Knowledge catalogue
arXiv:2604.23051v1 Announce Type: new Abstract: Language models are increasingly deployed in interactive settings where users reason about facts over time rather than in isolation. In such scenarios,
arXiv:2604.24618v1 Announce Type: new Abstract: We evaluate the propensity of frontier models to sabotage or refuse to assist with safety research when deployed as AI research agents within a frontier
arXiv:2604.23743v1 Announce Type: cross Abstract: Deploying quantum machine learning on NISQ devices requires architectures where training overhead does not negate computational advantages. We systema
arXiv:2604.24549v1 Announce Type: cross Abstract: Coordinating large populations of grid-edge devices requires learning methods that remain fully decentralised in deployment while still respecting thr
arXiv:2604.23366v1 Announce Type: new Abstract: Autonomous multi-agent LLM systems are increasingly deployed to investigate operational incidents and produce structured diagnostic reports. Their trust
arXiv:2604.24551v1 Announce Type: cross Abstract: Quantum error mitigation (QEM) is essential for extracting reliable results from near-term quantum devices, yet practical deployments must balance mit
arXiv:2604.23547v1 Announce Type: cross Abstract: Efficient hardware implementation of nonlinear activation functions is a crucial task in deploying artificial neural networks on resource-constrained
arXiv:2604.23375v1 Announce Type: new Abstract: Convolutional neural networks (CNNs) have become increasingly difficult to deploy in resource-constrained environments due to their large memory and com
DigitalOcean describes their optimization and deployment of three large language models (DeepSeek V3.2, MiniMax-M2.5, and Qwen 3.5 397B) on their Serverless Inference platform, likely leveraging NVIDI
arXiv:2505.01651v4 Announce Type: replace Abstract: This paper introduces the Human-AI Governance (HAIG) framework, contributing to the AI Governance (AIG) field by foregrounding the relational dynami
arXiv:2604.23467v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved strong performance across natural language and multimodal tasks, yet their practical deployment remains con
arXiv:2604.23478v1 Announce Type: new Abstract: Large language models are increasingly deployed as automated judges for evaluating other models, yet the stability of their verdicts under semantically
arXiv:2509.10546v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed in finance, where unsafe behavior can lead to serious regulatory risks. However, most r
arXiv:2604.23809v1 Announce Type: new Abstract: Small language models (SLMs) are promising for real-world deployment due to their efficiency and low operational cost. However, their limited capacity s
arXiv:2604.24602v1 Announce Type: new Abstract: Vision-language models transfer well in zero-shot settings, but at deployment the visual and textual branches often shift asymmetrically. Under this con
This article discusses the practical implementation of AI systems in government agencies to detect and prevent fraudulent activities, likely covering deployment challenges, best practices, and real-wo
OpenAI outlines its commitment to implementing safety measures and responsible practices in the development and deployment of AI systems to protect users and communities. The statement likely covers O
arXiv:2505.16888v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed via third-party system prompts downloaded from public marketplaces. We identify a criti
arXiv:2604.23600v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in persona-driven applications such as education, customer service, and social platforms, where m
arXiv:2501.02548v2 Announce Type: replace-cross Abstract: Deploying learned decision-making systems often requires transferring to new sites where the sensing pipeline differs. In such cases, observat
arXiv:2604.24020v1 Announce Type: cross Abstract: Autonomous AI agents deployed on platforms such as OpenClaw face prompt injection, memory poisoning, supply-chain attacks, and social engineering, yet
arXiv:2604.24295v1 Announce Type: new Abstract: Inefficient driving behaviors, such as overly conservative yielding, remain a key obstacle to deployment of autonomous vehicles (AVs). Instantaneous dri
arXiv:2503.05587v3 Announce Type: replace-cross Abstract: Robustness has become a critical attribute for the deployment of RAG systems in real-world applications. Existing research focuses on robustne
arXiv:2602.04120v2 Announce Type: replace-cross Abstract: Though Explainable AI (XAI) has made significant advancements, its inclusion in edge and IoT systems is typically ad-hoc and inefficient. Most
arXiv:2510.12826v2 Announce Type: replace-cross Abstract: As large language model (LLM) agents are deployed autonomously in diverse contexts, evaluating their capacity for strategic deception becomes
arXiv:2512.12844v2 Announce Type: replace-cross Abstract: Reliable uncertainty quantification is essential for deploying machine learning systems in high-stakes domains. Conformal prediction provides
arXiv:2604.23662v1 Announce Type: new Abstract: The increasing global deployment of solar photovoltaic (PV) systems needs robust, scalable, and automated inspection technologies capable of detecting a
arXiv:2604.24380v1 Announce Type: new Abstract: While Large Vision Language Models (LVLMs) demonstrate impressive capabilities, their substantial computational and memory requirements pose deployment
arXiv:2604.23205v1 Announce Type: cross Abstract: Deploying proprietary Deep Neural Networks (DNNs) on commodity edge devices demands hardware-backed Digital Rights Management (DRM) capable of withsta
Google LLC and Anthropic PBC are two of the most powerful forces shaping enterprise AI. As agentic deployments demand platforms that are both capable and safe to build on, the Google-Anthropic partner
