Search: “deployment”

SlugTrails: An Egocentric Benchmark for Floor Plan Localization in Large Buildings

model-releasesarxiv-cs-cv

arXiv:2609.19876v1 Announce Type: new Abstract: Floor-plan-based indoor visual localization enables infrastructure-free positioning, but most methods are developed and evaluated in small residential e

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Safety

StageGuard: Learning Stage Transitions for Long-Horizon Robot Tasks via Agentic Distillation

DGX agent

arXiv:2609.20791v1 Announce Type: new Abstract: Hierarchical planning frameworks combine skills from multiple robot control policies for long-horizon task execution, where determining when to terminat

safetyarxiv-cs-ro

18 Sept 2026

Strategic Transformer for Resource-Constrained Multi-Object Navigation in Ultra-Large-Scale Environments

model-releasesarxiv-cs-ro

arXiv:2609.20227v1 Announce Type: new Abstract: Resource-constrained multi-object navigation in vast indoor environments (>2,000ext{ m}^2) poses significant challenges for efficiency and strategic pla

Stress-testing Alignment Midtraining

arXiv:2609.20412v1 Announce Type: cross Abstract: When aligning frontier models through post-training techniques, it is not possible to directly demonstrate all of the behaviours we want a model to ex

TacSushi: Tactile-Grounded World-Action Modeling for Dexterous Sushi Manipulation

arXiv:2609.19613v1 Announce Type: cross Abstract: Dexterous food manipulation requires control under deformation, occlusion, and uncertain contact. We present TacSushi, a tactile-grounded, Cosmos3-bas

Tutorials

Tailored to you: longitudinal effects of personalising language models

arXiv:2609.20077v1 Announce Type: new Abstract: Interest in developing personalised language models is rapidly growing. While personalisation is often viewed as a mechanism to better serve diverse use

tutorialsarxiv-cs-ai

The Neutral Mask: How Alignment Training Provides Shallow Alignment while Leaving Partisan Structure Intact in a Large Language Model

model-releasesarxiv-cs-cl

arXiv:2606.09735v2 Announce Type: replace Abstract: The ambition behind alignment training is to make large language models safe and useful. The primary mechanisms, reinforcement learning from human f

TinyCNN: A 193K-Parameter Network for On-Device Plant Disease Detection, with a Cross-Dataset Robustness Diagnosis

model-releasesarxiv-cs-cv

arXiv:2609.20290v1 Announce Type: new Abstract: Detecting crop disease early is central to sustainable agriculture and food security under United Nations Sustainable Development Goal 2 (Zero Hunger),

Towards High-DoF Dexterous Manipulation through VLA Post-Training

arXiv:2609.19666v1 Announce Type: new Abstract: Imitation-learned vision--language--action (VLA) foundation models acquire broad manipulation capabilities by scaling robot data across tasks and embodi

TRACE: Accountable Agentic Retrieval for Source Discovery in Digital Archives

arXiv:2609.19897v1 Announce Type: new Abstract: Historical archives pose a difficult retrieval problem for retrievalaugmented generation systems: documents are OCR-degraded, heterogeneous across genre

TraceFlow: Guiding Frozen Flow-Matching Robot Policies with Success and Failure Traces

arXiv:2609.20646v1 Announce Type: new Abstract: A vision-language-action (VLA) policy with a flow-matching action expert generates each action chunk (a short command sequence) by integrating a learned

Transformer fault diagnosis using an efficient simulation-driven variational quantum classifier with domain-aware feature encoding

arXiv:2609.20214v1 Announce Type: cross Abstract: Early transformer fault diagnosis is challenged by nonlinear dissolved-gas interactions, overlapping fault signatures, and limited labeled data, while

ULOHA: An Underwater Bimanual Robot System for Robot Learning

arXiv:2609.19200v1 Announce Type: new Abstract: Underwater visuomotor policy learning has focused primarily on single manipulators, while bimanual imitation learning has been studied largely in air. W

UniExo: Unified Multi-Skill Policies for Musculoskeletal Locomotion and Co-Adaptive Exoskeleton Control

arXiv:2609.19690v1 Announce Type: cross Abstract: Daily locomotion encompasses diverse activities and frequent transitions between them, yet most exoskeleton controllers are designed for a single acti

safetyarxiv-cs-lg

ViLoMan: Learning Visual-Proprioceptive Whole-Body Loco-Manipulation Skills for Humanoid Robots

arXiv:2609.19340v1 Announce Type: new Abstract: Humanoid loco-manipulation requires adaptive whole-body coordination to seamlessly integrate locomotion and physical interaction. Despite recent advance

When Does Retrieval Help Time-Series Forecasting?

