Search: “deployment”

Calibration-Aware Uncertainty Cascades for Efficient Heterogeneous Model Collaboration

arXiv:2609.11446v1 Announce Type: new Abstract: Heterogeneous model collaboration seeks to exploit the complementary strengths of different models to balance predictive performance and inference cost.

researcharxiv-cs-ai

12 Sept 2026

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Model Releases

Nex-N2.5-mini-MLX-4bit on Apple M5 Max — 133.6 tok/s — llm-bench.io

DGX agent

Another new model dropped in the course of this week that is well deployable on consumer hardware: Nex N2.5 Mini I went with the recommended settings for the best generation quality and ran a few benc

model-releasesr-localllama

12 Sept 2026

A-JIT: Agentic Just-In-Time Software Construction

arXiv:2609.10248v1 Announce Type: cross Abstract: Traditional software delivery assumes a static paradigm: code is constructed prior to execution and deployed as a fixed artifact. We present Agentic J

agentsarxiv-cs-ai

Beyond Training: A Feasibility Taxonomy for Inference-Time AI Governance

arXiv:2609.10105v1 Announce Type: cross Abstract: Compute governance today is a governance of training: the thresholds, reporting requirements, and frontier-AI regimes now in force attach to training

agentsarxiv-cs-ai

Build interactive MCP Apps using Amazon Bedrock AgentCore

model-releasesaws-ml-blog

Learn how to build and deploy an MCP App with interactive HTML widgets on Amazon Bedrock AgentCore. Because MCP Apps is a host-agnostic standard, the same server delivers the same rich experience acro

Certifying Lower Bounds for Risk-Sensitive Reinforcement Learning under Adversarial State Perturbations

arXiv:2609.10866v1 Announce Type: new Abstract: Reinforcement learning (RL) agents deployed in real-world environments are often vulnerable to adversarial perturbations in state observations, creating

CMoE: Contrastive Mixture of Experts for Motion Control and Terrain Adaptation of Humanoid Robots

arXiv:2603.03067v2 Announce Type: replace Abstract: For effective deployment in real-world environments, humanoid robots must autonomously navigate a diverse range of complex terrains with abrupt tran

applicationsarxiv-cs-ro

Emergent Risks in Generative Multi-Agent Systems

arXiv:2603.27771v3 Announce Type: replace-cross Abstract: Multi-agent systems composed of large generative models are rapidly moving from laboratory prototypes to real-world deployments, where they jo

agentsarxiv-cs-cl

HALDETECT at ImageEval 2026 Shared Tasks: Answer-First Contrastive Grounding with QLoRA

arXiv:2609.11236v1 Announce Type: new Abstract: Large multimodal models tend to hallucinate visual detail fluently, which limits their deployment for fine-grained interpretation. We present HALDETECT,

LexAgentHallu: A Hierarchical Benchmark for Profiling Hallucinations in Legal Agents

arXiv:2609.09754v1 Announce Type: new Abstract: As large language models are increasingly deployed as tool-augmented legal agents, they introduce agentic hallucinations where tool-call and reasoning e

On the Impact of Anonymization on the Performance of Large Language Models

arXiv:2609.11335v1 Announce Type: new Abstract: As large language models are increasingly deployed in sensitive domains, anonymizing input data to protect personally identifiable information has becom

researcharxiv-cs-cl

Pairit: A Platform for Live Experiments on Human-AI Collaboration

arXiv:2609.09789v1 Announce Type: cross Abstract: Organizational design in the era of artificial intelligence requires experimental methods that can test how human-AI groups coordinate, delegate, and

researcharxiv-cs-ai

PRAGMA: Evaluating Personalized Guidance with Memory Alignment in Lifelong Conversations

arXiv:2609.09664v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as personalized assistants that interact with users over extended periods of time. As conversatio

A Risk-Sensitive and Uncertainty-Aware Decision-Making and Control Framework for Safe and Robust Autonomous Driving

arXiv:2609.09650v1 Announce Type: new Abstract: Reinforcement learning (RL) has demonstrated considerable potential for autonomous driving decision-making. However, its deployment in urban autonomous

safetyarxiv-cs-ro

AgentBrew: Offline Tool-Use Agent Learning from Raw Real-World Trajectories

arXiv:2609.05837v1 Announce Type: new Abstract: LLM-based agents are increasingly deployed in real-world applications through tool-use APIs, yet training them for specific environments remains fundame

safetyarxiv-cs-ai

API Benchmark Scores Do Not Reliably Transfer to Chatbot Interfaces

arXiv:2609.08861v1 Announce Type: new Abstract: Benchmark scores are a central currency in model releases: they inform purchasing decisions, shape public trust, and influence policy. Yet, a key assump

