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SpecGradFilter: A Spectral Gradient Filtering Framework for Taming Federated Heterogeneity

arXiv:2607.04189v1 Announce Type: new Abstract: Federated Learning (FL) is fundamentally challenged by statistical heterogeneity, where non-identically distributed (non-IID) data induces client drift

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Spectral Gradient Descent Mitigates Anisotropy-Driven Misalignment: A Case Study in Phase Retrieval

DGX agent

arXiv:2601.22652v2 Announce Type: replace-cross Abstract: Spectral gradient methods, such as the Muon optimizer, modify gradient updates by preserving directional information while discarding scale, a

safetyarxiv-cs-lg

7 Jul 2026

StageCraft: Execution Aware Mitigation of Distractor and Obstruction Failures in VLA Models

arXiv:2603.20659v2 Announce Type: replace Abstract: Large scale pre-training on text and image data along with diverse robot demonstrations has helped Vision Language Action models (VLAs) to generaliz

safetyarxiv-cs-ro

STAPO: Selective Trajectory-Aware Policy Optimization for LLM Agent Training

arXiv:2607.04963v1 Announce Type: new Abstract: Reinforcement Learning (RL) is the dominant paradigm for training Large Language Model (LLM) agents on long-horizon tasks. However, sparse and delayed r

States are asking for a $1.4 trillion fine of Meta over addicting kids. Basically they are saying Facebook's business model is a result of c…

States are asking for a $1.4 trillion fine of Meta over addicting kids. Basically they are saying Facebook's business model is a result of crime, and all of Mark Zuckerberg's property should be forfei

Strategic Buying Agents

arXiv:2607.04708v1 Announce Type: cross Abstract: Agentic AI is shifting online shopping from search toward delegated purchasing, where autonomous buying agents monitor markets and decide when to buy

STRATOS: Bridging the Symbolic-to-Numeric Gap in Spatio-Temporal Text-to-SQL for Meteorological Data

arXiv:2607.03501v1 Announce Type: cross Abstract: Copernicus, the European Union's Earth observation program, produces petabytes of Earth observation and climate data, offering immense potential for r

Structure-Guided Self-Supervised Matching for One-Shot Medical Landmark Detection

arXiv:2203.01687v3 Announce Type: replace Abstract: Medical landmark detection usually requires accurate expert annotations, which are laborious and difficult to scale across anatomical regions. In th

TaoSR-AGRL: Adaptive Guided Reinforcement Learning Framework for E-commerce Search Relevance

arXiv:2510.08048v4 Announce Type: replace-cross Abstract: Query-product relevance prediction is fundamental to e-commerce search and has become even more critical in the era of AI-powered shopping, wh

Teaming Up with AI: Coordination and Cooperation

arXiv:2607.03181v1 Announce Type: cross Abstract: Successful diffusion of AI in the workforce hinges on the economic value that AI brings to human endeavors. Bringing AI into the workforce is more tha

Telescope: Improving Zero Shot Detection of LLM Generated Content By Measuring Token Repetition Probability

arXiv:2607.04061v1 Announce Type: cross Abstract: Distinguishing Large Language Model (LLM) generated text from human writing is a critical and difficult challenge. While LLMs are trained to write lik

Tensor-Train Joint Modeling for Few-Step Discrete Diffusion

arXiv:2607.03788v1 Announce Type: new Abstract: Discrete diffusion promises orders-of-magnitude faster generation than autoregressive (AR) models for sequential discrete data, yet its full potential o

Text as Partial Constraint: Core-Residual Alignment for Robust Vision-Language Learning

arXiv:2607.03143v1 Announce Type: cross Abstract: Vision-language alignment powers open-vocabulary recognition, retrieval, and LVLM grounding, yet natural captions are often underspecified, making sim

Text Dictates, Music Decorates: Energy-based Attention for Editable Dance Motion Generation

arXiv:2606.22726v2 Announce Type: replace Abstract: Choreographic motion generation poses unique challenges for AI, demanding precise semantic control over complex, temporally structured, and expressi

The agent creates, we validate: A Lightweight Framework for Agentic Artifact Generation

arXiv:2607.02615v1 Announce Type: cross Abstract: Generating structured artifacts with Large Language Models - e.g. database queries, threat framework mappings, entity schemas - is relatively straight

The AI boom is just not sustainable; Apollo’s Torsten Slok joins the chorus.

