Search: “deployment”

Prototype Fusion: A Training-Free Multi-Layer Approach to OOD Detection

arXiv:2603.23677v2 Announce Type: replace Abstract: Deep learning models are increasingly deployed in safety-critical applications, where reliable out-of-distribution (OOD) detection is essential to e

safetyarxiv-cs-cv

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Robust Promptable Video Object Segmentation

DGX agent

arXiv:2605.12006v1 Announce Type: new Abstract: The performance of promptable video object segmentation (PVOS) models substantially degrades under input corruptions, which prevents PVOS deployment in

model-releasesarxiv-cs-cv

13 May 2026

See What Matters: Differentiable Grid Sample Pruning for Generalizable Vision-Language-Action Model

arXiv:2605.11817v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown remarkable promise in robotics manipulation, yet their high computational cost hinders real-time deploy

SEVO: Semantic-Enhanced Virtual Observation for Robust VLA Manipulation via Active Illumination and Data-Centric Collection

arXiv:2605.11114v1 Announce Type: new Abstract: Vision-Language-Action (VLA) and imitation-learning policies trained via community toolchains on low-cost hardware frequently fail when deployed outside

safetyarxiv-cs-ro

Local Ai

SOMA: Efficient Multi-turn LLM Serving via Small Language Model

arXiv:2605.11317v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in multi-turn dialogue settings where preserving conversational context across turns is essential

local-aiarxiv-cs-cl

Toxicity Detection Should Measure Contextual Harm, Not Text-Intrinsic Badness

arXiv:2503.16072v4 Announce Type: replace-cross Abstract: Toxicity detection has become core safety infrastructure for online moderation, dataset filtering, and deployed language-model systems. Yet mo

safetyarxiv-cs-cl

VERDI: Single-Call Confidence Estimation for Verification-Based LLM Judges via Decomposed Inference

arXiv:2605.11334v1 Announce Type: cross Abstract: LLM-as-Judge systems are widely deployed for automated evaluation, yet practitioners lack reliable methods to know when a judge's verdict should be tr

VNDUQE: Information-Theoretic Novelty Detection using Deep Variational Information Bottleneck

arXiv:2605.11551v1 Announce Type: cross Abstract: Detecting out-of-distribution (OOD) samples is critical for safe deployment of neural networks in safety-critical applications. While maximum softmax

safetyarxiv-cs-cv

A Robust Out-of-Distribution Detection Framework via Synergistic Smoothing

arXiv:2605.08191v1 Announce Type: cross Abstract: Reliable out-of-distribution (OOD) detection is a critical requirement for the safe deployment of machine learning systems. Despite recent progress, s

Local Ai

AdaSwitch: Adaptive Switching between Small and Large Agents for Effective Cloud-Local Collaborative Learning

arXiv:2410.13181v2 Announce Type: replace Abstract: Recent advancements in large language models (LLMs) have been remarkable. Users face a choice between using cloud-based LLMs for generation quality

local-aiarxiv-cs-cl

Agent-ValueBench: A Comprehensive Benchmark for Evaluating Agent Values

arXiv:2605.10365v1 Announce Type: new Abstract: Autonomous agents have rapidly matured as task executors and seen widespread deployment via harnesses such as OpenClaw. Safety concerns have rightly dra

An Empirical Analysis of Calibration and Selective Prediction in Multimodal Clinical Condition Classification

arXiv:2603.02719v2 Announce Type: replace Abstract: As artificial intelligence systems move toward clinical deployment, ensuring reliable prediction behavior is fundamental for safety-critical decisio

safetyarxiv-cs-lg

AnyDepth-DETR/-YOLO: Any-depth object detection with a single network

arXiv:2605.09407v1 Announce Type: new Abstract: Modern object detectors are static, fixed-depth networks optimized for a single operating point, requiring separate models for different deployment scen

BaseCal: Unsupervised Confidence Calibration via Base Model Signals

arXiv:2601.03042v4 Announce Type: replace Abstract: Reliable confidence is essential for trusting the outputs of LLMs, yet widely deployed post-trained LLMs (PoLLMs) typically compromise this trust wi

researcharxiv-cs-cl

Beyond Autonomy: A Dynamic Tiered AgentRunner Framework for Governable and Resilient Enterprise AI Execution

arXiv:2605.10223v1 Announce Type: new Abstract: Current large language model agent frameworks prioritize autonomy but lack the governability mechanisms required for enterprise deployment. High-risk wr

safetyarxiv-cs-ai

Beyond Hard Writes and Rigid Preservation: Soft Recursive Least-Squares for Lifelong LLM Editing

arXiv:2601.15686v2 Announce Type: replace Abstract: Model editing updates a pre-trained LLM with new facts or rules without retraining while preserving unrelated behavior. In real deployment, edits ar

researcharxiv-cs-lg

Beyond Position Bias: Shifting Context Compression from Position-Driven to Semantic-Driven

arXiv:2605.09463v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated exceptional performance across diverse tasks. However, their deployment in long-context scenarios faces h

safetyarxiv-cs-cl

Beyond Toy Benchmarks: A Systematic Evaluation of OOD Detection Methods For Plant Pathology Classification

arXiv:2605.08618v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection is essential for reliable deployment of deep learning systems, yet the majority of existing methods are evaluated on

