Search: “safety”

DynaMiCS: Fine-tuning LLMs with Performance Constraints using Dynamic Mixtures

arXiv:2605.10770v1 Announce Type: new Abstract: Multi-domain fine-tuning of large language models requires improving performance on target domains while preserving performance on constrained domains,

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f-GRPO and Beyond: Divergence-Based Reinforcement Learning Algorithms for General LLM Alignment

DGX agent

arXiv:2602.05946v3 Announce Type: replace Abstract: Recent work shows that preference alignment objectives can be interpreted as divergence estimators between aligned (preferred) & unaligned (less-pre

safetyarxiv-cs-lg

12 May 2026

Intelligent Autonomous Orchestration for Distributed Cloud Resources using Complex-Stability Analysis

arXiv:2605.08139v1 Announce Type: cross Abstract: In modern distributed cloud environments, efficient resource allocation is required as traditional scaling mechanisms are often subject to cloud thras

MedFL-Stress: A Systematic Robustness Evaluation of Federated Brain Tumor Segmentation under Cross-Hospital MRI Appearance Shift

arXiv:2605.09025v1 Announce Type: new Abstract: Federated learning enables hospitals to collaboratively train segmentation models without sharing patient data. However, current evaluation protocols re

MoMo: Conditioned Contrastive Representation Learning for Preference-Modulated Planning

arXiv:2605.08512v1 Announce Type: new Abstract: Temporally contrastive representation learning induces a latent structure capable of reducing long-horizon planning to inference in a low-dimensional li

Muninn: Your Trajectory Diffusion Model But Faster

arXiv:2605.09999v1 Announce Type: new Abstract: Diffusion-based trajectory planners can synthesize rich, multimodal robot motions, but their iterative denoising makes online planning and control prohi

Mutual Information Optimal Density Control of Linear Systems and Generalized Schrodinger Bridges with Reference Refinement

arXiv:2605.09349v1 Announce Type: cross Abstract: We consider a mutual information (MI) regularized version of optimal density control of a discrete-time linear system. MI optimal control has been pro

Not All Turns Matter: Credit Assignment for Multi-Turn Jailbreaking

arXiv:2605.08778v1 Announce Type: new Abstract: Deploying LLMs in multi-turn dialogues facilitates jailbreak attacks that distribute harmful intent across seemingly benign turns. Recent training-based

On Uniform Error Bounds for Kernel Regression under Non-Gaussian Noise

arXiv:2605.09757v1 Announce Type: new Abstract: Providing non-conservative uncertainty quantification for function estimates derived from noisy observations remains a fundamental challenge in statisti

PHMForge: Evaluating LLM Agents on Industrial Prognostics through MCP-Native, Algorithm-Grounded Tools

arXiv:2604.01532v2 Announce Type: replace Abstract: LLM agents are beginning to invoke industrial asset-management tools through the Model Context Protocol (MCP), yet whether they can act reliably on

Research on Security Enhancement Methods for Adversarial Robust Large Language Model Intelligent Agents for Medical Decision-Making Tasks

arXiv:2605.08257v1 Announce Type: cross Abstract: Motivated by the challenge to improve the adversarial robustness, security, and trust of medical decision making intelligent agents, this study develo

Self-ReSET: Learning to Self-Recover from Unsafe Reasoning Trajectories

arXiv:2605.08936v1 Announce Type: new Abstract: Large Reasoning Models possess remarkable capabilities for self-correction in general domain; however, they frequently struggle to recover from unsafe r

SHIELD: Scalable Optimal Control with Certification using Duality and Convexity

arXiv:2605.09171v1 Announce Type: new Abstract: We present SHIELD, a hierarchical algorithm that reduces both the decision-variable dimension and the constraint set in ell_1-regularized convex program

SnareNet: Flexible Repair Layers for Neural Networks with Hard Constraints

arXiv:2602.09317v2 Announce Type: replace-cross Abstract: Neural networks are increasingly used as fast surrogate models across various domains, but unconstrained predictions can violate physical, ope

Supervised Mixture-of-Experts for Surgical Grasping and Retraction

arXiv:2601.21971v2 Announce Type: replace-cross Abstract: Imitation learning has achieved remarkable success in robotic manipulation, yet its application to surgical robotics remains challenging due t

The Art of the Jailbreak: Formulating Jailbreak Attacks for LLM Security Beyond Binary Scoring

arXiv:2605.09225v1 Announce Type: cross Abstract: Jailbreak attacks -- adversarial prompts that bypass LLM alignment through purely linguistic manipulation -- pose a growing operational security threa

