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Variational Quantum Transformer Architecture for Synthetic Language Generation

arXiv:2609.18565v1 Announce Type: cross Abstract: We propose a compact NISQ-compatible quantum transformer architecture for synthetic QNLP sequence modelling. The model preserves the autoregressive ne

tutorialsarxiv-cs-cl

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Human DGX agent

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Research

VCTP: Vehicle-Conditioned Terrain Planning for Off-Road Navigation

DGX agent

arXiv:2609.17834v1 Announce Type: new Abstract: A vehicle's heading affects both the surfaces beneath its tires and its pitch and roll. We present Vehicle-Conditioned Terrain Planning (VCTP), which re

researcharxiv-cs-ro

17 Sept 2026

Veo-Act: Enhancing VLA Policies with Frontier Video Models

arXiv:2604.04502v2 Announce Type: replace Abstract: Video generation models can produce coherent vi- sual sequences depicting object motion and interactions. We in- vestigate how frontier video genera

Version- and Scope-Aware Question Answering over Normative Documents: A Deployed System and an End-to-End Evaluation at Production Scale

arXiv:2609.18769v1 Announce Type: new Abstract: Correctly answering a question grounded in normative documents often depends on information outside any single passage: whether the retrieved document i

Local Ai

VibeAvatar: Aligning Phonetic Kinematics and Human Aesthetics for High-Fidelity Talking Avatar Synthesis

arXiv:2609.18632v1 Announce Type: new Abstract: Multi-modal talking avatar synthesis aims to generate realistic talking videos from a reference portrait and speech. Despite rapid progress in diffusion

local-aiarxiv-cs-cv

vidax: A Unified JAX Framework for Video Generative Models on Accelerator Meshes

model-releasesarxiv-cs-lg

arXiv:2609.18077v1 Announce Type: cross Abstract: Open-source video generative models ship almost exclusively as PyTorch/CUDA reference implementations. This leaves Cloud TPU pods without a production

Video-Based Markerless Motion Capture for Clinical and Rehabilitation Biomechanics: A PRISMA-ScR Scoping Review of Validated Architectures, Clinical Readiness, and Emerging Methods

arXiv:2609.18667v1 Announce Type: new Abstract: Background.. Video-based markerless motion capture promises movement analysis without the cost, space and skin-marker constraints of optoelectronic syst

researcharxiv-cs-cv

Vision-Language Grounded Task-Context-Aware Imitation Learning for Robotic Disassembly

arXiv:2609.17714v1 Announce Type: new Abstract: Real-world robotic disassembly requires long-horizon execution, where robots must perform ordered sequences of manipulation tasks across multiple parts

Visual Autoregressive Priors for RAW-to-sRGB Image Signal Processing

model-releasesarxiv-cs-cv

arXiv:2609.18302v1 Announce Type: new Abstract: RAW-to-sRGB image signal processing (ISP) must recover perceptually faithful colors and fine details from sensor measurements, often under imperfect spa

Visual Compliance via Executable Safety Rule Entailment

arXiv:2609.18328v1 Announce Type: new Abstract: Recent advances in LLMs and VLMs have enabled safety systems to reason beyond simple risk patterns toward more contextual and semantic safety concerns.

Visual Input and Its Framing Affect Attribute-based Descriptions Produced by Large Vision-Language Models

arXiv:2609.18345v1 Announce Type: new Abstract: Large vision-language models (LVLMs) are commonly used with only a single text prompt as the input, or plus an image. In this paper, we demonstrate that

researcharxiv-cs-cv

Hardware

Visual Perception Engine: Fast and Flexible Multi-Head Inference for Robotic Vision Tasks

arXiv:2508.11584v3 Announce Type: replace-cross Abstract: Deploying multiple machine learning models on resource-constrained robotic platforms for different perception tasks often results in redundant

hardwarearxiv-cs-ai

VLA-ULAP: Interleaving Cloud VLA Calls with Ultra-Lightweight Local Action Prediction at the Edge

model-releasesarxiv-cs-lg

arXiv:2609.18663v1 Announce Type: cross Abstract: Billion-parameter vision--language--action (VLA) policies demand substantial onboard power, while communication delays in remote inference hinder time

VLEM: Real-Time 3D Vision-Language Embedding Mapping

arXiv:2508.06291v2 Announce Type: replace Abstract: Semantic scene understanding in robotics requires representations that are both metric-accurate and queryable via natural language in real-time. Whi

VLM-MPPI: Grounding Natural Language in Behaviorally Diverse Trajectories for Aerial Navigation

arXiv:2609.18451v1 Announce Type: new Abstract: We present a hierarchical UAV navigation framework that aligns natural-language intent with dynamically feasible flight behaviors in cluttered indoor en

