Search: “safety”

M3imic: Learning a Versatile Whole-Body Controller for Multimodal Motion Mimicking

arXiv:2606.04829v1 Announce Type: new Abstract: Building a general-purpose whole-body controller is essential for enabling diverse motion capabilities in humanoid robots across a wide range of downstr

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MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation

DGX agent

arXiv:2510.01532v2 Announce Type: replace Abstract: In semi-supervised segmentation, capturing meaningful semantic structures from unlabeled data is essential. This is particularly challenging in hist

safetyarxiv-cs-cv

4 Jun 2026

Meta's Oversight Board says Meta's account deactivations lack due process, violations are flagged without clarity, and there's little support for appeals (Sarah Perez/TechCrunch)

Sarah Perez / TechCrunch: Meta's Oversight Board says Meta's account deactivations lack due process, violations are flagged without clarity, and there's little support for appeals — Meta's Oversight B

safetytechmeme

(Mis)generalization of Helpful-only Fine-tuning

arXiv:2606.04413v1 Announce Type: new Abstract: Helpful-only models, that is, models that are trained to always follow user intent, are valuable for dangerous capability evaluations and other areas of

MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&A

arXiv:2606.04231v1 Announce Type: cross Abstract: Recent advances in multimodal retrieval-augmented generation (MM-RAG) have shifted toward minimal parsing, relying on page-level images for producing

Model Evaluations: Prove Your Routing Policy Actually Works

This article discusses methods and tools for evaluating routing policies in machine learning models, likely covering techniques to validate that model routing decisions are effective and functioning a

safetydigitalocean

MorphoQuant: Modality-Aware Quantization for Omni-modal Large Language Models

arXiv:2606.04349v1 Announce Type: cross Abstract: Conventional Post-Training Quantization (PTQ) methods struggle with 4-bit Omni-modal Large Language Models (OLLMs) due to the extreme distribution het

MusaCoder: Native GPU Kernel Generation with Full-Stack Training on Moore Threads GPU

arXiv:2606.04847v1 Announce Type: cross Abstract: Native GPU kernel generation turns high-level tensor programs into executable, efficient low-level code. Existing Large Language Models (LLMs) struggl

Neetyabhas: A Framework for Uncertainty-Aware Public Policy Optimization in Rational Agent-Based Models

arXiv:2606.04562v1 Announce Type: new Abstract: Purpose The WHO's COVID-19 non-pharmaceutical interventions (e.g., lockdowns, vaccinations) effectively curb transmission but impose heavy economic stra

OA-CutMix: Correcting the Label Bias of CutMix

arXiv:2606.04820v1 Announce Type: cross Abstract: CutMix has become the de facto standard mixing augmentation, yet its label assignment rests on a flawed assumption: The area of the pasted patch faith

Offering a “damn, I’m truly impressed” award, for the first person or team to build a domain-general AI system that can play @baldursgate3, …

Offering a “damn, I’m truly impressed” award, for the first person or team to build a domain-general AI system that can play @baldursgate3, start to finish. Will make the prize especially sweet if you

Offering @kevinroose $100,000 bet that no domain-general AI system can do this before his book comes out. (mimicking walkthroughs doesn’t co…

Offering @kevinroose $100,000 bet that no domain-general AI system can do this before his book comes out. (mimicking walkthroughs doesn’t count.) Offering a “damn, I’m truly impressed” award, for the

On-the-fly Repulsion in the Contextual Space for Rich Diversity in Diffusion Transformers

arXiv:2603.28762v2 Announce Type: replace-cross Abstract: Modern Text-to-Image (T2I) diffusion models have achieved remarkable semantic alignment, yet they often suffer from a significant lack of vari

Optimal Transport Flow Matching by Design

arXiv:2606.04092v1 Announce Type: new Abstract: Flow matching models learn to transport samples from a simple prior distribution to a complex data distribution. When prior-data pairs are coupled via o

safetyarxiv-cs-cv

Optimal Transport under Group Fairness Constraints

arXiv:2601.07144v3 Announce Type: replace-cross Abstract: Ensuring fairness in matching algorithms is a key challenge in allocating scarce resources and positions. Focusing on Optimal Transport (OT),

