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arXiv:2606.31163v1 Announce Type: cross Abstract: Large language models deployed in regulated industries operate under two constraints: compliance enforcement and cost efficiency. Personally identifia

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Research

Criticality-Constrained Iterative Pruning for Energy-Efficient Spiking Neural Networks via Combined Importance Scoring

DGX agent

arXiv:2606.30676v1 Announce Type: cross Abstract: Deploying spiking neural networks (SNNs) on neuromorphic hardware demands aggressive synaptic pruning while preserving temporal computation integrity.

researcharxiv-cs-lg

1 Jul 2026

No Adaptation Without Observation: Observability-Constrained Test-Time Prompt Tuning for LiDAR Semantic Segmentation

arXiv:2606.30937v1 Announce Type: new Abstract: LiDAR semantic segmentation often degrades under real-world deployment due to evolving sensing conditions, while collecting new annotations for retraini

safetyarxiv-cs-cv

Scenario Generation for Testing of Autonomous Driving Systems Using Real-World Failure Records

arXiv:2606.31131v1 Announce Type: new Abstract: To ensure safe on-road behavior, pre-deployment testing and failure discovery of Autonomous Driving Systems (ADS) is crucial. Present day simulation bas

agentsarxiv-cs-ai

The Consistency Dilemma in LLMs: Generator-Evaluator Agreement and Vulnerability to Mistakes

arXiv:2606.30653v1 Announce Type: cross Abstract: Large language models are increasingly deployed in agentic pipelines that depend on the model evaluating its own outputs without external verification

agentsarxiv-cs-ai

Budgeted Act-or-Defer Multi-Agent LLM Deliberation with Local Reliability Bounds

arXiv:2606.29654v1 Announce Type: new Abstract: Multi-agent deliberation among LLMs can improve reasoning, but deployment requires deciding when the current answer is reliable enough to act on and whe

Hardware

CSAR: Containerized System Architecture for Robotics

arXiv:2606.30293v1 Announce Type: new Abstract: Robotic applications increasingly rely on distributed computational infrastructures that combine embedded devices, edge servers, and cloud resources. Th

hardwarearxiv-cs-ro

Defeat Devices in AI Systems

model-releasesarxiv-cs-ai

arXiv:2606.28863v1 Announce Type: cross Abstract: AI systems increasingly exhibit behavior that differs systematically between evaluation and deployment contexts. Alignment faking, sandbagging, benchm

Exploiting Local Flatness for Efficient Out-of-Distribution Detection

model-releasesarxiv-cs-ai

arXiv:2606.29952v1 Announce Type: cross Abstract: Detecting out-of-distribution (OOD) data is crucial for reliable machine learning deployment. Among detection strategies, post-hoc methods are particu

HASTE: A Framework for Training-Free, Dynamic, and Steerable Compression of Pre-Trained Convolutional Neural Networks

arXiv:2606.30516v1 Announce Type: new Abstract: Deploying large convolutional neural networks (CNNs) on resource-constrained devices is challenging due to their high computational cost. While dynamic

researcharxiv-cs-cv

HJ-SafeDMP: Hamilton-Jacobi Reachability-Guided Dynamic Movement Primitives for Provably Safe Robot Motion

arXiv:2606.28995v1 Announce Type: new Abstract: Robots deployed in safety-critical environments must execute motions that are simultaneously robust to disturbances and provably safe from collisions. D

safetyarxiv-cs-ro

Proof-of-Guardrail in AI Agents and What (Not) to Trust from It

arXiv:2603.05786v2 Announce Type: replace-cross Abstract: As AI agents become widely deployed as online services, users often rely on an agent developer's claim about how safety is enforced, which int

Synthetic Interaction Data for Scalable Personalization in Large Language Models

arXiv:2602.12394v2 Announce Type: replace Abstract: Personalized prompting offers large opportunities for deploying large language models (LLMs) to diverse users, yet existing prompt optimization meth

agentsarxiv-cs-lg

Toward Secure and Reliable PDDL Formalization of Large Language Models with Planner-in-the-Loop Feedback

model-releasesarxiv-cs-ai

arXiv:2606.29700v1 Announce Type: new Abstract: Planning often requires symbolic specifications that are both executable and verifiable. For large language models deployed in autonomous or decision-su