arXiv:2603.15941v2 Announce Type: replace Abstract: Automated diagnosis from chest computed tomography (CT) scans faces two persistent challenges in clinical deployment: distribution shift across acqu
arXiv:2604.23505v1 Announce Type: cross Abstract: Uncertainty in large language model (LLM)-based systems is often studied at the level of a single model output, yet deployed LLM applications are comp
arXiv:2604.22985v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed to autonomously solve real-world tasks. A key ingredient for this is the LLM Function-Calling par
arXiv:2604.23356v1 Announce Type: new Abstract: Large language models (LLMs) show promise in medical diagnosis, but real-world deployment remains challenging due to high-stakes clinical decisions and
arXiv:2604.22872v1 Announce Type: new Abstract: Autonomous vehicles (AVs) rely on real-time perception systems to understand road environments and ensure safe navigation. However, implementing reliabl
arXiv:2604.23767v1 Announce Type: new Abstract: Deploying machine learning models across diverse well portfolios requires generalisation to wells with design parameters outside the training distributi
arXiv:2603.04328v2 Announce Type: replace Abstract: We study the deployment performance of machine learning based enforcement systems used in cryptocurrency anti money laundering (AML). Using forward
arXiv:2604.22236v1 Announce Type: cross Abstract: Human decision-makers often face choices about complex cases with many potentially relevant features, but limited bandwidth to inspect and integrate a
arXiv:2604.21936v1 Announce Type: new Abstract: Medical imaging research is increasingly shifting from controlled benchmark evaluation toward real-world clinical deployment. In such settings, applying
arXiv:2604.22313v1 Announce Type: new Abstract: NL2SQL systems deployed in industry settings often encounter ambiguous or unanswerable queries, particularly in interactive scenarios with incomplete us
arXiv:2604.22238v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models promise generalist robot manipulation, but are typically trained and deployed as short-horizon policies that assume
Cognition is partnering with @MercedesBenz to accelerate software engineering across their global engineering teams, representing one of the most extensive deployments of AI software engineering in th
arXiv:2604.22119v1 Announce Type: new Abstract: As reasoning capacity and deployment scope grow in tandem, large language models (LLMs) gain the capacity to engage in behaviors that serve their own ob
arXiv:2604.21982v1 Announce Type: new Abstract: Solar panels are increasingly deployed in cities on rooftops, walls, and urban infrastructure. Although the panel costs have fallen in recent years, the
arXiv:2508.15919v3 Announce Type: replace-cross Abstract: Large language model (LLM) serving faces the dual challenge of meeting strict user-specific service-level objectives (SLOs) while minimizing c
arXiv:2506.09520v2 Announce Type: replace-cross Abstract: Human planning is efficient--it frugally deploys limited cognitive resources to accomplish difficult tasks--and flexible--adapting to novel pr
arXiv:2604.22750v1 Announce Type: new Abstract: The wide adoption of AI agents in complex human workflows is driving rapid growth in LLM token consumption. When agents are deployed on tasks that requi
Pay attention to this one, AI devs. If you're building multi-agent systems, you're probably wiring static org charts. New research argues they should look more like a labor market. The paper introduce
Agentic AI is flooding enterprise pipelines with possibility, but regulated industries are learning that the speed of deployment and the discipline of governance cannot be decoupled — and that AI proc
arXiv:2604.22082v1 Announce Type: cross Abstract: As AI systems begin to automate complex tasks, supervision increasingly relies on weaker models or limited human oversight that cannot fully verify ou
Microsoft and OpenAI announced an expanded partnership extending their collaboration on AI development and deployment. The partnership likely involves increased investment from Microsoft, expanded int
arXiv:2506.17299v2 Announce Type: replace-cross Abstract: As large language models (LLMs) become increasingly deployed in safety-critical applications, the lack of systematic methods to assess their v
arXiv:2509.20979v2 Announce Type: replace Abstract: In modern GPU inference, cache efficiency remains a major bottleneck, and heuristic policies such as extsc{LRU} can perform far worse than the offli
OpenAI's core principles outline the company's commitment to developing artificial intelligence safely and beneficially, emphasizing responsible AI development and deployment. These principles likely
arXiv:2604.21568v1 Announce Type: new Abstract: Autonomous robots deployed in mass casualty incidents (MCI) face the challenge of making critical decisions based on incomplete and noisy perceptual dat
arXiv:2602.13211v2 Announce Type: replace-cross Abstract: Compared with IP multicast, Overlay Multicast (OM) offers better compatibility and flexible deployment in heterogeneous, cross-domain networks
arXiv:2505.09971v3 Announce Type: replace Abstract: Airborne laser scanning (ALS) point cloud semantic segmentation is a fundamental task for large-scale 3D scene understanding. Fixed models deployed
arXiv:2601.22703v2 Announce Type: replace Abstract: Detecting out-of-distribution (OOD) inputs is a critical safeguard for deploying machine learning models in the real world. However, most post-hoc d
As enterprises pivot from AI experimentation to production deployment, AI storage infrastructure is emerging as the critical bottleneck determining whether massive chip investments deliver real return
arXiv:2604.20985v1 Announce Type: cross Abstract: In machine learning applications, privacy requirements during inference or deployment time could change constantly due to varying policies, regulation