safety

Skill is Not One-Size-Fits-All: Model-Aware Skill Alignment for LLM Agents

arXiv:2605.30723v1 Announce Type: new Abstract: LLM agents increasingly retrieve externally curated skills-procedural instructions retrieved at decision time-to improve performance on long-horizon int

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Smaller Models are Natural Explorers for Policy-Level Diversity in GRPO

DGX agent

arXiv:2605.30789v1 Announce Type: cross Abstract: We identify a new dimension for enhancing rollout diversity in Group Relative Policy Optimization (GRPO) for LLMs. While GRPO relies on diverse rollou

safetyarxiv-cs-ai

1 Jun 2026

Softly Constrained Denoisers for Diffusion Models Applied to Partial Differential Equations

arXiv:2512.14980v4 Announce Type: replace Abstract: Diffusion models have become a powerful generative prior for solutions of partial differential equations (PDEs). Existing approaches enforce physica

SpongeBob: Sync-Aware Harmonious Audio-Visual Generative Editing

arXiv:2605.25193v2 Announce Type: replace Abstract: Visual and acoustic events in the physical world are inherently coupled, yet existing video editing methods typically adopt decoupled pipelines, lac

Stateful Online Monitoring Catches Distributed Agent Attacks

arXiv:2605.31593v1 Announce Type: cross Abstract: Language models can find thousands of severe software vulnerabilities, and agents are increasingly being misused for cyberattacks. To avoid detection,

Structural Bias Beyond Homophily: A Study of Fairness in Link Prediction

arXiv:2602.11802v2 Announce Type: replace Abstract: Graph link prediction (LP) plays a critical role in socially impactful applications such as job recommendation and friendship formation, making fair

Structure-Induced Information for Rerooting Levin Tree Search

arXiv:2605.30664v1 Announce Type: new Abstract: Subgoal-based policy tree search, which uses a policy to guide search, is effective for complex single-agent deterministic problems but often relies on

Supervised Learning as Lossy Compression: Characterizing Generalization and Sample Complexity via Finite Blocklength Analysis

arXiv:2602.04107v2 Announce Type: replace Abstract: This paper presents a novel information-theoretic perspective on generalization in machine learning by framing the learning problem within the conte

Supervised Training Rapidly Degrades Early Visual Cortex Alignment Across Biologically Plausible Learning Rules

arXiv:2605.30556v1 Announce Type: new Abstract: Random, untrained neural networks consistently match or exceed trained networks in representational similarity to early visual cortex. This puzzling fin

Surface Constraint Policy for Learning Surface-Constrained and Dynamically Feasible Robot Skills

arXiv:2605.31321v1 Announce Type: new Abstract: Diffusion-based imitation learning methods have driven rapid progress in robot dexterous manipulation tasks. However, they have limitations when applied

safetyarxiv-cs-ro

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation

arXiv:2511.11440v3 Announce Type: replace-cross Abstract: Performance gains of Vision Language Models (VLMs) obtained by fine-tuning are generally based on ad hoc data collection and annotation of rea

TALON: Token-Aligned Lightweight Adapters for 6-DoF Spacecraft Pose Estimation

arXiv:2605.31217v1 Announce Type: new Abstract: Monocular 6-DoF spacecraft pose estimation methods predominantly process individual frames, discarding the temporal information present in an image sequ

TARIC: Memory-Augmented Traversability-Aware Outdoor VLN under Interrupted Semantic Cues

arXiv:2605.31121v1 Announce Type: cross Abstract: Outdoor vision-language navigation (VLN) in long-range, open-world environments is frequently disrupted by semantic-cue interruptions, where informati

Task-Focused Memorization for Multimodal Agents

arXiv:2605.31075v1 Announce Type: new Abstract: Long-term memory is essential for multimodal agents to build coherent experience, accumulate world knowledge, and achieve continual learning. However, c

THE DEFINITIVE AI CONVERSATION OF THE YEAR. MUST LISTEN. @JG_Nuke @GaryMarcus @MacrostrategyP https://open.substack.com/pub/georgenoble/p/ai…

THE DEFINITIVE AI CONVERSATION OF THE YEAR. MUST LISTEN. @JG_Nuke @GaryMarcus @MacrostrategyP https://open.substack.com/pub/georgenoble/p/ai-the-biggest-capital-misallocation-d18?r=35saq&utm_medium=io