The AI boom is just not sustainable; Apollo’s Torsten Slok joins the chorus. Torsten Slok argues that the AI boom can only be seen in the hyperscalers and semiconductor companies and that this is caus

The Foreign Policy AI Evaluation Gap

arXiv:2607.02955v1 Announce Type: cross Abstract: We argue that AI systems used in conducting foreign policy tasks - broadly enacting 'statecraft' - should be a priority test case for technical AI gov

The ‘Ghost’ in the Database: Recovering Active ADFS Signing Keys via Machine DPAPI

Written by: Shebin Mathew Introduction The 'Golden SAML' technique, first described by CyberArk researchers in 2017, and further detailed by Mandiant researchers in 2021, remains one of the most effec

safetygoogle-cloud-ai

The Three Regimes of Offline-to-Online Reinforcement Learning

arXiv:2510.01460v4 Announce Type: replace-cross Abstract: Offline-to-online reinforcement learning (RL) has emerged as a practical paradigm that leverages offline datasets for pretraining and online i

TokAN: Accent Normalization Using Self-Supervised Speech Tokens

arXiv:2607.03928v1 Announce Type: cross Abstract: Accent normalization (AN) seeks to convert non-native (L2) accented speech into standard (L1) speech while preserving speaker identity. The current te

Towards Data-Driven Metrics for Social Robot Navigation Benchmarking

arXiv:2509.01251v3 Announce Type: replace Abstract: This paper presents a joint effort towards the development of a data-driven Social Robot Navigation metric to facilitate benchmarking and policy opt

safetyarxiv-cs-ro

Training Verifiably Robust Agents Using Set-Based Reinforcement Learning

arXiv:2408.09112v2 Announce Type: replace Abstract: Reinforcement learning policies parametrized by deep neural networks have achieved strong performance for continuous control, yet even small input p

Trajectory-Anchor Optimization for Overconfident Thermal Visual Place Recognition: Zero-Leakage OOD Auditing and Kidnapped-Robot Recovery

arXiv:2607.04745v1 Announce Type: cross Abstract: Modern thermal visual place recognition (TIR-VPR) frontends based on foundation models achieve remarkable closed-set retrieval but suffer from an over

Transformer-Based Multi-Agent Reinforcement Learning for Networked Systems with Long-Range Interactions

arXiv:2511.13103v2 Announce Type: replace Abstract: Multi-agent reinforcement learning (MARL) has shown promise for large-scale network control, yet existing methods face two major limitations. First,

Trust Region Policy Distillation

arXiv:2607.04751v1 Announce Type: cross Abstract: Big goals are hard to achieve all at once; breaking them into small steps is wiser. We present Trust Region Policy Distillation (TOP-D), which transfo

Turning Off-Policy Tokens On-Policy: A Plug-in Approach for Improving LLM Alignment

arXiv:2607.04728v1 Announce Type: cross Abstract: Reinforcement learning (RL) post-training for large language models (LLMs) follows a efficient paradigm of 'rollout then update', which inevitably res

Two Black Boxes, One Solver: Encoder Probing and Decoder Attribution for Neural Multi-Attribute VRP under Hard-Mask and Recourse Decoders

arXiv:2607.04487v1 Announce Type: cross Abstract: Neural autoregressive solvers for the Multi-Attribute Vehicle Routing Problem (MAVRP) reach competitive cost but offer no per-step justification, a pr

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning

arXiv:2607.04425v1 Announce Type: cross Abstract: Recent advances in multimodal foundation models and agent systems have driven GUI agents from single-platform task execution toward cross-platform int

Uncertainty-Aware Abstention in Large Language Models with Provable Alignment Guarantees

arXiv:2607.04430v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in question answering (QA) systems, yet they may generate hallucinated or misaligned responses wi

safetyarxiv-cs-cl

Uncertainty-Aware Last-Layer Adaptation of RETFound for Referable Diabetic Retinopathy Screening Under Dataset Shift

arXiv:2607.02569v1 Announce Type: new Abstract: This paper presents a safety-centered empirical evaluation of uncertainty-aware last-layer adaptation for referable diabetic retinopathy screening using

Uncertainty Quantification for Regression: A Unified Framework based on kernel scores

arXiv:2510.25599v2 Announce Type: replace Abstract: Regression tasks, notably in safety-critical domains, require reliable uncertainty quantification, yet the literature remains largely classification

UNDREAM: Bridging Differentiable Rendering and Photorealistic Simulation for End-to-end Adversarial Attacks

arXiv:2510.16923v3 Announce Type: replace-cross Abstract: Deep learning models deployed in safety critical applications like autonomous driving use simulations to test their robustness against adversa

