safety

WD-FQDet: Multispectral Detection Transformer via Wavelet Decomposition and Frequency-aware Query Learning

arXiv:2605.13621v1 Announce Type: new Abstract: Infrared-visible object detection improves detection performance by combining complementary features from multispectral images. Existing backbone-specif

Safety

“we are working harder to manage our tools than we are to solve the actual problems they were meant to fix.”

DGX agent

“we are working harder to manage our tools than we are to solve the actual problems they were meant to fix.” Harvard Business Review research reveals that excessive interaction with AI is causing a sp

safetygary-marcus--x

14 May 2026

What properties of reasoning supervision are associated with improved downstream model quality?

arXiv:2605.13290v1 Announce Type: new Abstract: Validating training data for reasoning models typically requires expensive trial-and-error fine-tuning cycles. In this work, we investigate whether the

safetyarxiv-cs-ai

What to Ignore, What to React: Visually Robust RL Fine-Tuning of VLA Models

arXiv:2605.13105v1 Announce Type: new Abstract: Reinforcement learning (RL) fine-tuning has shown promise for Vision-Language-Action (VLA) models in robotic manipulation, but deployment-time visual sh

When Backdoors Meet Partial Observability: Attacking Real-World Reinforcement Learning

arXiv:2601.14104v2 Announce Type: replace-cross Abstract: Backdoor attacks can cause reinforcement learning (RL) policies to behave normally under clean inputs while executing malicious behaviors when

When to Act, Ask, or Learn: Uncertainty-Aware Policy Steering

arXiv:2602.22474v2 Announce Type: replace-cross Abstract: Policy steering is an emerging way to adapt robot behaviors at deployment-time: a learned verifier analyzes low-level action samples proposed

When to Trust Confidence Thresholding: Calibration Diagnostics for Pseudo-Labelled Regression

arXiv:2605.12780v1 Announce Type: cross Abstract: Calibrated probability outputs of trained classifiers are increasingly used as inputs to downstream regression estimands such as effects, prevalences,

a dude who plays fast and loose with a nonprofit’s money like this is going to play fast and loose with you future. it’s time to boycott Ope…

a dude who plays fast and loose with a nonprofit’s money like this is going to play fast and loose with you future. it’s time to boycott OpenAI. 🚨 OpenAI's original board REJECTED Altman's Helion deal

A Survey of On-Policy Distillation for Large Language Models

arXiv:2604.00626v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) continue to grow in both capability and cost, transferring frontier capabilities into smaller, deployable stud

A survey predicted that 43% of companies would be using AI “as scale” by now. In reality, the figure is just 19%.

A survey predicted that 43% of companies would be deploying AI at scale by the current date, but actual adoption has reached only 19%, indicating a significant gap between forecasted and realized AI i

A Theory of Time-Sensitive Language Generation: Sparse Hallucination Beats Mode Collapse

arXiv:2605.11302v1 Announce Type: cross Abstract: We study language generation in the limit under a global preference ordering on strings, as introduced by Kleinberg and Wei. As in [arXiv:2504.14370,

A Unified Graph Language Model for Multi-Domain Multi-Task Graph Alignment Instruction Tuning

arXiv:2605.12197v1 Announce Type: new Abstract: Leveraging Graph Neural Networks (GNNs) as graph encoders and aligning the resulting representations with Large Language Models (LLMs) through alignment

ACSAC: Adaptive Chunk Size Actor-Critic with Causal Transformer Q-Network

arXiv:2605.11009v1 Announce Type: new Abstract: Long-horizon, sparse-reward tasks pose a fundamental challenge for reinforcement learning, since single-step TD learning suffers from bootstrapping erro

Adaption, co-founded by ex-Cohere VP of AI research Sara Hooker, unveils AutoScientist, which can automate the research loop behind model training and alignment (Russell Brandom/TechCrunch)

Russell Brandom / TechCrunch: Adaption, co-founded by ex-Cohere VP of AI research Sara Hooker, unveils AutoScientist, which can automate the research loop behind model training and alignment — For yea

