Search: “deployment”

Faithful, Interpretable Chest X-ray Diagnosis with Artifact-free B-cos Networks

arXiv:2507.16761v3 Announce Type: replace Abstract: Faithfulness and interpretability are essential for deploying deep neural networks (DNNs) in safety-critical domains such as medical image analysis.

safetyarxiv-cs-cv

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Research

Feed the Panel Dimensions, Not Verdicts: Rubric-Decomposed Fusion of Vision-Language Aesthetic Judges

DGX agent

arXiv:2609.27110v1 Announce Type: cross Abstract: Vision-language models (VLMs) are deployed as zero-shot judges of image aesthetics, and panels of several models are recommended, on thin evidence, as

researcharxiv-cs-cl

24 Sept 2026

Finite-Sample Probabilistic Safety Certification for AI-Based Grid-Edge Coordination

arXiv:2609.28182v1 Announce Type: new Abstract: Coordinating large population of flexible grid-edge devices can alleviate the need for time-consuming and capital-intensive network upgrades, and AI-bas

safetyarxiv-cs-ai

FLINT: Fast Lightweight Inference for Traversability

arXiv:2609.26857v1 Announce Type: cross Abstract: Navigation in off-road conditions is challenging due to the lack of structure. There is no fixed vocabulary for what is traversable. The traversabilit

Humanoid Locomotion with a Fly-Inspired Recurrent Controller

arXiv:2609.27001v1 Announce Type: new Abstract: We investigate humanoid locomotion with a fly-inspired recurrent controller and identify the pathways supporting its deployed behavior. The controller c

safetyarxiv-cs-ro

Issuer-Sovereign Agentic Payments

arXiv:2609.27452v1 Announce Type: cross Abstract: AI agents are beginning to make real payments. Current approaches let an agent pay by relying on a credential provider that, in the approaches deploye

Applications

Lend me an Ear: Speech Enhancement Using a Robotic Arm with a Microphone Array

arXiv:2602.17818v2 Announce Type: replace Abstract: Speech enhancement performance degrades significantly in noisy environments, limiting the deployment of speech-controlled technologies in industrial

applicationsarxiv-cs-ro

LWCal: Loss-Weighted Calibration for Tabular Classifiers with Noisy Calibration Labels

arXiv:2609.26839v1 Announce Type: cross Abstract: Post-hoc probability calibration is usually evaluated under an optimistic assumption: the held-out calibration labels are clean. In many AI deployment

researcharxiv-cs-ai

NGN: Learning Neural Network Size as a Differentiable Count

arXiv:2609.27291v1 Announce Type: new Abstract: Neural network size is usually chosen before training, separating architecture selection from weight optimization. We introduce the Neurogenesis Network

researcharxiv-cs-lg

Safe learning-based control via function-based uncertainty quantification

arXiv:2604.01173v2 Announce Type: replace-cross Abstract: Uncertainty quantification is essential when deploying learning-based control methods in safety-critical systems. This is commonly realized by

SafeTutors: Benchmarking Pedagogical Safety in AI Tutoring Systems

arXiv:2603.17373v2 Announce Type: replace Abstract: Large language models are rapidly being deployed as AI tutors, yet current evaluation paradigms assess problem-solving accuracy and generic safety i

Speaker-labeled transcription with WhisperX on SageMaker AI

The AWS WhisperX Deep Learning Container packages Whisper, wav2vec2 forced alignment, and speaker diarization into a GPU-ready image. Learn how to deploy it to Amazon SageMaker AI real-time and asynch

safetyaws-ml-blog

Spec2COBOLRot: An Agentic-AI Degradation Loop for Realistic COBOL Corpus Generation

arXiv:2609.26835v1 Announce Type: cross Abstract: COBOL remains widely deployed, yet representative corpora reflecting real production code are rarely available, limiting rigorous benchmarking of mode

The Capability Manifold and ML Scaling Laws

arXiv:2609.27588v1 Announce Type: cross Abstract: Existing machine learning (ML) scaling laws relate predictive loss to compute, model parameters, and data. However, as models are increasingly deploye

Wave-Robust Passive AUV Localization Using FP-MUSIC

arXiv:2609.27712v1 Announce Type: new Abstract: Localizing an autonomous underwater vehicle without pre-deployed seabed transponders, or direct access to onboard vehicle sensors remains a core challen

agentsarxiv-cs-ro

When Context Misleads: In-context Learning with Jurisdiction in Large Language Models

arXiv:2609.27603v1 Announce Type: cross Abstract: In-Context Learning (ICL) has become a cornerstone of modern LLM deployment. However, existing ICL post-training methods have a critical blind spot: t

