Search: “safety”

CrystalGRPO: Target-Aligned and Coverage-Preserving Reinforcement Learning for Flow-Based Crystal Structure Prediction

arXiv:2608.06582v1 Announce Type: new Abstract: Flow-based generative models can efficiently produce candidate structures for crystal structure prediction (CSP), but their pretrained objectives do not

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Density-Functional Excited-State Gradients and Nonadiabatic Couplings on a Consumer GPU from a Contraction-DAG

DGX agent

arXiv:2608.06536v1 Announce Type: cross Abstract: Nonadiabatic dynamics needs an excited-state gradient and an interstate nonadiabatic coupling matrix element (NACME) at every nuclear geometry, and a

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10 Aug 2026

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training

arXiv:2608.07147v1 Announce Type: new Abstract: Reinforcement learning with Verifiable Reward (RLVR) has emerged as a powerful paradigm for training coding agents, where the execution feedback from co

Do 3D Medical Foundation Models See Through MRI Artifacts? A Controlled Study of Representation Robustness

arXiv:2608.06613v1 Announce Type: cross Abstract: Self-supervised 3D medical foundation models are increasingly used as general-purpose feature extractors, yet their sensitivity to MRI artifacts remai

Does Splitting a Triage Decision Across Agents Hide Bias or Help Catch It? A Multi-Agent Simulation Study of LLM-Based Resource Allocation Under Audit Capacity Constraints

arXiv:2608.06949v1 Announce Type: new Abstract: Prior benchmarking work has shown that a single large language model (LLM), forced to make life-or-death resource-allocation decisions, exhibits measura

Dual-Space Modality Consistency Learning for Universal Cross-Modal Re-Identification

arXiv:2608.06943v1 Announce Type: new Abstract: Cross-modal Re-Identification (ReID) aims to retrieve the same identity across heterogeneous imaging modalities and has been widely studied in visible-i

Explore or Converge? Stage-Guided Per-Step Optimization for Diffusion Models

arXiv:2608.06768v1 Announce Type: new Abstract: Diffusion models have strong generative capabilities. However, their maximum likelihood training objective only focuses on reconstructing the data distr

From Optimal Actions to World Models: Identifiability of Transition Kernels in Discounted MDPs

arXiv:2608.07301v1 Announce Type: new Abstract: We study what can be recovered about the transition probabilities of a Markov decision process from optimal actions alone. This is closely related to th

Gated-BEPO: Confidence-Gated Bellman Credit Assignment for Large Language Model Agents

arXiv:2608.06861v1 Announce Type: new Abstract: Training large language model agents in long-horizon environments requires assigning credit from sparse terminal outcomes to individual actions. Existin

GOPI: Generation-Oriented 3D Pose Inference for Furniture Insertion from Single-View RGB-D Indoor Scenes

arXiv:2608.06836v1 Announce Type: new Abstract: We study the problem of inserting new furniture into indoor scene images. Under masked single-view 2D image-plane conditioning, however, the physical sc

Grad-ECLIP: Gradient-based Visual and Textual Explanations for CLIP

arXiv:2502.18816v3 Announce Type: replace Abstract: Significant progress has been achieved on the improvement and downstream usages of the Contrastive Language-Image Pre-training (CLIP) vision-languag

Graph Machine: Exploring Edge Mechanisms as an Inductive Bias

arXiv:2608.06834v1 Announce Type: new Abstract: Transformers provide a powerful architecture for global content-based matching, but reasoning problems may benefit from a stronger inductive bias toward

High-dimensional ridgeless least squares interpolation under spiked covariance structures

arXiv:2608.07281v1 Announce Type: cross Abstract: This paper investigates the asymptotic behavior of the out-of-sample prediction risk of the high-dimensional ridgeless least-squares estimator when th

How Much, Then Where: Credit-Conserving Action-to-Token Allocation for Multi-Turn Agent Reinforcement Learning

arXiv:2608.07118v1 Announce Type: new Abstract: Credit assignment in multi-turn agent reinforcement learning operates at two levels: assigning trajectory-level credit to actions and distributing each

How WPP operationalizes platform and data engineering for AI marketing

Between chaotic levels of market fragmentation and economic volatility, marketing and communications agencies can no longer rely on the human intuition they’ve traditionally used to win clients and op

safetygoogle-cloud-ai

Identity as Presence: Towards Appearance and Voice Personalized Joint Audio-Video Generation

arXiv:2603.17889v4 Announce Type: replace Abstract: Recent advances in video synthesis have enabled realistic integration of real individuals, driving demand for identity-aware generation. While emerg

