Search: “deployment”

PolyUMI: Accessible Visual-Tactile-Audio Data Collection for Object Inference and Manipulation

arXiv:2609.29760v1 Announce Type: new Abstract: Humans typically rely on vision, touch, hearing, and proprioception to perceive contact and adapt their actions during manipulation. Providing robots wi

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PROOF: Profiling Reliability of Object-Level Facts in Large Language Models

DGX agent

arXiv:2609.29504v1 Announce Type: cross Abstract: Aggregate factuality scores hide where a language model succeeds, which relations it confuses, and whether an answer survives innocuous changes to the

model-releasesarxiv-cs-ai

25 Sept 2026

Local Ai

PUBG Ally: A Conversational Embodied Agent as an AI Teammate

arXiv:2609.29837v1 Announce Type: new Abstract: We introduce PUBG Ally, an embodied agent for PUBG: BATTLEGROUNDS that can reason, act autonomously, and play alongside players as a voice-enabled teamm

local-aiarxiv-cs-ai

Qwen-Planner-Agent: A Closed-Loop AI-for-AI Framework for Real-World Mobile Planner Agents

arXiv:2609.29892v1 Announce Type: new Abstract: The rapid progression of large language models is extending AI from passive content generation into the active workflows of engineering and scientific d

RAPID: Robot Agentic Programming from Demonstrations

arXiv:2609.30249v1 Announce Type: cross Abstract: Coding agents have demonstrated enormous success in solving complex programming problems. To leverage their potential for robot systems, this work int

Requirement-Bound Verified Commissioning: A Frozen Four-Billion-Parameter Local Model as a Candidate Generator under an External Acceptance Layer with Verification and Release Authority

arXiv:2609.30219v1 Announce Type: cross Abstract: An acceptance protocol is developed for sensor-coordinate and polarity binding in mechatronic commissioning. Candidate generation is separated from re

Revalidation Beats Stateful Routing for Scientific Surrogates Under Distribution Shift

arXiv:2609.29715v1 Announce Type: cross Abstract: Surrogate models are often chosen during development and then left in place as new measurements arrive. That practice becomes risky when noise, input

Sampling-Based MuJoCo MPC for Double-Pendulum Sway Suppression on a Shipboard Crane

arXiv:2603.16407v4 Announce Type: replace Abstract: Transferring heavy payloads in maritime settings relies on efficient crane operation, limited by hazardous double-pendulum payload sway. This sway m

researcharxiv-cs-ro

Scope Before You Persist: Preventing Cross-Family Interference in Agent Memory

arXiv:2609.29144v1 Announce Type: new Abstract: Persistent memory lets language-model agents improve prompts and skills without updating model weights. We show that matching retrieval scope to certifi

SEEK: Skill-Routed Evaluation with Evolvable Knowledge for Industrial Search

arXiv:2609.29803v1 Announce Type: cross Abstract: Search quality evaluation provides essential supervision and diagnostic signals for the development and iteration of industrial search systems. Althou

researcharxiv-cs-ai

Small yet Assistive: Spatially-Aware Post-Training for Low Vision

model-releasesarxiv-cs-cl

arXiv:2609.28757v1 Announce Type: cross Abstract: An estimated 1 billion people worldwide live with vision impairment, yet current vision-language models (VLMs) produce descriptions too vague for safe

TactileStep: Sole Tactile Learning for Regulating Foot-Terrain Interaction in Humanoid Locomotion

arXiv:2609.28959v1 Announce Type: new Abstract: Humanoid parkour policies can traverse various terrains, but task completion may mask challenges of harsh landings, edge contacts, and unstable stance c

Task-Aware Spectral Pruning: A Mixture-of-Masks Framework for Efficient LLM Inference

model-releasesarxiv-cs-lg

arXiv:2609.29499v1 Announce Type: new Abstract: Static pruning imposes one sparse structure on every prompt, even though reasoning, retrieval, generation, coding, and translation can depend on differe

Temporal Taxation Compounds Under Post-Training Compression of Whisper Models

arXiv:2609.28739v1 Announce Type: new Abstract: Automatic speech recognition models are audited for demographic fairness at full precision, yet the models that ship to production have been quantized,

safetyarxiv-cs-cl

The Gold in Bias: Maturing the AI Design Process through Verification

arXiv:2609.29730v1 Announce Type: new Abstract: Bias in AI systems is typically framed as a flaw to be minimized, yet it also serves as a critical indicator of underlying weaknesses in data, modeling

Three Ways Classical Test Theory Misleads for LLM Judges

arXiv:2609.29709v1 Announce Type: cross Abstract: An LLM judge scores a bank of responses against a rubric, and the reliability comes back at 0.52. What has been measured? Judge evaluation has begun b

researcharxiv-cs-cl

Uncertainty-Gated Exploration Noise Suppresses Task Collapse in Online RL Fine-Tuning of a Flow-Matching Vision-Language-Action Policy

model-releasesarxiv-cs-lg

arXiv:2609.28838v1 Announce Type: cross Abstract: Online reinforcement learning fine-tuning of pretrained flow-matching vision-language-action (VLA) policies promises robots that keep learning after d

