Search: “safety”

Moloch's Bargain: Emergent Misalignment When LLMs Compete for Audiences

arXiv:2510.06105v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly shaping how information is created and disseminated, from companies using them to craft persuasive adv

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More with Less: a Large Scale Remote Sensing VLM with a Simple Recipe

DGX agent

arXiv:2607.15942v2 Announce Type: replace Abstract: Remote sensing vision-language models are increasingly expected to support open-ended reasoning over Earth Observation data and a variety of tasks.

safetyarxiv-cs-cv

2 Oct 2026

Nous: Learning and Certifying Memory Decisions Before Source Calibration

arXiv:2610.00094v1 Announce Type: cross Abstract: Belief-based agent memory needs reliable decisions about current state, yet its evidence may be noisy, copied, or stale. Must a memory calibrate its s

OmniSeek: Native Tool Integration for Multi-turn Audio-Visual Reasoning

arXiv:2610.02181v1 Announce Type: new Abstract: We present OmniSeek, an agentic framework that transforms an Omni Large Language Model (Omni-LLM) into an active, multi-turn reasoning agent with native

On Evaluating Quantum Kernel Robustness for Low-Resource Cross-Corpus Audio Deepfake Detection

arXiv:2610.00649v1 Announce Type: cross Abstract: Synthetic speech detection is critical for audio security, but performance can degrade when labeled data are scarce and evaluation conditions differ f

PAGER: Partial-to-global Alignment via Geometric and Relational Distillation

arXiv:2610.01589v1 Announce Type: new Abstract: Pretrained 3D encoders are typically developed on globally reconstructed scenes expressed in a consistent world coordinate frame, whereas embodied syste

Participation-Sensitive Convergence and the Fragment First, Converge Later Pattern in Asynchronous Online Learning: A Topological Analysis Across 22 OULAD Courses

arXiv:2610.01738v1 Announce Type: cross Abstract: Asynchronous online learning offers temporal flexibility at a structural cost: learning communities tend to fragment rather than cohere. eta_0, the nu

PhGPO: Pheromone-Guided Policy Optimization for Long-Horizon Tool Planning

arXiv:2602.13691v2 Announce Type: replace Abstract: Recent advancements in Large Language Model (LLM) agents have demonstrated strong capabilities in executing complex tasks through tool use. However,

PixelDense: Dense Prediction as Representation Alignment for Pixel Diffusion

arXiv:2610.00483v1 Announce Type: new Abstract: Representation alignment (REPA) accelerates diffusion transformer training, but its alignment targets are almost exclusively semantic encoders such as D

🚨Potentially huge deal: @SpeakerMenin has introduced NYC legislation that would OBLIGATE third party validation of AI models. Self-policing…

🚨Potentially huge deal: @SpeakerMenin has introduced NYC legislation that would OBLIGATE third party validation of AI models. Self-policing is not enough. Thrilled to see NYC stepping in where the fed

safetygary-marcus--x

PPO-HRAP: Proximal Policy Optimization with a Hybrid Regime-Aware Policy for Risk-Controlled Trading

arXiv:2610.01325v1 Announce Type: new Abstract: Reinforcement learning for trading often struggles to balance upside participation with drawdown control. Profit-only policies can collapse toward passi

PROMO: Preference-conditioned Multi-Objective Reinforcement Learning for Quadrupedal Robots

arXiv:2610.01260v1 Announce Type: cross Abstract: Quadrupedal locomotion requires balancing conflicting objectives such as command tracking, stability, and energy efficiency, yet conventional reinforc

[R] Would you keep a robot demonstration if hand tracking missed the moment the plug went in?

Suppose you’re recording a human plugging a cable into a socket to collect demonstrations for robot learning. The hand tracker captures the approach accurately. Then occlusion causes the hand estimate

safetyr-machinelearning

Range-GRPO: Policy Optimization via Pairwise Relations among Reward Intervals

arXiv:2610.01548v1 Announce Type: new Abstract: As the use of large language models (LLMs) expands, post-training has become increasingly important for adapting them to downstream tasks. However, obta

Rate-Optimal Algorithm for Adversarial Linear CMDPs

arXiv:2610.00927v1 Announce Type: new Abstract: We study episodic adversarial linear constrained Markov decision processes (CMDPs) with unknown transitions, where both the loss and constraint function

