Search: “deployment”

Collective Bias Mitigation via Model Routing and Collaboration

arXiv:2610.03240v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in public health, finance, and governance, requiring both accuracy and societal value alignment.

safetyarxiv-cs-cl

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Decoupling Memory from Context: Structured Memory for Token-Efficient Test-Time Continual Learning

DGX agent

arXiv:2610.02687v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in enterprise, scientific, and medical applications, where agents must incorporate domain-specifi

model-releasesarxiv-cs-ai

5 Oct 2026

Hardware

Efficient Memory Crystallization for Graph Learning under Non-Stationary Distribution Shifts

arXiv:2610.02795v1 Announce Type: new Abstract: Deep graph learning models deployed in real-world systems often need to cope with non-stationary environments, where the underlying graph distribution d

hardwarearxiv-cs-lg

FALCON: A Model and Dataset Agnostic Framework for Synthetic Data Generation for NL2SQL Pairs

model-releasesarxiv-cs-cl

arXiv:2610.03625v1 Announce Type: new Abstract: Relational databases are among the most widely deployed forms of structured knowledge, and natural language access to them requires grounding language o

Filter-Aware Fine-Tuning for Safe Humanoid Whole-Body Tracking

arXiv:2610.02341v1 Announce Type: new Abstract: Safe whole-body motion is essential for deploying humanoid robots in unstructured environments. Modern humanoid control commonly separates reference spe

safetyarxiv-cs-ro

From Language Priors to Field Adaptation: Preference Learning for Traversability Estimation

arXiv:2610.02974v1 Announce Type: cross Abstract: Image-based traversability estimation is inherently dependent on the robot platform, deployment domain, and mission preferences, which limits the appl

Harness-Aware Distillation for Small Language Model Agents

arXiv:2610.02858v1 Announce Type: new Abstract: Language model agents are deployed with a harness, the software around the model that manages its context, tools, and feedback. When such an agent is di

Heads, Tails, and AI Fails: LLMs, Randomness, and Human Judgments

arXiv:2406.00092v2 Announce Type: replace Abstract: Randomness is central to human cognition and to many applications in which large language models are deployed, yet probabilistic token generation do

Applications

LOCUS: Landmark-Oriented Container Discrimination Using Spatial Graphs

arXiv:2610.02803v1 Announce Type: new Abstract: As robots are increasingly deployed in unstructured, real-world environments, the ability to reason about complex spatial and semantic relationships amo

applicationsarxiv-cs-ro

Low-Cost Video--Time Priors as a Strong Baseline for EEG--fNIRS Emotion Regression on Familiar Videos

arXiv:2610.03618v1 Announce Type: new Abstract: Continuous emotion regression estimates moment-to-moment valence and arousal while a viewer watches a video. In familiar-video deployment, responses fro

MobiAgent: Dual-Loop Recursive Policy Self-Improvement for Long-Horizon Mobile Manipulation

arXiv:2610.03476v1 Announce Type: cross Abstract: Long-horizon mobile manipulation presents significant challenges due to compounding execution errors and capacity interference between locomotion and

Online Verification of Language Model Responses Under Cost Constraints

arXiv:2610.02632v1 Announce Type: new Abstract: As large language models are increasingly deployed for multi-step reasoning, verifying the correctness of their outputs has become essential for maintai

safetyarxiv-cs-lg

ReFract: Benchmarking Perspective Awareness in Language Model Agents with Text World Models

model-releasesarxiv-cs-ai

arXiv:2610.03356v1 Announce Type: new Abstract: Large Language Model (LLM) agents are increasingly deployed in high-stakes settings such as industrial maintenance and equipment fault troubleshooting,

Rethinking World-Action Model for Compositional and In-Context Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2610.02368v1 Announce Type: new Abstract: Long-horizon compositional manipulation has become increasingly important for real-world robot deployment, where a single task involves multiple coordin

Robust Adversarial Quantification via Conflict-Aware Evidential Deep Learning

arXiv:2506.05937v3 Announce Type: replace-cross Abstract: Reliability of deep learning models is critical for deployment in high-stakes applications, where out-of-distribution or adversarial inputs ma

