Search: “safety”

The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing

arXiv:2606.25108v1 Announce Type: new Abstract: Autonomous AI systems are transitioning from advisory to autonomous roles for medication prescriptions. Recent United States bill H.R. 238 and Utah's pr

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems

DGX agent

arXiv:2606.24937v1 Announce Type: cross Abstract: The Hitchhiker's Guide to Agentic AI is a comprehensive practitioner's reference for building autonomous AI systems. The book covers the full stack fr

safetyarxiv-cs-cl

25 Jun 2026

The saddest thing about AI is how many people are using it to become worse, lesser, and disempower themselves.

Connor Leahy expresses concern that AI tools are being used by many people in ways that diminish their capabilities and agency rather than enhance them. The post suggests that rather than leveraging A

safetyconnor-leahy--x

TIDAL: Temporally Interleaved Diffusion and Action Loop for High-Frequency VLA Control

arXiv:2601.14945v2 Announce Type: replace Abstract: Large-scale Vision-Language-Action (VLA) models offer semantic generalization but suffer from high inference latency, limiting them to low-frequency

safetyarxiv-cs-ro

Towards Scalable Multi-Task Reinforcement Learning with Large Decision Models

arXiv:2606.24962v1 Announce Type: new Abstract: Recent progress in large-scale sequence modeling has shown that a single model can learn useful representations across highly diverse data distributions

Transferability for General Reasoning: An Automated Curriculum for Multi-Domain RLVR

arXiv:2606.25178v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has been extended from single-domain training to multi-domain reasoning suites spanning mathematic

TTSA3R: Training-Free Temporal-Spatial Adaptive Persistent State for Streaming 3D Reconstruction

arXiv:2601.22615v3 Announce Type: replace Abstract: Streaming recurrent models enable efficient 3D reconstruction by maintaining persistent state representations. However, they suffer from catastrophi

Uncertainty-aware reinforcement learning for chemical language models

arXiv:2606.24990v1 Announce Type: new Abstract: Reinforcement Learning (RL) has become a powerful paradigm for de novo molecular design, enabling Chemical Language Models (CLMs) to navigate and explor

update with some context and additional thoughts: https://open.substack.com/pub/garymarcus/p/the-generative-ai-fizzle?utm_source=app-post-st…

Gary Marcus discusses concerns about generative AI's limitations and potential overhyping of the technology, arguing that initial enthusiasm may not translate into sustained practical breakthroughs as

VolSplat: Rethinking Feed-Forward 3D Gaussian Splatting with Voxel-Aligned Prediction

arXiv:2509.19297v3 Announce Type: replace Abstract: Feed-forward 3D Gaussian Splatting (3DGS) has emerged as a highly effective solution for novel view synthesis. Existing methods predominantly rely o

Weird to see this just as the administration is delaying a model for the second time in weeks, apparently without clarity about its criteria…

Weird to see this just as the administration is delaying a model for the second time in weeks, apparently without clarity about its criteria. You can’t be pro-growth, pro-innovation, and opaque at all

What Does It Mean to Break a Distillation Defense?

arXiv:2606.25059v1 Announce Type: cross Abstract: Black-box LLMs (accessible only via API) are vulnerable to distillation attacks, in which an attacker queries the model and trains a student on its ou

When Do Conservation Laws Survive Learned Representations? Certified Horizons for Latent World Models

arXiv:2606.24945v1 Announce Type: new Abstract: We ask a representation-learning question about physical world models: when does a conservation law remain certifiable after a model learns a latent rep

When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification?

arXiv:2606.26053v1 Announce Type: cross Abstract: Synthetic data augmentation is widely used to mitigate class imbalance, but its theoretical effects on score-based classification remain poorly unders

Why Multi-Step Tool-Use Reinforcement Learning Collapses and How Supervisory Signals Fix It

arXiv:2606.26027v1 Announce Type: new Abstract: Tool use enables large language models (LLMs) to perform complex tasks, and recent agentic reinforcement learning (RL) methods show promise for enhancin

safetyarxiv-cs-cl

wouldn’t it be funny if greed undid OpenAI?

wouldn’t it be funny if greed undid OpenAI? 🚨BREAKING: OPENAI IPO DELAYED Altman told everyone OpenAI is worth a TRILLION dollars His own advisors warned him that retail investors aren’t buying it… ga