Can We Trust LLMs for Mental Health Screening? Consistency, ASR Robustness, and Evidence Faithfulness

arXiv:2605.09634v1 Announce Type: new Abstract: LLMs can estimate Hospital Anxiety and Depression Scale (HADS) scores from speech in a zero-shot manner, but clinical deployment requires reliability ac

Can You Keep a Secret? Involuntary Information Leakage in Language Model Writing

arXiv:2605.10794v1 Announce Type: cross Abstract: Language models are deployed in settings that require compartmentalization: system prompts should not be disclosed, chain-of-thought reasoning is hidd

Coarse-to-Fine: Progressive Image Compression for Semantically Hierarchical Classification

arXiv:2605.08266v1 Announce Type: cross Abstract: Recent advances in learned image compression (LIC) have enabled practical deployments, spurring active research into image compression for machines an

researcharxiv-cs-cv

Conformity Generates Collective Misalignment in AI Agents Societies

arXiv:2605.10721v1 Announce Type: cross Abstract: Artificial intelligence safety research focuses on aligning individual language models with human values, yet deployed AI systems increasingly operate

safetyarxiv-cs-cl

ConQuR: Corner Aligned Activation Quantization via Optimized Rotations for LLMs

model-releasesarxiv-cs-lg

arXiv:2605.10793v1 Announce Type: new Abstract: Large language models (LLMs) are costly to deploy due to their large memory footprint and high inference cost. Weight-activation quantization can reduce

CoreQ: Learning-Free Mismatch Correction and Successive Rounding for Quantization

arXiv:2602.05902v2 Announce Type: replace-cross Abstract: Post-training quantization (PTQ) enables efficient deployment of large language models by mapping pretrained weights to low-bit formats withou

Debugging the Debuggers: Failure-Anchored Structured Recovery for Software Engineering Agents

arXiv:2605.08717v1 Announce Type: cross Abstract: Software engineering agents are increasingly deployed in evaluable engineering environments, yet post-failure recovery remains costly, manual, and ad

safetyarxiv-cs-ai

Deepfake Detection that Generalizes Across Benchmarks

arXiv:2508.06248v4 Announce Type: replace Abstract: The generalization of deepfake detectors to unseen manipulation techniques remains a challenge for practical deployment. Although many approaches ad

Agents

Delivering Science as a Service: Sci-Orchestra's Cloud-Native Approach to HPC

arXiv:2605.08396v1 Announce Type: new Abstract: The increasing complexity of modern computational environments often burdens researchers with infrastructure management, authentication protocols, and c

agentsarxiv-cs-cv

Detecting Multi-Agent Collusion Through Multi-Agent Interpretability

arXiv:2604.01151v2 Announce Type: replace Abstract: As LLM agents are increasingly deployed in multi-agent systems, they introduce risks of covert coordination that may evade standard forms of human o

Different Prompts, Different Ranks: Prompt-aware Dynamic Rank Selection for SVD-based LLM Compression

model-releasesarxiv-cs-lg

arXiv:2605.08568v1 Announce Type: new Abstract: Large language models (LLMs) have rapidly grown in scale, creating substantial memory and computational costs that hinder efficient deployment. Singular

Applications

Distilling 3D Spatial Reasoning into a Lightweight Vision-Language Model with CoT

arXiv:2605.09719v1 Announce Type: cross Abstract: Large-scale 3D vision-language models (VLMs) like LLaVA-3D offer strong spatial reasoning but are difficult to deploy due to high computational costs.

applicationsarxiv-cs-ai

Applications

Dynamic Cross-Modal Prompt Generation for Multimodal Continual Instruction Tuning

arXiv:2605.10765v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, yet real-world deployment often requires continual cap

applicationsarxiv-cs-ai

EdgeFlowerTune: Evaluating Federated LLM Fine-Tuning Under Realistic Edge System Constraints

arXiv:2605.08636v1 Announce Type: new Abstract: Federated fine-tuning offers a promising paradigm for adapting large language models (LLMs) on edge devices by leveraging the rich, diverse, and continu

