Search: “models”

SchemaFill: Efficient LLM Tool Calling via Slot-Parallel Speculative Decoding

arXiv:2610.07086v1 Announce Type: cross Abstract: LLM agents interact with external systems by generating structured tool calls. Given a user request, conversational context, and a catalog of tool sch

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Stabilizing Off-Policy Training for Long-Horizon LLM Agent via Turn-Level Importance Sampling and Clipping-Triggered Normalization

DGX agent

arXiv:2511.20718v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) algorithms such as PPO and GRPO are widely used to train large language models (LLMs) for multi-turn agentic tasks

safetyarxiv-cs-ai

7 Oct 2026

Stable Scores, Unstable Answers: Frame Phase and Option Order in Video Multiple-Choice Evaluation

arXiv:2610.08649v1 Announce Type: new Abstract: Video-language models are ranked by multiple-choice accuracy on frames from a uniform grid. The grid has two parameters, a rate and a phase, and benchma

Statistical Turbulence and High-Fidelity Disturbance Fields for Quadrotor Flight Control

arXiv:2610.06874v1 Announce Type: cross Abstract: Reinforcement-learning quadrotor controllers are usually trained under simplified wind models, yet the impact of wind-field fidelity, as opposed to ma

Hardware

T-CCL: Resource Efficient and Performant Collective Communication using Tensor Memory Accelerator

arXiv:2610.07098v1 Announce Type: cross Abstract: Large transformer-based models increasingly depend on multi-GPU execution, which requires frequent collective communication among GPUs. Existing commu

hardwarearxiv-cs-ai

Agents

Text2Dashboard: A Governed Agent Architecture for Natural-Language Dashboard Generation over Enterprise DataBrain

arXiv:2610.06914v1 Announce Type: new Abstract: Text2Dashboard is a DataBrain-specific prototype that turns natural-language analytic requests into inspectable dashboards. An installable Codex plugin

agentsarxiv-cs-ai

TRIAGE: Direction-Aware Mismatch Stabilization of Native NVFP4 Reinforcement Learning

arXiv:2610.07043v1 Announce Type: cross Abstract: Low-precision execution can substantially accelerate reinforcement learning (RL) for large language models, but discrepancies between learner and samp

safetyarxiv-cs-ai

When Does AI Supervision Help? A Role-Aware Study of Network Fraud Decision Management with Blockchain Auditability

arXiv:2610.07434v1 Announce Type: new Abstract: When does a second artificial intelligence (AI) component improve a primary network-fraud decision rather than add operational burden? We study this que

safetyarxiv-cs-ai

When Does External Guidance Help LLM Reasoning? A Bias-Variance Theory of Guidance-Augmented GRPO

arXiv:2610.06861v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become the dominant paradigm for eliciting multi-step reasoning in large language models, an

safetyarxiv-cs-cl

Which Image Property Carries the Jailbreak? A Controlled Dissection of Image-to-Text Jailbreaks

arXiv:2610.07009v1 Announce Type: cross Abstract: Image-to-text jailbreaks place harmful intent in text, image content, or the relationship between them. We examine image-side factors across four publ

safetyarxiv-cs-ai

Tutorials

WildMatch: Weakly Supervised Image Matcher Adaptation for Wildlife Re-Identification

arXiv:2610.07384v1 Announce Type: cross Abstract: Individual animal re-identification from camera-trap imagery is an instance retrieval problem central to non-invasive wildlife monitoring: a query ima

tutorialsarxiv-cs-ai

An equality condition for the Dobrushin bound on attention rollout and how often it holds in trained transformers

arXiv:2610.05558v1 Announce Type: new Abstract: The Dobrushin coefficient of each attention-rollout factor satisfies kappa(frac12(I+A))lefrac12(1+kappa(A)), and multiplying these inequalities over lay

Anatomy-preserving unpaired cone-beam CT refinement for image-guided radiotherapy using pseudo-label guided diffusion

arXiv:2610.06094v1 Announce Type: new Abstract: Cone-beam computed tomography (CBCT) is widely used in image-guided radiotherapy, but scatter, beam hardening, noise, truncation, and other artifacts li