ACLArena: Agent Continue Learning in Multi-stage Post-training

arXiv:2609.23989v1 Announce Type: new Abstract: Building general-purpose agents for industrial deployment requires integrating multiple capabilities, each typically acquired at a distinct stage of tra

safetyarxiv-cs-ai

AgentBetta: Verification-Driven Adaptive Configuration of an AI Nano-Agent through Selective Expansion and Verified Contraction

arXiv:2609.23512v1 Announce Type: new Abstract: Large language model agents are typically deployed with predefined configurations, although the required model capability, context, tools, permissions,

Tools

🔬Bio-security is an AI Arms Race - Eric Nguyen (CEO, Radical Numerics)

Radical Numerics, led by CEO Eric Nguyen, is deploying “biological chain‑of‑thought” and multimodal perception techniques to stay ahead of the evolving bio‑defense arms race, enabling rapid design of

toolslatent-space

Block-Level Weight-Space Structure Persists Under Post-Training: An Empirical Study Across LLM Families

arXiv:2609.26147v1 Announce Type: new Abstract: Modern LLMs are deployed as families of post-trained variants (base, instruct, chat, code) derived from a shared set of pre-trained weights. We present

Calibrated Confidence Expression for Radiology Report Generation

arXiv:2603.29492v2 Announce Type: replace Abstract: Safe deployment of Large Vision-Language Models (LVLMs) in radiology report generation requires not only accurate predictions but also clinically in

researcharxiv-cs-cl

Can You Delete a Year of Market Data? Machine Unlearning Against Exact Retraining Oracles

arXiv:2609.26242v1 Announce Type: new Abstract: When a data license expires, deleting stored records does not remove influence encoded in a trained forecaster. Machine unlearning seeks to remove this

Decoding Guardrails: XAI-Guided Perturbation Analysis of Prompt Injection Detection

arXiv:2609.24801v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in production systems, raising concerns about their exposure to adversarial manipulation throug

safetyarxiv-cs-ai

EDGEGEN: Improving Tool-Calling Agents Beyond Happy Paths with Synthetic Edge Case Generation

arXiv:2609.24115v1 Announce Type: new Abstract: Tool-calling LLM agents are increasingly deployed in enterprise applications. However, effective evaluation and optimization require high-quality, diver

Epi-Logic: A Conceptual Framework for Epistemic Runtime Control, Schema Validity Checking, and Controlled Accommodation in Autonomous AI Agents

arXiv:2609.24755v1 Announce Type: new Abstract: Autonomous AI agents are increasingly deployed in areas where wrong decisions are hard to reverse. This paper examines schema mismatch: the condition in

Applications

How AI data foundations are rewriting enterprise architecture

In the quest to deploy artificial intelligence at scale, enterprises are discovering a basic truth: Model capability is no longer the only issue, but data access and control can determine success or f

applicationssiliconangle

Learning to Defer with Guidance on Real World Medical Data

arXiv:2609.26384v1 Announce Type: new Abstract: Medical image interpretation is high-volume and time-consuming, and while AI interpretation can reduce workload, fully autonomous deployment carries pot

Learning to Fluctuate: Statistical Foundations for Causal Tabular Pretraining

arXiv:2609.26290v1 Announce Type: cross Abstract: Causal tabular foundation models amortize effect estimation across synthetic mechanisms, but latent-effect supervision rewards posterior shrinkage ins

Modality-Gated Deep Adapters: Adding a Modality to a Frozen Embedding Model with Exact Preservation

arXiv:2609.26182v1 Announce Type: new Abstract: Multimodal embedding models are deployed at scale: retrieval indices, benchmark results, and behavioral audits all depend on the base model's exact outp

Optimal Sequential Annotations for Off-Policy Evaluation

arXiv:2609.26707v1 Announce Type: cross Abstract: Offline reinforcement learning and off-policy evaluation evaluates dynamic treatment rules based on retrospectively collected data prior to deployment

QuantWM: Temporally Consistent 2-Bit KV Cache Quantization for World Models and Video Generation

arXiv:2609.26425v2 Announce Type: new Abstract: KV cache memory has become a major deployment bottleneck for video generation and world models, which motivates low-bit quantization study for efficienc

researcharxiv-cs-cv

RGSQ: Riemannian Geometry-Sensitive Quantization for Large Vision-Language Models

arXiv:2609.25492v1 Announce Type: new Abstract: Large vision-language models (VLMs) can be efficiently deployed under stringent memory and latency constraints through post training quantization (PTQ).

