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DRT: Dense Reasoning Trace for Efficient and Grounded Multimodal Reasoning

arXiv:2609.21675v1 Announce Type: new Abstract: Despite the remarkable progress in Multimodal Large Language Models (MLLMs), prevailing Chain-of-Thought (CoT) paradigms remain confined to the natural-

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Human DGX agent

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dSTAR: Straggler Tolerant and Byzantine Resilient Distributed SGD

DGX agent

arXiv:2412.07151v1 Announce Type: cross Abstract: Distributed model training needs to be adapted to challenges such as the straggler effect and Byzantine attacks. When coordinating the training proces

researcharxiv-cs-ai

21 Sept 2026

Dual-Interest Sequential Product Recommendation With Multi-Granular SSM

arXiv:2609.21548v1 Announce Type: new Abstract: Sequential recommendation aims to predict the next item a user will interact with based on their historical behavior. Advances in Transformers have sign

safetyarxiv-cs-ai

Duty Factor Predicts Robust Constrained Quadrupedal Locomotion Across Gait Types

model-releasesarxiv-cs-ro

arXiv:2609.22073v1 Announce Type: new Abstract: Quadrupedal robots are increasingly deployed in environments where locomotion must remain robust to disturbances and constrained terrain. Gait type, suc

Dynamic Lagging using Stable-Prefix Training for Simultaneous Translation

arXiv:2609.05799v2 Announce Type: replace Abstract: In streaming simultaneous speech translation, the speech translation system is trained to learn a read-write policy that alternates between consumin

safetyarxiv-cs-cl

Dynamic Modeling and LQR Control of a Single Coaxial Drone with 2DOF Thrust Vectoring Mechanism

arXiv:2609.21099v1 Announce Type: new Abstract: Coaxial rotor drones have generated considerable interest because of energy efficiency and small size, but they are afflicted with inherent underactuati

Dynamics-Induced Commitment in Learning-Based Robotic Penalty Kicks

arXiv:2609.21100v1 Announce Type: new Abstract: Learning in robotic games is constrained not only by strategic information but also by what the body can still execute. We study this coupling in a hier

ECG-Mamba-V2: Architectural Refinements to a Bidirectional State Space Model for Multi-Label 12-Lead ECG Classification

arXiv:2510.13046v3 Announce Type: replace Abstract: State space models offer linear-time sequence modeling and are a promising backbone for multi-label 12-lead ECG classification, but the design choic

ECG Mirage: Revealing and Mitigating the Underutilisation of ECGs in Vision-Language Models for Clinical Prediction

arXiv:2609.21755v1 Announce Type: new Abstract: Emergency department (ED) decision-making relies on heterogeneous clinical information, including patient history, vital signs, laboratory results, and

researcharxiv-cs-ai

Edit-VAR: Taming Visual Autoregressive Model for Precise Video Editing

arXiv:2609.21268v1 Announce Type: new Abstract: Text-guided video editing modifies target content while preserving the appearance and temporal coherence of unedited regions. Training-based approaches

Efficient Architecture Search under Leave-One-Subject-Out Evaluation

arXiv:2609.21457v1 Announce Type: new Abstract: Deep neural architectures are widely used for signal processing in automated pain assessment systems. However, architecture design has remained largely

Efficient Bayes-Adaptive Reinforcement Learning with Temporal Logic Specifications

arXiv:2609.20954v1 Announce Type: new Abstract: We present a novel end-to-end model-based Reinforcement Learning (RL) algorithm for efficient policy synthesis under given Linear Temporal Logic (LTL) s

Efficient Benchmarking in Production: A Study of an Evolving LLM Agent

arXiv:2609.21267v1 Announce Type: new Abstract: Production LLM agents are evaluated repeatedly as they evolve, but full agent benchmarks are costly to rerun. We study efficient recurring evaluation fo

EgoPush: Egocentric Multi-Object Rearrangement for Mobile Robots via Constrained Teacher Observability

arXiv:2602.18071v2 Announce Type: replace Abstract: Humans rearrange objects in cluttered environments using egocentric perception, actively moving to keep task-relevant spatial cues in view. Mobile r

Local Ai

Elastic Threshold Attention: Learned Contextual Sparsity for Long-Context Decoding

arXiv:2609.20888v1 Announce Type: new Abstract: Massive KV caches can cause severe memory-bandwidth bottlenecks during long-context decoding. Sparse attention methods mitigate this via selective loadi

local-aiarxiv-cs-lg

End-to-End Hard-Label Cryptanalytic Model Extraction Using Efficient Sign Recovery

arXiv:2609.21941v1 Announce Type: cross Abstract: The importance of deep neural networks (DNNs) is widely recognized, and the parameters obtained through training are regarded as valuable assets. Rece

