Search: “safety”

Can Semantic Methods Enhance Team Sports Tactics? A Methodology for Football with Broader Applications

arXiv:2601.00421v2 Announce Type: replace Abstract: This paper explores how semantic-space reasoning, traditionally used in computational linguistics, can be extended to tactical decision-making in te

Safety

Catching the Infection Before It Spreads: Foresight-Guided Defense in Multi-Agent Systems

DGX agent

arXiv:2605.01758v1 Announce Type: new Abstract: Large multimodal model-based Multi-Agent Systems (MASs) enable collaborative complex problem solving through specialized agents. However, MASs are vulne

safetyarxiv-cs-ai

6 May 2026

C’mon BBC. Zilis was sharp as a tack on the stand, on her role in the OpenAI nonprofit board, and how she managed conflicts as they began …

C’mon BBC. Zilis was sharp as a tack on the stand, on her role in the OpenAI *nonprofit* board, and how she managed conflicts as they began to develop, and this (which is not really even news since it

Coherent Hierarchical Multi-Label Learning to Defer for Medical Imaging

arXiv:2605.02734v1 Announce Type: new Abstract: Learning to Defer (L2D) enables a model to predict autonomously or defer to an expert, but prior work largely assumes flat label spaces. We study the fi

Correction: the jury is advisory only. It’s the judge who decides; if she sees it as I do, OpenAI loses.

Gary Marcus clarifies that in the legal proceeding he's discussing, the jury serves an advisory role while the judge retains decision-making authority on the case outcome. Marcus expresses confidence

Deciphering Shortcut Learning from an Evolutionary Game Theory Perspective

arXiv:2605.02658v2 Announce Type: new Abstract: Shortcut learning causes deep learning models to rely on non-essential features within the data. However, its formation in deep neural network training

Descent-Guided Policy Gradient for Scalable Cooperative Multi-Agent Learning

arXiv:2602.20078v3 Announce Type: replace-cross Abstract: Scaling cooperative multi-agent reinforcement learning (MARL) is fundamentally limited by cross-agent noise. When agents share a common reward

DGPO: Distribution Guided Policy Optimization for Fine Grained Credit Assignment

arXiv:2605.03327v1 Announce Type: new Abstract: Reinforcement learning is crucial for aligning large language models to perform complex reasoning tasks. However, current algorithms such as Group Relat

Discovering Reinforcement Learning Interfaces with Large Language Models

arXiv:2605.03408v1 Announce Type: new Abstract: Reinforcement learning systems rely on environment interfaces that specify observations and reward functions, yet constructing these interfaces for new

DMGD: Train-Free Dataset Distillation with Semantic-Distribution Matching in Diffusion Models

arXiv:2605.03877v1 Announce Type: new Abstract: Dataset distillation enables efficient training by distilling the information of large-scale datasets into significantly smaller synthetic datasets. Dif

@Dr_Gingerballs At least actual ponzi schemes don't light their cash on fire... they just cant meet redemptions at the level of their inflat…

@Dr_Gingerballs At least actual ponzi schemes don't light their cash on fire... they just cant meet redemptions at the level of their inflated fake earnings. After all of the hyperscalers burn every l

FIBER: A Differentially Private Optimizer with Filter-Aware Innovation Bias Correction

arXiv:2605.03425v1 Announce Type: new Abstract: Differentially private (DP) training protects individual examples by adding noise to gradients, but the injected noise interacts nontrivially with adapt

FINER-SQL: Boosting Small Language Models for Text-to-SQL

arXiv:2605.03465v1 Announce Type: cross Abstract: Large language models have driven major advances in Text-to-SQL generation. However, they suffer from high computational cost, long latency, and data

safetyarxiv-cs-cl

From SFT to RL: Demystifying the Post-Training Pipeline for LLM-based Vulnerability Detection

arXiv:2602.14012v2 Announce Type: replace-cross Abstract: The integration of LLMs into vulnerability detection (VD) has shifted the field toward more interpretable and context-aware analysis. While po

@GaryMarcus I can't believe this is still a thing that 'experts' haven't caught up with. @GaryMarcus has been saying this forever, and those…

@GaryMarcus I can't believe this is still a thing that 'experts' haven't caught up with. @GaryMarcus has been saying this forever, and those of us who have actually dug into the tech, analyzed it, use

Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling

arXiv:2507.07982v2 Announce Type: replace Abstract: Videos inherently represent 2D projections of a dynamic 3D world. However, our analysis suggests that video diffusion models trained solely on raw v