Where Does Exactly-Once Live? Model, Harness, and Tool-Contract Effects on Duplicate Side Effects in LLM Agents

arXiv:2609.29095v1 Announce Type: cross Abstract: When a tool-using agent's write times out or returns a server error, the action may already have taken effect. Retrying blindly duplicates it -- a sec

Working with Agentic `Teammates': When a New Organizational Actor Collides with the Human Ecosystem of Work

arXiv:2609.29901v1 Announce Type: cross Abstract: Enterprise AI is transitioning from single-user, reactive tools toward proactive, multi-user 'teammates,' but our empirical understanding of this tran

A Systematic Evaluation of Infrastructure-Based Radar System for Highway Traffic Monitoring

model-releasesarxiv-cs-cv

arXiv:2609.27143v1 Announce Type: new Abstract: Infrastructure-based radar systems offer robust and long-range solutions for traffic monitoring, yet their detection and tracking performance under real

Tutorials

Active Learning for Biodiversity Monitoring: From Label Efficiency to Reliable Ecological Inference

arXiv:2609.27409v1 Announce Type: new Abstract: Limited expert annotation capacity is a pervasive constraint in biodiversity monitoring. Passive acoustic recorders and camera traps generate data faste

tutorialsarxiv-cs-lg

An Open Pipeline and Dashboard for Systemic-Risk Evidence under the EU AI Act's Code of Practice

arXiv:2609.28335v1 Announce Type: new Abstract: Claims about AI safety reach audiences well beyond the AI community, yet many rely on opaque evidence or static assessments, when supporting evidence is

Backdoors in Learning-Based Industrial Robotic Arm Manipulation: An Empirical Security Study

arXiv:2609.26868v1 Announce Type: new Abstract: Learning-based models (e.g., visuomotor and Vision-Language-Action (VLA)) are increasingly explored for industrial robotic manipulation, where model pre

researcharxiv-cs-ro

Beyond Information Seeking: Severity-Aware Question Supervision for Proactive Medical Dialogue

arXiv:2608.24521v3 Announce Type: replace Abstract: Proactive medical dialogue requires an agent to decide what to ask from incomplete patient information. Existing information-seeking approaches comm

safetyarxiv-cs-cl

Bounded Loops: Pre-Run Spend Bounds, Proved Termination, and Verified Completion for Agent Harnesses

arXiv:2609.27871v1 Announce Type: cross Abstract: In mainstream agent frameworks, a step ends when the agent's own output says it has finished. Durable-execution platforms bound retries and time, but

Can One Adapted Model Do It All? Fine-Tuning Strategy Selection for Customer Support LLMs

model-releasesarxiv-cs-cl

arXiv:2609.27262v1 Announce Type: new Abstract: Production customer-support systems often require LLMs to support multiple skills, such as intent classification, question answering, summarization, or

CART: Closed-Loop Adaptive Red Teaming for Large Language Models

arXiv:2609.27336v1 Announce Type: new Abstract: Automated red teaming often replays a fixed set of prompts, which measures known risks but cannot learn from failures found during testing. We present C

CoRelNav: Collaborative Relational Navigation for Multi-Robot Spatially Constrained Semantic Navigation

arXiv:2609.27720v1 Announce Type: new Abstract: Spatially constrained semantic navigation requires robots to identify targets specified not only by semantic categories but also by relations to surroun

agentsarxiv-cs-ro

DAVIS: A Depth-Only End-to-End Active-Vision Framework for Humanoid Soccer Skills

arXiv:2609.28175v1 Announce Type: new Abstract: Humanoid soccer contact skills require more than producing high-impact foot-ball contacts: the robot must close the loop over perception, approach, alig

DRSR: Learning Set-Level Deletion Risk for Efficient Long-Horizon Agents

arXiv:2609.27276v1 Announce Type: new Abstract: Long-horizon language-model agents accumulate reasoning traces, tool exchanges, and observations whose relevance changes with the current decision. Exis

DUGM-R: Uncertainty-Aware Dynamic Grid Mapping and Risk-Triggered Recovery for Learned Local Navigation

model-releasesarxiv-cs-ro

arXiv:2609.27338v1 Announce Type: new Abstract: Learned local navigation in crowded indoor environments is sensitive to how dynamic obstacle motion is represented, while collision-prone behaviour may

EnComp: Lightweight Encoder-Only Context Compression for Retrieval-Augmented Question Answering

arXiv:2603.09222v2 Announce Type: replace Abstract: Efficient context compression is critical for retrieval-augmented question answering in resource-constrained settings, where long retrieved contexts

researcharxiv-cs-cl

Evaluation Choices Decide the Forecasting Leaderboard: Evidence from a Production Marketplace Panel

model-releasesarxiv-cs-lg

arXiv:2609.27867v1 Announce Type: new Abstract: A forecasting benchmark reports which method won. We show that the answer is set by the evaluator's choices before any model is fitted. We benchmark 24

