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Asking the Crowd the Right Question: Bias-Cancelling Weights for Federated Learning

arXiv:2610.04671v1 Announce Type: cross Abstract: A federated objective is a weighted sum of client risks, and the weights are almost always fixed in advance. We treat them instead as the only instrum

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Assembling Insights for Agentic Machine Learning Engineering Systems

DGX agent

arXiv:2610.04927v1 Announce Type: cross Abstract: Agentic machine learning engineering (MLE) is an emerging AI4SE application for complex ML tasks and a step toward recursive self-improvement of AI sy

model-releasesarxiv-cs-lg

6 Oct 2026

AstraSR: Real-World Thermal Super-Resolution with GPT-6 Astra

arXiv:2610.05910v1 Announce Type: new Abstract: Real-world thermal super-resolution (SR) is constrained by limited sensor resolution and the difficulty of obtaining corresponding high-resolution (HR)

applicationsarxiv-cs-cv

Asymptotically Optimal Best Arm Identification with Fixed-Budget under Differential Privacy

arXiv:2610.04600v1 Announce Type: new Abstract: Best arm identification under differential privacy is a pure-exploration problem in which both statistical efficiency and privacy protection must be ach

Asynchronous Tracking, Optical Communication and 3D Motion Capture using Event-based Sensors

arXiv:2610.04342v1 Announce Type: cross Abstract: Inter-robot communication is a key enabling technology in cooperative robotics applications. While wireless communication is ubiquitous, simple, and e

researcharxiv-cs-cv

Local Ai

Atomic Visual Entailment: Enhancing Zero-Shot Vision-Language Reasoning through Atomic Fact Decomposition and Learned Selection

arXiv:2610.05630v1 Announce Type: cross Abstract: Visual entailment (VE) asks whether an image supports, contradicts, or leaves undecided a textual hypothesis. Strong results come from fine-tuning lar

local-aiarxiv-cs-cv

Attention-Based Surface Representation Learning for Robot State Prediction and Open-Ended Surface Classification

arXiv:2610.04240v1 Announce Type: cross Abstract: For ground robots operating in outdoor environments, understanding the properties of the underlying terrain is essential for ensuring reliable operati

Attention Tax, Handoff Tax: A Stylised Model of When Multi-Agent LLM Systems Help

arXiv:2610.06069v1 Announce Type: cross Abstract: Recent work on multi-agent LLM systems reaches sharply different conclusions: some results show that a single agent with the same information and comp

agentsarxiv-cs-lg

AUTOPILOT An Advanced Perception, Localization and Path Planning Techniques for Autonomous Vehicles Using YOLOv7 and MiDaS

arXiv:2610.06232v1 Announce Type: cross Abstract: Self driving vehicles have emerged as a reliable technology that has the capability to transform transportation and mobility. The development of self

agentsarxiv-cs-lg

Autoware in Construction: Gap Analysis and LiDAR Perception Toward Off-Road Autonomous Driving

arXiv:2610.04187v1 Announce Type: new Abstract: Autoware is an open-source autonomous driving software platform widely adopted by researchers and industry developers. Originally developed primarily fo

agentsarxiv-cs-ro

BabelFake: A Multilingual Audio-Visual DeepFake Benchmark

arXiv:2610.06339v1 Announce Type: new Abstract: Reliable and practical audio-visual DeepFake detection requires benchmarks that reflect diverse linguistic contexts and modern data synthesis pipelines

Backdoor Attacks on Discrete Graph Diffusion Models

arXiv:2503.06340v2 Announce Type: replace-cross Abstract: Diffusion models have demonstrated remarkable generative capabilities in continuous data domains such as images and videos. Recently, discrete

Tutorials

Backward-Consistent Diffusion Sampling for Sparsely Observed PDE Inverse Problems

arXiv:2610.04624v1 Announce Type: new Abstract: Recovering Partial Differential Equation (PDE) coefficient fields from extremely sparse observations is a severely ill-posed inverse problem for which g

tutorialsarxiv-cs-lg

BARQ: Balanced Codebook Refinement for Low-Bit LLM Quantization

arXiv:2610.04490v1 Announce Type: new Abstract: As large language models (LLMs) grow in parameter count, model storage and parameter memory traffic have become major bottlenecks to efficient deploymen

Barrier-Shaped Recurrent Reinforcement Learning for Autonomous Landing on a Heaving Ship Deck

arXiv:2610.03924v1 Announce Type: cross Abstract: This paper addresses autonomous landing of unmanned aerial vehicle (UAV) rotorcrafts on a heaving ship deck using a recurrent policy trained via asymm

safetyarxiv-cs-ro

Base Models Can Reason By Taking a Cue From Training Data

arXiv:2610.06851v1 Announce Type: new Abstract: In this paper, we study how training data creates associations between the tokens at the start of a base model's response and the reasoning behavior tha