BIO-MEMART: Biometric-Aware KV Cache Memory for Multi-User LLM Agents

arXiv:2609.08566v1 Announce Type: new Abstract: KV cache is evolving from a serving optimization into an external memory substrate for long-term LLM agents. In a shared multi-user deployment, however,

researcharxiv-cs-ai

CIVI: A Framework for Diagnosing Search Agent Failures in Civic Information

arXiv:2609.08094v1 Announce Type: new Abstract: Large Language Models are increasingly deployed in public-sector settings, where incorrect guidance can cause irreversible harm. We introduce CIVI, the

Closing the Consistency Gap: Self-Evolving Agents That Learn to Stay on Course

arXiv:2609.08832v1 Announce Type: new Abstract: Large language model (LLM)-powered agents can be accurate on average yet unreliable in production, a discrepancy that has been observed but remains larg

Diffs vs. Whole Files: An Empirical Comparison of Iterative Edit-Based and Direct Generation for Flutter/Dart Code Models

arXiv:2609.05779v1 Announce Type: cross Abstract: Large language models used for code editing can be trained and deployed in at least two output regimes: direct generation, where the model emits the e

DiSCo: A Distribution-First Steering and Cultural Prior Evaluation Framework for Measuring Cultural Preference Bias in LLMs

arXiv:2609.10253v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in globally used assistants, yet their default choices in culturally grounded everyday situations

local-aiarxiv-cs-cl

Do Web Agents Investigate Before They Decide?

arXiv:2602.05354v3 Announce Type: replace Abstract: Autonomous web agents are increasingly deployed in moderation and policy enforcement, where correct decisions often depend on evidence that is not i

Dreamina Seedance 2.0 is now available to US companies on fal, hosted entirely on US infrastructure. One of the world’s most capable video m…

applicationssonya-huang--x

Dreamina Seedance 2.0 is now available to US companies on fal, hosted entirely on US infrastructure. One of the world’s most capable video models can now be deployed by US teams through fal, with infe

Explaining AI Agents Through Execution Traces

arXiv:2609.06063v1 Announce Type: new Abstract: AI Agents are increasingly deployed in real-world settings, where they interact with external tools and make sequential decisions with limited human ove

agentsarxiv-cs-ai

Generator-Independent Runtime Assurance under Partial Observation

arXiv:2609.06036v1 Announce Type: new Abstract: Proposal-based controllers---learned policies, language-model planners, and other black-box generators---are increasingly deployed behind runtime verifi

safetyarxiv-cs-ai

Gravwell adds five AI agents that gather their own investigation context

model-releasessiliconangle

Security data platform company Gravwell Inc. today released Gravwell 5.10, an update that puts five artificial intelligence agents inside a customer’s deployment. The agents collect the context an inv

IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier

arXiv:2609.10494v1 Announce Type: new Abstract: Enterprises deploy systems, not checkpoints. Usable capability depends jointly on weights, serving route, precision, output contract, and harness, yet a

applicationsarxiv-cs-cl

LightMedSeg-ISLES: Stroke Lesion Segmentation with 81x Fewer Parameters than nnU-Net

arXiv:2609.09634v1 Announce Type: new Abstract: Large networks and ensembles often lead medical image segmentation challenges, but their storage and inference demands complicate deployment. We present

MOSAIC: A Universal Agent-Level Interface for Cross-Paradigm Agent Mixing and Human-AI Collaboration

arXiv:2603.01260v2 Announce Type: replace Abstract: Existing infrastructure cannot deploy agents from different decision-making paradigms within the same environment, making fair cross-paradigm compar

PELM: Power Efficient On-Device LLM Inference with Speculative Decoding and Dynamic Voltage Frequency Scaling

arXiv:2609.09662v1 Announce Type: cross Abstract: Deploying Large Language Models (LLMs) directly on mobile platforms at the edge is gaining traction due to a myriad of benefits, such as increased pri

local-aiarxiv-cs-cl

Personalized Execution Time Optimization for Billion-Scale Scheduled Jobs

arXiv:2203.06158v3 Announce Type: replace Abstract: Scheduled batch jobs are widely used on asynchronous computing platforms to execute enterprise applications such as promotional notifications and ca

safetyarxiv-cs-lg

PRIMUS: Identity, Governance, and Verification for Multi-Agent Federations

arXiv:2609.07910v1 Announce Type: new Abstract: Multi-agent federations need governance that answers three questions under adversarial conditions: who participated (identity), did they conform (enforc

safetyarxiv-cs-ai

Procedural Graphs: Self-Evolving Execution Structures for LLM Agents

arXiv:2609.09153v1 Announce Type: new Abstract: Large language models are increasingly deployed as agents that plan over long horizons and act through external tools. Most agents select actions throug

local-aiarxiv-cs-ai

RiLM: Parameter-Efficient Language Modeling via Geodesic Decoding

model-releasesarxiv-cs-cl

arXiv:2609.10305v1 Announce Type: new Abstract: Language models under one million parameters matter for edge deployment, domain adaptation, and reproducible research, yet a two-layer LSTM or Transform

