) imaging remains challenging due to inter-slice

Long-MDR: Long-Context Reinforcement Learning for Multimodal Deep-Research Agents

arXiv:2610.05195v1 Announce Type: new Abstract: The next generation of multimodal research agents must reason over long-lived research histories rather than short model completions. During a single ta

Loss-Invariant Projections as Passive Probes of Learned Representations

arXiv:2610.06195v1 Announce Type: cross Abstract: Learned feature representations in neural networks often contain structure beyond that directly used by the final task output. We study this structure

MAGEFormer: Learning Metric-Consistent Representations for Anisotropic CT Segmentation

arXiv:2610.04036v1 Announce Type: new Abstract: Vision Transformers (ViTs) have shown strong performance in volumetric segmentation, but their effectiveness on clinical CT is limited by an isotropic E

MambaVF: State Space Model for Efficient Video Fusion

arXiv:2602.06017v2 Announce Type: replace Abstract: Video fusion aims to integrate complementary information from multiple source videos while preserving temporal consistency. Effective modeling of te

MarvisNav: Making Memory Visible on Route Choices for Zero-Shot Object Navigation

arXiv:2610.06510v1 Announce Type: new Abstract: When searching for an object, people choose their next move by considering both likely target locations and places already explored. The current view ca

Memory-State Critic for Asymmetric Actor-Critic with Application to Vision-Based Pursuit-Evasion

arXiv:2610.03830v1 Announce Type: new Abstract: In partially observable Markov decision processes, the optimal policy generally depends on the history of observations and past actions. Asymmetric acto

MemPilot: Orchestrating On-Demand Multimodal Memory Curation for LLM Agents

arXiv:2610.06830v1 Announce Type: cross Abstract: Memory has become integral to the LLM agent ecosystem, supporting information retention and reuse across interactions. However, most existing agent me

MeSD: Multi-Evidence Self-Distillation for VideoLLM

arXiv:2610.06342v1 Announce Type: new Abstract: While reinforcement learning with verifiable rewards provides reliable outcome supervision for VideoLLMs, sequence-level rewards offer limited token-lev

MGPO: Manifold-Guided Diffusion Alignment for Task-Aware Dataset Distillation

arXiv:2610.05252v1 Announce Type: new Abstract: Diffusion-based dataset distillation (DD) suffers from a fundamental objective mismatch: likelihood-driven diffusion models prioritize density approxima

MIND: Microstructure INverse Design with Generative Hybrid Neural Representation

arXiv:2502.02607v2 Announce Type: replace Abstract: The inverse design of microstructures plays a pivotal role in optimizing metamaterials with specific, targeted physical properties. While traditiona

Mind the Drift: Diagonal Linear Networks Under Large Learning Rates

arXiv:2610.06120v1 Announce Type: new Abstract: Large learning rates can qualitatively change the trajectory of neural network training, often pushing optimization into regimes far from classical grad

Minority Collective Action for User-Side Fairness

arXiv:2508.15374v3 Announce Type: replace Abstract: Machine learning models often preserve biases present in training data, leading to unfair treatment of certain minority groups. Despite an array of

MoCAR: Motion-code Coordinate-aware AutoRegression for Continuous Trajectory Forecasting

arXiv:2610.06210v1 Announce Type: new Abstract: Autoregressive generation is natural for language, where predicted tokens can be directly reused as the next prediction state, but trajectory forecastin

Monitorability Disposition in Large Reasoning Models

arXiv:2610.04914v1 Announce Type: cross Abstract: Monitoring the chain-of-thought (CoT) of large reasoning models (LRMs) is a common way to detect misbehavior in real-world practice. However, current

Mulligan: Performance-Guided Data Collection for Efficient On-Robot Learning

arXiv:2610.05882v1 Announce Type: cross Abstract: Learning from human demonstrations is a reliable way to teach robots new tasks, but the gains from each additional demonstration shrink as the policy

Neural Barriers: An Online Certifiable Learning-enhanced Adaptive High Order Safety Critical Control

arXiv:2610.05542v1 Announce Type: new Abstract: Control barrier functions are an effective model-based tool to formally certify the safety of a system. However, transferring their theoretical guarante

Occlusion-Aware Contingency Safety-Critical Planning for Autonomous Driving

arXiv:2502.06359v3 Announce Type: replace Abstract: Ensuring safe driving while maintaining travel efficiency for autonomous vehicles in dynamic and occluded environments is a critical challenge. This

Off-Policy Merging Beats On-Policy Self-Distillation for Continual Learning

arXiv:2610.05872v1 Announce Type: new Abstract: A long-standing goal of AI is a model that can continually learn and improve itself. On post-trained models, supervised finetuning (SFT) on new data oft

On architectural choices for interpretability and thermodynamic consistency in Physically Recurrent Neural Networks in the low-data regime

arXiv:2610.04067v1 Announce Type: new Abstract: In this paper, we unravel the effect of different decoder architectures on the interpretability of the latent space of the Physically Recurrent Neural N

On Semi-Markov Suboptimality in Hierarchical Reinforcement Learning

arXiv:2610.05338v1 Announce Type: new Abstract: Hierarchical reinforcement learning uses temporally extended subtasks for exploration, yet committing to their execution can restrict both deployment an

On the Geometry of Multimodal Saturation: Riemannian VICReg

arXiv:2610.06096v1 Announce Type: new Abstract: In self-supervised learning, a third modality should improve, or at least preserve, performance. Across nine image-text-tabular datasets, we show that i

Optimizer Geometry Sets the Pace: Spectral Learning Dynamics in Matrix Factorization

arXiv:2610.04249v1 Announce Type: new Abstract: Recent successes of matrix- and curvature-based optimizers have renewed interest in how update geometry shapes learning. These methods normalize or prec