Local Ai

BAT-NO: A Boundary-Condition-Aware Transformer Neural Operator for Crashworthiness Prediction of Vehicle Components

arXiv:2610.03854v1 Announce Type: new Abstract: High-fidelity finite-element simulations provide accurate crashworthiness predictions, but their cost limits iterative design exploration. Deep learning

local-aiarxiv-cs-lg

Bayes-Sufficient Compression Is Not Enough: How Does Communication Help Multi-Agent Systems?

arXiv:2610.03769v1 Announce Type: new Abstract: Multi-agent LLM systems pair a sender with broad context and an executor with a limited local view. We study when a short message improves the executor'

Bayesian Data Augmentation for DNN Retraining with Binomial Outcomes in Vision-Based UAV Landing

arXiv:2610.05674v1 Announce Type: cross Abstract: In GPS-denied or cluttered urban environments, vision-based landing is essential for reliable UAV missions. Real-world landing sites are often unstruc

applicationsarxiv-cs-cv

Tutorials

Bayesian Entropy-based Reordering for Calibrated Diffusion Language Models

arXiv:2610.05125v1 Announce Type: new Abstract: Masked Diffusion Language Models (MDLMs) generate sequences by iteratively replacing masked tokens with model predictions. At each denoising step, the d

tutorialsarxiv-cs-lg

Bayesian Optimization in Sequence-to-Architecture Latent Space for Zero-Shot NAS

arXiv:2610.06167v1 Announce Type: new Abstract: Zero-shot Neural Architecture Search removes the prohibitive cost of traditional NAS, but its search process is typically based on the evolutionary algo

BazaarBench: Delegation Safety in Decentralized C2C Marketplaces Run by LLM Agents

arXiv:2610.06748v1 Announce Type: cross Abstract: In decentralized consumer-to-consumer (C2C) marketplaces, people list goods, negotiate with strangers, and rate one another, so trust rests on reputat

BeliefGraph-JEPA: Structured Latent World Models for Action-Conditioned Time Series

arXiv:2610.05409v1 Announce Type: new Abstract: Action-conditioned time-series forecasting requires accounting for how future actions and exogenous forcings influence multiple targets through partiall

Bellman-Centric Learning: Near-Optimal Regret for Linear Bandits with Memory

arXiv:2610.05659v1 Announce Type: new Abstract: We study linear bandits with memory, where past actions induce endogenous nonstationarity through an arbitrary known, bounded matrix-valued memory map.

Benchmarking CLIP for Zero-Shot Face and Periocular Gender Estimation

arXiv:2610.06102v1 Announce Type: new Abstract: We investigate CLIP for zero-shot gender estimation from full-face and periocular images. Three CLIP backbones are evaluated on 11,299 frontal images fr

safetyarxiv-cs-cv

Benchmarking Generative Trajectory Models for Active-Inference Control

model-releasesarxiv-cs-ro

arXiv:2610.05692v1 Announce Type: new Abstract: Learning from trajectory demonstrations offers a route to active-inference control of complex systems whose dynamics are difficult to model explicitly.

Best-of-N Guidance for Test-time Diffusion Alignment

arXiv:2610.05108v1 Announce Type: new Abstract: Diffusion models achieve strong generative performance but often struggle to align generated samples with human preferences measured by a reward model.

Better Call Reward: Reward Hacking as Strategic Abstention in Legal Reasoning Models

arXiv:2610.06439v1 Announce Type: new Abstract: What happens when a legal AI model learns to look like a lawyer instead of reasoning like one? We fine tune Qwen3-8B with Group Relative Policy Optimisa

Beyond In-Distribution Preservation: Recovering Generalization in Quantized VLAs via Vulnerability-Oriented Tuning

arXiv:2610.05745v1 Announce Type: new Abstract: Post-training quantization has been shown to preserve VLA performance under standard evaluation conditions, but whether it preserves the full-precision

Local Ai

Beyond LLM Serving: Characterizing Vision-Language-Action Workloads for Embodied AI System Design

arXiv:2610.05062v1 Announce Type: cross Abstract: Vision-language-action (VLA) models translate multimodal observations into low-level robot actions. During robot operation, each control period sets a

local-aiarxiv-cs-ro

Beyond Masked Sparsity: SNACK Enables Truly Sparse Neural Networks on GPU

arXiv:2610.04093v1 Announce Type: new Abstract: Deep neural networks continue to grow in parameter count, driving up training and inference cost on GPUs. Sparse neural networks and Dynamic Sparse Trai

Beyond Overparameterization: Provable Learning of Input-Convex Multi-Layer Polynomial Networks with Active Queries

arXiv:2610.04999v1 Announce Type: new Abstract: The theoretical understanding of multi-layer neural networks is largely confined to overparameterized settings, which obscure parameter identifiability