Vocabulary-Guided Gait Recognition

arXiv:2609.18413v1 Announce Type: new Abstract: What is a gait? Appearance-based gait networks consider a gait as the human shape and motion information from images. Model-based gait networks treat a

tutorialsarxiv-cs-cv

Voice of Reason: Reinforcement Learning for Spoken Math

arXiv:2609.18677v1 Announce Type: new Abstract: Speech language models enable richer spoken interactions between humans and machines than cascaded systems, allowing access to paralinguistic informatio

VoiceTrace: A Benchmark and Retrieval Framework for Who-Said-What Speech Retrieval

arXiv:2609.18521v1 Announce Type: cross Abstract: Speech retrieval has become increasingly important as spoken content continues to grow across meetings, lectures, podcasts, and videos. Existing bench

Walking the Score Manifold: Continuous-time Generative Dynamics on Learned Data Manifolds

arXiv:2609.17901v1 Announce Type: cross Abstract: Generative modeling of time-dependent data is typically formulated on a discrete temporal grid, restricting supervision to the observed timestamps in

tutorialsarxiv-cs-ai

Local Ai

WARD: Runtime Workload-Adaptive Vision TRansformer Framework for Dependable Edge AI

arXiv:2609.17556v1 Announce Type: cross Abstract: Edge-deployed AI operate under dynamically changing power budgets, reliability requirements, and input distributions, requiring continuous adaptation.

local-aiarxiv-cs-ai

WAVE-Go: World-Model Navigation with Adaptive Execution for Wheel-Legged Robots

arXiv:2609.18193v1 Announce Type: new Abstract: World models can anticipate the consequences of navigation actions, but predicted action sequences may become invalid during execution, especially when

researcharxiv-cs-ro

WaveTLM: Reliable Time-Series Language Modeling through Task Compilation

model-releasesarxiv-cs-lg

arXiv:2609.18812v1 Announce Type: new Abstract: Time-series language models provide a shared natural-language interface across temporal tasks, but plausible text does not guarantee reliable task outpu

Weakening Neurons: An Input-Output Functionality in Transformers with Outsize Influence

arXiv:2609.18612v1 Announce Type: cross Abstract: We analyze the learned input-output behavior of GLU-based neurons in large language models (LLMs). We propose a simple analysis method: For each neuro

Hardware

WeaveRL: Weaving Reconstruction into Scene-Aware Fabrics for Perceptive Reinforcement Learning

arXiv:2609.18685v1 Announce Type: new Abstract: Reinforcement learning allows robots to acquire complex skills, but producing policies for geometrically complex manipulation remains difficult. A promi

hardwarearxiv-cs-ro

WetRobo: A Reproducible Robot Kit for Coding Agents in Biological Laboratories

arXiv:2609.18435v1 Announce Type: new Abstract: Automating biological research requires general-purpose, reproducible robot systems that allow individual wet-lab researchers to delegate robot tasks wi

Hardware

WFM: Wiki Foundation Model for Complex Agentic Reasoning

arXiv:2609.18182v1 Announce Type: new Abstract: Real-world agents fundamentally require persistent non-parametric knowledge for dynamic reasoning, i.e., long-term memory and retrieval-augmented genera

hardwarearxiv-cs-ai

Agents

What Counts as Strategic Reasoning? A Systematic Mapping of Chess Research on Humans, Engines, and Language Models

arXiv:2609.18286v1 Announce Type: new Abstract: Chess has long served as a model domain for studying search, expertise, decision-making, and artificial intelligence. The emergence of large language mo

agentsarxiv-cs-ai

What You Can't See Is Still What You Learn: A Preregistered Sixty-Society Confirmation That Evidence Masking Drives Compositional Generalization

arXiv:2609.17637v1 Announce Type: new Abstract: Restricting what a module can read may improve what a system learns to compute. We test this in a preregistered confirmation with sixty four-cell system

tutorialsarxiv-cs-ai

'What's going to happen after I'm gone?': Parent Perspectives on Technology in Supporting Independent Living for Adults with Intellectual Disabilities

arXiv:2609.18970v1 Announce Type: new Abstract: Adults with intellectual and developmental disabilities (IDD) are increasingly transitioning from family homes towards semi-inde-pendent living. As pare

researcharxiv-cs-ro

When AI Generates Covariates: Causal Typing and Estimand Drift in Sequential Experiments

arXiv:2609.17772v1 Announce Type: cross Abstract: AI-generated covariates from notes, conversations, images, and wearable streams can change the causal question when their roles are left unspecified.