Virtual Category-Guided Continual Generalized Category Discovery

arXiv:2607.04984v1 Announce Type: new Abstract: Continual Generalized Category Discovery (C-GCD) aims to incrementally identify novel categories from sequential unlabeled data while preserving recogni

Vision Non-Causal Trapezoidal Mamba: Eliminating Directional Scanning in Vision SSMs with Second-Order Dynamics

arXiv:2607.03589v1 Announce Type: new Abstract: State Space Models (SSMs) have emerged as an alternative to Vision Transformers, yet most vision SSMs inherit directional token scanning from causal seq

VISOR++: Universal Visual Inputs based Steering for Large Vision Language Models

arXiv:2509.25533v2 Announce Type: replace-cross Abstract: As Vision Language Models (VLMs) are deployed across safety-critical applications, understanding and controlling their behavioral patterns has

VISOR: Visual Input-based Steering for Output Redirection in Vision-Language Models

arXiv:2508.08521v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) are increasingly being used in a broad range of applications, bringing their security and behavioral control to

VLA Grounder: Language-Conditioning Space Optimization for Black-Box VLA Models

arXiv:2607.04517v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are commonly treated as end-to-end action policies conditioned on natural-language task descriptions. In practice, h

VLM-CASE: Vision-Language Model Enabled Context-Adaptive Safety Envelopes for Anticipatory Safe Autonomous Driving

arXiv:2607.05180v1 Announce Type: cross Abstract: Adverse driving conditions, such as bad weather, remain a principal barrier to autonomous driving because they degrade two things at once: what the ve

Weak-to-Strong Generalization via Direct On-Policy Distillation

arXiv:2607.05394v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is a powerful recipe for improving language-model reasoning, but it is expensive to repeat on ev

When Agents Lie: Premeditation, Persistence, and Exploitation in Repeated Games

arXiv:2607.05132v1 Announce Type: cross Abstract: As large language models are deployed as autonomous agents that communicate intentions before acting, a critical safety question is whether agents tha

safetyarxiv-cs-cl

Who is entitled to benefit from major advances in technology—and on what basis? This new paper with @Dr_Atoosa argues that the benefits of t…

Who is entitled to benefit from major advances in technology—and on what basis? This new paper with @Dr_Atoosa argues that the benefits of technology—including AI—belong to the world in the sense that

WinTA-GIL: Windowed Trajectory Alignment for GNSS-IMU-LiDAR Heading Refinement in Intermittent Signal Environments

arXiv:2607.04879v1 Announce Type: new Abstract: Although multi-source fusion positioning systems have achieved significant progress, accurate and reliable heading estimation remains a critical challen

safetyarxiv-cs-ro

⚠️ Wow. The Treasury Department reportedly knows that the massive GenAI build out poses systemic risks to the US financial system – and does…

⚠️ Wow. The Treasury Department reportedly knows that the massive GenAI build out poses systemic risks to the US financial system – and doesn’t want to acknowledge it publicly. People may talk about t

You don't have to choose between 'AI is fake hype' and 'Superintelligence is inevitable, lie down and accept it.' There's a third option: hu…

You don't have to choose between 'AI is fake hype' and 'Superintelligence is inevitable, lie down and accept it.' There's a third option: humans deciding, through their governments, that machines smar

safetyconnor-leahy--x

A CEO who “vowed to fire anyone who doesn’t use AI in 2025” now says AI could not replace her executive assistant. This says a lot about how…

A CEO who “vowed to fire anyone who doesn’t use AI in 2025” now says AI could not replace her executive assistant. This says a lot about how many big believers in AI have realized that AI is not as go

F1 in Britain: Automated software to blame for crushing expectations

Race control displayed a 'Safety Car in this lap' message during the 2026 British Grand Prix at Silverstone, raising expectations for a final lap restart that never materialized, as the message was er

safetyars-technica

Fact: Dario Amodei didn’t invent overhyping the impact of neural networks on employment, Geoff Hinton did.

Gary Marcus argues that Geoff Hinton, rather than Dario Amodei, was the originator of making exaggerated claims about neural networks' impact on employment. This appears to be part of a broader discus

GenAI isn’t good enough to replace millions of employees, so there is basically no way the capex is going to earn out.

GenAI isn’t good enough to replace millions of employees, so there is basically no way the capex is going to earn out. If AI isn't going to wipe out millions of jobs every year, it has to generate pro