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EpiWorld: Grounding LLM Policy Agents in Epidemiological World Models

arXiv:2610.02744v1 Announce Type: new Abstract: Epidemic intervention policies are textual artefacts that human decision-makers interpret, justify, and revise through natural language, making large la

safetyarxiv-cs-cl

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Equivariant Flow Matching for Electron Density Prediction

DGX agent

arXiv:2610.02651v1 Announce Type: cross Abstract: Machine learning surrogates for density functional theory (DFT) have been increasingly used to reduce the cost of first-principles calculations. In th

model-releasesarxiv-cs-ai

5 Oct 2026

Equivariant Visual-Tactile Diffusion Policy for Contact-Rich Manipulation

arXiv:2610.03333v1 Announce Type: cross Abstract: Imitation learning for contact-rich manipulation requires high-quality expert data that is expensive to obtain. This makes learning a sample-efficient

Error Propagation in Dynamic Programming: From Stochastic Control to American Option Pricing

arXiv:2509.20239v2 Announce Type: replace-cross Abstract: This paper investigates theoretical and methodological foundations for stochastic optimal control (SOC) in discrete time. We start formulating

Estimating prevalence with precision and accuracy

arXiv:2507.06061v2 Announce Type: replace-cross Abstract: Unlike classification, whose goal is to estimate the class of each data point, quantification (or prevalence estimation) aims to estimate the

Evaluating and Improving the Robustness of Large Language Models to Input Sequence Variations

arXiv:2610.02432v1 Announce Type: cross Abstract: Large language models (LLMs) in production systems face prompt injections, trojans (backdoors), and manipulation of automatic quality metrics. This th

Evaluating LLM-as-a-Judge Beyond Score Alignment: A Psychometric Analysis of Residual Judging Difficulty

arXiv:2610.02877v1 Announce Type: new Abstract: Large language models (LLMs) are widely used as automatic judges, with validity typically assessed via alignment with human scores. However, aggregate a

safetyarxiv-cs-cl

Evaluating Multi-Dimensional Generalization of Large Language Models in Temporal Extraction Tasks

arXiv:2610.02549v1 Announce Type: new Abstract: Time and event expression extraction are fundamental temporal reasoning tasks, but the problem remains difficult due to annotation ambiguity, domain sen

researcharxiv-cs-cl

Evaluating the Retrieval Robustness of Large Language Models

model-releasesarxiv-cs-ai

arXiv:2505.21870v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) generally enhances large language models' (LLMs) ability to solve knowledge-intensive tasks. But RAG coul

Evaluating VQA in Vision Language Models using Cooperative Principles

arXiv:2610.02878v1 Announce Type: new Abstract: We evaluate the performance of Vision Language Models in Visual Question Answering (VQA) when questions violate Grice's maxims. To do this, we use VLMs

Evaluation format, not model capability, drives measured triage failure in the assessment of consumer health AI

model-releasesarxiv-cs-ai

arXiv:2603.11413v4 Announce Type: replace-cross Abstract: A recent Nature Medicine study reported that ChatGPT Health under-triages 51.6% of emergencies and concluded that consumer-facing AI triage po

Evaluation of Sampling Strategies and Physics-Informed Kolmogorov--Arnold Networks in Unbounded Domains

arXiv:2512.12074v2 Announce Type: replace Abstract: Physics-informed neural networks (PINNs) have emerged as an effective approach for solving partial differential equations (PDEs) by incorporating ph

Event-guided Neural Video Compression

arXiv:2610.02265v1 Announce Type: cross Abstract: Neural video codecs derive motion and temporal contexts mainly from RGB frames, leaving room for cross-modal guidance from complementary temporal obse

researcharxiv-cs-cv

EVEWorld: Physical Evolution Supervision for Embodied World Models

arXiv:2610.03374v1 Announce Type: new Abstract: Embodied world models enable scalable simulation of embodied interactions for robot learning. However, existing models are prone to Model Laziness, as t

safetyarxiv-cs-cv

Local Ai

EviDent-CBCT: Evidence-Bottlenecked Report Generation from Dental CBCT under Non-Exhaustive Report Supervision

arXiv:2610.02375v1 Announce Type: cross Abstract: Dento-maxillofacial cone-beam CT (CBCT) reports may contain dozens of tooth-specific, anatomical, and spatial findings from a single 3D scan. Learning

local-aiarxiv-cs-ai

EVOL: Simulator-Guided Evolutionary Expert Synthesis for Deployment-Free Learning Path Recommendation

arXiv:2610.03273v1 Announce Type: new Abstract: Reinforcement learning (RL) for learning path recommendation (LPR) faces two coupled obstacles. First, the policy must commit to a sequence of L concept

