Knowledge catalogue
arXiv:2607.23292v1 Announce Type: cross Abstract: Deep learning-based visual-infrared fused face detection models are increasingly deployed across a wide range of applications, yet they remain suscept
arXiv:2607.22568v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed on mobile and embedded devices to improve privacy and reduce network latency. Yet on-device infer
arXiv:2607.23704v1 Announce Type: cross Abstract: The deployment of embodied agents in self-driving laboratories could accelerate scientific discovery, yet their reliability is constrained by the irre
Learn how to architect and deploy a production-ready multi-agent AI system using LangGraph for workflow orchestration and Strands for agent reasoning on Amazon Bedrock AgentCore. This post walks throu
arXiv:2607.22556v1 Announce Type: new Abstract: Continual learning (CL) is essential for small language models (SLMs) to adapt to evolving real-world needs in resource-constrained deployments. However
@mitsuhiko Turns out the 'third-party provider' with the sandbox that was used for the attack was Modal, though they blame one of their customers for deploying an endpoint without authentication: http
arXiv:2607.23047v1 Announce Type: cross Abstract: Mixed-precision quantization improves the accuracy of post-training quantization by allocating higher bitwidths to sensitive layers, but existing meth
arXiv:2602.05547v2 Announce Type: replace-cross Abstract: RL-based post-training with GRPO is widely used to improve large language models on individual reasoning tasks. However, real-world deployment
arXiv:2510.17168v2 Announce Type: replace Abstract: As Large Language Models (LLMs) are increasingly deployed as social agents and trained to produce humor and irony, a question emerges: when encounte
arXiv:2607.23365v1 Announce Type: cross Abstract: Artificial intelligence (AI) systems are increasingly deployed in high-stakes domains such as healthcare, autonomous driving, finance, and education.
arXiv:2607.22621v1 Announce Type: new Abstract: While large language models (LLMs) enable strong question answering (QA), budgeted deployment is complicated by nondeterminism and heterogeneous resourc
arXiv:2607.23194v1 Announce Type: new Abstract: Scene Text Recognition (STR) models are trained almost exclusively on word crops of at most 25 characters, yet real deployments (signage, product labels
arXiv:2607.23680v1 Announce Type: new Abstract: Semantic segmentation in agricultural imagery is often evaluated under in-domain protocols, yet practical deployment requires robustness to appearance p
arXiv:2607.22797v1 Announce Type: cross Abstract: Trustworthy deployment of AI-based diagnosis in safety-critical mechanical systems hinges on validation: whether a prediction can be checked against p
arXiv:2607.23121v1 Announce Type: cross Abstract: Dynamic Product Ads (DPA) require retrieving relevant items from multi-million product catalogs, balancing two competing objectives: retargeting (re-s
arXiv:2607.23346v1 Announce Type: new Abstract: Modern deep neural networks are potent catalysts for scientific and industrial impact, yet excessive parameter counts impede deployment in low-compute s
arXiv:2607.23512v1 Announce Type: new Abstract: Offensive language detection models generally suffer performance degradation when deployed across datasets and across languages, yet most existing studi
arXiv:2607.22587v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong performance across diverse tasks but their deployment is constrained by the memory and compute cost of their
arXiv:2607.23888v1 Announce Type: cross Abstract: In the United States, artificial intelligence (AI) is rapidly deployed amid limited federal regulation. With courts become a recurring forum in which
Traditional data replication techniques often struggle to deliver the data freshness that modern workflows require. To help organizations overcome this challenge, SAP and Google Cloud are announcing t
arXiv:2607.21958v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed, reliable and efficient mechanisms for distinguishing AI-generated text from human-written c
arXiv:2607.21615v1 Announce Type: cross Abstract: The rapid deployment of generative AI has amplified the critical need for Training Data Attribution to ensure transparency and accountability. However
arXiv:2601.12291v2 Announce Type: replace-cross Abstract: Scalable and maintainable maps are fundamental to large-scale navigation and the long-term deployment of robots in real-world environments. Ho
arXiv:2607.22465v1 Announce Type: cross Abstract: Routing to select large language models (LLMs) with different cost-quality trade-offs has become a fundamental deployment feature of enterprise AI. Ex
arXiv:2607.21985v1 Announce Type: cross Abstract: The increasing deployment of large language models (LLMs) has magnified the computational and memory bottlenecks of autoregressive decoding, where low
As AI expands into robotics and other real-world applications, the focus is shifting toward integrated platforms that simplify development and deployment. Open ecosystems and standardized architecture
The Gemma series is an amazing set of highly performant open-weight models. They have proven extremely effective in industrial settings where site-deployed agents need exactly this as a base for domai
arXiv:2607.18317v2 Announce Type: replace-cross Abstract: We present TTSYoruba, a rule-based concatenative diphone speech synthesizer for Yoruba, deployed at online as part of the YorubaName.com open
arXiv:2607.21076v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) require huge memory and computational costs, which limits their practical deployment. Post-training quantizatio
arXiv:2607.20852v1 Announce Type: new Abstract: Visible tests are a common gate for LLM-generated code, but passing them does not certify specification correctness. We study a deployment-like monitori
arXiv:2607.20526v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in settings where fluent but incorrect answers can be costly. In these settings, accuracy alone i
arXiv:2607.20490v1 Announce Type: new Abstract: Edge Intelligence has emerged as a key paradigm for enabling real-time applications in smart cities by shifting computation from centralized cloud data
arXiv:2607.20561v1 Announce Type: cross Abstract: LoRA adapters provide an efficient way to specialize a pretrained model for many downstream tasks, but deploying one adapter per task requires adapter
arXiv:2607.21243v1 Announce Type: new Abstract: AI-enabled visual perception systems are increasingly deployed in intelligent transportation infrastructure and autonomous vehicle related applications.