Enhancing Audio Reasoning via Semantic Summary Prediction

arXiv:2609.20849v1 Announce Type: new Abstract: Large Audio Language Models (LALMs) perform well on complex question answering but often show a reasoning gap, where explicit Chain-of-Thought (CoT) red

researcharxiv-cs-cl

EnSol: an environment-aware graph neural network for molecular solubility prediction

arXiv:2609.21151v1 Announce Type: cross Abstract: Molecular solubility directly affects key aspects of molecular development such as reaction feasibility, formulation performance, separation efficienc

EnterpriseVal: Quantifying the Efficacy, Reliability and Value of Generative AI in the Enterprise

arXiv:2609.21841v1 Announce Type: new Abstract: Frontier language models now produce professional deliverables that expert graders judge to match human work on a substantial share of economically valu

Evaluating In-Context Learning and Retrieval Strategies for Devanagari Post-OCR Correction

model-releasesarxiv-cs-cl

arXiv:2609.21595v1 Announce Type: new Abstract: In-context learning using Large Language Models (LLMs) offers a compelling path to training-free post-OCR correction, yet its effectiveness for Devanaga

Applications

EventGeM: Global-to-Local Feature Matching for Event-Based Visual Place Recognition

arXiv:2603.05807v2 Announce Type: replace Abstract: Event cameras are rapidly rising in popularity for robotic and computer vision tasks because their sparse activation delivers energy-efficient, high

applicationsarxiv-cs-cv

ExpBoN: Exponential-Noise Best-of-n for Efficient Test-Time LLM Alignment

arXiv:2609.21899v1 Announce Type: new Abstract: Best-of-n (BoN) sampling is a simple yet effective inference-time alignment method, but hard maximization provides only coarse control over the trade-of

Explainable Multimodal Aspect-Based Sentiment Analysis with Dependency-guided Large Language Model

arXiv:2601.06848v2 Announce Type: replace Abstract: Multimodal aspect-based sentiment analysis (MABSA) aims to identify aspect-level sentiments by jointly modeling textual and visual information, whic

researcharxiv-cs-cl

Extending Decoupled Attention to Dense Prediction and Masked Training for Multi-Channel Images

arXiv:2609.21629v1 Announce Type: new Abstract: Multi-Channel imaging (MCI) data differs fundamentally from natural images, as each channel records a semantically distinct signal rather than a colour

Extreme classification: beating chance with one training example from each class

arXiv:2609.20897v1 Announce Type: cross Abstract: We study a minimal classification problem: Given independent labeled observations Xsim P and Zsim Q from two unknown distributions P,Q, and given an i

Fact Grounded Attention: Eliminating Hallucination in Large Language Models Through Attention Level Knowledge Integration

arXiv:2509.25252v3 Announce Type: replace Abstract: 'The greatest enemy of knowledge is not ignorance, it is the illusion of knowledge.' Large Language Models have conquered natural language but remai

FairLMs: A Turnkey Library for Fairness in Language Models

model-releasesarxiv-cs-cl

arXiv:2609.21296v1 Announce Type: cross Abstract: Fairness research on language models involves measuring bias, applying mitigation methods, and examining the evidence on which an evaluation rests. Ex

Applications

FAN: Foresight Action Normalization for Continual Adaptation of Vision-Language-Action Models

arXiv:2609.21358v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models pre-trained on large-scale, closed datasets have demonstrated remarkable success across diverse robotic manipulation

applicationsarxiv-cs-ro

Fast And Accurate Text Content File Type Identification

arXiv:2609.21306v1 Announce Type: new Abstract: A common requirement across organizations is to have a tool that can identify file types based on their contents, particularly in the cybersecurity doma

Hardware

FastLoop: Parallel Loop Closing with GPU-Acceleration in Visual SLAM

arXiv:2603.17201v2 Announce Type: replace Abstract: Visual SLAM systems combine visual tracking with global loop closure to maintain a consistent map and accurate localization. Loop closure is a compu

hardwarearxiv-cs-ro

FedeRage: Provably Convergent Agnostic Federated Learning under General Client Drift

arXiv:2609.21057v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training without sharing raw data, but its performance degrades under non-IID data and stochastic cl

