Search: “deployment”

Searching for Privacy Risks in LLM Agents via Simulation

arXiv:2508.10880v3 Announce Type: replace-cross Abstract: The widespread deployment of LLM-based agents is likely to introduce a critical privacy threat: malicious agents that proactively engage other

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

SHARP: A Self-Evolving Human-Auditable Rubric Policy for Financial Trading Agents

DGX agent

arXiv:2605.06822v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed for autonomous financial trading, a domain requiring continuous adaptation to noisy, non-stationa

safetyarxiv-cs-lg

11 May 2026

Sparse Autoencoders as Plug-and-Play Firewalls for Adversarial Attack Detection in VLMs

arXiv:2605.07447v1 Announce Type: cross Abstract: Vision-language models (VLMs) have advanced rapidly and are increasingly deployed in real-world applications, especially with the rise of agent-based

safetyarxiv-cs-ai

The Context Gathering Decision Process: A POMDP Framework for Agentic Search

arXiv:2605.07042v1 Announce Type: new Abstract: Large Language Model (LLM) agents are deployed in complex environments -- such as massive codebases, enterprise databases, and conversational histories

Why Does Agentic Safety Fail to Generalize Across Tasks?

arXiv:2605.06992v1 Announce Type: new Abstract: AI agents are increasingly deployed in multi-task settings, where the task to perform is specified at test time, and the agent must generalize to unseen

Hardware

XiYOLO: Energy-Aware Object Detection via Iterative Architecture Search and Scaling

arXiv:2605.06927v1 Announce Type: cross Abstract: Object detection on heterogeneous edge devices must satisfy strict energy, latency, and memory constraints while still providing reliable perception f

hardwarearxiv-cs-ai

Tools

How Superset built the IDE for AI agents on Vercel

Superset developed an integrated development environment (IDE) for AI agents deployed on Vercel's platform, enabling developers to build, test, and manage AI agent applications more efficiently. The s

toolsvercel-blog

10 May 2026

@NousResearch @OpenRouter Congrats! 🙌

Nous Research announced a congratulatory message regarding a collaboration or achievement involving OpenRouter, likely related to AI model deployment or integration. The post celebrates a milestone or

researchnous-research--x

9 May 2026

Kimi K2.6 on @baseten is ~5x cheaper than Opus 4.7 For a large majority of tasks, it's roughly the same performance If you want to use open …

Kimi K2.6, deployed on Baseten, offers approximately 5x cost savings compared to Claude Opus 4.7 while maintaining comparable performance on most tasks, making it a viable open-source alternative for

agentsharrison-chase--x

8 May 2026

Running Codex safely at OpenAI

This OpenAI article outlines safety practices and guidelines for responsibly deploying and using Codex, their code generation model. It likely covers potential risks associated with automated code gen

agentsopenai

8 May 2026

3.5 years into the “AI revolution” Basically no sign that it has mattered for productivity.

Gary Marcus argues that despite 3.5 years of rapid AI development and deployment, there is minimal empirical evidence that AI has significantly impacted overall economic productivity metrics. The post

safetygary-marcus--x

A Workflow-Oriented Framework for Asynchronous Human-AI Collaboration in Hybrid and Compute-Intensive HPC Environments

arXiv:2605.03743v1 Announce Type: cross Abstract: Human involvement is critical in training and deploying AI systems in high-stakes defence and security contexts. However, real-time interaction is imp

local-aiarxiv-cs-ai

Applications

Beyond Semantics: An Evidential Reasoning-Aware Multi-View Learning Framework for Trustworthy Mental Health Prediction

arXiv:2605.05121v1 Announce Type: new Abstract: Automated mental health prediction using textual data has shown promising results with deep learning and large language models. However, deploying these

applicationsarxiv-cs-cl

Continual Knowledge Updating in LLM Systems: Learning Through Multi-Timescale Memory Dynamics

arXiv:2605.05097v1 Announce Type: cross Abstract: LLMs are trained once, then deployed into a world that never stops changing. External memory compensates for this, but most systems manage it explicit

researcharxiv-cs-cl

CRAFT: Counterfactual-to-Interactive Reinforcement Fine-Tuning for Driving Policies

arXiv:2605.04470v1 Announce Type: new Abstract: Open-loop imitation learning has advanced modern autonomous driving policy architectures, but closed-loop deployment remains vulnerable to policy-induce

EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation

arXiv:2605.04062v1 Announce Type: new Abstract: Recent years have witnessed an increasing interest in deploying LLMs on resource-constrained devices, among which quantization has emerged as a promisin

researcharxiv-cs-lg

Enhancing Agent Safety Judgment: Controlled Benchmark Rewriting and Analogical Reasoning for Deceptive Out-of-Distribution Scenarios

arXiv:2605.03242v1 Announce Type: new Abstract: Tool-using agent systems powered by large language models (LLMs) are increasingly deployed across web, app, operating-system, and transactional environm

Applications

Every so often I think about how, in 2022, for $24B we could had 'prototype vaccines ready for each of the 26 known viral families that caus…

applicationsethan-mollick--x

Every so often I think about how, in 2022, for $24B we could had 'prototype vaccines ready for each of the 26 known viral families that cause human disease' so they can be deployed in 100 days if ther

Explaining and Preventing Alignment Collapse in Iterative RLHF

arXiv:2605.04266v1 Announce Type: new Abstract: Reinforcement learning from human feedback (RLHF) typically assumes a static or non-strategic reward model (RM). In iterative deployment, however, the p

FASQ: Flexible Accelerated Subspace Quantization for Calibration-Free LLM Compression

arXiv:2605.04084v1 Announce Type: new Abstract: Compressing large language models (LLMs) for deployment on commodity GPUs remains challenging: conventional scalar quantization is limited to fixed bit-

Laundering AI Authority with Adversarial Examples

arXiv:2605.04261v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly deployed as trusted authorities -- fact-checking images on social media, comparing products, and modera

LLMs Uncertainty Quantification via Adaptive Conformal Semantic Entropy

arXiv:2605.04295v1 Announce Type: new Abstract: LLMs' overconfidence, particularly when hallucinating, poses a significant challenge for the deployment of the models in safety-critical settings and ma

Order Matters: Improving Domain Adaptation by Reordering Data

arXiv:2605.05084v1 Announce Type: new Abstract: Domain shift remains a key challenge in deploying machine learning models to the real world. Unsupervised domain adaptation (UDA) aims to address this b

Personalized Spiking Neural Networks with Ferroelectric Synapses for EEG Signal Processing

arXiv:2601.00020v3 Announce Type: replace-cross Abstract: Electroencephalography (EEG)-based brain-computer interfaces (BCIs) are strongly affected by non-stationary neural signals that vary across se

local-aiarxiv-cs-lg

ProgramBench: Can Language Models Rebuild Programs From Scratch?

arXiv:2605.03546v1 Announce Type: cross Abstract: Turning ideas into full software projects from scratch has become a popular use case for language models. Agents are being deployed to seed, maintain,

Applications

Quantization and Fast Inference (MEAP) - How much performance are you actually getting from quantization in production? [D]

applicationsr-machinelearning

This discussion examines the practical performance gains of quantization techniques when deployed in production environments, particularly addressing whether theoretical speedups translate to real-wor

Variance Matters: Improving Domain Adaptation via Stratified Sampling

arXiv:2512.05226v2 Announce Type: replace Abstract: Domain shift remains a key challenge in deploying machine learning models to the real world. Unsupervised domain adaptation (UDA) aims to address th

Hardware

When Agents Handle Secrets: A Survey of Confidential Computing for Agentic AI

arXiv:2605.03213v1 Announce Type: cross Abstract: Agentic AI systems, specifically LLM-driven agents that plan, invoke tools, maintain persistent memory, and delegate tasks to peer agents via protocol

hardwarearxiv-cs-ai

AI and Open-data Driven Scalable Solar Power Profiling

arXiv:2605.02738v1 Announce Type: new Abstract: Solar photovoltaic (PV) deployment is expanding rapidly, yet detailed, up-to-date information on the spatial distribution and capacity of rooftop PV rem

Complexity Horizons of Compressed Models in Analog Circuit Analysis

arXiv:2605.02285v1 Announce Type: new Abstract: The deployment of Large Language Models (LLMs) for specialized engineering domains, such as circuit analysis, often faces a trade-off between reasoning

CoVSpec: Efficient Device-Edge Co-Inference for Vision-Language Models via Speculative Decoding

arXiv:2605.02218v1 Announce Type: new Abstract: Vision-language models (VLMs) have demonstrated strong capabilities in multimodal perception and reasoning. However, deploying large VLMs on mobile devi

researcharxiv-cs-ai

Disentangling Intent from Role: Adversarial Self-Play for Persona-Invariant Safety Alignment

arXiv:2605.01899v1 Announce Type: new Abstract: The growing capabilities of large language models (LLMs) have driven their widespread deployment across diverse domains, even in potentially high-risk s

safetyarxiv-cs-ai

Distributed Deep Variational Approach for Privacy-preserving Data Release

arXiv:2605.03069v1 Announce Type: cross Abstract: Federated learning (FL) lets distributed nodes train a shared model without exchanging their raw data, but in privacy-sensitive deployments medical se