Generalizable Robotic Insertion with World Models

arXiv:2609.28258v1 Announce Type: cross Abstract: Robotic assembly in high-mixture settings requires adaptable systems that can handle diverse parts, yet current approaches typically rely on policies

researcharxiv-cs-cv

Geospatial embeddings detect old-growth forests but buffered spatial validation narrows their advantage over Sentinel features

arXiv:2609.28194v1 Announce Type: cross Abstract: Old-growth forests develop over centuries under minimal anthropogenic disturbance, producing structurally complex and biodiverse stands. In Europe, pr

researcharxiv-cs-cv

Local Ai

Improving Ensemble Filters with Flow Matching

arXiv:2609.28015v1 Announce Type: cross Abstract: Data assimilation estimates a dynamical state from partial and noisy observations. Classical ensemble filters are efficient but restrict analysis upda

local-aiarxiv-cs-lg

InfiNoVA: Infinite Novel View Augmentation for Viewpoint Invariant Robot Policies

arXiv:2609.27734v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) policies often rely strongly on the camera viewpoints seen during training, causing substantial performance degradation w

Learning a Speed-adaptive Hip Exoskeleton Control Policy Via Sim-to-real Reinforcement Learning

arXiv:2609.28027v1 Announce Type: new Abstract: Providing personalized exoskeleton assistance across varying walking speeds remains challenging. Existing online optimization methods are sample-ineffic

Learning What to Activate: Combinatorial Capability Allocation for Long-Horizon Multimodal Agents

arXiv:2609.27869v1 Announce Type: new Abstract: Long-horizon multimodal agents rely on specialized capabilities for perception, retrieval, reasoning, verification, and execution. Existing designs typi

Median Temporal Ensembling: Training-Free Robust Aggregation for Action-Chunked Visuomotor Policies

arXiv:2609.27167v1 Announce Type: cross Abstract: Action-chunked visuomotor policies predict overlapping trajectories, so every executed action is covered by several predictions. Temporal ensembling s

safetyarxiv-cs-lg

MWE-ECL: Recoverable Long-Range Context Does Not Always Override Local Lexical Priors

model-releasesarxiv-cs-cl

arXiv:2609.27590v1 Announce Type: new Abstract: Long-context evaluations often test whether a model can recover distant evidence, but recoverability does not guarantee behavioral influence. We test th

NaviScale: Generating Large-Scale Semantic Map Datasets for Object Navigation

arXiv:2609.27218v1 Announce Type: new Abstract: Embodied navigation requires spatial representations that generalize across unseen environments, yet collecting large amounts of annotated data from rea

researcharxiv-cs-ro

NavProbe: Evidence-Grounded Reasoning with Active Memory Retrieval for Zero-Shot Navigation

arXiv:2609.27526v1 Announce Type: new Abstract: Long-horizon navigation requires an agent to revise its intermediate objectives as evidence accumulates. Full visual histories are costly to process, wh

PEARL: A Lightweight Prompt-based Feature Interpreter Framework for Real-Time, Anonymous, and Heterogeneous Collaborative Perception

arXiv:2609.27123v1 Announce Type: new Abstract: Heterogeneity across Collaborative Perception (CP) agents is a major challenge for emerging CP frameworks due to domain gaps from differing sensors, arc

agentsarxiv-cs-cv

Pro-Bench: Prompt-Robust Open-Vocabulary Visual Grounding Across Real-World Heterogeneous Environments

model-releasesarxiv-cs-cv

arXiv:2609.27076v1 Announce Type: new Abstract: Open-vocabulary visual grounding enables robots to localise task-relevant entities from natural-language queries without dependence on predefined percep

Prompt, Probe, Train, or Annotate? Single-camera sports video understanding in amateur settings

arXiv:2609.28049v1 Announce Type: cross Abstract: Video understanding is usually benchmarked on curated, single-actor, or professionally filmed clips, and a strong score there is routinely read as evi

Local Ai

Psychoacoustically Aligned Latent Smoothing for Adversarial Robustness of Full-Duplex Speech-to-Speech Dialogue Models

arXiv:2609.27378v1 Announce Type: cross Abstract: End-to-end speech-to-speech dialogue models listen and speak simultaneously, so a continuously open acoustic channel is exposed to adversarial manipul

local-aiarxiv-cs-ai

Quantization-Robust Unlearning through the Lens of Retain-Forget Loss Landscapes Interaction

arXiv:2609.27355v1 Announce Type: cross Abstract: Unlearning ensures LLM compliance by removing the influence of private or copyrighted training data. However, since LLM models typically undergo post-

researcharxiv-cs-ai