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Do we need to answer that question? Salience and Answerability of Potential Questions in Naturalistic Dialogue

arXiv:2609.31130v1 Announce Type: new Abstract: We empirically investigate Question Under Discussion based modelling in naturalistic dialogue by studying whether the salience of generated potential qu

safetyarxiv-cs-cl

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Does Thinking Help Fairness? Reasoning Tokens Resolve Some Biases but Create More

DGX agent

arXiv:2609.30768v1 Announce Type: new Abstract: Thinking in reasoning language models (RLMs) has been subject to debate on whether it resolves or amplifies bias. Prior works have shown competing concl

model-releasesarxiv-cs-ai

28 Sept 2026

Does Understanding Inform Generation in Unified Multimodal Models? From Analysis to Path Forward

arXiv:2511.20561v3 Announce Type: replace-cross Abstract: Recent years have witnessed significant progress in Unified Multimodal Models, yet a fundamental question remains: Does understanding truly in

Does Uniform Discrete Diffusion Need Time?

arXiv:2609.30977v1 Announce Type: cross Abstract: Uniform discrete diffusion models (UDMs) commonly use explicit time conditioning, but we find that it can often be unnecessary in practice. In this pa

researcharxiv-cs-ai

Don't CLAP: Are Music-Text Models Bag-of-Words?

arXiv:2609.30540v1 Announce Type: cross Abstract: Text-to-music systems are assessed on audio quality and on how faithfully the music follows its prompt, and the CLAP score, the cosine similarity betw

Double-stream registration with pyramid fusion for HDR video with alternating exposures

arXiv:2609.31108v1 Announce Type: new Abstract: High dynamic range (HDR) video reconstruction from al-ter-na-ting-exposure sequences remains challenging, especially in regions with extreme luminance v

dRVG: Quadtree-Guided, Resolution-Complete Online Motion Planning for Polygonal Robots in Unknown Environments

arXiv:2609.31412v1 Announce Type: new Abstract: We present the dynamic rotation-stacked visibility graph (dRVG), an online motion planner that guides polygonal robots to specified goals in initially u

researcharxiv-cs-ro

Agents

DualManip: Agentic Dynamic Manipulation via Dual-Path Semantic Reasoning and Geometric Adaptation

arXiv:2609.31112v1 Announce Type: new Abstract: Vision-language models (VLMs) enable open-vocabulary reasoning for robot manipulation, but their high inference latency limits responsiveness in dynamic

agentsarxiv-cs-ro

Local Ai

DyMD: Preserving Interaction Dynamics through Distribution Matching Distillation in Few-Step Video World Models

arXiv:2609.31349v1 Announce Type: cross Abstract: Large video diffusion models offer expressive priors for embodied prediction and learning, yet their many-step sampling remains costly for interactive

local-aiarxiv-cs-ai

Dynamic Regret in Online Convex Optimization with Indicator Switching Costs

arXiv:2609.30556v1 Announce Type: new Abstract: We study dynamic regret in online convex optimization with an indicator switching cost: a fixed penalty incurred whenever two consecutive decisions diff

Dynamic Welfare-Maximizing Pooled Testing

arXiv:2601.22419v2 Announce Type: replace-cross Abstract: Pooled testing uses one test to certify several agents as healthy when the pooled result is negative. We study a budget-constrained welfare pr

Agents

DynBranch: Speculative Subgraph Reuse for Dynamic Agentic LLM Serving

arXiv:2609.31047v1 Announce Type: cross Abstract: Agentic LLM workflows decide their execution paths at runtime. Downstream computation may be predictable, or may have run before, yet it cannot begin

agentsarxiv-cs-ai

Hardware

EAServe: Encode-Aware Disaggregated Serving for Multimodal Large Language Models

arXiv:2609.31551v1 Announce Type: cross Abstract: Disaggregating the two stages, Prefill and Decode, onto separate GPU pools is now a standard optimization for (text-only) LLM serving. However, multim

hardwarearxiv-cs-lg

Effects of Transcript Compression on LLM-based Medical Misinformation Detection in Japanese YouTube Videos

arXiv:2609.30882v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to assess long-form medical videos, but their effectiveness may depend on whether transcripts are pro

Efficient Constrained Graph Search for Post-hoc Error Correction in Binary Classifiers

arXiv:2401.04282v2 Announce Type: replace Abstract: We introduce a model-agnostic framework for constrained post-hoc error correction in binary classifiers. Given a frozen base classifier, the method

Efficient Safety Benchmarking via Item Response Theory

arXiv:2606.20626v2 Announce Type: replace-cross Abstract: Safety benchmarks for language models are typically evaluated using static paradigms that treat all items as equally informative for all model