Recova: Agent-Guided Failure Recovery for Autonomous Robotic Manipulation

arXiv:2610.01178v1 Announce Type: new Abstract: Manipulation failures can leave scenes in states from which a task policy cannot recover. Learning corrective behaviors requires scalable failure explor

safetyarxiv-cs-ro

Rethinking Memorization Mitigation in Diffusion Models: Reinforcing Text Conditioning

arXiv:2610.01723v1 Announce Type: new Abstract: Text-to-image diffusion models have achieved remarkable progress in image synthesis, yet can exhibit memorization by closely reproducing individual trai

Rethinking Probability-Based Reinforcement Learning From Posterior Concentration

arXiv:2610.01458v1 Announce Type: new Abstract: Verifier-free reinforcement learning with probability-based rewards offers a promising way to train LLMs on general reasoning tasks where external verif

Rethinking the Global Convergence of Softmax Policy Gradient with Linear Function Approximation: The Case of Multi-Armed Bandits

arXiv:2505.03155v2 Announce Type: replace Abstract: Policy gradient (PG) methods have played an essential role in the empirical successes of reinforcement learning. In order to handle large state-acti

Rethinking the Information Bottleneck: Structured Decomposition under Label-Induced Partitions

arXiv:2610.01175v1 Announce Type: new Abstract: Standard information bottleneck (IB) regularization constrains representations via a single scalar I(Z;X), implicitlytreating all information as homogen

Reusing Past Samples in Proximal Policy Optimization: When and How Does It Help?

arXiv:2610.01399v1 Announce Type: new Abstract: Among on-policy deep reinforcement learning methods, Proximal Policy Optimization (PPO) has become the de facto standard, due to its consistently strong

Revision-Aware Independent Agent Graphs for Dynamic Reasoning

arXiv:2610.01249v1 Announce Type: new Abstract: Conventional reasoning protocols present a fixed, preselected task, so they cannot test whether an agent propagates relevant updates, preserves unaffect

Reward as Observation: Learning Reward-Based Policies for Rapid Adaptation

arXiv:2610.00729v1 Announce Type: new Abstract: This paper explores a reward-based policy to achieve zero-shot transfer between source and target environments with completely different observation spa

Robust Nash Alignment under Preference Uncertainty

arXiv:2610.00715v1 Announce Type: new Abstract: Preference-based alignment methods typically optimize against a single preference model, and can therefore be brittle when pairwise preferences are unce

Same Scene, Different Task: Skill Alignment for Compositional Generalization in VLAs

arXiv:2610.00524v1 Announce Type: cross Abstract: Vision-language-action (VLA) models often struggle to generalize to skill combinations absent from their fine-tuning demonstrations, even when every c

Scalable Multi-Task Inverse Reinforcement Learning

arXiv:2610.00758v1 Announce Type: new Abstract: By learning transferable rewards, inverse reinforcement learning (IRL) enables counterfactual evaluation of agents under modified environments. Such tra

SemanTok: Predictable Semantic Tokens for Efficient Autoregressive Video Generation

arXiv:2610.00686v1 Announce Type: new Abstract: Recent video-based world models pair the scalability of autoregressive (AR) prediction with the visual quality of diffusion models. The choice of scene

Sharpen Before You Adapt: Data-Free Entry-State Sharpening for Test-Time Reinforcement Learning

arXiv:2610.00903v1 Announce Type: new Abstract: Test-time reinforcement learning (TTRL) adapts language models on unlabeled test problems using supervision derived from their own samples. This makes t

SHARPO: Segment-Level Credit Assignment for Agentic Reinforcement Learning

arXiv:2610.00838v1 Announce Type: cross Abstract: Agentic reinforcement learning (RL) trains a large language model (LLM) to act over long, multi-step interactions. However, a single localized error c

Signed Lexical Confidence for Risk-Calibrated Intent Routing

arXiv:2610.00262v1 Announce Type: cross Abstract: Selective intent routing allows an assistant to act on reliable predictions while deferring uncertain requests. Standard confidence scores primarily r

Stochastic Optimal Control for Continuous-Time fMRI Representation Learning

arXiv:2502.04892v2 Announce Type: replace Abstract: Learning robust representations from functional magnetic resonance imaging (fMRI) is fundamentally challenged by the temporal irregularity and noise

Symmetry Discovery in Quantum Learning: Observable-Level and Task-Level Inference from Finite Measurements

arXiv:2610.00157v1 Announce Type: cross Abstract: Symmetry reduces the capacity of a quantum learning model, but the imposed group must match both the measured information and the label transformation

T2SPO: Trajectory-to-Step Policy Optimization for Agentic Reinforcement Learning

arXiv:2610.00388v1 Announce Type: cross Abstract: Reinforcement learning enables large language model (LLM) agents to learn multi-step behaviors through interaction with their environments. However, r