Scaling Post-Training Ternarisation to Qwen3-8B Capability Retention, Reproduction, Lossless Packing, and Packed Execution

arXiv:2609.09240v1 Announce Type: new Abstract: Ultra-low-bit language models promise reductions in storage and memory traffic, but a nominal '1.58-bit' label does not specify the deployed representat

researcharxiv-cs-lg

SRD-GUARD: A Defense Framework of LLMs via Semantic Rewriting and Joint Multi-Model Scoring for Latent Intent Exposure

arXiv:2609.06540v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in safety-critical applications, yet jailbreak attacks can conceal harmful intent through role-

VANTAGE-Bench: Evaluating the Infrastructure AI Gap in Vision-Language Models

arXiv:2609.09396v1 Announce Type: new Abstract: As Vision-Language Models (VLMs) advance toward physical deployment, the focus has remained on action-oriented Embodied AI evaluated on subject-centric

A Black-Box Adversarial Attack on Human Pose Estimation and Keypoint-Based Action Recognition Models

arXiv:2609.08013v1 Announce Type: new Abstract: Human pose estimation and keypoint-based action recognition models are increasingly deployed as components of video understanding pipelines, yet their v

researcharxiv-cs-cv

Approaching the Harm of Gradient Attacks While Only Flipping Labels

arXiv:2503.00140v3 Announce Type: replace-cross Abstract: Machine learning systems deployed in distributed or federated environments are highly susceptible to adversarial manipulations, particularly a

applicationsarxiv-cs-lg

Artificial Foveated Perception for Mitigating Shortcut Learning in Robotic Foundation Models

arXiv:2607.10655v2 Announce Type: replace Abstract: Robotic foundation models still need task-specific fine-tuning before deployment, and the fine-tuned policies often break under modest changes in sc

safetyarxiv-cs-ro

Automatic Red Teaming for Implicit Vulnerabilities of Text-to-Image Models

arXiv:2609.06094v1 Announce Type: new Abstract: Red-teaming Text-to-Image (T2I) models is essential for safe deployment, yet it remains particularly challenging against implicit adversarial prompts. U

safetyarxiv-cs-cv

Beyond Retraining-Free MoE Compression: A Cost-Normalized Study of Post-Compression Adjustment

arXiv:2609.06076v1 Announce Type: new Abstract: Retraining-free MoE compression reduces deployment memory by pruning or merging experts, but often treats the compressed checkpoint as the final artifac

Hardware

Dense Structural Compression of Transformers via Gauge-Correct Channel Removal

arXiv:2609.07264v1 Announce Type: new Abstract: Inference energy per token drives the cost and carbon footprint of deployed transformers. It is dominated by dense matrix products that incur fused mult

hardwarearxiv-cs-lg

Dex-X: Learning Visual-Tactile Dexterous Manipulation From Human Videos with Simulated Interaction

arXiv:2609.07747v1 Announce Type: new Abstract: Human videos are an abundant source of dexterous manipulation behaviors, but they lack tactile information that is crucial for contact-rich interaction.

safetyarxiv-cs-ro

Do Input-Level Defenses Transfer to Observation-Level Attacks on VideoLLMs?

arXiv:2609.08331v1 Announce Type: new Abstract: Video Large Language Models (VideoLLMs) are increasingly deployed in safety-critical applications such as content moderation and video analytics. To pro

local-aiarxiv-cs-cv

Emo-DVS: A Multimodal Benchmark for Privacy-Aware Emotion Recognition with Event Cameras

arXiv:2609.06928v1 Announce Type: new Abstract: Emotion analysis is a fundamental task in computer vision, but its practical deployment remains constrained by the privacy risks inherent to conventiona

Hardware

EStream: Fast and Memory-Efficient MoE Prefill through Expert Virtualization on Mobile NPUs

arXiv:2609.06551v1 Announce Type: cross Abstract: Mobile vendors and application developers increasingly deploy LLMs on smartphones for diverse prefill-only services. Yet current systems rely mainly o

hardwarearxiv-cs-lg

FMMO: Detecting the Divergence Between Local Attribution and Global Drift

arXiv:2609.06173v1 Announce Type: new Abstract: Post-deployment drift poses a critical risk to algorithmic accountability, particularly when ground truth labels are delayed and performance degradation