safety

Vision-Guided Outdoor Flight and Obstacle Evasion via Reinforcement Learning

arXiv:2605.24449v1 Announce Type: cross Abstract: Although quadcopters boast impressive traversal capabilities enabled by their omnidirectional maneuverability, the need for continuous pilot control i

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What Gets Cited: Competitive GEO in AI Answer Engines

DGX agent

arXiv:2605.25517v1 Announce Type: new Abstract: AI answer engines generate answers from retrieved pages but cite only a few sources. This makes visibility depend not just on ranking, but on being cite

safetyarxiv-cs-ai

26 May 2026

When Does Multi-Agent RL Improve LLM Workflows? Workflow, Scale, and Policy-Sharing Tradeoffs

arXiv:2605.24202v1 Announce Type: new Abstract: Multi-agent LLM workflows route inference through specialized roles to lift end-task accuracy, but jointly training those roles with reinforcement learn

When Self-Belief Misleads: Active Label Acquisition for Reinforcement Learning with Verifiable Rewards

arXiv:2605.25864v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable advancements in reasoning capabilities empowered by Reinforcement Learning with Verifiable Rewar

when you think about it SpaceX has cumulatively lost a lot less than OpenAI so maybe it’s a bargain? or maybe we just shouldn’t value compan…

when you think about it SpaceX has cumulatively lost a lot less than OpenAI so maybe it’s a bargain? or maybe we just shouldn’t value companies at a trillion dollars until they actually show evidence

Workflow cleanup tools for ComfyUI: Visual Fold, group folding, and node alignment

Visual Fold is a tool for simple visual organization of ComfyUI workflows that does not turn selected nodes into a subgraph or change workflow logic. Group folding and node alignment features enable c

safetyr-stablediffusion

World-VLA-Loop: Closed-Loop Learning of Video World Model and VLA Policy

arXiv:2602.06508v2 Announce Type: replace Abstract: Reinforcement learning (RL) can refine Vision-Language-Action (VLA) policies beyond behavior cloning, but real-world RL remains expensive due to ext

X-DiffVLA: X-Embodied Diffusion Action Heads for Vision-Language-Action Models

arXiv:2605.25044v1 Announce Type: new Abstract: Learning universal policies from cross-embodied data remains a fundamental challenge in robotics. Although Vision-Language-Action (VLA) models are pre-t

XRPO: Pushing the limits of GRPO with Targeted Exploration and Exploitation

arXiv:2510.06672v3 Announce Type: replace Abstract: Reinforcement learning algorithms such as GRPO have driven recent advances in large language model (LLM) reasoning. While scaling the number of roll

100% agree. and this is important. and it will affect you, personally.

100% agree. and this is important. and it will affect you, personally. The SpaceX IPO is the most brazen retail fleecing in modern market history. NASDAQ has REWRITTEN the index rules specifically for

4DThinker: Thinking with 4D Imagery for Dynamic Spatial Understanding

arXiv:2605.05997v2 Announce Type: replace Abstract: Dynamic spatial reasoning from monocular video is essential for bridging visual intelligence and the physical world, yet remains challenging for vis

A look at the UK's AI Safety Institute, whose researchers probe AI models for safety gaps, as its work becomes a blueprint for other governments' AI policies (New York Times)

New York Times: A look at the UK's AI Safety Institute, whose researchers probe AI models for safety gaps, as its work becomes a blueprint for other governments' AI policies — The government's A.I. Se

safetytechmeme

A Novel Approach for the Counting of Wood Logs Using cGANs and Image Processing Techniques

arXiv:2605.23775v1 Announce Type: new Abstract: This study tackles the challenge of precise wood log counting, where applications of the proposed methodology can span from automated approaches for mat

ALIVE: Awakening LLM Reasoning via Adversarial Learning and Instructive Verbal Evaluation

arXiv:2602.05472v2 Announce Type: replace Abstract: The quest for expert-level reasoning in Large Language Models (LLMs) has been hampered by a persistent extit{reward bottleneck}: traditional reinfor

ARMS: Automatic Reward Shaping for Sparse-Reward Multi-Agent Reinforcement Learning

arXiv:2605.23562v1 Announce Type: cross Abstract: Sparse rewards are a major bottleneck in multi-agent reinforcement learning (MARL), where simultaneous learning induces non-stationarity and makes rew

As one of the first people to warn about a possible AI backlash—years ago—let me tell you this: it’s going to get much, much worse. It break…