When Audit Quality Fails to Predict Downstream Utility: A Counterfactual Study of Synthetic-Data Selectors for Low-Resource African NLP

arXiv:2609.18960v1 Announce Type: new Abstract: Quality-aware synthetic-data selection rests on a proxy: examples that an LLM judge rates as good should also help a downstream model learn. In a contro

tutorialsarxiv-cs-cl

When Cognitive Graphs Meet LLMs: BDEI Cognitive Pathways for Panic Emotional Arousal Prediction

model-releasesarxiv-cs-cl

arXiv:2606.15121v3 Announce Type: replace Abstract: Predicting the timing of individual and collective panic emotional arousal before manifestation is essential for timely emergency intervention. Exis

When Edit Flows are Edit Jumps: replicating Edit Flows and EvoFlows

arXiv:2609.18745v1 Announce Type: new Abstract: Antibody lead optimization calls for a small, bounded set of edits to an existing candidate: substitutions, but also insertions and deletions. Edit-base

researcharxiv-cs-lg

When Is Graph Structure Worth Its Cost? The Case for Structure Pricing in Retrieval-Augmented Generation

arXiv:2609.18099v1 Announce Type: new Abstract: Graph-based retrieval-augmented generation (RAG) can help answer questions that require information from many documents. However, building a graph often

researcharxiv-cs-ai

When the Gradient Sees Rank: Provable Necessity, Causal Recruitment, and Composition in Trained Matrix Memories

arXiv:2609.17594v1 Announce Type: new Abstract: Can gradient-based training learn the rank needed to store and compose associations in a matrix memory? In our earlier study, we used a matrix-augmented

tutorialsarxiv-cs-lg

When to Call an LLM: A Confidence-Gated Hybrid for Cost-Effective Emotion Recognition in Conversational AI

arXiv:2609.17977v1 Announce Type: new Abstract: Emotion recognition in conversation (ERC) is a production capability behind agent-assist prompts, escalation routing, and post-call analytics in contact

Where Grokking Happens: Distributed Utility and Fourier Recoding Without a Module Switch

arXiv:2609.17571v1 Announce Type: cross Abstract: Where in a Transformer is the change from memorization to generalization functionally expressed? We introduce Transition Games--behavior-aligned exact

Where Should Agents Live? Energy-Memory Characterization of Agentic AI for the Edge-Cloud Continuum

arXiv:2609.18283v1 Announce Type: new Abstract: As telecommunication networks evolve toward autonomous 5G-Advanced and 6G operations, agentic artificial intelligence (AI) workflows, where large langua

Which Demographics do LLMs Default to During Annotation?

arXiv:2410.08820v4 Announce Type: replace Abstract: Demographics and cultural background of annotators influence the labels they assign in text annotation -- for instance, an elderly woman might find

Which LLM is Best for Translating Natural Language Goals to PDDL

arXiv:2609.18731v1 Announce Type: new Abstract: Bridging the gap between human intent and machine execution remains a challenge in automated planning, where expressing goals in formal languages like P

Who Audits Whom, on What Substrate, with What Evidence? An Independence-Graded Audit Protocol for Agentic AI

arXiv:2609.18272v1 Announce Type: new Abstract: Agentic AI systems plan, invoke tools and act with limited supervision; they are now both the subject of audits and, increasingly, the auditor. Independ

Who Judges Matters: Measuring Family-Conditioned Preference in LLM-as-Judge Panels

arXiv:2609.17857v1 Announce Type: cross Abstract: Who the judge is can affect an LLM-as-judge result, but measuring that effect without confusing it with candidate quality is difficult. We study four

Applications

WholeBodyWAM: Learning Whole-Body World Action Models with Scalable Motion Priors

arXiv:2609.18197v1 Announce Type: new Abstract: Humanoid whole-body manipulation requires coordinated whole-body dynamics, yet large-scale trajectories from a target robot are expensive to collect and

applicationsarxiv-cs-ro

Whom Do AI Agents Work For? Role Assignment Induces Sponsorship Bias in LLM Recommenders

arXiv:2609.17989v1 Announce Type: cross Abstract: Large language models (LLMs) now serve as conversational shopping assistants on platforms that also sell advertising. These AI agents face a conflict

Wind on Trees: Testing Physical Grounding in Dynamic 4D Gaussian Splatting

model-releasesarxiv-cs-cv

arXiv:2609.17810v1 Announce Type: new Abstract: Monocular reconstruction of wind-driven vegetation is severely underconstrained: motion along the viewing direction is largely unobservable, a moving ca

Local Ai

WISE: A Lightweight, Weakly-Supervised Model for Onboard Fire Smoke Detection and Localization

arXiv:2609.18227v1 Announce Type: new Abstract: Wildfire smoke detection from satellite imagery is critical for early warning and rapid response. For onboard satellite deployment, detection systems mu

local-aiarxiv-cs-cv

WordPolo: Evaluating Language Models Through Iterative Semantic Feedback

arXiv:2609.19006v1 Announce Type: cross Abstract: Large Language Models (LLMs) and Large Reasoning Models (LRMs) are typically evaluated on challenging benchmarks through dataset accuracy alone, provi

'You are an expert annotator': Automatic Best-Worst-Scaling Annotations for Emotion Intensity Modeling

arXiv:2403.17612v3 Announce Type: replace Abstract: Labeling corpora constitutes a bottleneck to create models for new tasks or domains. Large language models mitigate the issue with automatic corpus