Search: “models”

Entropy-Regularized Mean-Field Games with Average-Reward Criterion: Span-Seminorm Contractions and Model-Free Learning

arXiv:2610.15871v1 Announce Type: cross Abstract: We study stationary discrete-time mean-field games with finite state and action spaces under the average-reward criterion, with an entropy-regularized

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Model Releases

Explicitly Aligned Multi-View Breast Cancer Risk Prediction from Variable-Length Screening Histories

DGX agent

arXiv:2610.16329v1 Announce Type: new Abstract: Accurate breast cancer risk prediction from screening mammography is essential for improved risk stratification and personalized screening. It can benef

model-releasesarxiv-cs-cv

14 Oct 2026

FDTD Model of Noise Pollution from an AI Data Center

arXiv:2610.14899v1 Announce Type: cross Abstract: The sudden growth in AI data centers is raising numerous environmental concerns. Low-frequency noise pollution is a concern for local residents, that

local-aiarxiv-cs-lg

From Prototype to Massing: Learning Graph Correspondence for Scalable Facade Modeling

arXiv:2610.14098v1 Announce Type: new Abstract: Facade articulation at building scale is usually resolved manually or through parametric systems that encode design intent as explicit rules. This paper

local-aiarxiv-cs-lg

Game2Real-Bench: Do Toy Games Reveal and Transfer to Real-World Visual Reasoning?

arXiv:2610.18834v1 Announce Type: new Abstract: Visual reasoning is an important capability for vision-language models (VLMs) to understand visual evidence, spatial structure, constraints, and state c

How to Train Your Latent Control Barrier Function: Smooth Safety Filtering Under Hard-to-Model Constraints

arXiv:2511.18606v2 Announce Type: replace-cross Abstract: Latent safety filters extend Hamilton-Jacobi (HJ) reachability to operate on latent state representations and dynamics learned directly from h

safetyarxiv-cs-lg

inLoRA-MINT: Internal Representations for Auditing Training Data in Domain-adapted LLMs

arXiv:2610.18051v1 Announce Type: new Abstract: This work investigates the application of the Membership Inference Test (MINT) to large language models (LLMs) fine-tuned with LoRA, aiming to determine

Learning When to Transfer: Reinforcement Learning for Carbon-Footprint Modeling in New Manufacturing Domains

arXiv:2610.18730v1 Announce Type: new Abstract: Manufacturers often need to characterize a new material, process, or product before enough operating data are available to train reliable predictive mod

safetyarxiv-cs-lg

LLM Agents for Automated Post-Training via Structured Recipe Optimization

arXiv:2610.18572v1 Announce Type: cross Abstract: Automated post-training transforms pre-trained large language models (LLMs) into models capable of handling diverse domains by automatically construct

Meta-Video: Efficient Standardized Evaluation for Video Understanding

arXiv:2610.18535v1 Announce Type: new Abstract: Video understanding benchmarks are the primary yardstick for measuring progress in video-language modeling, and a growing body of work relies on them to

Modeling and Control of a Rigid-soft Hybrid-link Humanoid Robot

arXiv:2610.15073v1 Announce Type: new Abstract: Physical flexibility plays a significant role not only in human motions but also in robot motions. For example, a humanoid robot with passive and flexib

researcharxiv-cs-ro

OmniCompass: A Holistic Benchmark for Omni-Modal Video Grounding and Understanding

arXiv:2610.18829v1 Announce Type: new Abstract: With the rapid development of multimodal large language models (MLLMs), evaluating their video understanding capabilities has become increasingly import

PRIME: Frequency-Aware Unified Pruning and Merging for MoE Compression

arXiv:2610.13982v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models have emerged as an effective paradigm for scaling large language models, yet their massive parameter footprint poses sev

Probabilistic Forecasting of Wildfire Size Using Conditional Flow Matching

arXiv:2610.15983v1 Announce Type: new Abstract: In this work we present a probabilistic model for predicting the temporal evolution of wildfire size using conditional generative algorithms, namely flo

ProML: Progressive Multi-label Learning

arXiv:2610.15614v1 Announce Type: new Abstract: Multi-label learning typically pursues model construction through the joint optimization of label fitting, correlation modeling, and structural regulari

RAIN: Robotic Task Generalization with Region-Aware Interaction Networks

arXiv:2610.14039v1 Announce Type: cross Abstract: Existing vision-language-action (VLA) models benefit from large-scale vision-language pretraining, but tend to memorize training trajectories, limitin