Safe Streaming Flow Planning by Aligning Sampling Dynamics with Execution Dynamics

arXiv:2610.03132v1 Announce Type: cross Abstract: Generative planners based on diffusion/flow matching can learn to synthesize long-horizon trajectories from demonstrations. However, real-world deploy

safetyarxiv-cs-lg

Agents

Self-Supervised Scaling of Terminal Environments for Scientific Domains

arXiv:2610.02710v1 Announce Type: cross Abstract: Terminal agents are increasingly deployed beyond software engineering in science and other specialized domains. Constructing training environments req

agentsarxiv-cs-ai

TACD: Distilling Efficient Text-to-Motion Models via Terminal Amplification Control

arXiv:2610.02867v1 Announce Type: new Abstract: Recent text-to-motion models have improved motion quality and instruction following, yet many-step denoising and large model components make deployment

Tutorials

The Fragility of Trigger-Tag Mechanisms for Misuse Detection in Open-Weight LLMs

arXiv:2610.03124v1 Announce Type: cross Abstract: Open-weight language models can be downloaded, modified, and deployed beyond their developers' control, limiting the effectiveness of centrally enforc

tutorialsarxiv-cs-ai

Who Went Where When on the Lunar Surface: Forensic Trajectory Analysis to Identify Byzantine Rovers

arXiv:2610.02694v1 Announce Type: new Abstract: Future planetary surface missions are likely to involve multiple independently operated rovers sharing the same deployment region, raising the need to v

safetyarxiv-cs-ro

b11384

model-releasesllama-cpp-releases

ci : pushing tag needs deploy key (#29937) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/52545484 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silic

4 Oct 2026

Syntheses

Synthesis: Arxiv-Cs-Ai

synthesisarxiv-cs-aiauto-generated

Auto-generated synthesis of 29846 entries about arxiv-cs-ai

4 Oct 2026

Local Ai

Adaptive Conformal Prediction for Image Regression Models with Application to an Inertial Confinement Fusion Emulator

arXiv:2610.00535v1 Announce Type: cross Abstract: Uncertainty quantification is critical in scientific machine learning, where black-box, image-based models are increasingly deployed in high-stakes se

local-aiarxiv-cs-lg

AI21 achieves an 83% reduction in time-to-start for AI workloads with AI Hypercomputer

Editor’s note: AI21 Labs is a leading global AI lab with a long track record of building foundation models, most notably the Jamba family, and today focuses on specialized LLMs and agent optimization

safetygoogle-cloud-ai

Constant-Time Planning for Chaining Collision-free Motion to Manipulation Behaviors

arXiv:2512.00939v3 Announce Type: replace-cross Abstract: Recent progress in contact-rich robotic manipulation has been striking, yet most deployed systems remain confined to simple, scripted routines

Constitutional Value Potentials: reading and steering internal priority margins in language models

arXiv:2606.15420v2 Announce Type: replace-cross Abstract: Safety evaluations test a policy on prompts that omit the incentive information deployment supplies: a commission, a performance score, a dash

Deep Learning for Anomaly Detection in Railway Systems: A Structured Survey

arXiv:2610.00363v1 Announce Type: cross Abstract: Ensuring safe and reliable operation of modern railway systems increasingly relies on data-driven monitoring and intelligent fault detection. Deep lea

Distilling Directional Verification

arXiv:2610.00997v1 Announce Type: cross Abstract: Knowledge distillation aims to transfer the factual knowledge of large language models to smaller models for efficient deployment. Yet a teacher may r

Distilling LLM Reasoning into Graph of Concept Predictors

arXiv:2602.03006v3 Announce Type: replace Abstract: Deploying Large Language Models (LLMs) for discriminative workloads is often limited by inference latency, compute, and API costs at scale. Active d

Explainability of Complex AI Models with Correlation Impact Ratio

arXiv:2601.06701v2 Announce Type: replace-cross Abstract: Complex AI systems make better predictions but often lack transparency, limiting trustworthiness, interpretability, and safe deployment. Commo

Greed Is Learned: Visible Incentives as Reward-Hacking Triggers

arXiv:2606.16914v2 Announce Type: replace Abstract: Safety evaluations test a policy on prompts that omit the incentive information deployment supplies: a commission, a performance score, a dashboard

Homomorphic Advantage Operator: Stabilizing Reinforcement Learning Under Fully Homomorphic Encryption Constraints

model-releasesarxiv-cs-ai

arXiv:2610.02074v1 Announce Type: new Abstract: Privacy-preserving machine learning presents significant deployment challenges on the cloud for intelligent systems with confidential data. Fully Homomo

How Divergence Becomes Decision Flips in Compressed Language Models

arXiv:2610.00694v1 Announce Type: cross Abstract: Compression reports summarize how far a compressed language model moved from the dense one, usually by a KL divergence; a deployment that relies on th

