safety

PReSS: An Automated Black-Box Framework for Evaluating Political Stance Stability in LLMs

arXiv:2504.17052v4 Announce Type: replace Abstract: Existing evaluations of political bias in large language models (LLMs) typically classify outputs as left- or right-leaning. We extend this perspect

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PRISM: Polynomial Representations for Interaction-Structured Motor Control

DGX agent

arXiv:2607.23473v1 Announce Type: new Abstract: Robot policies are typically MLPs mapping observations to actions. Yet robot observations are physical variables, and many action-relevant cues arise no

safetyarxiv-cs-ro

28 Jul 2026

PRISM: Prompt Refinement via Image-grounded Self-rewarding Mechanism for Text-to-Image Generation

arXiv:2607.24353v1 Announce Type: new Abstract: Text-to-image generation models can synthesize high-quality images from natural language descriptions, but their performance remains highly sensitive to

Progress-conditioned Group Policy Optimization for Long-Horizon Agentic Tasks

arXiv:2607.22724v1 Announce Type: cross Abstract: Group-based policy optimization has been increasingly used to train large language model (LLM) agents from sparse outcome rewards by comparing traject

Reasoning to Regulate: Chain-of-Thought for Traffic Rule Understanding

arXiv:2607.24199v1 Announce Type: new Abstract: Understanding and complying with traffic regulations is a safety-critical requirement for autonomous driving, yet remains challenging due to the diversi

Reconstruction of SINR Maps from Sparse Measurements using Group Equivariant Non-Expansive Operators

arXiv:2507.19349v3 Announce Type: replace Abstract: As sixth generation (6G) wireless networks evolve, accurate signal-to-interference-noise ratio (SINR) maps are becoming increasingly critical for ef

Regulating for AI Legitimacy

arXiv:2607.24391v1 Announce Type: cross Abstract: AI systems already govern. They rank speech and allocate attention, filter applicants and triage claims. The dominant frame for AI governance, alignme

Reinforcement Learning for Heterogeneous Sensor Selection in Maritime Surveillance

arXiv:2607.22667v1 Announce Type: new Abstract: This paper presents an information-gain-guided reinforcement-learning sensor-selection framework for single-vessel tracking in heterogeneous maritime se

Rethinking Classifier-Free Guidance in On-Policy Diffusion Distillation

arXiv:2607.24731v1 Announce Type: cross Abstract: On-policy distillation (OPD) adapts diffusion models by querying a teacher along trajectories generated by the current student, but how it should beha

RM-Distiller: Exploiting Generative LLM for Reward Model Distillation

arXiv:2601.14032v2 Announce Type: replace Abstract: Reward models (RMs) play a pivotal role in aligning large language models (LLMs) with human preferences. Due to the difficulty of obtaining high-qua

RMS@CC-MMD 2026: Multimodal Misogyny Detection via Geometric Interaction and Multi-View Consensus

arXiv:2607.22709v1 Announce Type: cross Abstract: The proliferation of internet memes has introduced new complexities to automated content moderation, particularly in detecting misogyny. Memes often r

Robust 6-DoF Object Pose Tracking with Built-In Recovery under Occlusions and Rapid Object Motions

arXiv:2607.23468v1 Announce Type: new Abstract: Real-time 6-DoF object pose tracking is essential for many robotics applications, and several approaches exist. Yet even today's approaches remain unrel

Robustness and Cybersecurity in the EU Artificial Intelligence Act

arXiv:2502.16184v3 Announce Type: replace Abstract: The EU Artificial Intelligence Act (AIA) establishes different legal principles for different types of AI systems. While prior work has sought to cl

Sam Altman: “Concentration of power with AI is a terrifying thing.” 'A lot of the talk about safety concerns is well-founded, and then a lot…

Sam Altman: “Concentration of power with AI is a terrifying thing.” 'A lot of the talk about safety concerns is well-founded, and then a lot of it is about people that just really, even if it's slight

safetyclem-delangue--x

Sampling Decisions: Exact Path-Space Correction, Prior Cancellation and Local-Boltzmann Guidance

arXiv:2503.14549v3 Announce Type: replace-cross Abstract: How can a cheap but biased sequential, finite-horizon sampler over a discrete space be corrected so that its terminal output follows a prescri

Self-Authored Verification Is Unreliable in Heuristic Self-Improving Agents

arXiv:2607.24300v1 Announce Type: new Abstract: Self-improving agents accumulate capability by repeatedly rewriting procedural policies, controllers, or heuristic rules. They typically rely on self-au