Federated Deep Clustering Networks for High-Dimensional and Heterogeneous Data

arXiv:2609.21829v1 Announce Type: cross Abstract: Clustering high-dimensional data is a fundamental task in unsupervised machine learning with applications to a variety of domains. In the centralized

safetyarxiv-cs-ai

Fewer Steps, Better Actions: Rethinking Flow-Matching Inference for VLA Policies

arXiv:2609.21216v1 Announce Type: cross Abstract: Vision-language-action (VLA) policies based on flow matching generate action chunks through repeated evaluations of an action expert. Increasing the n

safetyarxiv-cs-ai

Field Tracking of Insects Using a Stereoscopic Event-Based Camera Setup

arXiv:2609.21354v1 Announce Type: new Abstract: High-speed tracking of small, fast-moving organisms in their natural environments is important to better understand their behavior and ecology. Traditio

Tutorials

FOCAL-VLA: Subtask-Guided Geometry Distillation and Implicit World Modeling for Vision-Language-Action Models

arXiv:2609.21228v1 Announce Type: cross Abstract: Vision-language-action (VLA) models built on pretrained vision-language models have demonstrated strong performance across diverse robotic manipulatio

tutorialsarxiv-cs-ai

FootQuery: Future-Touchdown-Guided Retrieval from Depth History for Perceptive Humanoid Locomotion

arXiv:2609.21447v1 Announce Type: cross Abstract: Humanoid locomotion over complex terrain requires anticipating footholds that may no longer be visible at touchdown. Limited camera coverage and self-

ForceTwin: Physics-informed Digital Twins for Robotic Manipulation from Instrumented Human Interaction

arXiv:2609.21751v1 Announce Type: cross Abstract: Manipulating objects requires understanding not only their motion, but also the physical properties that determine it. For articulated objects, these

Tutorials

ForeTac-VLA: A Forecasting-Based Tactile-Vision-Language-Action Model for Contact-Rich Robotic Manipulation

arXiv:2609.20980v1 Announce Type: new Abstract: Vision-language-action (VLA) models have demonstrated strong capabilities in robotic manipulation, yet their reliance on visual perception limits robust

tutorialsarxiv-cs-ro

FORTE: Task-Adaptive Force Capability Optimization for Mobile Manipulators

arXiv:2609.21497v1 Announce Type: new Abstract: Effective physical interaction control in robotic manipulation requires not only kinematically feasible motion but also sufficient force-interaction cap

Fragment-Aware Vision Transformers for Fresco-Fragment Style Classification

arXiv:2609.21012v1 Announce Type: new Abstract: Artistic style classification is usually studied on complete artworks, where models can exploit global composition, spatial organisation, and iconograph

From Code Archival to Knowledge Graph: Bridging Software Heritage, COAR Notify and Wikidata

arXiv:2609.21667v1 Announce Type: cross Abstract: Software is a first-class scientific object, yet validated links between source code and the scholarly record remain largely absent from the Linked Op

researcharxiv-cs-ai

Agents

From Discharge Notes to Patient Understanding: Persona-Grounded, Open-Ended Simulation of LLMs as Discharge Educators

arXiv:2609.20827v1 Announce Type: new Abstract: Hospital discharge education is an interactive teaching task: a clinician adapts a discharge plan to a patient's literacy, recall, and personality. Exis

agentsarxiv-cs-cl

From Generation to Detection: Exploration of Discourse Driven Scenario based LLM Generated Fake News

arXiv:2609.20838v1 Announce Type: new Abstract: In this study, we examine how modern LLMs generate and detect fake news under controlled settings across four manipulation scenarios. These are open-end

researcharxiv-cs-cl

From Memory to Behavior: A Behavior-Aware Role-Playing Framework for Social Media Influencers

arXiv:2609.21349v1 Announce Type: cross Abstract: Large language models have shown strong potential as role-playing agents for real individuals, yet faithful impersonating remains challenging. Existin

researcharxiv-cs-ai

From Pretraining to Proficiency: Real-World Subtask RL for Long-Horizon Manipulation with Minimal Human Intervention

arXiv:2609.21788v1 Announce Type: cross Abstract: A pretrained robot foundation policy may execute most of a long-horizon task yet repeatedly fail at a few critical subtasks. Collecting additional ful

From Retrieval to Recognition:How Vision--Language Models Become OCR Specialists

arXiv:2609.21543v1 Announce Type: new Abstract: Does a general vision--language model acquire specialized OCR ability by developing a new reading circuit or by reusing an existing mechanism? We addres

From Stress to Affect: Multimodal Deep Learning for Physiological Emotion Recognition Across Wearable Sensor Modalities

arXiv:2609.20991v1 Announce Type: new Abstract: Physiological emotion recognition using wearable sensors has important applications in mental health monitoring, affective computing, and human-computer

From Switching to Dynamic Regret: A Simple Reduction via Unbiased Random Sequences

arXiv:2609.20968v1 Announce Type: new Abstract: In non-stationary online learning, dynamic regret has attracted increasing attention as a measure of how well an online learner performs against a time-