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Considering Context: When World Models Need Context Encoders

arXiv:2610.06651v1 Announce Type: new Abstract: Methods for generalization in model-based reinforcement learning typically assume that an agent cannot recover the latent context governing the environm

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Constrained Goal-directed Planar Graph Generation with Grammar-based Reinforcement Learning

DGX agent

arXiv:2610.06244v1 Announce Type: new Abstract: Planar graphs are central to applications across science and engineering, yet existing generators provide limited support for goal-directed generation u

model-releasesarxiv-cs-lg

6 Oct 2026

Construction and Evaluation of Machine Learning Models for Near-Real-Time Fire Detection from MTG FCI Imagery

arXiv:2610.05154v1 Announce Type: new Abstract: Geostationary satellite observations are important for wildfire detection and monitoring. The current study evaluates machine learning models for MTG FC

researcharxiv-cs-cv

Continual Action Quality Assessment via Adaptive Manifold-Aligned Graph Regularization

arXiv:2510.06842v2 Announce Type: replace Abstract: Action Quality Assessment (AQA) quantifies human actions in videos, supporting applications in sports scoring, rehabilitation, and skill evaluation.

Continual Humanoid Motion Learning

arXiv:2610.04231v1 Announce Type: new Abstract: Humanoid whole-body controllers can now track a diverse set of dynamic motions, but they are typically trained offline and then frozen, so teaching such

safetyarxiv-cs-ro

Contrastive Time Series Forecasting with Anomalies

arXiv:2512.11526v2 Announce Type: replace Abstract: Time series forecasting predicts future values from past data. In real-world settings, some anomalous events have lasting effects and influence the

Controllable and Photorealistic Pedestrian Risky Motion Generation for End-to-End Driving Safety Evaluation

model-releasesarxiv-cs-ro

arXiv:2610.06171v1 Announce Type: new Abstract: Evaluating end-to-end autonomous driving under rare, safety-critical vehicle-pedestrian interactions requires photorealistic, sensor-level scenarios. Ho

Controllable Road Marking Generation

arXiv:2610.05771v1 Announce Type: new Abstract: Lane and road markings provide critical guidance for vehicle navigation and multi-agent coordination, yet authoring them at scale remains a manual workf

Convergence of Statistical Estimators via Mutual Information Bounds

arXiv:2412.18539v3 Announce Type: replace-cross Abstract: Recent advances in statistical learning theory have revealed profound connections between mutual information (MI) bounds, PAC-Bayesian theory,

Cooperative Sheaf Neural Networks

arXiv:2507.00647v4 Announce Type: replace Abstract: Sheaf diffusion has recently emerged as a promising design pattern for graph representation learning due to its inherent ability to handle heterophi

Correct Verdicts, Flawed Reasoning: Structured Auditing of LLM-based Vulnerability Reasoning

arXiv:2610.06366v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed for automated software vulnerability analysis. Binary classification alone is insufficient; pra

COSMOS: Soft Mechanism Mixtures with Verifiable Routing for Long-Horizon PDE Forecasting

arXiv:2610.04427v1 Announce Type: new Abstract: Neural operators offer an efficient alternative to classical PDE solvers, but most learn a monolithic map per equation family and discretization. Real s

Coupling Noisy Pairwise Knowledge to the DAG Posterior for Causal Discovery

arXiv:2610.04559v1 Announce Type: new Abstract: External causal reports can improve structure learning from limited observations, but their reliability varies across sources and variable pairs. We int

COVER: Learning to Accept More in Selective Sleep Staging

arXiv:2610.03911v1 Announce Type: new Abstract: Traditional sleep-staging methods apply the same model to every EEG epoch. Such uniform deployment expends computation on epochs that a smaller model co

Critical Points of Degenerate Metrics on Algebraic Varieties: A Tale of Overparametrization

arXiv:2512.21029v2 Announce Type: replace-cross Abstract: We study the critical points over an algebraic variety of an optimization problem defined by a quadratic objective that is degenerate. This sc

Cross-dataset harmonization for robust endoscopic image analysis

arXiv:2610.06663v1 Announce Type: new Abstract: A significant problem in endoscopic image analysis is that the machine learning (ML) models used for this purpose usually underperform when applied on i

researcharxiv-cs-cv

Cross-Modal Contrastive Learning for the Retrieval of Immunotherapy-Associated Molecular Signatures from Histopathology

arXiv:2610.05157v1 Announce Type: new Abstract: Gastric Adenocarcinoma is a leading cause of cancer mortality. Although 'Inflamed/Non-Inflamed' subtypes have been proposed to predict immunotherapy res