arXiv:2607.20531v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed over Model Context Protocol (MCP) servers, yet the benchmarks used to evaluate them score th
arXiv:2607.21227v1 Announce Type: new Abstract: Force-conditioned reinforcement learning (RL) enables tight-clearance assembly under a commanded force ceiling, but practical deployment requires determ
arXiv:2607.21471v1 Announce Type: new Abstract: Dynamic-scene reconstruction is almost always evaluated inside the observed time window, yet deployment settings such as AR overlays, robot interaction,
arXiv:2607.20460v1 Announce Type: cross Abstract: Current full-duplex (FD) spoken dialogue systems can produce fluid interactions, yet it remains unclear whether they can adapt their turn-taking behav
arXiv:2607.20734v1 Announce Type: new Abstract: As LLMs become more capable, they are increasingly deployed as collaborative agents, taking on user-delegated tasks through iterative interaction. Yet g
Enterprise AI ambition is far outpacing the readiness of the enterprise AI stack, and the growing rift between proof-of-concept deployments and production-scale agent operations is where most organiza
Model quantization has become essential for efficient large language model deployment, yet existing approaches involve clear trade-offs: methods such as GPTQ and AWQ achieve practical compression but
arXiv:2607.20528v1 Announce Type: new Abstract: Online recommendation platforms increasingly use Large Language Models (LLMs) to extract structured features from ad creatives. While deploying a single
arXiv:2607.20558v1 Announce Type: cross Abstract: AI Assistants are increasingly deployed in high-stakes settings, such as healthcare or government services. Yet their real-world behavior remains poor
arXiv:2607.21151v1 Announce Type: new Abstract: As Video Large Language Models are increasingly deployed in real-world applications, ensuring their safety alignment has become critical. Counterintuiti
arXiv:2602.09345v3 Announce Type: replace-cross Abstract: AI agents are increasingly deployed in multi-tenant cloud environments, where they execute diverse tool calls within sandboxed containers, eac
At @NVIDIAAI we continue to push open data, techniques and models forward because we know that every organization needs the freedom to build and deploy AI in their own way. We're now the biggest insti
arXiv:2607.19353v1 Announce Type: new Abstract: Confidential computing is becoming a practical deployment requirement for AI inference workloads that process sensitive inputs or protect proprietary mo
arXiv:2607.19382v1 Announce Type: cross Abstract: Deep learning models have shown strong potential for time series forecasting, yet their deployment in real-world environmental monitoring remains chal
arXiv:2507.05458v2 Announce Type: replace Abstract: For effective real-world deployment, robots should adapt to human preferences, such as balancing distance, time, and safety in delivery routing. Act
arXiv:2607.20293v1 Announce Type: new Abstract: Diffusion policies achieve strong visuomotor control by iteratively denoising action chunks, but repeated denoising makes real-time deployment computati
arXiv:2607.19349v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as always-on online services, making efficient LLM serving a critical systems challenge. Achievin
arXiv:2607.20277v1 Announce Type: new Abstract: Machine learning models achieve high predictive accuracy in regression tasks, but their deployment in safety-critical and regulated domains requires int
arXiv:2607.19811v1 Announce Type: new Abstract: The Segment Anything Model 2 (SAM2) has advanced temporal promptable segmentation, yet its deployment remains hindered by heavy memory cross-attention o
arXiv:2607.19064v2 Announce Type: replace-cross Abstract: Large-scale visual generators are increasingly capable but costly to train, fine-tune, and deploy. We introduce Mage-Flow, a compact 4B-scale
arXiv:2607.19695v1 Announce Type: new Abstract: Robots deployed in delivery, campus, and emergency-response settings often need to navigate from buildings to streets within a single continuous episode
arXiv:2607.20002v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) have emerged as general-purpose models for time series analysis, but pretraining alone is often insufficient for
arXiv:2602.04401v4 Announce Type: replace Abstract: Visual Place Recognition (VPR) is a key component for localization in Global Navigation Satellite System (GNSS)-denied environments, but its perform
arXiv:2607.19701v1 Announce Type: new Abstract: VLMs are increasingly deployed in AD systems, creating an urgent need for rigorous safety evaluation under rare yet safety-critical scenarios. Among the
arXiv:2607.17890v2 Announce Type: replace Abstract: Concept erasure aims to remove semantic concepts from a trained generative model and is increasingly important for responsible AI deployment. Howeve
arXiv:2607.19524v1 Announce Type: cross Abstract: Federated learning (FL) offers a promising approach to privacy-preserving clinical risk prediction, but its deployment remains limited by restricted d