Tutorials

Riemannian Graph Tokenizer for Structural Knowledge Transfer

arXiv:2610.15427v1 Announce Type: new Abstract: Foundation models are at the forefront of artificial intelligence. A tokenizer, converting the raw input into discrete representations that the model ca

tutorialsarxiv-cs-lg

SyncReward: Human-Aligned Synchronization Reward for Audio-Visual Generation

arXiv:2610.16338v1 Announce Type: new Abstract: Recent years have witnessed rapid progress in joint audio--visual generation. However, audio--visual synchronization remains a challenge, as generated s

The Effect of Layer Ordering in Hybrid Architectures

arXiv:2610.18104v1 Announce Type: new Abstract: Hybrid attention is rapidly becoming the dominant architectural choice for frontier models. Despite their successes, such architectures complicate model

To Align or Not To Align: Check Your COMPASS Before You Train

arXiv:2610.18638v1 Announce Type: cross Abstract: Combining pretrained models is a common strategy for building AI systems, whether by aligning a vision encoder with a language model or merging fine-t

local-aiarxiv-cs-lg

Towards Dependable Optical Flow: Benchmarks for Real-World Spatiotemporal Corruptions

arXiv:2411.14865v2 Announce Type: replace-cross Abstract: Optical flow estimation is a fundamental task in dynamic scene understanding and computer vision. While recent deep models achieve impressive

Variable Input Masked Autoencoders for Wrist-Worn Accelerometry

arXiv:2610.17383v1 Announce Type: new Abstract: Large-scale repositories of wearable sensor data have enabled the development of foundation models for human activities and behavior. However, existing

Video-Arena: A Scalable Agentic Video Benchmark for Spatiotemporal Capability

arXiv:2610.14158v1 Announce Type: new Abstract: Understanding and tracking a dynamic world are fundamental to visual intelligence. Existing video benchmarks provide limited insight into whether models

Vision-Predictor-Action: Towards Efficient Embodied AI via Neuro-Symbolic JEPA Predictors

arXiv:2610.15556v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models commonly generate robot actions token by token through Large Language Model (LLM) decoders, causing inference late

WisdomNet: trustable machine learning toward error-free classification

arXiv:2610.15299v1 Announce Type: new Abstract: Misclassification is a critical problem in many machine learning applications. Since even the classifier models with high accuracy (e.g., > 95%) still i

AudioTETRIS: Token Elimination via Transitional Importance Signals for Efficient Audio LLMs

arXiv:2610.13600v1 Announce Type: cross Abstract: Large Audio Language Models (LALMs) achieve strong performance on speech-text tasks, but standard audio encoders produce hundreds of tokens for even s

Evo^2: Evolving the Evolver through Weight-Harness Co-Evolution

model-releasesarxiv-cs-cl

arXiv:2610.13025v1 Announce Type: new Abstract: Self-evolving agents are increasingly used to improve language model systems without human intervention through either weight updates or harness improve

Local Diagnostics of Learned Event Representations for Anomaly Detection at the HL-LHC

arXiv:2610.13437v1 Announce Type: new Abstract: Anomaly detection, the identification of collision events that deviate from Standard Model expectations, is one of the most promising model-agnostic str

MolP2SBench: Do LLMs Really Reason from Molecular Properties to Structure?

arXiv:2610.12522v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in molecular discovery and structure elucidation, where decisions depend on physical observations.

MoPrO-SD: Modular Prompt Optimization for Stance Detection

model-releasesarxiv-cs-cl

arXiv:2610.13386v1 Announce Type: new Abstract: Stance detection remains challenging for large language models (LLMs) due to implicit opinions, sarcasm, and context-dependent expressions of stance. Al

Provenance-Aware Belief Updating in Human-AI Feedback Loops

arXiv:2610.13008v1 Announce Type: new Abstract: In repeated human-AI interaction, a single observation can be quoted, summarized, and returned to a model as multiple reports, making repetition look li