Tutorials

Cross-Modal Solar Image Synthesis: Adapting the Surya Foundation Model from He I 10830 {AA} to EUV Translation and Coronal Hole Segmentation

arXiv:2610.04553v1 Announce Type: cross Abstract: The long observational record of He I 10830 {AA} offers a means to investigate solar morphology before modern extreme-ultraviolet (EUV) imaging. We ad

tutorialsarxiv-cs-cv

Cross-Time Directional Selection in Diffusion Sampling

arXiv:2610.05199v1 Announce Type: new Abstract: How strongly do the remaining diffusion-sampling steps amplify a perturbation at a late latent state? Standard measurements answer this question with ne

Cross-Trait Transfer in Subliminal Learning

arXiv:2610.04260v1 Announce Type: new Abstract: Subliminal learning is a phenomenon where a student language model acquires a teacher model's behavioral traits by training on semantically unrelated ou

CT-Miner: Fast and Coarse-Grained Time-Series Pattern Mining via Cartesian Trees

arXiv:2610.05330v1 Announce Type: cross Abstract: Time series often contain recurring structural patterns, and efficiently mining such patterns into compact representations is essential for scalable a

CURIO: Curiosity-Driven Test-Time Learning for Open-Ended Discovery

arXiv:2610.04851v1 Announce Type: new Abstract: Open-ended discovery requires learning from repeated attempts while continuing to explore directions whose value is not yet apparent. Search with a froz

Tutorials

CurveCodec 2: Skeleton-agnostic animation compression with a learned entropy model

arXiv:2610.04211v1 Announce Type: cross Abstract: Skeletal motion is stored as every joint's transform at every frame, yet most of it is implied by the body rather than by what the motion is about. Co

tutorialsarxiv-cs-ro

Hardware

Cut Binary Cross Entropy: Efficient Large-Vocabulary Loss and Gradient Kernels for Sequential Recommendation

arXiv:2610.05559v1 Announce Type: new Abstract: Industrial sequential recommender systems operate over massive item catalogs (e.g., 10^5--10^7 items). Multi-label recommendation models are trained wit

hardwarearxiv-cs-lg

Agents

CyTReX: Explainable AI-Based Cybersecurity Threat Reasoning Framework for DER Networks

arXiv:2610.04286v1 Announce Type: cross Abstract: Distributed Energy Resource (DER) environments rely on network communication protocols to coordinate control commands, measurements, and device states

agentsarxiv-cs-lg

D-DOIT: Training-free Adaptation of Discrete Diffusion via Doob's h-Transform

arXiv:2610.04938v1 Announce Type: new Abstract: We propose D-DOIT (Discrete Doob-Oriented Inference-time Transformation), a training-free and efficient adaptation method for discrete diffusion models

DABACO: A Multi-Camera Dataset and Benchmark for Screen Localization and Pointing Estimation

arXiv:2610.03928v1 Announce Type: new Abstract: Screen localization and pointing estimation are key to low-cost interactive devices. Yet developing and evaluating these algorithms requires realistic d

DASH: A da Vinci Adapter for Serial-link and Humanoid Robots as an Accessible Platform for Surgical Robotics Research

arXiv:2610.05792v1 Announce Type: new Abstract: Robotic minimally invasive surgery offers well-documented clinical benefits, but the cost and infrastructure requirements of purpose-built platforms lim

researcharxiv-cs-ro

DASH: Fast, Valid Counterfactuals for Deep Networks via Batched Directional Search

arXiv:2610.04783v1 Announce Type: new Abstract: Counterfactual explanations are most useful when they can be generated with low latency, remain close to the factual input, and satisfy input-domain, ca

Dataset-Free Compliant Humanoid Loco-Manipulation with Dynamic Online Posture

arXiv:2610.05678v1 Announce Type: new Abstract: Most humanoid loco-manipulation controllers require human motion data to learn whole-body coordination and posture, leaving policies reliant on external

safetyarxiv-cs-ro

De-occluding broadband metalens

arXiv:2601.19403v2 Announce Type: replace-cross Abstract: Obstructions such as raindrops, fences, or dust degrade captured images, especially when mechanical cleaning is infeasible. Conventional solut

researcharxiv-cs-cv

Decouple, Purify and Unite: Semantic-Structural Prototype Learning for Federated Medical Segmentation

arXiv:2610.04700v1 Announce Type: new Abstract: Federated learning enables medical institutions to train a global model without sharing data, yet feature heterogeneity from diverse scanners or protoco

safetyarxiv-cs-cv

Local Ai

Decoupling Time and Space: A Temporally Conditioned Refinement for EEG Source Imaging

arXiv:2610.06726v1 Announce Type: new Abstract: Electroencephalography (EEG) offers millisecond temporal resolution, but inferring underlying neural sources is a severely ill-posed spatial inverse pro

local-aiarxiv-cs-lg

Deep Fair Learning: Task-Aware Fair Representations via Joint Distance-Covariance Regularization

arXiv:2504.06470v2 Announce Type: replace-cross Abstract: Ensuring fairness is essential as machine learning increasingly informs consequential decisions. However, many fairness-aware methods focus on