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Adaptive Policy Learning Under Unknown Network Interference

arXiv:2605.11191v1 Announce Type: cross Abstract: Adaptive experimentation under unknown network interference requires solving two coupled problems: (i) learning the underlying dynamics of interferenc

Adaptive TD-Lambda for Cooperative Multi-agent Reinforcement Learning

arXiv:2605.11880v1 Announce Type: new Abstract: TD(lambda) in value-based MARL algorithms or the Temporal Difference critic learning in Actor-Critic-based (AC-based) algorithms synergistically integra

Adaptive Teacher Exposure for Self-Distillation in LLM Reasoning

arXiv:2605.11458v1 Announce Type: cross Abstract: On-policy self-distillation has become a strong recipe for LLM reasoning, where a privileged teacher supervises the student's own rollouts while condi

agreed. RL is not (at least by itself) the way to alignment

agreed. RL is not (at least by itself) the way to alignment Yoshua Bengio says Reinforcement Learning is a dangerous path for building superintelligence It can create systems with hidden goals, reward

AIA: Rethinking Architecture Decoupling Strategy In Unified Multimodal Model

arXiv:2511.22663v5 Announce Type: replace Abstract: Unified multimodal models for image generation and understanding represent a significant step toward AGI and have attracted widespread attention fro

.@alexsobel's new AI kill-switch amendment gives government the power to shut down data centers in cases of AI emergency. It's also the firs…

.@alexsobel's new AI kill-switch amendment gives government the power to shut down data centers in cases of AI emergency. It's also the first piece of proposed UK law recognizing ASI as the national s

safetyconnor-leahy--x

Aligning Flow Map Policies with Optimal Q-Guidance

arXiv:2605.12416v1 Announce Type: new Abstract: Generative policies based on expressive model classes, such as diffusion and flow matching, are well-suited to complex control problems with highly mult

AlphaGRPO: Unlocking Self-Reflective Multimodal Generation in UMMs via Decompositional Verifiable Reward

arXiv:2605.12495v1 Announce Type: new Abstract: In this paper, we propose AlphaGRPO, a novel framework that applies Group Relative Policy Optimization (GRPO) to AR-Diffusion Unified Multimodal Models

Anti-Self-Distillation for Reasoning RL via Pointwise Mutual Information

arXiv:2605.11609v1 Announce Type: cross Abstract: On-policy self-distillation, where a student is pulled toward a copy of itself conditioned on privileged context (e.g., a verified solution or feedbac

Assessment of cloud and associated radiation fields from a GAN stochastic cloud subcolumn generator

arXiv:2605.11968v1 Announce Type: cross Abstract: Modern Earth System Models (ESMs) operate on horizontal scales far larger than typical cloud features, requiring stochastic subcolumn generators to re

Asymmetric Advantage Modulation Calibrates Entropy Dynamics in RLVR

arXiv:2604.04894v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has substantially improved the reasoning ability of large language models (LLMs), but it often

Augmented Lagrangian Method for Last-Iterate Convergence for Constrained MDPs

arXiv:2605.11694v1 Announce Type: new Abstract: We study policy optimization for infinite-horizon, discounted constrained Markov decision processes (CMDPs). While existing theoretical guarantees typic

Autoregressive Learning in Joint KL: Sharp Oracle Bounds and Lower Bounds

arXiv:2605.12316v1 Announce Type: new Abstract: We study the fundamental and timely problem of learning long sequences in autoregressive modeling and next-token prediction under model misspecification

Beyond Point-wise Neural Collapse: A Topology-Aware Hierarchical Classifier for Class-Incremental Learning

arXiv:2605.11904v1 Announce Type: new Abstract: The Nearest Class Mean (NCM) classifier is widely favored in Class-Incremental Learning (CIL) for its superior resistance to catastrophic forgetting com

BSO: Safety Alignment Is Density Ratio Matching

arXiv:2605.12339v1 Announce Type: new Abstract: Aligning language models for both helpfulness and safety typically requires complex pipelines-separate reward and cost models, online reinforcement lear

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

Can Graphs Help Vision SSMs See Better?