Evolving Hybrid Quantum-Classical Architectures for Image Classification

arXiv:2610.03220v1 Announce Type: cross Abstract: Hybrid quantum classical neural networks integrate parameterized quantum circuits (PQCs) with established deep learning architectures, but their perfo

researcharxiv-cs-ai

EvoRiskBench: An Evolving Benchmark for Runtime Security Risks in Workspace Agents

model-releasesarxiv-cs-ai

arXiv:2610.03153v1 Announce Type: cross Abstract: Workspace agents combine large language models with execution harnesses to perform stateful, multi-step tasks that access or modify external resources

Exact Memory-Time Optimization for Prefix-Cached Language Model Serving

arXiv:2610.02766v1 Announce Type: new Abstract: Retaining language-model prefix states trades recomputation against storage time. Optimizing each cached block independently can overcount savings: a re

Expected Utility Regret Rule: Minimax and Bayes Optimal Portfolio Choice

arXiv:2610.02290v1 Announce Type: cross Abstract: This study considers the problem of portfolio choice, where we recommend a portfolio to an investor to maximize the expected utility of their wealth.

Explainable Molecular Structure Inference from GC--MS with Diffusion Models and LLM Reranking

arXiv:2610.03066v1 Announce Type: cross Abstract: GC--EI--MS is an important technique for analyzing volatile and semivolatile compounds in complex samples. However, conventional methods rely heavily

Tutorials

Exploring Subnetwork Interactions in Heterogeneous Brain Network via Prior-Informed Graph Learning

arXiv:2603.19307v2 Announce Type: replace-cross Abstract: Modeling the complex interactions among functional subnetworks is crucial for the diagnosis of mental disorders and the identification of func

tutorialsarxiv-cs-ai

Exploring the Trade-Off Between Structured Pruning and Fault Tolerance in Deep Neural Networks for Space Applications

arXiv:2610.03117v1 Announce Type: new Abstract: Deep Neural Networks (DNNs) inherently exhibit a degree of robustness to bit-level faults due to their distributed representation of information. As a m

EyeRobot 2.0: Active Gaze for Precise Manipulation without Wrist Cameras

arXiv:2610.03710v1 Announce Type: cross Abstract: Inspired by human vision, we introduce a framework using active gaze to enable fine-grained bimanual manipulation with only a single stereo camera. Ey

Local Ai

FactorSplat: Appearance-Controllable Gaussian Proxies for Medical Volume Rendering

arXiv:2610.02382v1 Announce Type: new Abstract: Transfer functions (TFs) control color and visibility in medical volume rendering, but image-trained Gaussian proxies typically bake one transfer functi

local-aiarxiv-cs-cv

FALCON: A Model and Dataset Agnostic Framework for Synthetic Data Generation for NL2SQL Pairs

arXiv:2610.03625v1 Announce Type: new Abstract: Relational databases are among the most widely deployed forms of structured knowledge, and natural language access to them requires grounding language o

Agents

Fast Models, Slow Evidence: A Paired and Self-Audited Evaluation of System-1 Decision Models for LLM Agent Harnesses

arXiv:2610.02267v1 Announce Type: new Abstract: Agent harnesses make many small, typed decisions per task: which model to call, which tool to use, whether retrieved text is relevant, whether an input

agentsarxiv-cs-ai

FASTDIAR: Frame-level speaker encoder for Streaming Diarization

arXiv:2610.02941v1 Announce Type: cross Abstract: Real-time conversational agents require speaker diarization that streams and runs on a CPU. Most systems apply an utterance-level speaker encoder to s

FastOPD: On-Policy Distillation for Lightweight VLA Deployment

arXiv:2610.02832v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) foundation models have scaled rapidly to enhance manipulation performance and generalizability, but this scaling incurs h

Local Ai

Fed-ADApt: Federated Anytime Depth Adaptation for Resource-Aware Medical Image Segmentation

arXiv:2610.03474v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative training of medical image segmentation models without sharing raw patient data, yet existing approaches as

local-aiarxiv-cs-cv

Feedforward Novel View Synthesis for Heterogeneous Cameras

arXiv:2610.03522v1 Announce Type: new Abstract: Feed-forward novel view synthesis has recently shown promising results from sparse posed images, but most existing methods assume that context and targe

researcharxiv-cs-cv

FiberGeoText: A Vision-Language Model for Population- Level Organization of Superficial White Matter

arXiv:2610.02755v1 Announce Type: new Abstract: The superficial white matter (SWM), a critical brain region for cognition across the lifespan and brain disease, contains abundant short-range associati