The Flip Side of RLHF: On-Policy Feedback for Reward Model Self-Supervised Improvement

arXiv:2605.30888v1 Announce Type: new Abstract: Building strong reward models (RMs) for language model alignment is bottlenecked by the cost and difficulty of acquiring diverse and reliable preference

The Global Landscape of Environmental AI Regulation: From the Cost of Reasoning to a Right to Green AI

arXiv:2603.00068v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) systems impose substantial and growing environmental costs, yet transparency about these impacts has declined eve

The OpenAI Foundation is doing a lot of wonderful things. Helping society become resilient to AI is going to be incredibly important. Much m…

The OpenAI Foundation is doing a lot of wonderful things. Helping society become resilient to AI is going to be incredibly important. Much more to come here! AI is advancing quickly. Society’s ability

safetysam-altman--x

The Refutability Gap: Challenges in Validating Reasoning by Large Language Models

arXiv:2601.02380v4 Announce Type: replace-cross Abstract: Recent reports claim that Large Language Models (LLMs) have achieved the ability to derive new science and exhibit human-level general intelli

The Sword, Shield, and Achilles' Heel: Characterizing the Linguistic Inductive Bias of Large Language Models for Spatial Reasoning in Navigation Planning

arXiv:2605.31404v1 Announce Type: cross Abstract: Large Language Model (LLM)-based navigation systems commonly construct explicit spatial representations (e.g., topological graphs, semantic raster map

“The technology worked. The value didn’t arrive,” Bain concluded in the report. https://www.bloomberg.com/news/newsletters/2026-06-01/bain-s…

“The technology worked. The value didn’t arrive,” Bain concluded in the report. https://www.bloomberg.com/news/newsletters/2026-06-01/bain-survey-ai-delivers-less-cost-reduction-than-many-firms-predic

This was right five years ago, and still is: “Large scale pretrained models are certainly likely to figure prominently in artificial intelli…

This was right five years ago, and still is: “Large scale pretrained models are certainly likely to figure prominently in artificial intelligence for the near future, and play an important role in com

Traceable by Design: An LLM Pipeline and Dashboard for EU Regulatory Consultation Analysis

arXiv:2605.30995v1 Announce Type: cross Abstract: Public consultations generate large volumes of data in the form of stakeholder submissions that are practically unfeasible to analyse manually. We pre

Trust-Region Behavior Blending for On-Policy Distillation

arXiv:2605.31159v1 Announce Type: cross Abstract: On-policy distillation (OPD) trains a student on prefixes sampled from its own policy while matching a stronger teacher. This addresses the prefix mis

TunerDiT: Training-free Progressive Steering of Diffusion Transformer for Multi-Event Video Generation

arXiv:2605.31590v1 Announce Type: cross Abstract: Text-to-video (T2V) generation faces challenging questions when generating videos with long horizons containing multiple events. Inspired by the intri

TUX: Measuring Human--AI Tacit Understanding

arXiv:2605.30930v1 Announce Type: cross Abstract: As large language models (LLMs) increasingly act as collaborative partners, human--AI alignment is often evaluated through explicit task success, accu

Ubiquity of Emergent Hebbian Dynamics in Regularized Learning

arXiv:2505.18069v3 Announce Type: replace Abstract: Hebbian and anti-Hebbian plasticity are widely observed in the brain and are classically modeled as mechanistic, local homosynaptic rules stabilized

Uncertainty-Aware and Temporally Regulated Expert Advice in Reinforcement Learning for Autonomous Driving

arXiv:2605.30576v1 Announce Type: new Abstract: Exploration in reinforcement learning for autonomous driving is inherently unsafe: agents must experience novel behaviors to learn, yet exploration can

Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization

arXiv:2506.22304v3 Announce Type: replace-cross Abstract: Continuous Normalizing Flows (CNFs) enable elegant generative modeling but remain bottlenecked by their iterative nature requiring costly samp

UniAudio-Token: Empowering Semantic Speech Tokenizers with General Audio Perception

arXiv:2605.31521v1 Announce Type: new Abstract: Semantic speech tokenizers have become a widely used interface for Audio-LLMs, owing to their compact single-codebook design and strong linguistic align

UniRTL: Unifying Code and Graph for Robust RTL Representation Learning

arXiv:2605.31040v1 Announce Type: new Abstract: Developing effective representations for register transfer level (RTL) designs is crucial for accelerating the hardware design workflow. Existing approa