Global and Local Topology-Aware Attention with Persistent Homology and Euler Biases for Time-Series Forecasting

arXiv:2605.03163v1 Announce Type: new Abstract: Scientific time series often encode predictive geometric structure, including connectivity, cycles, shell-like geometry, directional changes, and nonlin

GRAFT: Auditing Graph Neural Networks via Global Feature Attribution

arXiv:2605.03377v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) achieve strong performance on node classification tasks but remain difficult to interpret, particularly with respect to whi

Grounding Multi-Hop Reasoning in Structural Causal Models via Group Relative Policy Optimization

arXiv:2605.01482v1 Announce Type: new Abstract: Multi-Hop Fact Verification (MHFV) necessitates complex reasoning across disparate evidence, posing significant challenges for Large Language Models (LL

GRPO-TTA: Test-Time Visual Tuning for Vision-Language Models via GRPO-Driven Reinforcement Learning

arXiv:2605.03403v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) has recently shown strong performance in post-training large language models and vision-language models. It ra

Healthcare AI GYM for Medical Agents

arXiv:2605.02943v1 Announce Type: new Abstract: Clinical reasoning demands multi-step interactions -- gathering patient history, ordering tests, interpreting results, and making safe treatment decisio

Heterogeneous Graph Importance Scoring and Clustering with Automated LLM-based Interpretation

arXiv:2605.02919v1 Announce Type: new Abstract: Urban bridge networks are critical infrastructure whose disruption can cascade into severe impacts on transportation, emergency services, and economic a

HiMAC: Hierarchical Macro-Micro Learning for Long-Horizon LLM Agents

arXiv:2603.00977v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents have recently demonstrated strong capabilities in interactive decision-making, yet they remain fundamentally

How Sam ('You parachute him onto a cannibal island, and he comes back five years later as king”) Altman operates:

How Sam ('You parachute him onto a cannibal island, and he comes back five years later as king”) Altman operates: counsel: 'by fall of 2023 did you percieve altman was not candid with you? truthful? h

I repeat, the bubble is in the 'e' not the 'p' in today's PE ratios. The hucksters and talking heads will, as always, fail to realize until …

I repeat, the bubble is in the 'e' not the 'p' in today's PE ratios. The hucksters and talking heads will, as always, fail to realize until it's too late. But it's a very simple set up. Hyperscalers g

If Forbes had only waited to hear the testimony at this week’s trial Or read @_KarenHao’s book Or @RonanFarrow’s @newyorker investigation Or…

If Forbes had only waited to hear the testimony at this week’s trial Or read @_KarenHao’s book Or @RonanFarrow’s @newyorker investigation Or my own writings since fall 2023 They would have realized ho

Important nuance: the jury at this Musk-OpenAI trial is an advisory jury, hence not binding on the judge, and is only looking at liability…

Important nuance: the jury at this Musk-OpenAI trial is an *advisory* jury, hence not binding on the judge, and is only looking at liability (not damages, if any). Thanks to @bahhradx for correcting a

Intervention Complexity as a Canonical Reward and a Measure of Intelligence

arXiv:2605.02175v1 Announce Type: new Abstract: The Legg--Hutter universal intelligence measure provides a rigorous scalar assessment of general intelligence as expected reward across all computable e

Model Releases

Jiao: Bridging Isolation and Customization in Mixed Criticality Robotics

model-releasesarxiv-cs-ro

arXiv:2605.03641v1 Announce Type: new Abstract: Consumer robotics demands consolidation of safety-critical control, perception pipelines, and user applications on shared multicore platforms. While sta

just want to go back to how much intense pushback i got on this story at all levels of the company at the time, and how people speak very di…

just want to go back to how much intense pushback i got on this story at all levels of the company at the time, and how people speak very differently when under the threat of perjury lot of names etch

Khala: Scaling Acoustic Token Language Models Toward High-Fidelity Music Generation

arXiv:2605.01790v1 Announce Type: cross Abstract: A common design pattern in high-quality music generation is to handle structure and fidelity in different representation spaces: a generator first mod

Large Language Models are Universal Reasoners for Visual Generation

arXiv:2605.04040v1 Announce Type: new Abstract: Text-to-image generation has advanced rapidly with diffusion models, progressing from CLIP and T5 conditioning to unified systems where a single LLM bac

Like tricksters, LLMs have perfected the art of plausibility, says Tim Harford: https://ft.trib.al/5Foo2YD