OSCAR: Omni-Embodiment Skeleton-Conditioned World Action Model for Robotics

arXiv:2606.04463v1 Announce Type: new Abstract: We present OSCAR, a precise action-conditioned video world model that generalizes across different robot embodiments and enables robot policy evaluation

safetyarxiv-cs-ro

Outcome-Based RL Provably Leads Transformers to Reason, but Only With the Right Data

arXiv:2601.15158v4 Announce Type: replace-cross Abstract: Transformers trained via Reinforcement Learning (RL) with outcome-based supervision can spontaneously develop the ability to generate intermed

Outcome-Grounded Advantage Reshaping for Fine-Grained Credit Assignment in Mathematical Reasoning

arXiv:2601.07408v2 Announce Type: replace Abstract: Group Relative Policy Optimization (GRPO) has emerged as a promising critic-free reinforcement learning paradigm for reasoning tasks. However, stand

Path-conditioned training: a principled way to rescale ReLU neural networks

arXiv:2602.19799v2 Announce Type: replace-cross Abstract: Despite recent algorithmic advances, we still lack principled ways to leverage the well-documented rescaling symmetries in ReLU neural network

PersonaTree: Structured Lifecycle Memory for Person Understanding in LLM Agents

arXiv:2606.04780v1 Announce Type: new Abstract: Persistent LLM agents require memory representations that make the formation of person understanding explicit across long term interaction. Existing age

Physics-Informed Machine Learning for Short-Term Flood Prediction

arXiv:2606.04143v1 Announce Type: cross Abstract: Accurate flood forecasting is essential for mitigating disaster risks and protecting communities. However, purely data-driven machine learning models

Plug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstruction

arXiv:2602.23214v2 Announce Type: replace Abstract: Plug-and-Play diffusion prior (PnPDP) frameworks have emerged as a powerful paradigm for solving imaging inverse problems by treating pretrained gen

safetyarxiv-cs-cv

POLARIS: Guiding Small Models to Write Long Stories

arXiv:2606.04095v1 Announce Type: cross Abstract: Small open-weight models struggle at long-form creative writing: their generated stories either fall far short of the requested length, or their quali

Policy Gradient for Continuous-Time Robust Markov Decision Processes

arXiv:2606.04335v1 Announce Type: new Abstract: The framework of robust Markov decision processes (RMDPs) allows the design of reinforcement learning agents that satisfy performance guarantees under w

Potential-Guided Flow Matching for Vision-Language-Action Policy Improvement

arXiv:2606.04968v1 Announce Type: new Abstract: Large vision-language-action (VLA) policies are increasingly trained as conditional generative models over action chunks. Yet deployment produces mixed-

safetyarxiv-cs-ro

Probing Outcome-Level Resemblance and Mechanism-Level Alignment in LLM Risk Decisions: Evidence from the St. Petersburg Game

arXiv:2606.04978v1 Announce Type: new Abstract: LLMs can appear cautious in risk decision-making tasks, yet cautious-looking outputs do not necessarily indicate alignment with human decision-making me

ps no fair just mimicing walkthroughs.

This post likely discusses concerns about AI systems that merely imitate or reproduce existing walkthroughs and instructional content rather than demonstrating genuine understanding or original proble

Reducing the Filtering Effect in Public School Admissions: A Bias-aware Analysis for Targeted Interventions

arXiv:2004.10846v5 Announce Type: replace-cross Abstract: Problem definition: Traditionally, New York City's top 8 public schools have selected candidates solely based on their scores in the Specializ

REGAIN: REconciliation GAIN-driven Auxiliary Direction Learning

arXiv:2606.04380v1 Announce Type: cross Abstract: Forecast reconciliation usually starts from a fixed measurement system and asks how forecasts should be projected onto a coherent space. We ask a diff

Reinforcement Learning from Rich Feedback with Distributional DAgger

arXiv:2606.05152v1 Announce Type: cross Abstract: Reasoning models have advanced rapidly, but the dominant reinforcement learning from verifiable rewards (RLVR) recipe remains surprisingly narrow: sam

Remember when Dario called pausing AI 'most extreme'? '...we should just pause ... that extreme position doesn't make much sense to me eithe…