Electric Vehicle Charging Station Location Selection using Geospatial Artificial Intelligence (GeoAI)

arXiv:2609.30417v1 Announce Type: new Abstract: As electric vehicle (EV) adoption increases, ensuring efficient and well-distributed charging infrastructure has become a critical challenge. While many

safetyarxiv-cs-lg

EmoZone-Talker: Regional Semantic Control of Audio-Driven 3DGS Talking Heads via Facial Action Units

model-releasesarxiv-cs-cv

arXiv:2606.15848v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has shown strong potential for high-fidelity talking head synthesis. However, enabling fine-grained, interpretable, and

Enabling a Unified Cross-Domain Representation for Two-Finger Gripper Manipulation via Interaction-Centric Modeling

model-releasesarxiv-cs-cv

arXiv:2609.31207v1 Announce Type: cross Abstract: Achieving robust cross-embodiment generalization in imitation learning demands overcoming a critical representation flaw that inextricably entangles t

Hardware

ENAS: An Efficient Hardware-Aware Neural Architecture Search Framework for TinyML on Resource-Constrained Microcontrollers

arXiv:2609.30272v1 Announce Type: cross Abstract: We present extbf{ENAS}, a hardware-aware Neural Architecture Search (NAS) framework that combines a static feasibility check, a cell-based search spac

hardwarearxiv-cs-ai

Encryptability As a Coordinate Choice: Depth-One Homomorphic Federated Learning of Quantum Neural Networks

arXiv:2609.30581v1 Announce Type: cross Abstract: Encrypted training relies on keeping server-side updates low-degree. This constraint traditionally excludes models whose weights inhabit a compact Lie

Energy Efficiency of Locally Deployed LLMs: A Preliminary Quantitative GPU Power Benchmark on Consumer Hardware

arXiv:2608.00008v2 Announce Type: replace Abstract: The local deployment of large language models (LLMs) is gaining traction due to privacy concerns and the desire for on-premise inference. However, t

Energy-efficient operation of neural operators for virtual sensing

arXiv:2609.30580v1 Announce Type: new Abstract: Virtual sensing repeatedly reconstructs physical fields from changing observations, often on a fixed geometry. We investigate how shared spatial computa

Enhancing Assessment of Self-Consistency in LLM Explanations using Perturbation Strength

arXiv:2609.30849v1 Announce Type: new Abstract: Prior work has examined the self-consistency of LLM-generated explanations using surface-level perturbation methods. However, the strength of these pert

Enhancing Photogrammetric Digital Surface Models with Pretrained Diffusion Models and Multimodal Conditioning

arXiv:2609.31199v1 Announce Type: cross Abstract: Large-scale Digital Surface Models (DSMs) can be produced cost-effectively from satellite images via stereo-photogrammetry. However, the resulting 3D

Entropy Regularization: A Free Correction to Cross-Entropy for Verified Demonstrations

arXiv:2609.30572v1 Announce Type: new Abstract: Large language models are often post-trained on expert demonstrations using cross-entropy (CE), even when the downstream objective is not to imitate the

safetyarxiv-cs-lg

EPOC: Endpoint-Preserving Online Correction With Compressed Residual State for Multi-Horizon Time Series Forecasting

arXiv:2609.30929v1 Announce Type: new Abstract: Completed multi-horizon forecasts provide residual feedback for a fixed forecaster, but retaining full residual blocks increases auxiliary state. We pro

Epstein Files Engine: Agentic Search for Investigative Journalism

model-releasesarxiv-cs-cl

arXiv:2609.30611v1 Announce Type: cross Abstract: On Jan. 30, 2026, the U.S. Department of Justice released a mixed-media collection concerning Jeffrey Epstein, including about three million pages of

Equation discovery with Bayesian tree-adjoining grammars

model-releasesarxiv-cs-lg

arXiv:2609.31368v1 Announce Type: cross Abstract: Tree-Adjoining Grammars (TAGs) have recently been introduced to Nonlinear System Identification (NLSI) as a means of encoding an entire model class as

Applications

Estimating and Orthogonalizing Unknown Pre-training Gradients for Continual Fine-tuning of Large Language Models

arXiv:2609.30935v1 Announce Type: cross Abstract: Continual fine-tuning is essential for large language models (LLMs) to dynamically adapt to real-world environments, yet it inevitably suffers from ca

applicationsarxiv-cs-ai

Evaluating Cultural Awareness of LLMs for Haitian Creole

arXiv:2609.31506v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit substantial performance disparities between high- and low-resource languages. Beyond lower task performance, they

Agents

Evaluating Real-Time Voice Agents: From Component Quality to Grounded Outcomes

arXiv:2609.30798v1 Announce Type: new Abstract: Real-time voice agents have moved from research prototypes to production deployments, yet the literature describing them is fragmented across three comm

agentsarxiv-cs-ai

Evaluating Sycophancy in Chinese Large Language Models on Factual Questions Derived from Online Search Queries

model-releasesarxiv-cs-cl

arXiv:2609.30986v1 Announce Type: new Abstract: As large language models increasingly mediate information access, factually accurate and independent answers are critical. However, these models can exh