arXiv:2605.11300v1 Announce Type: new Abstract: Vision state space models inherit the efficiency and long-range modeling ability of Mamba-style selective scans. However, their performance depends crit

Causal Bias Detection in Generative Artifical Intelligence

arXiv:2605.11365v1 Announce Type: cross Abstract: Automated systems built on artificial intelligence (AI) are increasingly deployed across high-stakes domains, raising critical concerns about fairness

Causal Fairness for Survival Analysis

arXiv:2605.11362v1 Announce Type: new Abstract: In the data-driven era, large-scale datasets are routinely collected and analyzed using machine learning (ML) and artificial intelligence (AI) to inform

Certified Gradient-Based Contact-Rich Manipulation via Smoothing-Error Reachable Tubes

arXiv:2602.09368v2 Announce Type: replace Abstract: Gradient-based methods can efficiently optimize controllers by leveraging differentiable simulation and physical priors. However, contact-rich manip

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

CheXTemporal: A Dataset for Temporally-Grounded Reasoning in Chest Radiography

arXiv:2605.11304v1 Announce Type: new Abstract: Chest radiograph interpretation requires temporal reasoning over prior and current studies, yet most vision-language models are trained on static image-

Clarity: The Flexibility-Interpretability Trade-Off in Sparsity-aware Concept Bottleneck Models

arXiv:2601.21944v2 Announce Type: replace Abstract: The widespread adoption of deep learning models in computer vision has intensified concerns about interpretability. Despite strong performance, thes

Cluster-Aware Neural Collapse Prompt Tuning for Long-Tailed Generalization of Vision-Language Models

arXiv:2605.11939v1 Announce Type: new Abstract: Prompt learning has emerged as an efficient alternative to fine-tuning pre-trained vision-language models (VLMs). Despite its promise, current methods s

Combining On-Policy Optimization and Distillation for Long-Context Reasoning in Large Language Models

arXiv:2605.12227v1 Announce Type: new Abstract: Adapting large language models (LLMs) to long-context tasks requires post-training methods that remain accurate and coherent over thousands of tokens. E

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

Controllable User Simulation

arXiv:2605.11519v1 Announce Type: cross Abstract: Using offline datasets to evaluate conversational agents often fails to cover rare scenarios or to support testing new policies. This has motivated th

Cooperative Robotics Reinforced by Collective Perception for Traffic Moderation

arXiv:2605.11972v1 Announce Type: new Abstract: Collisions at non-line-of-sight (NLOS) intersections remain a major safety concern because drivers have limited visibility of approaching traffic. V2X b

Coordinated Diffusion: Generating Multi-Agent Behavior Without Multi-Agent Demonstrations

arXiv:2605.11485v1 Announce Type: new Abstract: Imitation learning powered by generative models has proven effective for modeling complex single-agent behaviors. However, teaching multi-agent systems,

counterpoint: i have no interest in a company that says we will roll everything out to anybody regardless of how dangerous it is.

counterpoint: i have no interest in a company that says we will roll everything out to anybody regardless of how dangerous it is. boris cherny recently says that, 'anthropic has no plans to roll out m

Couple to Control: Joint Initial Noise Design in Diffusion Models

arXiv:2605.11311v1 Announce Type: cross Abstract: Diffusion models typically generate image batches from independent Gaussian initial noises. We argue that this independence assumption is only one cho

Cross-Modal-Domain Generalization Through Semantically Aligned Discrete Representations

arXiv:2605.12145v1 Announce Type: new Abstract: Multimodal learning seeks to integrate information across diverse sensory sources, yet current approaches struggle to balance cross-modal generalizabili

Debiased Model-based Representations for Sample-efficient Continuous Control

arXiv:2605.11711v1 Announce Type: new Abstract: Model-based representations recently stand out as a promising framework that embeds latent dynamics information into the representations for downstream

Debiasing Message Passing to Mitigate Popularity Bias in GNN-based Collaborative Filtering

arXiv:2605.11145v1 Announce Type: cross Abstract: Collaborative filtering (CF) models based on graph neural networks (GNNs) achieve strong performance in recommender systems by propagating user-item s