UniVAD v2: Unified Visual Anomaly Detection via Support-Conditioned Boundary Construction

arXiv:2606.29714v1 Announce Type: new Abstract: Unified visual anomaly detection seeks to train a single detector that can be deployed across categories, domains, and application scenarios. In the few

researcharxiv-cs-cv

When Can Conformal Risk Control Certify LLM Outputs? Bounds, Impossibility, and Adaptation for Structured Generation

arXiv:2606.29054v1 Announce Type: new Abstract: Large language models (LLMs) deployed for structured generation (NER, JSON extraction, QA, and classification) lack formal reliability guarantees, and s

researcharxiv-cs-lg

Tutorials

FlexMoE: One-for-All Nested Intra-Expert Pruning for MoE Language Models

arXiv:2606.27866v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) language models scale model ability with sparsely activated experts, making this architecture a standard recipe for modern larg

tutorialsarxiv-cs-lg

29 Jun 2026

QuantV2X: A Fully Quantized Multi-Agent System for Cooperative Perception

arXiv:2509.03704v2 Announce Type: replace Abstract: Cooperative perception through Vehicle-to-Everything (V2X) communication offers significant potential for enhancing vehicle perception by mitigating

29 Jun 2026

Yuvion LLM: An Adversarially-Aware Large Language Model for Content And AI Safety

arXiv:2606.27632v1 Announce Type: new Abstract: As large language models are increasingly deployed in real-world systems, safety failures can still lead to harmful outputs and dangerous misuse. We arg

29 Jun 2026

Adaptive Utility driven Resource Orchestration for Resilient AI (AURORA-AI)

arXiv:2606.27005v1 Announce Type: new Abstract: Modern AI systems are increasingly deployed under non-stationary computational, demographic, and operational conditions in which static resource allocat

Cascaded Multi-Granularity Pruning for On-Device LLM Inference in Industrial IoT

arXiv:2606.26861v1 Announce Type: new Abstract: Deploying large language models (LLMs) on Industrial Internet of Things (IIoT) edge devices demands extreme compression, yet existing structured pruning

Governing Actions, Not Agents: Institutional Attestation as a Governance Model for Autonomous AI Systems

arXiv:2606.26298v1 Announce Type: new Abstract: Autonomous AI agents may begin to perform consequential, irreversible actions such as clinical prescribing and production software deployment. This pape

Learning from Equivalence Queries, Revisited

arXiv:2604.04535v2 Announce Type: replace Abstract: Modern machine learning systems, such as generative models and recommendation systems, often evolve through a cycle of deployment, user interaction,

researcharxiv-cs-lg

LiveEdit: Towards Real-Time Diffusion-Based Streaming Video Editing

arXiv:2606.26740v1 Announce Type: new Abstract: Streaming video editing has made rapid progress, yet practical deployment is still limited by two core issues: maintaining stable backgrounds and non-ed

What Survives When You Compress a Recursive Reasoner for the Edge?

arXiv:2606.26488v1 Announce Type: new Abstract: Recursive reasoning models can solve complex structured tasks with only a few million parameters by repeatedly updating a latent state. Deploying these

researcharxiv-cs-lg

A Survey of Toxicity Detection and Mitigation Strategies for Multilingual Language Models

arXiv:2606.25380v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed across languages, but their safety behavior remains uneven across linguistic and cultural context

safetyarxiv-cs-cl

Constraint Tax in Open-Weight LLMs: An Empirical Study of Tool Calling Suppression Under Structured Output Constraints

arXiv:2606.25605v1 Announce Type: new Abstract: Tool Calling and Structured Output are two core capabilities of modern Agent systems, yet their interaction under joint deployment conditions remains in

Detect, Unlearn, Restore: Defending Text Summarization Models Against Data Poisoning

arXiv:2606.26036v1 Announce Type: new Abstract: Training-time data poisoning during fine-tuning poses a significant threat to large language models (LLMs) deployed for abstractive text summarization,

Efficient Real-World Dehazing via Physics-Inspired Global-Local Decoupling

arXiv:2606.25732v1 Announce Type: new Abstract: Real-world single image dehazing is highly ill-posed due to spatially and spectrally varying scattering, while practical deployment demands lightweight

GROVE: Grounded Pedestrian Simulation via Natural Language for Interactive Social Robot Navigation

arXiv:2606.25504v1 Announce Type: new Abstract: Pedestrian simulation is a critical component for training and deploying social robot navigation approaches, yet it remains a largely rigid system that

researcharxiv-cs-ro

Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift

arXiv:2606.25152v1 Announce Type: new Abstract: Deployed approaches for AI text detection often rely on training-time access to labeled datasets of both human-written and AI-generated text. This appro

MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction

arXiv:2606.25651v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed in healthcare settings, accurate error detection and correction in generated or existing text

safetyarxiv-cs-cl

PERRY: Policy Evaluation with Confidence Intervals using Auxiliary Data

arXiv:2507.20068v3 Announce Type: replace Abstract: Off-policy evaluation (OPE) methods estimate the value of a new reinforcement learning (RL) policy prior to deployment. Recent advances have shown t

safetyarxiv-cs-lg

RAVEN: Long-Horizon Reasoning & Navigation with a Visuo-Spatio-Temporal Memory

arXiv:2606.25206v1 Announce Type: cross Abstract: Long-term robot deployment requires a compact and scalable memory that preserves fine-grained visual semantics, grounds observations in space and time

agentsarxiv-cs-cl

Applications

Towards Continuous Power Forecasting: Practical Continual Learning for Real-World Energy Systems in Nonstationary Time Series

arXiv:2606.24955v1 Announce Type: new Abstract: Power forecasting models deployed in real-world energy markets must operate under nonstationary conditions, where data distributions continually evolve

applicationsarxiv-cs-lg

When Multi-Sensor Fusion Fails to Generalize: Cattle Posture Classification Under Animal-Level and Temporal Distribution Shift

model-releasesarxiv-cs-lg

arXiv:2606.24986v1 Announce Type: new Abstract: Automated cattle posture-classification systems frequently report near-perfect accuracy, yet their robustness under realistic deployment conditions rema

Grounding Generative Policies in Physics: Optimization-Guided Diffusion for Robot Control

arXiv:2606.24208v1 Announce Type: new Abstract: Diffusion models sample effectively from high-dimensional, multimodal distributions, but their outputs may violate deployment constraints. For task-spac

researcharxiv-cs-ro

24 Jun 2026

To Compare, or Not to Compare: On Methodological Practices in Evaluating Social Bias

arXiv:2606.24596v1 Announce Type: new Abstract: As Large Language Models are increasingly deployed in critical applications, robustly evaluating their social biases is paramount. However, the current

24 Jun 2026

A Digital Twin Framework for Traffic-Aware UAV Pavement Monitoring without Lane Closure

arXiv:2606.20742v1 Announce Type: new Abstract: UAV-based pavement inspection can reduce the cost and risk of road-surface monitoring, but real-world deployment remains difficult when traffic, pedestr

agentsarxiv-cs-ro

ACE-GS: Acing the Trade-off with Accurate, Compact and Efficient 3D Gaussian Splatting

arXiv:2606.21244v1 Announce Type: new Abstract: 3D Gaussian Splatting achieves exceptional real-time rendering, but its substantial computational and storage demands hinder widespread deployment. Exis

APEX: Action Priors Enable Efficient Exploration for Robust Motion Tracking on Legged Robots

arXiv:2505.10022v4 Announce Type: replace Abstract: Learning natural, animal-like locomotion from demonstrations has become a core paradigm in legged robotics. While motion tracking can reproduce refe

safetyarxiv-cs-ro

BELLS-O: Evaluating the Operational Trade-offs of LLM Supervision Systems

model-releasesarxiv-cs-lg

arXiv:2606.20668v1 Announce Type: cross Abstract: LLM supervision systems, namely input/output moderation filters and jailbreak detectors, are the primary safeguard against misuse in deployed AI appli

Applications

Changing Modalities: Adapting Remote Sensing Models to New Satellites and Sensors

arXiv:2606.23356v1 Announce Type: new Abstract: Machine learning models for remote sensing are trained and deployed on a static set of modalities. However, as we equip newer satellites with novel sens

applicationsarxiv-cs-cv

ClayBuddy: A Framework, Evaluation, & Mitigation of Coding Agent Failures

arXiv:2606.19380v2 Announce Type: replace-cross Abstract: Software engineering and deployment are increasingly delegated to AI coding agents. The scale of their adoption is surfacing rare, but highly

safetyarxiv-cs-lg

Cross-Modal Corroboration for Annotation-Free Wildlife Monitoring

arXiv:2606.21613v1 Announce Type: new Abstract: Scaling wildlife monitoring for real-world conservation deployments requires automated analysis of smart sensors that operate under severe annotation sc

safetyarxiv-cs-cv

Demystifying Numerical Instability in LLM Inference: Achieving Reproducible Inference for Mission-Critical Tasks with HEAL

model-releasesarxiv-cs-lg

arXiv:2606.21023v1 Announce Type: new Abstract: As Large Language Models (LLMs) deploy into mission-critical domains (e.g., finance, medicine, and law), output reproducibility has become a strict syst

LOLLA: Deep Reinforcement Learning for Closed-Loop Link Adaptation Towards a GPU-Accelerated AI-RAN

arXiv:2606.23110v1 Announce Type: cross Abstract: Outer-loop link adaptation (OLLA) is widely deployed in 5G NR to track channel variations, yet its reliance on first-order, single-bit feedback degrad

safetyarxiv-cs-lg

PolicyTrim: Boosting Intrinsic Policy Efficiency of Vision-Language-Action Models

arXiv:2606.22540v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models provide a unified paradigm for robotic manipulation, yet their real-world deployment is often bottlenecked by execut

safetyarxiv-cs-cv