The Value of Mechanistic Priors in Sequential Decision Making

arXiv:2605.10018v1 Announce Type: new Abstract: Hybrid mechanistic models, physical priors with learned residuals, promise to reduce the data required for good decisions, but have no computable criter

There will be no AI jobpocalypse. The story that AI will lead to massive unemployment is stoking unnecessary fear. AI — like any other techn…

There will be no AI jobpocalypse. The story that AI will lead to massive unemployment is stoking unnecessary fear. AI — like any other technology — does affect jobs, but telling overblown stories of l

safetyandrew-ng--x

Token Buncher: Shielding LLMs from Harmful Reinforcement Learning Fine-Tuning

arXiv:2508.20697v3 Announce Type: replace-cross Abstract: As large language models (LLMs) continue to grow in capability, so do the risks of harmful misuse through fine-tuning. While most prior studie

Unlearners Can Lie: Evaluating and Improving Honesty in LLM Unlearning

arXiv:2605.08765v1 Announce Type: cross Abstract: Unlearning in large language models (LLMs) aims to remove harmful training data while preserving overall utility. However, we find that existing metho

Variational Inference for Levy Process-Driven SDEs via Neural Tilting

arXiv:2605.10934v1 Announce Type: cross Abstract: Modelling extreme events and heavy-tailed phenomena is central to building reliable predictive systems in domains such as finance, climate science, an

When a Robot is More Capable than a Human: Learning from Constrained Demonstrators

arXiv:2510.09096v3 Announce Type: replace-cross Abstract: Learning from demonstrations enables experts to teach robots complex tasks using interfaces such as kinesthetic teaching, joystick control, an

Beyond Confidence: Rethinking Self-Assessments for Performance Prediction in LLMs

arXiv:2605.07806v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in settings where reliable self-assessment is critical. Assessing model reliability has evolved fro

Future-proof your data strategy: AlloyDB adds PostgreSQL 18 and new Extended Support

As you look out at your 2026 infrastructure roadmap, your goal is to balance the need for rapid innovation with operational stability. You shouldn't have to choose between adopting the latest database

safetygoogle-cloud-ai

Intention assimilation control for accurate tracking with variable impedance in teleoperation

arXiv:2605.07037v1 Announce Type: new Abstract: Robot systems for teleoperation commonly use a spring-like force pulling the follower robot towards the leader's position to track their movements. With

Learned Lyapunov Shielding for Adaptive Control

arXiv:2605.06934v1 Announce Type: new Abstract: We augment the Slotine--Li adaptive controller for Euler--Lagrange systems with three learned components: a structured-quadratic Lyapunov function (V_ps

Probabilistic Object Detection with Conformal Prediction

arXiv:2605.07549v1 Announce Type: new Abstract: Conformal Prediction (CP) is a distribution-free method for constructing prediction sets with marginal finite-sample coverage guarantees, making it a su

Sensitivity-Based Robust NMPC for Close-Proximity Offshore Wind Turbine Inspection with a Tilted Multirotor

arXiv:2605.07771v1 Announce Type: new Abstract: Close-proximity offshore wind turbine inspection requires strict clearance control around large cylindrical structures under wind and model mismatch. No

The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment

arXiv:2605.07462v1 Announce Type: cross Abstract: Moltbook is a Reddit-like platform where OpenClaw agents post, comment, and vote at scale - a so far unprecedented incident that comes with serious sa

Theoretical Limits of Language Model Alignment

arXiv:2605.07105v1 Announce Type: cross Abstract: Language model (LM) alignment improves model outputs to reflect human preferences while preserving the capabilities of the base model. The most common

RVPO: Risk-Sensitive Alignment via Variance Regularization

Current critic-less RLHF methods aggregate multi-objective rewards via an arithmetic mean, leaving them vulnerable to constraint neglect: high-magnitude success in one objective can numerically offset

safetyapple-ml-research

8 May 2026

The human brain🧠 is incredibly efficient because it only activates the specific neurons needed for a thought. Modern LLMs naturally try to …

The human brain🧠 is incredibly efficient because it only activates the specific neurons needed for a thought. Modern LLMs naturally try to do this too (> 95% of neurons in feedforward layers stay sile

safetydavid-ha--x

8 May 2026

Anatomy of a failure: When, how, and why deep vision fails in scientific domains

arXiv:2605.04231v1 Announce Type: new Abstract: Mirroring its ubiquity in popular media and all human activities, the use of deep learning (DL) is rapidly growing in scientific imaging modalities. How