Terence Tao on today's AI: 'you look at how it's done and it doesn't feel like intelligence, like its some trick, you just cobble together t…

Terence Tao on today's AI: 'you look at how it's done and it doesn't feel like intelligence, like its some trick, you just cobble together these neural networks and we ran some algorithm and we were l

safetygary-marcus--x

The Weakest Link: Distilling LLM Reasoning with Worst-Case Constrained Reinforcement Learning

arXiv:2610.00332v1 Announce Type: cross Abstract: Distilling the reasoning capabilities of large language models (LLMs) into smaller students is a central challenge for efficient deployment. Current a

TOAST: Stochastic Robot Action Tokenization for Autoregressive Vision-Language-Action Models

arXiv:2610.00899v1 Announce Type: cross Abstract: Autoregressive Vision-Language-Action models often represent continuous robot actions as discrete token sequences, enabling action prediction with sta

Token Communication-Assisted Collaborative Embodied Artificial Intelligence: Concepts, Framework, and Opportunities

arXiv:2610.01826v1 Announce Type: cross Abstract: Collaborative embodied artificial intelligence (CEAI) enables multiple physical agents to perceive, reason, and act cooperatively in dynamic environme

Token-World: World Modeling in Vision-Language Model Token Space for Robot Manipulation

arXiv:2610.00575v1 Announce Type: new Abstract: A common approach to world-model simulation for vision-language-action (VLA) systems is to predict future RGB observations and then re-encode them into

safetyarxiv-cs-ro

Tolerance-Based Fairness Auditing: Violation Certification and Sensitivity Screening

arXiv:2610.01005v1 Announce Type: cross Abstract: As artificial intelligence is increasingly deployed, algorithmic unfairness has raised growing concerns and intensified demands for transparent fairne

Towards a General Humanoid Loco-Manipulation Model via Egocentric Whole-Body Human Data Pretraining

arXiv:2610.00438v1 Announce Type: new Abstract: Humanoid whole-body manipulation has advanced rapidly, enabling policies to coordinate locomotion, posture, bimanual interaction, and dexterous hand mov

safetyarxiv-cs-ro

Towards Hierarchical Cyber Defense with Large Language Models: From Planning to Execution

arXiv:2610.00590v1 Announce Type: cross Abstract: An autonomous cyber defender trained with reinforcement learning (RL) is typically tied to the network on which it was trained, limiting its ability t

Towards Optimal Policy Improvement

arXiv:2610.01566v1 Announce Type: new Abstract: Practical Reinforcement Learning (RL) algorithms learn to solve Markov Decision Processes (MDPs) through iterative policy improvement in the presence of

Unsupervised Domain Adaptation for Enhanced Radiometer Image Precipitation Estimation using Conditional Flow Matching

arXiv:2610.01890v1 Announce Type: cross Abstract: Deep generative networks have recently achieved unprecedented performance in precise image and video editing using sophisticated textual prompts. Howe

US authorities arrest a California man suspected of smuggling $300M+ worth of restricted Nvidia AI chips to China via Malaysia and Singapore from 2023 to 2024 (Michael Shepard/Bloomberg)

Michael Shepard / Bloomberg: US authorities arrest a California man suspected of smuggling $300M+ worth of restricted Nvidia AI chips to China via Malaysia and Singapore from 2023 to 2024 — By continu

safetytechmeme

vFedProtoQNAS: Prototype-Guided Personalized Quantum Neural Architecture Search for Virtual Federated Learning

arXiv:2610.01718v1 Announce Type: new Abstract: Quantum federated learning (QFL) has emerged as a promising approach for collaboratively training compact quantum neural networks (QNNs) over distribute

VISPA: Pluralistic Alignment via Automatic Value Selection and Activation

arXiv:2601.12758v2 Announce Type: replace-cross Abstract: As large language models are increasingly used in high-stakes domains, it is essential that their outputs reflect not average} human preferenc

When a Data Artifact Isn't a Shortcut: Causal Auditing of Synthetic RLVR Corpora

arXiv:2610.00202v1 Announce Type: cross Abstract: Several recent pipelines build RLVR training data by masking a span of real corpus text and asking a language model to invent plausible wrong answers

When Harnesses Lose the Signal: Causal Evaluation of Recovery in LLM Agents

arXiv:2610.00372v1 Announce Type: new Abstract: Large language model agents rely on external harnesses to pass information between the model and its environment and to recover from execution errors. Y