What Should SFT Learn? Conditional Information Gain for Generalizable Post-Training

arXiv:2610.12995v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) remains a widely used approach for post-training large language models, because fixed demonstrations can directly introdu

safetyarxiv-cs-ai

Applications

Acting from Belief, Looking When Needed: A Bayesian Spatial World Model for Navigation under Intermittent Perception

arXiv:2610.11591v1 Announce Type: new Abstract: Robot navigation commonly uses wide-coverage, high-frequency sensing to reduce partial observability; this reliance becomes restrictive when another tas

applicationsarxiv-cs-ro

AuraLuxMuse: Adaptive Fusion Modeling for Aesthetic Stage Lighting Design with Music and Expert Guidance

arXiv:2610.11792v1 Announce Type: cross Abstract: We present AuraLuxMuse, a novel system for automated aesthetic stage lighting design that integrates expert knowledge, representation learning, and pr

safetyarxiv-cs-ai

Tutorials

Being-M0.7: A Latent World-Action Model for Humanoid Robots

arXiv:2610.11283v1 Announce Type: cross Abstract: Humanoid loco-manipulation requires coordinated locomotion and manipulation informed by future scene evolution and whole-body motion, yet learning the

tutorialsarxiv-cs-cv

Bi-FORK: Generative Modeling of High-Dimensional Bifurcating Systems

arXiv:2610.12449v1 Announce Type: cross Abstract: Bifurcations are ubiquitous in physical systems, from structural buckling to fluid and climate dynamics, yet they remain largely unexplored in deep le

researcharxiv-cs-ai

Tutorials

Curating Always-Loaded Context for LLM Agents: A Capacitated Assortment Model with Censored Feedback

arXiv:2610.11007v1 Announce Type: new Abstract: At the start of every session, LLM agents load a fixed context file, such as exttt{AGENTS.md}. Each loaded token in the file is charged again in every l

tutorialsarxiv-cs-ai

DataSense-Bench: The First Step Toward an AI Scientist

arXiv:2610.12190v1 Announce Type: new Abstract: As claims about recursive self-improvement (RSI) and artificial general intelligence (AGI) proliferate, we ask a simple question: do frontier AI models

Design Creativity Bench: Measuring creativity in LLM-Generated UI

arXiv:2610.11539v1 Announce Type: cross Abstract: As leading LLMs improve on capability evaluations, their limitations in producing creative outputs on design tasks remain insufficiently characterised

Easy to anticipate, hard to compute: boundary dependence finds the computed outputs that entropy patching misses

arXiv:2610.11790v1 Announce Type: new Abstract: Byte-level language models such as the Byte Latent Transformer (BLT) group bytes into patches and run their large global model once per patch. BLT start

researcharxiv-cs-cl

LIME: Link-based User-item Interaction Modeling with Decoupled XOR Attention for Efficient Test Time Scaling

arXiv:2510.18239v4 Announce Type: replace-cross Abstract: Scaling large recommendation systems requires advancing three major frontiers: processing longer user histories, expanding candidate sets, and

Low-Cost Sensor Calibration for Indoor Air Quality Monitoring: A Dataset, Evaluation Scenarios, and a Lightweight Model

arXiv:2610.11236v1 Announce Type: new Abstract: Low-cost sensors enable scalable indoor air quality monitoring but require calibration because of nonlinear distortions, noise, and temporal drift. The

Phase-HDC: Replacing Optimizer History with Gradient Thresholds in Discrete Phase Learning

arXiv:2610.10630v1 Announce Type: cross Abstract: Training a compact model often needs far more memory than storing it, because the optimizer keeps its own records of past gradients. For a hyperdimens

RideBench: A Large-Scale Exogenous-Aware Benchmark for Ride-Hailing Time Series Forecasting

arXiv:2610.11164v1 Announce Type: new Abstract: We release Ride-Hailing, a large-scale ride-hailing time series dataset synthesized from DiDi's marketplace data across 200 spatial areas. Ride-Hailing

SepGen: Multi-Stem Audio-Video Separation and Generation in a Single Model

arXiv:2610.11361v1 Announce Type: cross Abstract: A 4D audio-visual scene comprises a video, the dynamic geometry it depicts, and the sound sources that populate it, each with its own position and tra

local-aiarxiv-cs-cv

StoreBench: A Live-Commerce Environment for Evaluating and Training Autonomous Operator Agents

arXiv:2610.10942v1 Announce Type: new Abstract: Reinforcement learning environments are now a primary lever for improving large language model (LLM) capabilities in post-training, yet most agentic ben

Symbolic Density Estimators for Unnormalized Distributions

arXiv:2610.10807v1 Announce Type: new Abstract: Estimating the symbolic or analytical form of probability density functions (PDFs) from observed samples is a fundamental challenge in statistical and c

Verdict Without the Rule: Diagnosing and Auditing Regulatory Rule Sensitivity in LLM Compliance Systems

arXiv:2610.12313v1 Announce Type: new Abstract: Large language model compliance systems are deployed on the assumption that a verdict depends on the regulatory rule it is given. We test this directly

safetyarxiv-cs-ai