GISclaw: A Comprehensive Open-Source LLM Agent System for Realistic Multi-Step Geospatial Analysis

arXiv:2603.26845v2 Announce Type: replace-cross Abstract: Most LLM-driven GIS assistants solve narrow single-step tasks tightly coupled to proprietary platforms such as ArcGIS or QGIS, limiting their

local-aiarxiv-cs-ai

Kernel Affine Hull Machines for Compute-Efficient Query-Side Semantic Encoding

arXiv:2605.02950v1 Announce Type: new Abstract: Transformer-based semantic retrieval is highly effective, yet in many deployments the dominant cost lies in online query encoding rather than corpus ind

Moral Sensitivity in LLMs: A Tiered Evaluation of Contextual Bias via Behavioral Profiling and Mechanistic Interpretability

arXiv:2605.03217v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in settings that require nuanced ethical reasoning, yet existing bias evaluations treat model out

PIIGuard: Mitigating PII Harvesting under Adversarial Sanitization

model-releasesarxiv-cs-cl

arXiv:2605.03129v1 Announce Type: cross Abstract: Browsing-enabled LLM assistants can fetch webpages and answer contact-seeking queries, creating a practical channel for scraping contact-style persona

Pioneering AI-assisted code migration: How Google achieved 6x faster migration from TensorFlow to JAX

model-releasesgoogle-cloud-ai

AI coding agents are rapidly becoming ubiquitous across the software industry, fundamentally changing how developers write, test, and debug daily code. While these tools excel at localized, self-conta

Posterior-First Neural PDE Simulation: Inferring Hidden Problem State from a Single Field

arXiv:2605.03247v1 Announce Type: new Abstract: Neural PDE simulators often receive only a single observed field at deployment. In this setting, a field-to-future predictor can collapse distinct laten

researcharxiv-cs-lg

Reasoning Models Can be Accurately Pruned Via Chain-of-Thought Reconstruction

arXiv:2509.12464v2 Announce Type: replace Abstract: Reasoning language models such as DeepSeek-R1 produce long chain-of-thought traces during inference time which make them costly to deploy at scale.

Reward Hacking Benchmark: Measuring Exploits in LLM Agents with Tool Use

arXiv:2605.02964v1 Announce Type: new Abstract: Reinforcement learning (RL) trained language model agents with tool access are increasingly deployed in coding assistants, research tools, and autonomou

RFPrompt: Prompt-Based Expert Adaptation of the Large Wireless Model for Modulation Classification

arXiv:2605.03279v1 Announce Type: new Abstract: Automatic modulation classification (AMC) in real-world deployments demands robustness to distribution shifts arising from hardware impairments, unseen

RouteHijack: Routing-Aware Attack on Mixture-of-Experts LLMs

arXiv:2605.02946v1 Announce Type: new Abstract: Safety alignment is critical for the responsible deployment of large language models (LLMs). As Mixture-of-Experts (MoE) architectures are increasingly

local-aiarxiv-cs-lg

Safety and accuracy follow different scaling laws in clinical large language models

model-releasesarxiv-cs-cl

arXiv:2605.04039v1 Announce Type: new Abstract: Clinical LLMs are often scaled by increasing model size, context length, retrieval complexity, or inference-time compute, with the implicit expectation

SHIELD: A Diverse Clinical Note Dataset and Distilled Small Language Models for Enterprise-Scale De-identification

arXiv:2605.03301v1 Announce Type: new Abstract: De-identification of clinical text remains essential for secondary use of electronic health records (EHRs), yet public benchmarks such as i2b2 2006/2014

local-aiarxiv-cs-cl

Industry

SpaceXAI and @AnthropicAI have also expressed interest in partnering to develop multiple gigawatts of orbital AI compute capacity

SpaceX's AI division (xAI) and Anthropic have expressed interest in partnering to develop orbital AI compute capacity at the gigawatt scale. This collaboration would involve deploying computational in

industryelon-musk--x

The Cost of Consensus: Isolated Self-Correction Prevails Over Unguided Homogeneous Multi-Agent Debate

arXiv:2605.00914v1 Announce Type: cross Abstract: Multi-agent debate, where teams of LLMs iteratively exchange rationales and vote on answers, is widely deployed under the assumption that peer review

Trojan Hippo: Weaponizing Agent Memory for Data Exfiltration

arXiv:2605.01970v2 Announce Type: cross Abstract: Memory systems enable otherwise-stateless LLM agents to persist user information across sessions, but also introduce a new attack surface. We characte