Beyond Fixed Thresholds and Domain-Specific Benchmarks for Explainable Multi-Task Classification in Autonomous Vehicles

arXiv:2605.04299v1 Announce Type: new Abstract: Scene understanding is a vital part of autonomous driving systems, which requires the use of deep learning models. Deep learning methods are intrinsical

Encoding Predictability and Legibility for Style-Conditioned Diffusion Policy

arXiv:2603.16368v2 Announce Type: replace-cross Abstract: Striking a balance between efficiency and transparent motion is a core challenge in human-robot collaboration, as highly expressive movements

From Reach to Insert: Tactile-Augmented Precision Assembly under Sub-Millimeter Tolerances

arXiv:2605.04649v1 Announce Type: new Abstract: High-precision assembly frequently involves tight-tolerance insertions, where even slight pose errors can cause jamming or excessive interaction forces,

@GaryMarcus is on fire lately... follow him for #AI

Gary Marcus is an AI researcher and public intellectual who frequently shares commentary and insights about artificial intelligence developments on social media. His posts on X (formerly Twitter) cove

safetygary-marcus--x

How Does the Lagrangian Guide Safe Reinforcement Learning through Diffusion Models?

arXiv:2602.02924v2 Announce Type: replace Abstract: Diffusion policy sampling enables reinforcement learning (RL) to represent multimodal action distributions beyond suboptimal unimodal Gaussian polic

I'm really excited about this as a new tool in our interpretability tool kit

I'm really excited about this as a new tool in our interpretability tool kit In a new paper, we present NLAs, an unsupervised method for converting an LLM's internal state into human-readable text. I'

safetyjan-leike--x

InterFuserDVS: Event-Enhanced Sensor Fusion for Safe RL-Based Decision Making

arXiv:2605.04355v1 Announce Type: new Abstract: Autonomous driving systems rely heavily on robust sensor fusion to perceive complex envi- ronments. Traditional setups using RGB cameras and LiDAR often

It'll be 'impossible to slow down the ASI race' until it (very) suddenly isn't.

This post discusses the dynamics of artificial superintelligence (ASI) development as a competitive race, arguing that competitive pressures make it difficult to slow progress until a critical inflect

safetyconnor-leahy--x

Model Releases

Manifold of Failure: Behavioral Attraction Basins in Language Models

model-releasesarxiv-cs-lg

arXiv:2602.22291v3 Announce Type: replace Abstract: While prior work has focused on projecting adversarial examples back onto the manifold of natural data to restore safety, we argue that a comprehens

Misaligned by Reward: Socially Undesirable Preferences in LLMs

arXiv:2605.05003v1 Announce Type: new Abstract: Reward models are a key component of large language model alignment, serving as proxies for human preferences during training. However, existing evaluat

Predict-then-Diffuse: Adaptive Response Length for Compute-Budgeted Inference in Diffusion LLMs

arXiv:2605.04215v1 Announce Type: new Abstract: Diffusion-based Large Language Models (D-LLMs) represent a promising frontier in generative AI, offering fully parallel token generation that can lead t

Road Risk Monitor: A Deployable U.S. Road Incident Forecasting System with Live Weather and Road-Level Tiles

arXiv:2605.04242v1 Announce Type: new Abstract: Nationwide road-incident forecasting is a systems problem before it is a modeling problem. A usable service must connect historical incident archives, h

Sparse Tokens Suffice: Jailbreaking Audio Language Models via Token-Aware Gradient Optimization

arXiv:2605.04700v1 Announce Type: cross Abstract: Jailbreak attacks on audio language models (ALMs) optimize audio perturbations to elicit unsafe generations, and they typically update the entire wave

Terence Tao recognized that plausibility and veracity are not the same, and that current tools are better at the former than the latter. [ed…

Terence Tao recognized that plausibility and veracity are not the same, and that current tools are better at the former than the latter. [edit: the video is from 2024, and affirms what i said in 2019

safetygary-marcus--x

A Knowledge-Driven LLM-Based Decision-Support System for Explainable Defect Analysis and Mitigation Guidance in Laser Powder Bed Fusion

arXiv:2605.01100v1 Announce Type: new Abstract: This work presents a knowledge-driven decision-support system that integrates structured defect knowledge with LLM-based reasoning to provide explainabl