Evaluating the accuracy of KV cache reuse techniques

arXiv:2609.31415v1 Announce Type: new Abstract: Position-independent KV cache reuse aims to reduce latency in retrieval-augmented generation by reusing chunk-level KV caches across prompts. We show th

Evaluating the Impact of Adaptive Extended Reality on Human-Robot Interaction Across the Reality-Virtuality Continuum

arXiv:2609.31138v1 Announce Type: new Abstract: As populations in developed countries age and labor shortages intensify, Cybernetic Avatars (CAs) are proposed to extend human capabilities through robo

researcharxiv-cs-ro

Evaluation Is All You Need for Multi-Modal Autonomous Driving

arXiv:2609.30818v1 Announce Type: cross Abstract: Multi-modal planning is promising for autonomous driving by representing multiple plausible behaviors in ambiguous and long-tail scenarios. Existing m

Evaluation is All You Need: Strategic Overclaiming of LLM Reasoning Capabilities Through Evaluation Design

arXiv:2506.04734v3 Announce Type: replace Abstract: Reasoning models represented by the Deepseek-R1-Distill series have been widely adopted by the open-source community due to their strong performance

Evidence-Grounded Auditing of Identification Assumptions in Climate-Policy Causal Evaluations

model-releasesarxiv-cs-cl

arXiv:2609.30867v1 Announce Type: new Abstract: Difference-in-differences (DID) studies are widely used to evaluate climate policy, but assessing the evidence supporting their identification assumptio

EviDETR: Preserving Query-Relevant Temporal Evidence for Moment Retrieval and Highlight Detection

arXiv:2609.30724v1 Announce Type: new Abstract: Joint video moment retrieval and highlight detection requires identifying query-relevant temporal segments while estimating clip-level saliency, yet DET

Evolutionary Safety of Recursive Self-Improving AI: Taxonomy, Risk Discovery, and Evaluation

arXiv:2609.31186v1 Announce Type: new Abstract: Artificial intelligence is advancing rapidly, with increasingly capable systems taking larger roles in reasoning, decision-making, scientific discovery,

Applications

EXAONE Demand 1.0: A Time Series Foundation Model for Demand Forecasting

arXiv:2609.30880v1 Announce Type: new Abstract: Time series foundation models (TSFMs) are pretrained on series from diverse domains, where demand series make up only a small fraction. Demand data has

applicationsarxiv-cs-ai

ExoLaN: Physics-Consistent Context-Aware Dynamics Learning for Exoskeletons

arXiv:2609.31434v1 Announce Type: new Abstract: Task-agnostic assistive exoskeleton control based on human intention offers greater flexibility than conventional approaches that rely on predefined tas

researcharxiv-cs-ro

Exploiting Spatial Structure for Transductive Few-Shot Classification of Whole-Slide Images

arXiv:2609.31040v1 Announce Type: new Abstract: Automating the analysis of whole-slide images (WSIs), a key step in cancer diagnosis, has high clinical value, as it can reduce pathologist's workload w

Exploring the Benefits of Vision Foundation Models for Unsupervised Domain Adaptation

arXiv:2406.09896v3 Announce Type: replace Abstract: Achieving robust generalization across diverse data domains remains a significant challenge in computer vision. This challenge is important in safet

safetyarxiv-cs-cv

Externalized CPDAG Summaries Improve LLM Causal Deduction

arXiv:2609.31071v1 Announce Type: new Abstract: Corr2Cause asks whether a causal claim holds in every DAG compatible with observed correlations and conditional independencies. We frame this as latent-

Factorized axis convolutional gated recurrent unit with dynamic adaptive pooling for remaining useful life prediction of rolling bearings

arXiv:2609.30972v1 Announce Type: new Abstract: Convolutional neural networks (CNN) are widely used to predict the remaining useful life (RUL) of rolling bearings from time-frequency representations (

researcharxiv-cs-ai

Fake News Theories: Harnessing Disciplinary Insights for Computational Modeling, Detection, and Explanation

model-releasesarxiv-cs-lg

arXiv:2609.30427v1 Announce Type: new Abstract: Disinformation research has produced increasingly accurate automated fake-news detectors, but many systems remain difficult to interpret and are weakly

FARE: Forensic Acceptance Region Estimation for Catching Bait-and-Switch Image Generators

arXiv:2609.30982v1 Announce Type: cross Abstract: Modern AI image generators are increasingly deployed as opaque APIs, where customers can query the deployed service, but cannot inspect model weights