Self-Boosting Vision-Language Models with Noisy Student On-Policy Self-Distillation

arXiv:2607.23125v1 Announce Type: new Abstract: Post-training enables vision-language models (VLMs) to understand human instructions and perform various downstream tasks. Current post-training methods

Self-Motivated Growing Neural Network for Adaptive Architecture via Local Structural Plasticity

arXiv:2512.12713v2 Announce Type: replace-cross Abstract: Control policies are often implemented with fixed-capacity multilayer perceptrons trained by backpropagation, which require architecture selec

Shift-Aware Calibration for Fine-Tuned CLIP: Leveraging Image-Text Alignment

arXiv:2501.19060v4 Announce Type: replace Abstract: Vision-language models (VLMs), such as CLIP, adapt effectively to downstream tasks through prompt tuning, but fine-tuning can misalign predictive co

Sign-Symmetry Learning Rules are Robust Fine-Tuners

arXiv:2502.05925v2 Announce Type: replace-cross Abstract: Backpropagation (BP) has long been the predominant method for training neural networks due to its effectiveness. However, numerous alternative

SpecFormer: Mitigating Embedding and Attention Collapse via Spectral-Aware Transformer for Recommendation

arXiv:2607.24025v1 Announce Type: cross Abstract: Transformer architectures have achieved remarkable success across diverse domains; however, directly applying their standard self-attention mechanism

Spectral Dynamics of Semantic Drift in Clinical Multi-Agent Language Model Networks

arXiv:2607.22758v1 Announce Type: cross Abstract: The integration of iterative LLMs within multi-agent diagnostic frameworks requires a rigorous quantitative reevaluation of underlying communication t

StageGuard: Physiologically Constrained Sleep Staging

arXiv:2607.23284v1 Announce Type: new Abstract: Automated sleep staging is increasingly used in large-scale studies to derive sleep-architecture endpoints: total sleep time, REM latency, sleep efficie

STAIF: A Stage-wise Optimization for Complex Instruction Following

arXiv:2607.22649v1 Announce Type: new Abstract: Following complex instructions with multiple explicit constraints remains a fundamental challenge for large language models (LLMs). Existing alignment m

Structured Redundancy Modeling for Efficient Visual Token Pruning in High-Resolution MLLMs

arXiv:2607.23046v1 Announce Type: new Abstract: Recent high-resolution Multimodal Large Language Models (MLLMs) generate thousands of visual tokens per input, leading to a visual token explosion that

Synthetic Scenario Generation for Evaluation of Industry 4.0 Agents

arXiv:2607.22563v1 Announce Type: new Abstract: Industrial agent benchmarks require realistic evaluation scenarios that integrate telemetry, failure modes, maintenance records, and domain standards. H

SyRuP: Enhancing System-Prompt Following via Reward-Guided Prediction in LLM Decoding

arXiv:2607.23991v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly controlled through system prompts that specify roles, styles, formats, and safety requirements. However,

Systematic Analysis of Large Language Models and Transformer-Based Machine Translation for English-Tamil and Tamil-English Across Diverse Datasets

arXiv:2607.24515v1 Announce Type: new Abstract: The challenge of Machine Translation for low resource languages such as Tamil is primarily caused by the restricted amount of parallel data for these la

Tailored untruths: How personalisation challenges LLM safeguards

arXiv:2510.12993v3 Announce Type: replace Abstract: Large Language Models (LLMs) can generate highly persuasive disinformation, yet little is known about how effectively they personalise it across lan

Test-Time Adaptation via Dual Distillation for Videos Under Severe Distribution Shifts

arXiv:2607.24611v1 Announce Type: new Abstract: Deep learning models have achieved state-of-the-art performance in several computer vision tasks. However, they experience severe performance degradatio

The administration is very consistent: All aspects of foreign involvement in AI are banned: people (students and employees), hardware and mo…

The administration is very consistent: All aspects of foreign involvement in AI are banned: people (students and employees), hardware and models. And funding is down to a drip and capital allocation i

safetygary-marcus--x

The Agentic AI Foundation updates MCP with a fully stateless architecture, a hardened authentication model, a formal 12-month deprecation policy, and more (Michael Nuñez/VentureBeat)