Unsupervised Defect Detection for Surgical Instruments

arXiv:2509.21561v2 Announce Type: replace Abstract: Ensuring the safety of surgical instruments requires reliable detection of visual defects. However, manual inspection is prone to error, and existin

Used Car Salesbots? Honesty and Credulity of LLMs as Bargaining Agents under Partial Information

arXiv:2605.31445v1 Announce Type: cross Abstract: In this work we study agents in simulated bargaining scenarios, where a buyer and a seller communicate through a text channel and attempt to negotiate

UXR PoV for Neuroinclusive Emotion Regulation

arXiv:2605.31131v1 Announce Type: cross Abstract: Attention-deficit/hyperactivity disorder (ADHD) is a psychiatric disorder which presents itself in individuals through patterns of developmentally ina

Value Functions as Supermartingale Certificates

arXiv:2605.31524v1 Announce Type: new Abstract: Certification methods for stochastic systems provide sufficient proof rules, based on real-valued supermartingale certificates, to determine the almost-

VeriGate: Verifier-Gated Step-Level Supervision for GRPO

arXiv:2605.30451v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) is an effective recipe for training reasoning models with verifier-based outcome rewards, but its supervision

Vision-Language Models Suppress Female Representations Under Ambiguous Input

arXiv:2605.31556v1 Announce Type: cross Abstract: Alignment teaches vision-language models (VLMs) to avoid expressing demographic biases, and when gender is clearly visible they largely succeed. Far l

Wall-OSS-0.5 Technical Report

arXiv:2605.30877v1 Announce Type: new Abstract: Large-scale Vision-Language-Action (VLA) pretraining is increasingly adopted as the foundation for robot policies, yet the evidence for pretrained VLAs

safetyarxiv-cs-ro

@webisticsdawg @GaryMarcus Personally I think everyone w a 401(k) should be absolutely livid. I'm disgusted by this, it's so brazen. How dar…

@webisticsdawg @GaryMarcus Personally I think everyone w a 401(k) should be absolutely livid. I'm disgusted by this, it's so brazen. How dare the richest man in the world pick working Americans' pocke

What Am I Missing? Question-Answering as Hidden State Probing

arXiv:2605.31561v1 Announce Type: new Abstract: Test-time reasoning has become a significant field of study since the introduction of chain-of-thought reasoning in large language models (LLMs). Howeve

What if the skeptics are right and superintelligence is impossible? Great! Then a proactive ban costs us nothing, prevents massive compute w…

What if the skeptics are right and superintelligence is impossible? Great! Then a proactive ban costs us nothing, prevents massive compute waste, and hurts no one. But if they are wrong? We face an un

safetyconnor-leahy--x

When are LLMs Sufficient Policy Optimizers for Sequential RL Tasks?

arXiv:2605.30719v1 Announce Type: cross Abstract: We study when large language models (LLMs) can serve as effective black-box policy optimizers for reinforcement learning (RL) tasks, i.e., when can we

which is NOT new; see this quote from 5 years ago. the fact that all this is still true says a lot.

which is NOT new; see this quote from 5 years ago. the fact that all this is still true says a lot. This was right five years ago, and still is: “Large scale pretrained models are certainly likely to

Who Gets Credit or Blame? Attributing Accountability in Modern AI Systems

arXiv:2506.00175v5 Announce Type: replace-cross Abstract: Modern AI systems are typically developed through multiple stages-pretraining, fine-tuning rounds, and subsequent adaptation or alignment, whe

Why Linear Recurrent Memory Works in Partially Observable Reinforcement Learning

arXiv:2605.31261v1 Announce Type: cross Abstract: The family of linear recurrent neural networks has shown strong performance as recurrent memory units in partially observable reinforcement learning.

World Action Verifier: Self-Improving World Models via Forward-Inverse Asymmetry

arXiv:2604.01985v2 Announce Type: replace-cross Abstract: General-purpose world models promise scalable policy evaluation, optimization, and planning, yet achieving the required level of robustness re

World2Act: Latent Action Post-Training from World Model Dynamics

arXiv:2603.10422v2 Announce Type: replace Abstract: World Models (WMs) offer a promising mechanism for post-training Vision-Language-Action (VLA) policies by providing dynamics priors that improve gen

Your Teacher Can't Help You Here: Combating Supervision Fidelity Decay in On-Policy Distillation

arXiv:2605.30833v1 Announce Type: cross Abstract: On-policy distillation transfers reasoning capabilities by training a student model on its own generated trajectories using token-level feedback from