Tim Harford compares large language models to tricksters, arguing that LLMs excel at generating plausible-sounding text without necessarily ensuring accuracy or truthfulness. The article likely explor

LLM-XTM: Enhancing Cross-Lingual Topic Models with Large Language Models

arXiv:2605.03299v1 Announce Type: new Abstract: Cross-lingual topic modeling aims to discover shared semantic structures across languages, yet existing models depend on sparse bilingual resources and

safetyarxiv-cs-cl

Many trials feel like Rashomon with different witnesses. The amazing thing about Musk-OpenAI is how much agreement there has been (at least …

Many trials feel like Rashomon with different witnesses. The amazing thing about Musk-OpenAI is how much agreement there has been (at least so far) on the facts. The question is really whether what Op

MINT: Minimal Information Neuro-Symbolic Tree for Objective-Driven Knowledge-Gap Reasoning and Active Elicitation

arXiv:2602.05048v2 Announce Type: replace Abstract: Joint planning through language-based interactions is a key area of human-AI teaming. Planning problems in the open world often involve various aspe

Mix3R: Mixing Feed-forward Reconstruction and Generative 3D Priors for Joint Multi-view Aligned 3D Reconstruction and Pose Estimation

arXiv:2605.03359v1 Announce Type: new Abstract: Recent trends in sparse-view 3D reconstruction have taken two different paths: feed-forward reconstruction that predicts pixel-aligned point maps withou

Neuron-Anchored Rule Extraction for Large Language Models via Contrastive Hierarchical Ablation

arXiv:2605.03058v1 Announce Type: new Abstract: A key goal of explainable AI (XAI) is to express the decision logic of large language models (LLMs) in symbolic form and link it to internal mechanisms.

Nora: Normalized Orthogonal Row Alignment for Scalable Matrix Optimizer

arXiv:2605.03769v1 Announce Type: new Abstract: Matrix-based optimizers have demonstrated immense potential in training Large Language Models (LLMs), however, designing an ideal optimizer remains a fo

Normalized Matching Transformer

arXiv:2503.17715v3 Announce Type: replace Abstract: We introduce the Normalized Matching Transformer (NMT), a deep learning approach for efficient and accurate sparse semantic keypoint matching betwee

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies

arXiv:2605.03065v1 Announce Type: new Abstract: Generative control policies (GCPs), such as diffusion- and flow-based control policies, have emerged as effective parameterizations for robot learning.

On Surprising Effects of Risk-Aware Domain Randomization for Contact-Rich Sampling-based Predictive Control

arXiv:2605.03290v1 Announce Type: new Abstract: Domain randomization (DR) is widely used in policy learning to improve robustness to modeling error, but remains underexplored in contact-rich sampling-

safetyarxiv-cs-ro

OpenAI violated Canadian privacy laws in developing first ChatGPT model, probe finds https://www.theglobeandmail.com/business/article-openai…

OpenAI violated Canadian privacy laws in developing first ChatGPT model, probe finds https://www.theglobeandmail.com/business/article-openai-chatgpt-violated-canadian-privacy-laws-watchdogs-report/?ut

Optimal Posterior Sampling for Policy Identification in Tabular Markov Decision Processes

arXiv:2605.03921v1 Announce Type: new Abstract: We study the (arepsilon, elta)-PAC policy identification problem in finite-horizon episodic Markov Decision Processes. Existing approaches provide finit

Orientation-Aware Unsupervised Domain Adaptation for Brain Tumor Classification Across Multi-Modal MRI

arXiv:2605.03490v1 Announce Type: new Abstract: The clinical integration of deep learning models for brain tumor diagnosis in neuro-oncology is severely constrained by limited expert-annotated MRI dat

Poly-EPO: Training Exploratory Reasoning Models

arXiv:2604.17654v3 Announce Type: replace Abstract: Exploration is a cornerstone of learning from experience: it enables agents to find solutions to complex problems, generalize to novel ones, and sca

Population-Aware Imitation Learning in Mean-field Games with Common Noise

arXiv:2605.03357v1 Announce Type: new Abstract: Mean Field Games (MFGs) provide a powerful framework for modeling the collective behavior of large populations of interacting agents. In this paper, we

Power-Softmax: Towards Secure LLM Inference over Encrypted Data

arXiv:2410.09457v2 Announce Type: replace Abstract: Modern cryptographic methods for implementing privacy-preserving LLMs such as gls{HE} require the LLMs to have a polynomial form. Forming such a rep