Tutorials

Anosognosia in LLMs: Probing Self-Awareness of Quantized Computational Substrate

arXiv:2610.06174v1 Announce Type: new Abstract: Can LLMs recognize degradation in their own computational substrate? Inspired by anosognosia, a neurological condition in which patients fail to recogni

tutorialsarxiv-cs-lg

ARO: Aligned Representation learning for multi-Omics data

arXiv:2610.06443v1 Announce Type: new Abstract: The high cost of functional molecular assays, and prevalence of missing modalities and unmatched samples in computational biology, create significant ba

ArticuTable: Generating Instance-Level Interactive Rigid-Articulated 3D Tabletop Scenes from a Single Image

arXiv:2610.05249v1 Announce Type: new Abstract: Embodied agents benefit from 3D environments that combine visual fidelity to real-world observations with physical interactivity. Existing single-image

applicationsarxiv-cs-cv

ASD-FEAT: A Multi-Modal Infant Video-Derived Dataset for Early ASD Risk Prediction

arXiv:2610.04051v1 Announce Type: new Abstract: Accurate early screening for Autism Spectrum Disorder (ASD) is a precursor to timely intervention, which is critical for improving cognitive and behavio

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

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

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

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

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

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

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

Detecting Nighttime Anomalies from NASA Black Marble Using a Generalized Spatio-Temporally Robust Framework of Machine Leaning Ensembles

arXiv:2610.06674v1 Announce Type: cross Abstract: Nighttime lights from NASA's Black Marble product suite capture thermal and light emission signals from anomalous events including fires, volcanic eru

dIon: Fragmentation-Based Invariance for Self-Supervised Learning of Tandem Mass Spectra

arXiv:2610.06282v1 Announce Type: new Abstract: We introduce a novel invariance for peptide tandem mass spectrometry data, unlocking self-supervised representation learning that improves de novo seque

Dissecting Quantization Error: A Concentration-Alignment Perspective

arXiv:2603.04359v2 Announce Type: replace Abstract: Quantization can drastically increase the efficiency of large language and vision models, but typically incurs an accuracy drop. Recently, function-

Divide-and-Conquer CoT: RL for Reducing Latency via Parallel Reasoning

arXiv:2601.23027v2 Announce Type: replace Abstract: Long chain-of-thought reasoning (Long CoT) is now fundamental to state-of-the-art LLMs, especially in mathematical reasoning. However, LLM generatio

Agents

DREAM: Dynamic Resolution Assignment For Multimodal Multi-agent Debate

arXiv:2610.05615v1 Announce Type: cross Abstract: Multi-agent debate (MAD) has emerged as an effective paradigm to improve the reasoning capabilities of large language models (LLMs) and is increasingl

agentsarxiv-cs-cv

DynaMesh: Dynamic 3D Texture Generation

arXiv:2610.05529v1 Announce Type: new Abstract: We present DynaMesh, a dynamic texture generation method for 3D meshes. Given a textureless shape and a text prompt describing an effect, our method pro

EasyClassifier: Honest, Reproducible Machine-Learning Classification for Researchers Who Do Not Program

arXiv:2610.04758v1 Announce Type: new Abstract: Machine-learning classification is now used across medicine, the social sciences, economics, education and engineering, very often by researchers who do

EgoSteer: An Open-Source Full-Stack System Towards Steerable Dexterous Manipulation from Egocentric Videos

arXiv:2607.09701v2 Announce Type: replace Abstract: The enduring vision of general-purpose robots serving humanity hinges fundamentally on policy steerability. However, prevailing paradigms of learnin

safetyarxiv-cs-ro

EMG-GPT: Predictive Pretraining on Residual-Quantized EMG Tokens for Hand Pose Estimation

arXiv:2610.05235v1 Announce Type: new Abstract: Surface electromyography (sEMG) is a low-power, cost-effective biosignal for hand-pose estimation and gesture classification. In this work, we examine w