arXiv:2609.20193v1 Announce Type: new Abstract: Retrieval plug-ins supply a deep forecaster with information its lookback window cannot carry. Published evaluations report consistent gains, and each c

researcharxiv-cs-lg

🔥: “While AI comes with danger, we should be sceptical of any narrative that conveys inevitability around its trajectory. AI systems do not…

model-releasesgary-marcus--x

🔥: “While AI comes with danger, we should be sceptical of any narrative that conveys inevitability around its trajectory. AI systems do not build their own data centres. They do not manufacture their

Winning a Won Game: Strict Reach-Avoid-Stay Control Barrier Functions for High-Dimensional Black-Box Systems

arXiv:2609.19449v1 Announce Type: new Abstract: Robots must complete their tasks and maintain the achieved outcomes while avoiding safety failures at all times. Strict reach-avoid-stay (sRAS) formaliz

WorldContact: A Contact-Centric World Model for Scalable Robot Learning

arXiv:2609.19600v1 Announce Type: new Abstract: Adapting robots to new objects and tasks requires interaction experience that can be costly to obtain. We present WorldContact, a contact-centric world

Zarya: A Hybrid Autoregressive--Masked Diffusion Language Model with Flexible Training and Dual-Mode Inference

arXiv:2609.19868v1 Announce Type: cross Abstract: Autoregressive language models (ARMs) are constrained by sequential, left-to-right generation, while masked diffusion models (MDMs) enable parallel de

researcharxiv-cs-ai

A Comprehensive Review of Generative Physical Artificial Intelligence

arXiv:2609.18111v1 Announce Type: cross Abstract: The integration of large-scale foundation models with physical embodiments has led to significant advancements in robotics known as Generative Physica

A Convergence Framework for Deep V-Learning: Error Propagation and Sharp Action-Gap Bounds

arXiv:2609.18782v1 Announce Type: new Abstract: We establish convergence bounds for deep V-learning with horizon H. The algorithm fits a scalar value function to targets from executed transitions and

safetyarxiv-cs-lg

Agents

A Study of the Reliability of Agentic AI-Generated Programs

arXiv:2609.18298v1 Announce Type: cross Abstract: Agentic-AI based software development offers the promise of faster completion of the software, greater programmer efficiency, and more reliable code.

agentsarxiv-cs-ai

Hardware

Accelerating Stateful Network Applications with Performance Prediction on SoC SmartNICs

arXiv:2410.22229v2 Announce Type: replace-cross Abstract: Offloading stateful network functions to multi-threaded SoC SmartNICs promises significant performance and cost benefits. However, realizing t

hardwarearxiv-cs-cl

Adaptive hybrid coupling with operator inference, the overlapping Schwarz alternating method and reinforcement learning

arXiv:2609.17837v1 Announce Type: cross Abstract: Hybrid domain decomposition methods provide a flexible framework for coupling full order models (FOMs) and reduced order models (ROMs), but typically

An Empirical Evaluation of Cost-Efficient Large Language Models on Algorithmic Programming Tasks

arXiv:2609.18052v1 Announce Type: cross Abstract: This study empirically evaluates whether cost-efficient Large Language Models (LLMs) can be trusted to generate enterprise code to a written specifica

APGEM: Adaptive Policy-Guided Error Mitigation for Quantum Reinforcement Learning on a Real-World CVRP Case Study

arXiv:2609.18219v1 Announce Type: cross Abstract: Quantum Reinforcement Learning (QRL) represents policies as variational quantum circuits (VQCs), making it attractive for combinatorial optimization s

Ask the Tool, Don't Guess: Agent Tool Calls Hold Their Progress, and the Serving System Should Read It

arXiv:2609.18849v1 Announce Type: cross Abstract: An agentic request spends substantial wall-clock time waiting for tools, and its KV cache holds GPU memory the whole time. Serving systems decide whet

CALOS: Control-Affine Lyapunov On-manifold Safety Layer for Safe Deep Reinforcement Learning for Quadrotors

arXiv:2609.17758v1 Announce Type: cross Abstract: Deep Reinforcement Learning has demonstrated remarkable capability in quadrotor control, yet learned policies offer no guarantee of respecting safety

Characterizing Replay Retention Under Dynamics Shift in Model-Based Reinforcement Learning

arXiv:2609.18167v1 Announce Type: cross Abstract: Adapting to changes in robot dynamics requires learning from new data without discarding experience that may still be useful. In continual model-based

Collective Loss of Control in LLM Agent Systems: An Epidemic Account of Mutation, Contagion, and Recovery

arXiv:2609.18460v1 Announce Type: new Abstract: How does a multi-agent system evolve from a local deviation into collective loss of control? We propose an epidemic explanation organized around acciden

Decodability is Not Causality: Dissociating Probe Readouts from Behavioral Drivers via SAE Decomposition

arXiv:2609.18080v1 Announce Type: new Abstract: Linear probes can decode safety-relevant concepts such as truthfulness from language-model activations, but probe accuracy may show only decodability, n

Decoder-Agnostic Token Merging for Vision Transformers: A Systematic Study of G2TM

arXiv:2609.18279v1 Announce Type: new Abstract: Vision Transformers (ViTs) have achieved state-of-the-art performance across a range of computer vision tasks, mainly thanks to the self-attention mecha

researcharxiv-cs-cv

Designing Agentic AI Workflow Portfolios under Imperfect Selection and Compute Cost

arXiv:2609.18126v1 Announce Type: new Abstract: Agentic AI systems often approach the same task through multiple workflows that differ in reasoning strategy, verification structure, and compute cost.