Deep Learning Denoising of Real SWOT Sea Surface Height Observations

arXiv:2610.03739v1 Announce Type: cross Abstract: The SWOT (Surface Water Ocean Topography) mission is currently providing unpreceded high-resolution measurements of Sea Surface Height (SSH), revealin

Deep Learning for Sleep Heart Rate Estimation from Accelerometers: Toward Population-Scale Cardiac Insight Without Optical Sensors

arXiv:2610.06823v1 Announce Type: new Abstract: Large longitudinal cohorts often contain wrist accelerometry without optical heart-rate sensing, motivating recovery of cardiac information from motion

Deep Learning Reforms Image Matching: A Survey and Outlook

arXiv:2506.04619v2 Announce Type: replace Abstract: Image matching, which establishes correspondences between two images to recover 3D structure and camera geometry, is a cornerstone of computer visio

Deep Prior Learning for Embodied Perception

arXiv:2610.05531v1 Announce Type: new Abstract: Embodied systems need geometric perception that exploits available observations beyond images alone. Recent feed-forward 3D models incorporate geometric

DeepStock: Reinforcement Learning with Policy Regularizations for Inventory Management

arXiv:2603.19621v2 Announce Type: replace Abstract: Deep Reinforcement Learning (DRL) provides a general-purpose methodology for training inventory policies that can leverage big data and compute. How

DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts

arXiv:2412.10510v5 Announce Type: replace Abstract: The proliferation of disinformation demands reliable and scalable fact-checking solutions. We present Dynamic Evidence-based FAct-checking with Mult

Delay-coordinate reconstruction and conditional-moment causal diagnostics in stochastic systems

arXiv:2610.05632v1 Announce Type: new Abstract: Partial observation and delay-coordinate reconstruction are rooted in deterministic dynamical-systems theory, whereas there are many systems with intrin

DelegationBench: Measuring When AI Agents Should Ask Before Acting

arXiv:2610.05532v1 Announce Type: cross Abstract: AI agents that send emails, edit files, and make purchases must decide when to act on their own and when to check with the user first. This decision i

Demonstration-Calibrated Port-Hamiltonian Retuning for Manipulation Policies

arXiv:2610.05755v1 Announce Type: new Abstract: Diffusion and VLA policies for manipulation are often deployed through downstream impedance controllers. The stiffness and damping gains of these contro

safetyarxiv-cs-ro

Demystifying Hybrid Thinking: Can LLMs Truly Switch Between Think and No-Think?

arXiv:2510.12680v2 Announce Type: replace Abstract: Hybrid thinking enables LLMs to switch between reasoning and direct answering, offering a balance between efficiency and reasoning capability. Yet o

DeNoise: Learning Robust Graph Representations for Unsupervised Graph-Level Anomaly Detection

arXiv:2511.04086v2 Announce Type: replace Abstract: With the rapid growth of graph-structured data in critical domains, unsupervised graph-level anomaly detection (UGAD) has become a pivotal task. UGA

Dependable AI-Assisted Engineering: A Formal Framework for AI Participation and Assurance in Safety-Critical Workflows

arXiv:2610.04084v1 Announce Type: new Abstract: Generative AI can produce engineering artefacts, but generation alone does not determine whether or how those artefacts should enter safety-critical wor

safetyarxiv-cs-cv

DePICT: Decision-Preserving Interface for Constrained Downstream Tasks

arXiv:2610.03945v1 Announce Type: new Abstract: A constrained optimization problem may involve a parameter in its objective and active constraints, yet the final decision may remain insensitive to sma

Detecting Defects that Matter: An Application-Driven Benchmark for Anomaly Detection in Manufacturing and Retail Logistics (VAND 4.0 Challenge)

arXiv:2610.04392v1 Announce Type: new Abstract: Existing Anomaly Detection benchmarks are saturated and often unrealistic. As part of the VAND 4.0 Challenge, we introduce a hidden-test, application-dr