As one of the first people to warn about a possible AI backlash—years ago—let me tell you this: it’s going to get much, much worse. It breaks my heart that AI—something I spent my whole life thinking

Assessing Predictive Models for Fairness Based on Movement Patterns

arXiv:2605.23234v1 Announce Type: new Abstract: Assessing the spatial fairness of predictive models involves establishing whether they are statistically penalizing (favoring) individuals associated wi

B-GRTO: Bootstrapped Group Relative Tool Optimization for Referring Segmentation

arXiv:2605.23500v1 Announce Type: new Abstract: Segmentation is a fundamental task in computer vision, underpinning pixel-level scene understanding and serving as a cornerstone for applications rangin

BarrierSteer: LLM Safety via Learning Barrier Steering

arXiv:2602.20102v2 Announce Type: replace-cross Abstract: Despite the strong performance of large language models (LLMs) across diverse tasks, their susceptibility to adversarial attacks and unsafe co

Beyond Binary Edits Robust Multimodal Knowledge Editing with Adversarial Subspace Alignment

arXiv:2605.23780v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) need efficient mechanisms to update knowledge without degrading existing capabilities. While intrinsic multimod

Beyond VLM-Based Rewards: Diffusion-Native Latent Reward Modeling

arXiv:2602.11146v2 Announce Type: replace-cross Abstract: Preference optimization for diffusion and flow-matching models relies on reward functions that are both discriminatively robust and computatio

Bridging AI and Clinical Reasoning: Abductive Explanations for Alignment on Critical Symptoms

arXiv:2602.13985v2 Announce Type: replace Abstract: Artificial intelligence (AI) has demonstrated strong potential in clinical diagnostics, often achieving accuracy comparable to or exceeding that of

CapTrack: Multifaceted Evaluation of Forgetting in LLM Post-Training

arXiv:2603.06610v2 Announce Type: replace Abstract: Large language model (LLM) post-training enhances latent skills, unlocks value alignment, improves performance, and enables domain adaptation. Unfor

CarlaNCAP: A Framework for Quantifying the Safety of Vulnerable Road Users in Infrastructure-Assisted Collective Perception Using EuroNCAP Scenarios

arXiv:2512.11551v2 Announce Type: replace Abstract: The growing number of road users has significantly increased the risk of accidents in recent years. Vulnerable Road Users (VRUs) are particularly at

CBANet: A Compact Attention-Based CNN-BiLSTM Network for Aggressive Driving Event Detection

arXiv:2605.23471v1 Announce Type: cross Abstract: Aggressive driving is a major cause of traffic accidents and poses a serious threat to road safety. Although deep learning methods have shown promisin

ChainFlow-VLA: Causal Flow Planning with Vision-Language Models

arXiv:2605.23270v1 Announce Type: cross Abstract: Current end-to-end autonomous driving systems are fundamentally limited by a mismatch between temporal causal reasoning and global trajectory consiste

Class-Dependent Hybrid Data Augmentation for Multiclass Migraine Classification under Severe Class Imbalance

arXiv:2605.23453v1 Announce Type: new Abstract: We conducted a reproducibility-oriented re-evaluation of prior migraine classification studies, correcting for data leakage and metric bias. We then int

Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning

arXiv:2605.23138v1 Announce Type: cross Abstract: Variational Quantum Algorithms (VQAs) potentially offer a pathway to practical quantum advantage, but their optimization is heavily hindered by barren

ClimateChat-300K: A Multi-Modal Facebook Dataset for Understanding Diverse Perspectives in Climate Communication

arXiv:2605.23326v1 Announce Type: new Abstract: We present ClimateChat-300K, a large-scale dataset of 299,329 public Facebook posts about climate change collected between May 2020 and May 2024 through

Coloring the Noise: Adversarial Sobolev Alignment for Faithful Image Super Resolution

arXiv:2605.23264v1 Announce Type: cross Abstract: Generative priors in Image Super-Resolution (SR) often compromise faithful restoration, we attribute this limitation to a fundamental spectral misalig

Composing People Together: Iterative Pose-Image Generation for Multi-Person Interaction Scenes

arXiv:2605.23178v1 Announce Type: new Abstract: Despite recent progress, text-to-image models still struggle to generate semantically diverse and compositionally accurate multi-person interaction scen

Contrastive Distribution Matching for Amortized Sequential Monte Carlo in Discrete Diffusion

arXiv:2605.23346v1 Announce Type: new Abstract: Discrete diffusion models have emerged as powerful frameworks for generating structured categorical data. However, efficiently sampling from reward-tilt