A Comparative Study of Bayesian Contextual Bandits for Real-Time Warehouse Sorter Optimization

arXiv:2606.23977v1 Announce Type: new Abstract: Efficient sorter diversion control of automated material handling systems (MHS) is critical for optimizing operational efficiency in large-scale warehou

A global log for medical AI

arXiv:2510.04033v2 Announce Type: replace Abstract: Modern computer systems rely on syslog, a universal protocol that records critical events across heterogeneous infrastructure. Medicine's rapidly gr

A message to the Republicans in congress and the Trump administration. Please stop treating legal immigration like just another issue to be …

A message to the Republicans in congress and the Trump administration. Please stop treating legal immigration like just another issue to be fine tuned and start treating it like the existential threat

safetyelon-musk--x

A Robust Model-Based Approach for Continuous-Time Policy Evaluation with Unknown Levy Process Dynamics

arXiv:2504.01482v3 Announce Type: replace-cross Abstract: This paper develops a model-based framework for continuous-time policy evaluation (CTPE) in reinforcement learning, incorporating both Brownia

Abstractions of Queries in Ontology-Based Data Access

arXiv:2606.24618v1 Announce Type: new Abstract: In ontology-based data access (OBDA), multiple data sources are integrated via mappings to an ontology. We consider an OBDA setting based on existential

Accelerated Stochastic Min-Max Optimization Based on Bias-corrected Momentum

arXiv:2406.13041v3 Announce Type: replace Abstract: Lower-bound analyses for nonconvex strongly-concave minimax optimization problems have shown that stochastic first-order algorithms require at least

Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems

arXiv:2606.24416v1 Announce Type: new Abstract: Network operators' changing policies, service requirements, and stringent real-time constraints render existing methods designed with fixed objectives a

Aligning Audio Captions with Human Preferences

arXiv:2509.14659v3 Announce Type: replace-cross Abstract: Current audio captioning relies on supervised learning with paired audio-caption data, which is costly to curate and may not reflect human pre

An Introduction to Causal Reinforcement Learning

arXiv:2606.24160v1 Announce Type: new Abstract: Causal inference provides a set of principles and tools that allow one to combine data and knowledge about an environment to reason with questions of co

An LLM-based Two-Stage Transformer Framework for Cross-Domain Bearing Fault Diagnosis with Limited Data

arXiv:2606.24459v1 Announce Type: cross Abstract: Bearing fault diagnosis faces critical challenges when dataset heterogeneity, operating condition variations, and limited labeled data occur simultane

safetyarxiv-cs-cl

Are LLM Evaluators Really Narcissists? Sanity Checking Self-Preference Evaluations

arXiv:2601.22548v4 Announce Type: replace-cross Abstract: Recent research has shown that large language models (LLMs) favor their own outputs when acting as judges, undermining the integrity of automa

ARIA: Adaptive Region-Based Importance Allocation for Conditional Diffusion Distillation

arXiv:2606.23898v1 Announce Type: cross Abstract: Distilling conditional diffusion models aims to transfer the behavior of a large teacher to a smaller student while preserving alignment across condit

AsyncOPD: How Stale Can On-Policy Distillation Be?

arXiv:2606.24143v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on its own rollouts guided by teacher feedback and is becoming increasingly important for large language m

Audio-visual Contrastive Alignment for Diffusion-based Visual-conditioned Speech Enhancement

arXiv:2606.23712v1 Announce Type: cross Abstract: Audio-visual speech enhancement (AVSE) exploits visual cues such as lip movements to recover speech in noisy environments. Recent work introduced diff

Beyond Trajectory Imitation: Strategy-Guided Policy Optimization for LLM Reasoning

arXiv:2606.24064v1 Announce Type: new Abstract: Distilling reasoning capabilities from strong to weak language models typically involves imitating specific solution trajectories, effectively transferr

Beyond U-Net: A Latent-Representation-Aligned Skip-Free Backbone for Flow-Matching Speech Enhancement

arXiv:2606.24745v1 Announce Type: cross Abstract: Generative models, particularly diffusion and score-based approaches, have recently achieved strong performance in speech enhancement, but their itera

bit-equivalent on-policy rl for glm-5.2 has been achieved internally developing…

I cannot provide an accurate summary of this entry as the title appears incomplete and the URL/source information seems corrupted or mismatched (the handle doesn't match the URL). To create a reliable

safetysonya-huang--x

Boosting Text-Driven Video Segmentation via Geometry-Aware Distillation

arXiv:2606.24464v1 Announce Type: new Abstract: Text-driven Referring Video Object Segmentation (RVOS) aims to locate and segment target objects in videos given natural language. However, existing mod