Evaluating Zone-Guided Front Extraction for Glacier Calving-Front Delineation in SAR Imagery

arXiv:2610.04066v1 Announce Type: new Abstract: Automatic calving-front delineation from synthetic aperture radar imagery is challenging because the front is a thin and often ambiguous boundary betwee

Evolving LLM-Generated Features for Interpretable Classification

arXiv:2610.03951v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as classifiers, yet they operate as opaque systems whose decisions are difficult to interpret, which

Exact Fast Batch Simulation for Tabular Reinforcement Learning

arXiv:2610.04746v1 Announce Type: cross Abstract: Simulation is a fundamental computational primitive in reinforcement learning (RL), yet conventional simulation explicitly generates individual trajec

ExpertMuon-Compass: Alignment-Guided Step Sizes for Mixture-of-Experts Training

arXiv:2610.04140v1 Announce Type: new Abstract: Mixture-of-experts (MoE) language models send each token to a few experts, so each expert is trained on a different part of the data, and this part chan

FLASHSWIN: Unlocking Large Windows and Dense Tokens in Swin Vision Transformers with Memory Efficient Attention

arXiv:2610.04664v1 Announce Type: new Abstract: High-resolution vision backbones have long been forced to trade away local token density to afford larger receptive fields. Hierarchical Swin transforme

local-aiarxiv-cs-cv

Flip-Flop Consistency: Unsupervised Training for Robustness to Prompt Perturbations in LLMs

arXiv:2510.14242v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) often produce inconsistent answers when faced with different phrasings of the same prompt. In this paper, we prop

From Abusive Language Classification to Sequence Labeling Identification

arXiv:2610.06287v1 Announce Type: cross Abstract: Industrial content moderation must process massive message streams under tight latency constraints, yet most abusive language (AL) detection systems r

applicationsarxiv-cs-lg

From Kinematic Motion Planners to Dynamic Autonomous Navigation with Obstacle Avoidance (Extended version)

arXiv:2503.17589v3 Announce Type: replace Abstract: Kinematic motion planners are among the most widely used control approaches in robotic applications. By modeling the robot as a first-order system,

safetyarxiv-cs-ro

Global Communication or Graph-Specific Memory?

arXiv:2610.05874v1 Announce Type: new Abstract: Scalable Graph Transformers are commonly trained and evaluated on static large graphs in a transductive setup. Many scalable Graph Transformer component

Graph Data Augmentation via Contrastive Generator Inversion (exttt{DCBA})

arXiv:2610.05653v1 Announce Type: new Abstract: Graphs provide a natural representation of many complex systems, ranging from social platforms to ecosystems. However, the development of graph-based ma

applicationsarxiv-cs-lg

Groupwise Distortion Guarantees for Preference-Based Alignment

arXiv:2610.05450v1 Announce Type: new Abstract: Preference-based alignment methods such as reinforcement learning from human feedback (RLHF) and Nash learning from human feedback (NLHF) aggregate pair

Tutorials

HiER-BLS: A Hierarchy-Guided and Error-Correcting Robust Incremental Broad Learning System

arXiv:2610.05834v1 Announce Type: new Abstract: Broad Learning System (BLS) supports analytical training and incremental expansion, but its growth needs guidance on which inputs new blocks should lear

tutorialsarxiv-cs-lg

Hierarchical Credit Assignment for RLVR on Fused Gromov-Wasserstein Geometry

arXiv:2610.04344v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has been shown to improve the reasoning capability of large language models (LLMs) across diverse

local-aiarxiv-cs-lg

Hybrid-Basis Feature Forecasting for Diffusion Sampling Acceleration

arXiv:2610.05254v1 Announce Type: new Abstract: We propose Hybrid-Basis Feature Forecasting (HybridFF), a training-free, plug-and-play framework for accelerating diffusion sampling. To capture local s

local-aiarxiv-cs-cv