Controlled Personalization in Legacy Media Online Services: A Case Study in News Recommendation

arXiv:2510.09136v2 Announce Type: replace-cross Abstract: Personalized news recommendations have become a standard feature of large news aggregation services, optimizing user engagement through automa

Convergence Without Understanding: When Language Models Agree on Representations but Disagree on Reasoning

arXiv:2605.23315v1 Announce Type: cross Abstract: Large language models trained under diverse objectives and architectures have been shown to develop increasingly similar internal representations, an

CoReVAD: A Contextual Reasoning Framework for Training-Free Video Anomaly Detection

arXiv:2605.23116v1 Announce Type: cross Abstract: Existing Video Anomaly Detection (VAD) methods typically rely on task-specific training, leading to strong domain dependency and high training costs.

👇@davidSacks raises some interesting points here, which deserve an answer, but arguably gets the solution exactly backwards. Are we not now…

👇@davidSacks raises some interesting points here, which deserve an answer, but arguably gets the solution exactly backwards. Are we not now already leaving unelected private companies precisely the ab

Debiased Negative Mining Improves Out-of-distribution Detection with Pre-trained Vision-Language Models

arXiv:2605.23797v1 Announce Type: cross Abstract: Aiming at identifying unexpected inputs from unknown classes, out-of-distribution (OOD) detection has emerged as a pivotal approach to enhancing the r

Differences in Typological Alignment in Language Models' Treatment of Differential Argument Marking

arXiv:2602.17653v2 Announce Type: replace Abstract: Recent work has shown that language models (LMs) trained on synthetic corpora can exhibit typological preferences that resemble cross-linguistic reg

Diffusion and Flow Matching Models for Tabular Data: A Survey

arXiv:2502.17119v2 Announce Type: replace-cross Abstract: Deep generative models have made rapid progress in image, text, audio, and video generation, and are increasingly being applied to structured

Direct Dynamic Retargeting for Humanoid Imitation Learning from Videos

arXiv:2605.23762v1 Announce Type: new Abstract: Imitation Learning from monocular video demonstrations provides a scalable approach for teaching complex skills to humanoid robots. However, translating

Disentangling Interaction and Bias Effects in Opinion Dynamics of Large Language Models

arXiv:2509.06858v2 Announce Type: replace-cross Abstract: Large Language Models are increasingly used to simulate human opinion dynamics, yet the effect of genuine interaction is often obscured by sys

Dreaming Smoothly and Sample Efficiently with Gradient Penalized Latent Dynamics

arXiv:2605.23089v1 Announce Type: cross Abstract: Model-based reinforcement learning improves sample efficiency by learning a world model. However, existing latent world models such as DreamerV3 do no

Droneulator: A Portable UAV Simulator for Agricultural Workflows with RotorPy and Godot 4

arXiv:2605.23386v1 Announce Type: new Abstract: Agricultural UAV research requires simulators that integrate realistic 3D scenes, high-fidelity vehicle dynamics, and robotics middleware, while remaini

Dynamic Weight-based Temporal Aggregation for Low-light Video Enhancement Under Extreme Noise

arXiv:2510.09450v2 Announce Type: replace Abstract: Low-light video enhancement (LLVE) is challenging due to noise, low contrast, and color degradation. While learning-based methods enable fast infere

EDGE-OPD: Internalizing Privileged Context with Evidence Guided On-Policy Distillation

arXiv:2605.23493v1 Announce Type: new Abstract: On-Policy Distillation (OPD) has gained wide attraction as an LLM post-training paradigm due to its effectiveness in improving capabilities without intr

Empowering 9-1-1 Calltaking Training with Generative AI: Experiences and Lessons Learned

arXiv:2602.13241v2 Announce Type: replace-cross Abstract: Emergency call-takers form the first operational link in public safety response, handling over 240 million calls annually while facing a susta

Energy per Successful Goal: Goal-Level Energy Accounting for Agentic AI Systems

arXiv:2605.22883v1 Announce Type: new Abstract: Current AI energy benchmarks measure consumption at the granularity of a single model invocation or training run. For classical single-turn workloads th

Entropy-Aware On-Policy Distillation of Language Models

arXiv:2603.07079v2 Announce Type: replace-cross Abstract: On-policy distillation is a promising approach for transferring knowledge between language models, where a student learns from dense token-lev