How Many Categories Are Enough? Distribution-Free Certification Limits for Few-Shot Anomaly Thresholds

arXiv:2610.00236v1 Announce Type: new Abstract: Few-shot anomaly detectors are judged by ranking metrics, yet deployment requires an alarm threshold with a controlled false-alarm rate (FAR). We ask ho

Local Ai

Localisation-Aware Uncertainty for Pretrained Object Detection

arXiv:2610.01409v1 Announce Type: new Abstract: Reliable uncertainty estimation is essential for deploying object detectors when distribution/covariate shift and adversarial attacks may occur. Existin

local-aiarxiv-cs-cv

Hardware

MINCE: Shrinking LLM Evaluation Datasets via Few-Model Monte Carlo Calibration

arXiv:2606.22826v2 Announce Type: replace Abstract: Evaluating LLMs across many model variants---quantized, fine-tuned, or deployment-specific---requires running large benchmarks repeatedly, a process

hardwarearxiv-cs-ai

MOVE: Multimodal Open-world Verification and Expansion for Graph Learning

arXiv:2610.00268v1 Announce Type: new Abstract: Multimodal graph learning faces a fundamental challenge: new classes may emerge after deployment, while models are trained with a fixed label space. Exi

Applications

Not All Error Yields to Scale: Where Scaling Stops in Vision-Language Inference

arXiv:2610.01640v1 Announce Type: cross Abstract: Vision-language models (VLMs) face a fixed-budget trade-off between processing more visual information for fine-grained perception and using a larger

applicationsarxiv-cs-ai

Agents

Online Planning for Sparse Ground Target Search from a High-Altitude UAV under Partial Observability

arXiv:2610.01067v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs) searching for ground targets from a high altitude face a unique challenge, particularly when the target is already withi

agentsarxiv-cs-ro

PhaseAT: Fourier Phase Adversarial Training for Medical Image Domain Generalization

arXiv:2610.01807v1 Announce Type: new Abstract: Reliable clinical deployment of deep medical image models is hindered by distribution shifts across scanners, sites, and acquisition protocols. Existing

ProtoDCS: Towards Robust and Efficient Open-Set Test-Time Adaptation for Vision-Language Models

model-releasesarxiv-cs-ai

arXiv:2602.23653v3 Announce Type: replace-cross Abstract: Large-scale Vision-Language Models (VLMs) exhibit strong zero-shot recognition, yet their real-world deployment is challenged by distribution

Reachability Is Not Generalization: Understanding Verb--Noun Decomposition in Assembly Action Recognition

arXiv:2610.00064v1 Announce Type: new Abstract: Assembly actions are compositional: they combine a manipulation with a part or tool. In deployment, systems routinely encounter novel combinations of fa

Redundancy Meets Synergy: Dependency-aware Expert Selection for MoE via Submodular Optimization

arXiv:2610.00558v1 Announce Type: new Abstract: While Mixture-of-Experts (MoE) models effectively scale model capacity through sparse activation, their deployment is often bottlenecked by prohibitive

Agents

ReLiveGym: Evaluating Long-Lived Agents over Weeks of Replayed Reality

arXiv:2610.00710v1 Announce Type: new Abstract: As large language model (LLM) agents become widely adopted, they are increasingly deployed for tasks that require persistent monitoring or recurring act

agentsarxiv-cs-ai

Right In-Place (RiP) Convolution: A Simple, General, and Near-Optimal Strategy for Memory-Efficient CNN Inference

arXiv:2610.00586v1 Announce Type: cross Abstract: Activation memory, not compute, limits CNN inference on constrained hardware such as microcontrollers. Direct in-place convolution removes the dual-bu

Robust Evidential Learning Through Latent Consistency

arXiv:2610.01384v1 Announce Type: new Abstract: Reliable uncertainty quantification is essential for deploying deep learning models in high-stakes settings, where out-of-distribution and adversarial i

Safety in Self-Evolving Agents: A Survey

arXiv:2610.00093v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong general capabilities, yet their parameters typically remain fixed after deployment, limiting learning from

The Weakest Link: Distilling LLM Reasoning with Worst-Case Constrained Reinforcement Learning

arXiv:2610.00332v1 Announce Type: cross Abstract: Distilling the reasoning capabilities of large language models (LLMs) into smaller students is a central challenge for efficient deployment. Current a