Remember when Dario called pausing AI 'most extreme'? '...we should just pause ... that extreme position doesn't make much sense to me either.' Anthropic now: 'We believe it would be good for the worl

safetyconnor-leahy--x

RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities

arXiv:2606.05109v1 Announce Type: new Abstract: To leverage the full potential of multimodal data, we need representations that go beyond the state-of-the-art alignment and fusion approaches and explo

Representation Matters in Randomized Smoothing for Audio Classification

arXiv:2606.04210v1 Announce Type: cross Abstract: Randomized smoothing (RS) certifies robustness in the vector space where Gaussian noise is added. In audio classification, this space is often not uni

Reproducing, Analyzing, and Detecting Reward Hacking in Rubric-Based Reinforcement Learning

arXiv:2606.04923v1 Announce Type: cross Abstract: Rubric-based reinforcement learning (RL) uses an LLM-as-a-Judge (LaaJ) to score model outputs according to rubrics as rewards. However, policy models

Rethinking Continual Experience Internalization for Self-Evolving LLM Agents

arXiv:2606.04703v1 Announce Type: new Abstract: Experience internalization converts contextual experience from past interactions into reusable parametric capability, offering a promising path toward c

Rethinking Sales Lead Scoring with LLM-based Hierarchical Preference Ranking

arXiv:2606.04387v1 Announce Type: cross Abstract: Sales lead conversion in high-stakes domains (e.g., automotive, real estate) differs fundamentally from e-commerce recommendation due to prolonged dec

Reusing Trajectories in Policy Gradients Enables Fast Convergence

arXiv:2506.06178v3 Announce Type: replace Abstract: Policy gradient (PG) methods are a class of effective reinforcement learning algorithms, particularly when dealing with continuous control problems.

RL Excursions during Pre-Training: Re-examining Policy Optimization for LLM training

arXiv:2606.04272v1 Announce Type: new Abstract: The standard LLM training pipeline applies reinforcement learning (RL) only after pre-training and supervised fine-tuning (SFT). We question this status

Scaling Self-Evolving Agents via Parametric Memory

arXiv:2606.04536v1 Announce Type: new Abstract: Existing memory-augmented LLM agents store past experience exclusively in prompt space, as textual summaries or retrieved passages, while keeping model

Segment, Embed, and Align: A Universal Recipe for Aligning Subtitles to Signing

arXiv:2512.08094v2 Announce Type: replace Abstract: The goal of this work is to develop a universal approach for aligning subtitles (i.e., spoken language text with corresponding timestamps) to contin

Selective Coupling of Decoupled Informative Regions: Masked Attention Alignment for Data-Free Quantization of Vision Transformers

arXiv:2606.04373v1 Announce Type: cross Abstract: Data-Free Quantization (DFQ) addresses data security concerns by synthesizing samples, without accessing real data. It has garnered increasing attenti

Self-Distilled Policy Gradient

arXiv:2606.04036v1 Announce Type: new Abstract: On-policy self-distillation, where a language model conditions on privileged context to supervise its own generations, is a promising source of dense su

Semiparametric Preference Optimization: Your Language Model is Secretly a Single-Index Model

arXiv:2512.21917v3 Announce Type: replace-cross Abstract: Policy alignment to preference data typically assumes a known link function between observed preferences and latent rewards (e.g., Bradley-Ter

shit is getting real

shit is getting real Sam Altman, Dario Amodei, Demis Hassabis and many others have signed a letter urging Congress to increase security on orders of synthetic nucleic acids - and the equipment needed

Simplicial Embeddings Improve Sample Efficiency in Actor-Critic Agents

arXiv:2510.13704v2 Announce Type: replace-cross Abstract: Recent works have proposed accelerating the wall-clock training time of actor-critic methods via the use of large-scale environment paralleliz

Smart Transportation Without Neurons -- Fair Metro Network Expansion with Tabular Reinforcement Learning

arXiv:2606.04167v1 Announce Type: cross Abstract: We tackle the Metro Network Expansion Problem (MNEP), a subset of the Transport Network Design Problem (TNDP), which focuses on expanding metro system

SocialCoach: Personalized Social Skill Learning with RL-based Agentic Tutoring and Practice

arXiv:2606.04155v1 Announce Type: cross Abstract: Social skills such as negotiation and leadership are crucial for personal and professional success in today's interconnected world. However, scalable