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons

arXiv:2506.03392v2 Announce Type: replace Abstract: We propose a new ternary spiking neuron model to improve the representation capacity of binary spiking neurons in deep Q-learning. Although a ternar

Interpretable reinforcement learning with decision-tree pruning

arXiv:2608.07151v1 Announce Type: cross Abstract: Reinforcement learning policies are difficult to inspect, but interpreting them is a prerequisite for trustworthiness. Converting a trained policy int

Is Forward Prediction Enough? Physical State Grounding for JEPA World Models

arXiv:2608.06799v1 Announce Type: cross Abstract: Learning structured and control-relevant latent representations remains a key challenge for world models. Recent JEPA-based world models learn action-

KReF: Training-Free Retrieval for Long-Term Time-Series Forecasting and Predictive Uncertainty

arXiv:2608.06748v1 Announce Type: cross Abstract: Probabilistic long-term time-series forecasting commonly relies on trained models. Training-free conformal methods typically construct intervals aroun

Learning Suffers More Than the Policy Class Under Partial Observability: A Closed-Form Analysis

arXiv:2608.07228v1 Announce Type: new Abstract: When a reinforcement learning agent cannot observe the full state, we usually blame its policies: it cannot see enough to represent a good one. We show

Learning to Walk With Less: A Dyna-Style Approach to Quadrupedal Locomotion

arXiv:2509.06296v2 Announce Type: replace-cross Abstract: Traditional on-policy reinforcement learning (RL) controllers for quadrupedal locomotion often suffer from low data efficiency, requiring mill

Let's Unlearn Stereotypes Before Decision-Making: Assessing the Impact of Intrinsic Bias Mitigation on Downstream Fairness in LLMs

arXiv:2509.16462v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly used in high-stakes decision-making systems, where biased predictions can reinforce social and economi

LMM Modality Transfer: A Pre-requisite for Autonomous GIS Agents

arXiv:2608.06948v1 Announce Type: new Abstract: AI models are becoming increasingly adept at understanding and processing spatial information, thereby facilitating agentic problem-solving in spatial t

LyEvO: Lyapunov-Guided Evolutionary Optimization for Safe and Robust Sim-to-Real Policy Learning

arXiv:2608.06481v1 Announce Type: cross Abstract: Training controllers that are safe and robust in simulation, and systematically assessing their readiness for real-world deployment, remain key challe

MaskFlow: Precise, Consistent and Seamless Regional Image Editing

arXiv:2608.06929v1 Announce Type: cross Abstract: Regional image editing has attracted considerable attention for its spatial controllability. Although instruction-based and mask-reference-based editi

MemOPD: On-Policy Distillation through Memory State Alignment for Long-Horizon Agents

arXiv:2608.07068v1 Announce Type: new Abstract: Long-horizon agents accumulate growing contexts during interaction, impairing performance and stability. Compact memory mitigates this problem by compre

MemPrism: Task-Conditioned Relational Memory Views for Long-Horizon Agents

arXiv:2608.06745v1 Announce Type: new Abstract: Long-horizon agents rely on memory to reuse experiences, yet existing memory systems often assume that evidence can be directly consumed through a fixed

Mind the Gap: A Dual Knowledge Graph Framework for Unified Multi-task User Intent Inference

arXiv:2608.06752v1 Announce Type: new Abstract: This paper proposes DKG-MTI, a dual knowledge graph framework for unified multi-task user intent inference from online travel reviews. Existing approach

Minimal Ingredients for Reward Assignment from Expert Demonstrations

arXiv:2506.06793v2 Announce Type: replace-cross Abstract: Reward assignment from scarce demonstrations is a key challenge in both offline and online imitation learning. A common and intuitive strategy

MolBioKG: Grounding Out-of-Graph Molecules in Biomedical Knowledge Graphs via Multi-Resolution Structural Anchoring

arXiv:2608.06713v1 Announce Type: new Abstract: Biomedical knowledge graphs (KGs) accelerate drug discovery, but standard pipelines assume query molecules already exist as graph entities, leaving unre

NTDH: Complex Reasoning for Comprehensive Affective Analysis

arXiv:2608.06425v1 Announce Type: new Abstract: Comprehensive affective analysis is challenging for two reasons: it spans heterogeneous prediction tasks with continuous, ordinal, and multi-label outpu