Beyond Plausibility: Verifiable Fine-Grained Image Editing on Structured Assets

arXiv:2610.04381v1 Announce Type: new Abstract: Fine-grained image editing requires more than producing a visually plausible result: an editor must execute the requested attribute change precisely whi

Beyond the Good, the Bad, and the Ugly: Colormap Assessment through Data-Aware Perceptual Metric

arXiv:2610.03720v1 Announce Type: cross Abstract: Continuous colormaps are widely used to visualize scalar fields, and their quality is typically evaluated using measures such as discriminative power

researcharxiv-cs-cv

Beyond the Model: The Critical Role of Data Filtering in Clinical Machine Learning

arXiv:2610.06640v1 Announce Type: new Abstract: Machine learning (ML) studies using clinical data often rely on preprocessing and filtering pipelines before model development. The filtering decisions

Beyond Token Accuracy: Prioritizing What Matters for Visual Reconstruction

arXiv:2610.03822v1 Announce Type: new Abstract: Tokens have become a unified interface for multimodal foundation models, making visual-token communication a natural paradigm for efficient image delive

safetyarxiv-cs-cv

Beyond Transport Cost: Routing Differences between Flow Matching and Optimal Transport

arXiv:2610.05921v1 Announce Type: cross Abstract: In generative models, Optimal Transport (OT) is used to improve Flow Matching (FM) by reducing noise-data coupling cost. However, different noise-to-o

Bi-manual Stabilization of the Cervical Spine for Safe Physical Human-Robot Interaction in Rolling Maneuvers

arXiv:2610.04220v1 Announce Type: new Abstract: The neck, specifically the cervical spine, is a highly fragile and mobile part of the body with a high risk of catastrophic injury. Whether someone is i

safetyarxiv-cs-ro

Bilinear Flow Policy: Distributional Extrapolation for Goal-Conditioned Visuomotor Imitation

arXiv:2610.05765v1 Announce Type: new Abstract: Goal-conditioned imitation learning (GCIL) with flow matching is a promising framework that can represent multimodal behaviors while adapting to diverse

safetyarxiv-cs-ro

Billion-Scale Thumbnail Optimization for Uncurated Short-Form Videos via Multi-Armed Bandits

arXiv:2610.04931v1 Announce Type: new Abstract: This paper introduces a real-time thumbnail optimization system deployed at a global O(B) scale on a major short-form video platform. Unlike traditional

Block Disentanglement in CRL: Bridging Identifiability and Visual State Estimation

arXiv:2610.06809v1 Announce Type: new Abstract: Causal representation learning (CRL) is the process of recovering causally-related latent variables from high-dimensional observations. As a label-free

applicationsarxiv-cs-lg

Boosting Transferable Adversarial Attacks against Deep Reinforcement Learning

arXiv:2610.06083v1 Announce Type: new Abstract: Most adversarial attacks on deep reinforcement learning (DRL) assume white-box access to the victim policy, which rarely holds in practice. This paper s

BossouChimpanzee: Long-term Chimpanzee Video Dataset

arXiv:2610.05293v1 Announce Type: new Abstract: We describe the BossouChimpanzee video dataset, a unique long-term visual record of wild chimpanzees at an outdoor laboratory for field experiments in B

Bounded Reasoning: Cognitive Hierarchy in Human-versus-AI Cyber Defense

arXiv:2610.04878v1 Announce Type: cross Abstract: Human-agent evaluations often compress interaction into a single performance score, even when human and automated policies adapt differently over time

agentsarxiv-cs-lg

BrainTRACE: Tracing Longitudinal, Multimodal, and Volumetric Evidence in Brain MRI Clinical Reasoning

arXiv:2610.06571v1 Announce Type: new Abstract: Brain MRI interpretation is a longitudinal clinical reasoning problem: radiologists compare serial studies, integrate information across MRI sequences,

BRANCH-MoE: Balance-Aware Tree Routing for Large Embedding Models

arXiv:2610.06725v1 Announce Type: new Abstract: Mixture-of-experts (MoE) layers increase model capacity without a proportional increase in per-example computation. However, conventional flat routers c

BridgeCast: Bridging Ocean Wave Forecasts to Reanalysis via Flow Matching with Exogenous Variables

arXiv:2610.03759v1 Announce Type: cross Abstract: Ocean wave forecasting is essential for maritime safety, offshore operations, and coastal resilience, yet remains challenging due to systematic biases

Bridging the EHR Divide: Asymmetric Contrastive Learning for Cross-National Medical Representation Transfer

arXiv:2610.04946v1 Announce Type: new Abstract: Cross-system transfer of longitudinal Electronic Health Record (EHR) representations is challenging because clinical coding, patient populations, and he

applicationsarxiv-cs-lg