Efficient Ensemble Selection from Binary and Pairwise Feedback

arXiv:2605.09588v1 Announce Type: cross Abstract: Organizations increasingly deploy multiple AI systems across task domains, but selecting a small, high-performing ensemble can require costly model ca

Applications

Elastic MoE: Unlocking the Inference-Time Scalability of Mixture-of-Experts

arXiv:2509.21892v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) models typically fix the number of activated experts k at both training and inference. However, real-world deployment

applicationsarxiv-cs-ai

Execution Envelopes: A Shared Admission Contract for Backend AI Execution Requests

arXiv:2605.08267v1 Announce Type: cross Abstract: Enterprise AI backends increasingly admit heterogeneous execution requests across model deployment, inference, evaluation, data movement, and agentic

safetyarxiv-cs-ai

Fin-Bias: Comprehensive Evaluation for LLM Decision-Making under human bias in Finance Domain

arXiv:2605.09106v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in financial contexts, raising critical concerns about reliability, alignment, and susceptibility

Fitting Is Not Enough: Smoothness in Extremely Quantized LLMs

arXiv:2605.08894v1 Announce Type: cross Abstract: Large language models (LLMs) achieve strong performance but incur high deployment costs, motivating extremely low-bit but lossy quantization. Existing

From Ontology Conformance to Admissible Reconfiguration: A RoSO/SMGI Adequacy Argument for Robotic Service Governance

arXiv:2605.08185v1 Announce Type: cross Abstract: The Robotic Service Ontology (RoSO) gives service robotics a typed semantic vocabulary for services, functions, interactions, and deployment-sensitive

GAMBIT: A Three-Mode Benchmark for Adversarial Robustness in Multi-Agent LLM Collectives

arXiv:2605.09027v1 Announce Type: new Abstract: In multi-agent systems (MAS), a single deceptive agent can nullify all gains of an agentic AI collective and evade deployed defenses. However, existing

Agents

Generating synthetic electronic health record data using agent-based models to evaluate machine learning robustness under mass casualty incidents

arXiv:2605.09951v1 Announce Type: new Abstract: ML models in healthcare are typically evaluated using curated real-world EHR data. A key limitation of such evaluations is that they may fail to assess

agentsarxiv-cs-lg

HoReN: Normalized Hopfield Retrieval for Large-Scale Sequential Model Editing

arXiv:2605.08143v1 Announce Type: cross Abstract: Large language models encode vast factual knowledge that inevitably becomes outdated or incorrect after deployment, yet retraining is costly prohibiti

Agents

Insider Attacks in Multi-Agent LLM Consensus Systems

arXiv:2605.08268v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in multi-agent systems where agents communicate in natural language to solve tasks jointly. A k

agentsarxiv-cs-ai

Learning from Trials and Errors: Reflective Test-Time Planning for Embodied LLMs

arXiv:2602.21198v2 Announce Type: replace-cross Abstract: Embodied LLMs endow robots with high-level task reasoning, but they cannot reflect on what went wrong or why, turning deployment into a sequen

Learning When to Stop: Selective Imitation Learning Under Arbitrary Dynamics Shift

arXiv:2605.09183v1 Announce Type: new Abstract: Behavior cloning provides strong imitation learning guarantees when training and test environments share the same dynamics. However, in many deployment

safetyarxiv-cs-lg

LiteMedCoT-VL: Parameter-Efficient Adaptation for Medical Visual Question Answering

arXiv:2605.09384v1 Announce Type: cross Abstract: The reasoning gap between large and compact vision-language models (VLMs) limits the deployment of medical AI on portable clinical devices. Compact VL

Majority Bit-Aware Watermarking For Large Language Models

arXiv:2508.03829v2 Announce Type: replace Abstract: The growing deployment of Large Language Models (LLMs) has raised concerns about their misuse in generating harmful or deceptive content. To address

researcharxiv-cs-cl

MC^2: Monte Carlo Correction for Fast Elliptic PDE Solving

arXiv:2605.09288v1 Announce Type: cross Abstract: Partial differential equation (PDE) solvers underpin scientific computing, but real-world deployment is bounded by compute. Classical Monte Carlo solv

MedFL-Stress: A Systematic Robustness Evaluation of Federated Brain Tumor Segmentation under Cross-Hospital MRI Appearance Shift

arXiv:2605.09025v1 Announce Type: new Abstract: Federated learning enables hospitals to collaboratively train segmentation models without sharing patient data. However, current evaluation protocols re

safetyarxiv-cs-cv