Search: “safety”

Viverra: Text-to-Code with Guarantees

arXiv:2605.14972v1 Announce Type: cross Abstract: A fundamental limitation of Text-to-Code is that no guarantee can be obtained about the correctness of the generated code. Therefore, to ensure its co

15 May 2026

Asynchronous Reasoning: Training-Free Interactive Thinking LLMs

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

arXiv:2512.10931v3 Announce Type: replace Abstract: Many state-of-the-art LLMs are trained to think before giving their answer. Reasoning can greatly improve language model capabilities, but it also m

safetyarxiv-cs-lg

14 May 2026

Automated Rubrics for Reliable Evaluation of Medical Dialogue Systems

arXiv:2601.15161v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used for clinical decision support, where hallucinations and unsafe suggestions may pose direct

BEHAVE: A Hybrid AI Framework for Real-Time Modeling of Collective Human Dynamics

arXiv:2605.12730v1 Announce Type: new Abstract: Existing AI systems for modeling human behavior operate at the level of individuals or detect events after they occur. As a result, they systematically

DiffusionHijack: Supply-Chain PRNG Backdoor Attack on Diffusion Models and Quantum Random Number Defense

arXiv:2605.13115v1 Announce Type: cross Abstract: Diffusion models depend on pseudo-random number generators (PRNGs) for latent noise sampling. We present DiffusionHijack, a supply-chain backdoor atta

Loiter UAV Reinsertion Guidance for Fixed-wing UAV Corridors

arXiv:2605.13822v1 Announce Type: new Abstract: This paper considers fixed-wing unmanned aerial vehicle (UAV) corridors comprising a main lane, a circular loiter lane for managing traffic congestion,

Model Releases

Neurosymbolic Auditing of Natural-Language Software Requirements

model-releasesarxiv-cs-ai

arXiv:2605.13817v1 Announce Type: cross Abstract: Natural-language software requirements are often ambiguous, inconsistent, and underspecified; in safety-critical domains, these defects propagate into

No Attack Required: Semantic Fuzzing for Specification Violations in Agent Skills

arXiv:2605.13044v1 Announce Type: cross Abstract: LLM-powered agents can silently delete documents, leak credentials, or transfer funds on a routine user request, not because the agent was attacked, b

Runtime Monitoring of Perception-Based Autonomous Systems via Embedding Temporal Logic

arXiv:2605.12651v1 Announce Type: new Abstract: Runtime monitoring of autonomous systems traditionally relies on mapping continuous sensor observations to discrete logical propositions defined over lo

The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy

arXiv:2605.12735v1 Announce Type: new Abstract: We introduce and open-source the Unified Autonomy Stack, a system-level solution that enables resilient autonomy across diverse aerial and ground robot

VERA-MH: Validation of Ethical and Responsible AI in Mental Health

arXiv:2605.13318v1 Announce Type: new Abstract: Chatbot usage has increased, including in fields for which they were never developed for--notably mental health support. To that end, we introduce Valid

Can a Single Message Paralyze the AI Infrastructure? The Rise of AbO-DDoS Attacks through Targeted Mobius Injection

arXiv:2605.11442v1 Announce Type: cross Abstract: Large Language Model (LLM) agents have emerged as key intermediaries, orchestrating complex interactions between human users and a wide range of digit

Characterizing the Robustness of Black-Box LLM Planners Under Perturbed Observations with Adaptive Stress Testing

arXiv:2505.05665v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have recently demonstrated success in decision-making tasks including planning, control, and prediction, but thei

Continuous Discovery of Vulnerabilities in LLM Serving Systems with Fuzzing

arXiv:2605.11202v1 Announce Type: cross Abstract: LLM inference and serving systems have become security-critical infrastructure; however, many of their most concerning failures arise from the serving

Leveraging RAG for Training-Free Alignment of LLMs

arXiv:2605.11217v1 Announce Type: new Abstract: Large language model (LLM) alignment algorithms typically consist of post-training over preference pairs. While such algorithms are widely used to enabl

One Turn Too Late: Response-Aware Defense Against Hidden Malicious Intent in Multi-Turn Dialogue

arXiv:2605.05630v2 Announce Type: replace Abstract: Hidden malicious intent in multi-turn dialogue poses a growing threat to deployed large language models (LLMs). Rather than exposing a harmful objec

Robust LLM Unlearning Against Relearning Attacks: The Minor Components in Representations Matter

arXiv:2605.11685v1 Announce Type: new Abstract: Large language model (LLM) unlearning aims to remove specific data influences from pre-trained model without costly retraining, addressing privacy, copy

The DAWN of World-Action Interactive Models

arXiv:2605.11550v1 Announce Type: new Abstract: A plausible scene evolution depends on the maneuver being considered, while a good maneuver depends on how the scene may evolve. Existing World Action M

safetyarxiv-cs-cv

ActivationReasoning: Logical Reasoning in Latent Activation Spaces

arXiv:2510.18184v3 Announce Type: replace-cross Abstract: Large language models (LLMs) excel at generating fluent text, but their internal reasoning remains opaque and difficult to control. Sparse aut

Adversarial Attacks Against MLLMs via Progressive Resolution Processing and Adaptive Feature Alignment

arXiv:2605.09902v1 Announce Type: new Abstract: Adversarial perturbations can mislead Multimodal Large Language Models (MLLMs) recognize a benign image as a specific target object, posing serious risk

safetyarxiv-cs-cv

Beyond Autonomy: A Dynamic Tiered AgentRunner Framework for Governable and Resilient Enterprise AI Execution

arXiv:2605.10223v1 Announce Type: new Abstract: Current large language model agent frameworks prioritize autonomy but lack the governability mechanisms required for enterprise deployment. High-risk wr

Beyond Self-Play: Hierarchical Reasoning for Continuous Motion in Closed-Loop Traffic Simulation

arXiv:2605.09153v1 Announce Type: cross Abstract: Closed-loop traffic simulation requires agents that are both scalable and behaviorally realistic. Recent self-play reinforcement learning approaches d

Break the Brake, Not the Wheel: Untargeted Jailbreak via Entropy Maximization

arXiv:2605.10764v1 Announce Type: cross Abstract: Recent studies show that gradient-based universal image jailbreaks on vision-language models (VLMs) exhibit little or no cross-model transferability,

Model Releases

C-CoT: Counterfactual Chain-of-Thought with Vision-Language Models for Safe Autonomous Driving

model-releasesarxiv-cs-cv

arXiv:2605.10744v1 Announce Type: new Abstract: Safety-critical planning in complex environments, particularly at urban intersections, remains a fundamental challenge for autonomous driving. Existing

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,

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

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

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

safetyarxiv-cs-cv

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

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

11 May 2026

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

11 May 2026

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