Michael Nuñez / VentureBeat: The Agentic AI Foundation updates MCP with a fully stateless architecture, a hardened authentication model, a formal 12-month deprecation policy, and more — The Model Cont

safetytechmeme

The Curse of Precision: A Data Scaling Law for High-Precision Robotic Manipulation

arXiv:2607.23108v1 Announce Type: new Abstract: While scaling laws for imitation learning have primarily focused on generalization in open-world settings, the relationship between data and precision i

safetyarxiv-cs-ro

The Entropic Bound for Transformers: Why Static Rank Fails and Attention-Native Rank Recovers

arXiv:2607.23050v1 Announce Type: new Abstract: Neural scaling laws describe how loss decreases as models, data, and compute grow, but they do not answer a prior question: for a fixed task, what is th

The Physics of Multi-Turn Long-Horizon Planning: From Pre-training to Post-training via Single- and Multi-Teacher On-Policy Agentic Distillation

arXiv:2607.24720v1 Announce Type: cross Abstract: Multi-turn long-horizon planning is critical for foundation model agents, yet how to fundamentally improve it remains unclear. Existing models are tra

Towards Dual-Brain Minimal Sufficient Representation for Vision-Language Navigation

arXiv:2607.23181v1 Announce Type: new Abstract: Vision-and-Language Navigation in continuous environments (VLN-CE) requires an agent to ground language in egocentric observations and plan in unseen sc

TRACE-CTI: Auditable Post-Extraction Governance of TTP Claims with Knowledge Graphs

arXiv:2607.24563v1 Announce Type: new Abstract: Security Operations Centers increasingly rely on automated mapping of Cyber Threat Intelligence reports to MITRE ATT&CK, yet extractor outputs remain fa

Training with (Swap) Regret Loss in a Single-Layer Self-Attention Model: A Case Study on the Probability Simplex

arXiv:2607.23333v1 Announce Type: cross Abstract: We revisit the regret loss framework introduced in Park et al. (2025), which uses decision-theoretic regret as a direct loss function for training mod

Unequal Trips, Unequal Places: Diagnosing and Mitigating Delay Inequity in Autonomous Vehicle Fleet Coordination

arXiv:2607.24336v1 Announce Type: new Abstract: City-scale autonomous vehicle fleet coordinators are typically optimized for aggregate travel time, yet fleet averages conceal how delay is distributed

Unifying Complementarity Constraints and Control Barrier Functions for Safe Whole-Body Robot Control

arXiv:2504.17647v2 Announce Type: replace Abstract: Safety-critical whole-body robot control demands reactive methods that ensure collision avoidance in real-time. Complementarity constraints and cont

safetyarxiv-cs-ro

Variational Boosting for Physics-Informed Neural Networks

arXiv:2607.23940v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) solve differential equations by minimizing the residual of a nonlinear operator over a neural parameterization

Visual Token Compression Enhances Robustness of MLLMs

arXiv:2607.22716v1 Announce Type: new Abstract: In this paper, we show for the first time that visual token pruning enhances the robustness of Multimodal Large Language Models (MLLMs), mitigating vuln

What Can Be Enforced? A Theory of Certified Runtime Safety for Tool-Using Agents

arXiv:2607.22868v1 Announce Type: new Abstract: Runtime guardrails act before irreversible tool calls, but their guarantees depend on what policy state is representable, what a judge observes, and whe

What CLIP Knows but Cannot Say: Recovering Negation from Frozen Intermediate Features

arXiv:2607.23271v1 Announce Type: cross Abstract: Contrastive vision-language models such as CLIP map semantically opposite phrases (e.g., 'a dog' vs. 'not a dog') to nearly identical embeddings, rend

When Every Simulation Counts: Value-Based Reinforcement Learning for Accelerated Photonics Inverse Design

arXiv:2607.23469v1 Announce Type: cross Abstract: Photonic-crystal surface-emitting lasers (PCSELs) can combine high-power operation with narrow-divergence surface emission, but optimizing coupled par

When Should Active RAG Retrieve? A Budget-Aware Evaluation of Utility, Calibration, and Cost

arXiv:2607.24010v1 Announce Type: new Abstract: Active RAG systems decide when to retrieve external knowledge during generation, making them a budget-sensitive case of agentic RAG and self-adaptive re

Where Is the Cost of Third-Party API Routers in Agentic Software Development?

arXiv:2607.23624v1 Announce Type: cross Abstract: Third-party API routers have become a common layer that unifies access across increasingly diverse LLM providers. In coding-agent workflows, high-auto

White-hat hacking IS the defense to black-hat hacking. The techniques are the same. How does Dario expect companies to do it if their models refuse?

You patch security holes by intentionally finding them. If the models refuse to do it, how can companies protect themselves against rogue AIs, whether they are Chinese or OpenAI/Anthropic themselves?

safetyr-localllama