PAST: Prompt-Adaptive Sampling Termination for Efficient Diffusion Model

arXiv:2608.06794v1 Announce Type: new Abstract: While diffusion models have made significant progress in text-to-image tasks, they still exhibit limitations when directly optimizing downstream objecti

People Are Not Just Their Countries. Disentangling Social Determinants of LLM Value Alignment Across Europe

arXiv:2608.07367v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly used as a primary source of information and advice, understanding their alignment to humans in terms of

Progress-Certified Reversible Simplex Supervision of Goal-Reaching Reinforcement Learning

arXiv:2601.19499v2 Announce Type: replace Abstract: Task completion is difficult to certify when state aggregation, model mismatch, and disturbances invalidate nominal RL transitions. We present a rev

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Progressive Alignment of Recommender Foundation Model through Multi-Phase Post-Training

arXiv:2608.06792v1 Announce Type: cross Abstract: Foundation model(FM) for recommendation has shown strong ability to model long-horizon sequential user behavior. In practice, a single pretrained foun

R2S-EGO: Dual-Proxy Refinement for Sparse-Capture Real-to-Sim

arXiv:2608.06827v1 Announce Type: cross Abstract: Real-to-sim (R2S) depends on scene representations that render observations along robot ego trajectories, yet dense multi-view capture limits per-envi

Reading Copom's Tone: A Weighted LLM Framework for Hawkish-Dovish Sentiment, Forward Guidance, and Uncertainty

arXiv:2608.07251v1 Announce Type: cross Abstract: This paper documents an applied natural-language-processing framework for measuring the tone of Brazilian Monetary Policy Committee (Copom) statements

Really important point and nicely explained! Two further points worth noting: (1) if an AI system relies on a harness (which as Gary notes i…

Really important point and nicely explained! Two further points worth noting: (1) if an AI system relies on a harness (which as Gary notes in other writing is increasingly improtant in top models), th

safetygary-marcus--x

Representation-driven Endoscopic Visual Embedding Alignment for Latent Generation

arXiv:2608.07176v1 Announce Type: cross Abstract: Developing foundation generative models for endoscopy is limited by the gap between natural and clinical images and the computational cost of training

RibAssist 3D: Biplanar Rib-Fracture Detection, Addressing, and Selective 3D Localization from CT-Derived Projections

arXiv:2608.06914v1 Announce Type: new Abstract: Rib fractures are common, clinically significant, and time-consuming to localize on computed tomography (CT). We ask whether fractures detected in two o

Robust Average-Reward Markov Decision Processes: Minimax-Optimal Learning via Plug-in Reductions

arXiv:2608.06545v1 Announce Type: new Abstract: Distributionally robust Markov decision processes provide a principled framework for sequential decision making under model uncertainty. We study how ma

Self-Distillation Enables Continual Learning

arXiv:2601.19897v2 Announce Type: replace Abstract: Continual learning, enabling models to acquire new skills and knowledge without degrading existing capabilities, remains a fundamental challenge for

Shape Your Feed: An LLM-based Agentic System for Conversational Recommendation

arXiv:2608.06632v1 Announce Type: new Abstract: Industrial recommendation systems predominantly adopt a passive ranking paradigm that infers user preferences from implicit behavioral signals (e.g., cl

SNI-GNN: SmartNIC-Assisted Full-Graph GNN Training with In-Network Embedding Prediction

arXiv:2608.06441v1 Announce Type: new Abstract: Full-graph GNN training delivers high accuracy but scales poorly on multi-server clusters due to heavy, irregular inter-node embedding exchanges. We pre

Sources: officials say OpenAI risks its White House relationship by hiring Dean Ball, who has criticized Trump's AI strategy after leaving the administration (Thomas Barrabi/New York Post)

Thomas Barrabi / New York Post: Sources: officials say OpenAI risks its White House relationship by hiring Dean Ball, who has criticized Trump's AI strategy after leaving the administration — Top Open

safetytechmeme

TA-RAG: Tone Awareness as a Design Imperative for Retrieval-Augmented Generation

arXiv:2608.06672v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become a robust architecture for grounding large language models (LLMs) in trusted knowledge. However, standard

The Horizon Gap: Planning, Memory, Execution, Training, and Evaluation for Long-Horizon LLM Agents

arXiv:2608.06663v1 Announce Type: new Abstract: Frontier language models solve reasoning problems in a single forward pass that would have been research contributions years ago, yet fail at multi-hour