researcharxiv-cs-cv

Secure, intelligent experiences across every endpoint

model-releasesgoogle-cloud-ai

For years, the workday looked remarkably consistent. Employees logged into devices, opened browsers and toggled across a dozen disconnected applications. Users acted as the bridge between apps, copyin

Applications

Targeted Review for AI-Assisted Biodiversity Surveys: Active Continuous-Score Occupancy Modeling

arXiv:2609.25657v1 Announce Type: new Abstract: We increasingly use machine learning to label scientific datasets. The models we develop and deploy are improving all the time, but they are not and wil

applicationsarxiv-cs-lg

Toolcompass: Guiding Tool Trialing, Not Suppressing It

arXiv:2609.25678v1 Announce Type: cross Abstract: Large language model (LLM) agents must generalize from tools seen during training to unseen tools at deployment. A key challenge is tool trialing, i.e

researcharxiv-cs-lg

Local Ai

Universal Fractal Natural Language Decision Map: Real-Time Edge Triage Across Heterogeneous Domains

arXiv:2609.25498v1 Announce Type: cross Abstract: Deploying Large Language Models for runtime operational triage incurs prohibitive latency (>100-500 ms), high VRAM requirements (>4-8 GB), and excessi

local-aiarxiv-cs-cl

An LLM-Assisted AutoML Framework for Intrusion Detection in IoT Networks

arXiv:2609.23097v1 Announce Type: cross Abstract: Internet of Things (IoT) systems are increasingly deployed in smart homes, transportation, energy systems, and critical infrastructure. This broad con

Artificial Structure Function Search: Preserving Artificial Functional Connectivity for Structured Pruning

arXiv:2609.24401v1 Announce Type: new Abstract: Structured pruning is a model compression technique that is used to reduce the computational cost of deploying deep neural networks on resource-constrai

As part of our efforts to pace the frontier, we’re committed to supporting independent assessments with deep levels of access across trainin…

As part of our efforts to pace the frontier, we’re committed to supporting independent assessments with deep levels of access across training, evaluation, and deployment. That access should enable thi

model-releasesopenai--x

Local Ai

CHART: A Harness-Rotation Curriculum for Harness-Robust Search Agents

arXiv:2609.22247v1 Announce Type: new Abstract: Search agents are usually trained under a single harness. But once an agent is deployed in a real application, its harness is frequently updated (e.g.,

local-aiarxiv-cs-lg

Tutorials

Clustering-Based Collective Anomaly Detection in IoT Systems: A Graph Neural Network Approach

arXiv:2609.22166v1 Announce Type: new Abstract: The rapid advancement of Internet of Things (IoT) technology has led to the widespread deployment of smart, interconnected devices across a range of dom

tutorialsarxiv-cs-lg

Conflicting Pattern Formation by Teams of Anonymous, Fully Disoriented Robots

arXiv:2609.23454v1 Announce Type: cross Abstract: Two groups of autonomous, anonymous, and oblivious mobile robots are deployed in the two-dimensional Euclidean plane, each assigned a distinct task. W

agentsarxiv-cs-ro

Constrained Decoding Eliminates Structural Failures in Small LLMs but Reveals a Scale-Dependent Semantic Gap

arXiv:2609.23742v1 Announce Type: new Abstract: Small open-source large language models (LLMs) in the 0.6B-4B parameter range are increasingly deployed for structured output generation (JSON, function

Dial: A Knowledge-Grounded Dialect-Specific NL2SQL System

arXiv:2603.07449v2 Announce Type: replace-cross Abstract: Enterprises commonly deploy heterogeneous database systems, each of which owns a distinct SQL dialect with different syntax rules, built-in fu

Efficient Mixture-of-Experts with Speculative Decoding via Expert Coactivation

arXiv:2609.22471v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models are increasingly deployed alongside Speculative Decoding (SD) to accelerate inference, but combining the two is challe

Emergent Collusion in Long-Horizon LLM Agent Interaction

arXiv:2609.24967v1 Announce Type: cross Abstract: LLM agents are increasingly deployed in collaborative settings, yet long-term interaction may give rise to undesirable coordination. We study the emer

safetyarxiv-cs-cl

Ev-YOLO: Uncertainty-Aware Object Detection via a Unified Evidential Formulation

arXiv:2609.24668v1 Announce Type: new Abstract: Reliable uncertainty estimation is essential for deploying object detectors in autonomous systems operating in uncertain environments. Evidential Deep L

agentsarxiv-cs-cv

Fathom-Vaidya: Advancing Medical Reasoning with Rubric-Based Rewards

arXiv:2609.24480v1 Announce Type: cross Abstract: Deploying Large Language Models (LLMs) in healthcare requires robust performance across two complementary dimensions - diagnostic reasoning: the conve