researcharxiv-cs-cv

Filter-Aware Fine-Tuning for Safe Humanoid Whole-Body Tracking

arXiv:2610.02341v1 Announce Type: new Abstract: Safe whole-body motion is essential for deploying humanoid robots in unstructured environments. Modern humanoid control commonly separates reference spe

safetyarxiv-cs-ro

FinDialogLens: Event Extraction over Multi-Party Dialogue for Missed-Trade Identification in Financial Chatrooms

arXiv:2610.02455v1 Announce Type: cross Abstract: Multi-party financial chatrooms are vital for sales-and-trading professionals, but their complexity makes manual recovery of missed trades infeasible:

researcharxiv-cs-ai

Finding the Move Is Not Winning the Game: XiangqiBench for Closed-Loop Evaluation of LLM Agents

arXiv:2610.02425v1 Announce Type: new Abstract: Static evaluations credit a language model for naming the right move, but an agent must carry a plan through to a verified outcome while an opponent res

Fisher-Guided Submodular Data Selection for Continual Pre-Training of Large Language Models

arXiv:2610.02593v1 Announce Type: new Abstract: Data selection is already a central bottleneck in large-language-model training, where web-scale corpora are noisy and token budgets are finite. In cont

Hardware

FlashSinkhorn 2: Block-Sparse Entropic Optimal Transport

arXiv:2610.02395v1 Announce Type: new Abstract: Streaming GPU solvers for entropic optimal transport (EOT), such as FlashSinkhorn, avoid storing the dense kernel but still evaluate all nimes m point p

hardwarearxiv-cs-ai

Flow Map Denoisers: Traversing the Distortion-Perception Plane for Inverse Problems

model-releasesarxiv-cs-cv

arXiv:2606.19802v2 Announce Type: replace-cross Abstract: Image restoration faces a fundamental tradeoff: methods that minimize error produce blurry reconstructions, while those that maximize perceptu

Flow Matching for Fast Posterior Sampling in Bayesian Inverse Problems

arXiv:2610.02377v1 Announce Type: cross Abstract: Sampling from the posterior is the central task of computational Bayesian inverse problems. The standard workhorse in Bayesian inference - Markov chai

FlowHMR: Physically Plausible Motion Capture from Video

arXiv:2610.03691v1 Announce Type: new Abstract: We present FlowHMR, a framework for recovering physically plausible global 3D human motion from monocular video. Previous learning-based methods typical

safetyarxiv-cs-cv

Follow the Winners: Conservative Policy Improvement with the Cross-Entropy Method for Critic-Free RFT

arXiv:2610.03361v1 Announce Type: cross Abstract: Critic-free reinforcement fine-tuning (RFT) for agentic large language models is often done through GRPO-style methods, which compute a group baseline

Forecasting from Counterfactual Simulator Rollouts: A Sim2Real Evaluation

arXiv:2610.03662v1 Announce Type: new Abstract: Deploying a new decision policy creates a cold-start problem for prediction models whose targets depend on the policy's actions: historical observations

safetyarxiv-cs-lg

Tutorials

Foresight: planning future perception in streaming VLMs without retraining

arXiv:2610.03123v1 Announce Type: cross Abstract: Existing streaming vision-language models (VLMs) continuously perceive and reason over visual streams, but their computational pathways remain fixed t

tutorialsarxiv-cs-ai

Tutorials

ForestQuery: Boundary-Aware and Spatially Anchored Query Learning for Unified Forest Point Cloud Segmentation

arXiv:2610.03403v1 Announce Type: cross Abstract: Forest point cloud segmentation is fundamental for fine-grained 3D forest scene understanding, yet remains challenging due to irregular tree structure

tutorialsarxiv-cs-ai

Found but Not Read: When Extracted Text Closes the Retrieval-Reading Gap in Document Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2610.02880v1 Announce Type: new Abstract: Retrieval-augmented document question answering assumes that once the right page is found, a vision-language model (VLM) can read it. We show that this

Fragmentation is Efficiently Learnable by Quantum Neural Networks

arXiv:2512.00751v4 Announce Type: replace-cross Abstract: In certain classes of physical quantum systems, the exponentially large state space 'fragments' into many low-dimensional, dynamically disconn

Frequency Is Not Sensitivity Identifying Safety-Sensitive Experts in Sparse MoE LLM

arXiv:2610.02910v1 Announce Type: new Abstract: Suppressing a small set of routed experts can weaken the safety behavior of a sparse Mixture-of-Experts (MoE) language model without retraining. Which e

From Behavior to Provenance: Attributing Tabular Foundation Models to Synthetic Pretraining Data

arXiv:2610.02347v1 Announce Type: new Abstract: Training-data attribution aims to identify which training examples shape model behavior, yet validating such claims is difficult because causal training