Breaking Shortcut Learning for Cross-Trial EEG-Guided Target Speech Extraction via Two-Stage Training

arXiv:2606.24164v1 Announce Type: cross Abstract: Recent end-to-end models for EEG-guided target speech extraction report impressive results, underscoring potential for neuro-steered hearing technolog

Breaking the Filter Bubble: A Semantic Pareto-DQN Framework for Multi-Objective Recommendation

arXiv:2606.24042v1 Announce Type: new Abstract: Recommender systems often induce filter bubbles and semantic homogenization by monolithically optimizing for immediate user engagement. Standard single-

Breaking the Mirror: Activation-Based Mitigation of Self-Preference in LLM Evaluators

arXiv:2509.03647v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly serve as automated evaluators, yet they suffer from 'self-preference bias': a tendency to favor thei

Bridging the Manifold Gap: Riemannian Residual Line Search for One-Step Image Editing

arXiv:2606.24844v1 Announce Type: new Abstract: One-step diffusion editors are fast because they avoid inversion and iterative optimization, but a single transport update must be aggressive enough to

CALIBER: Calibrating Confidence Before and After Reasoning in Language Models

arXiv:2606.24281v1 Announce Type: cross Abstract: Reasoning language models are increasingly asked not only to answer difficult questions, but also to estimate their likelihood of success. Existing me

COMPANY BUILT ON IP THEFT RAGES AGAINST THEFT OF ITS OWN IP

COMPANY BUILT ON IP THEFT RAGES AGAINST THEFT OF ITS OWN IP ANTHROPIC ACCUSED ALIBABA OF UNAUTHORIZED ACCESS TO ITS AI MODELS, OUTLINING ITS ALLEGATIONS IN LETTERS SENT TO U.S. SENATORS AND THE WHITE

Congresswoman denies staff used AI to write defense funding amendment

Rep. Anna Paulina Luna (R-FL) says her staff used AI for 'spellcheck' in an amendment summary for a major defense bill, but denies it was used for the bill text itself and says 'NO Legislation is ever

safetythe-verge-ai

Cryptographic certificates of validity for trustworthy AI

arXiv:2606.23768v1 Announce Type: cross Abstract: We propose cryptographic certificates of validity for agentic AI systems. The core idea is to formally specify a correctness or policy condition as a

Decentralized SGD with Controlled Disagreement Finds Flatter Minima

arXiv:2602.02899v2 Announce Type: replace Abstract: Decentralized training is often regarded as inferior to centralized training because the consensus errors between workers are thought to undermine c

DREG: A Layer-Wise Jacobian Regularization as a General-Purpose Penalty

arXiv:2606.23942v1 Announce Type: new Abstract: We present a large-scale empirical study isolating the contributions of the Derivative Regularization penalty (DREG). Across a fully-crossed factorial s

Dynamic Symmetric Point Tracking: Tackling Non-ideal Reference in Analog In-memory Training

arXiv:2602.21321v2 Announce Type: replace Abstract: Analog in-memory computing (AIMC) performs computation directly within resistive crossbar arrays, offering an energy-efficient platform to scale lar

Enabling Robust Cloth Manipulation via Inference-Time Simulator-in-the-Loop Refinement

arXiv:2606.24552v1 Announce Type: new Abstract: Simulator-in-the-loop optimization offers a promising inference-time mechanism for robot manipulation. It uses a physical simulator as a backend rollout

safetyarxiv-cs-ro

Enforcing Human-like Kinematics in Dexterous Piano Playing via Adversarial Posture Regularization

arXiv:2606.23848v1 Announce Type: new Abstract: Reinforcement learning can train bimanual dexterous hands to play piano in physics simulation with high note accuracy, but for high-DoF dexterous hands,

safetyarxiv-cs-ro

Escaping the Self-Confirmation Trap: An Execute-Distill-Verify Paradigm for Agentic Experience Learning

arXiv:2606.24428v1 Announce Type: new Abstract: Experience-driven self-evolution is critical for large language model (LLM) agents to improve through open-world interaction. However, existing experien

safetyarxiv-cs-cl