DistAL: Distance-based Advantage Learning for VLA Fine-Tuning

arXiv:2609.18392v1 Announce Type: new Abstract: Vision-language-action models (VLAs) have trans- formed the field of robotic manipulation in recent years by combining the semantic understanding of LLM

DRT&R: Direct Radar Teach & Repeat

arXiv:2609.17766v1 Announce Type: new Abstract: Radar-based navigation is appealing for its robustness to adverse conditions involving airborne particles, such as precipitation, dust, fog, and smoke,

researcharxiv-cs-ro

EdgeReMIND: A Scalable, Top-Ranked Memorization Baseline for Temporal Multi-Relational Link Prediction

arXiv:2609.17916v1 Announce Type: new Abstract: Temporal link prediction on the Temporal Graph Benchmark 2.0 (TGB 2.0) faces a scalability ceiling: on the benchmark's three largest datasets, every exi

Evidence-Grounded Agentic Formulation Development in an Autonomous Laboratory

arXiv:2609.19099v1 Announce Type: new Abstract: Self-emulsifying drug delivery systems (SEDDS) can improve the oral bioavailability of poorly soluble drugs, but identifying high-performing formulation

EvolveTrade: Experience-Driven Policy Refinement for Self-Evolving LLM Trading Agents

arXiv:2609.17632v1 Announce Type: new Abstract: Large language model (LLM) trading agents can combine market data, news, and executable analysis, but their behavior is often controlled by static hand-

Agents

EvoUndo: Recoverability-Constrained Self-Evolution for LLM Agent Harnesses

arXiv:2608.28363v2 Announce Type: replace Abstract: LLM agents increasingly modify their own prompts, tools, middleware, resources, and execution harnesses at runtime. Such self-evolution can improve

agentsarxiv-cs-ai

FedASAP: Activation Statistics-driven Structured Adaptive Pruning for Efficient Personalized Federated Learning for Lesion Segmentation on brain MRI

model-releasesarxiv-cs-cv

arXiv:2609.19042v1 Announce Type: cross Abstract: In medical imaging, developing robust deep learning models requires data from various domains. However, regulatory policies protecting patient privacy

FRAUDSkill: Structured Frozen-Weight Skill Optimization for Audio Anti-Fraud Detection

model-releasesarxiv-cs-cl

arXiv:2609.18766v1 Announce Type: cross Abstract: Large audio-language models have shown promise for anti-fraud detection by directly processing speech and reasoning over fraud-related evidence. Their

From Gameplay to Policy: Towards Scalable Robot Data Collection via Gamified Robot-Free Interaction

model-releasesarxiv-cs-ro

arXiv:2609.18650v1 Announce Type: new Abstract: Learning generalizable robot manipulation policies requires large-scale and diverse interaction data, yet collecting real-world demonstrations remains c

From Pixels to Pairs: A Comprehensive Benchmark of LLM-Based Key-Value Extraction in Noisy Document Settings

model-releasesarxiv-cs-cl

arXiv:2609.17538v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for structured information extraction from documents, yet their behavior under realistic OCR noise re

Function-Preserving Data Generation for Zero-Shot Real-to-Sim-to-Real Manipulation

arXiv:2609.18293v1 Announce Type: new Abstract: Robotic data generation is a promising paradigm for scaling robot learning without collecting large-scale real-world data. However, generating geometric

FuseFi: Combining Irregularly Sampled CSI from Diverse Communication Packets and Frequency Bands for Wi-Fi Sensing

arXiv:2512.22143v2 Announce Type: replace-cross Abstract: Existing Wi-Fi sensing systems rely on injecting high-rate probing packets to extract channel state information (CSI), leading to communicatio

Agents

HINT-Plan: Human Intention-Aware Robot Task Planning in Context-Rich Environments using Vision Language Models

arXiv:2609.17771v1 Announce Type: cross Abstract: Approaches to incorporating human awareness into mobile robot decision-making mainly focus on collision avoidance in low-level motion planning, often

agentsarxiv-cs-ai

Hybrid coupling with numerics-informed neural networks and the overlapping Schwarz alternating method

arXiv:2609.17841v1 Announce Type: new Abstract: We develop a hybrid modeling framework for coupling pre-trained numerics-informed neural networks (NINNs) with classical full order models (FOMs) using

researcharxiv-cs-lg