Search: “applications”

arXiv:2605.28345v1 Announce Type: new Abstract: Progress in Prognostics and Health Management (PHM) is hindered by the lack of standardized and reusable evaluation practices across tasks, datasets, an

arXiv:2605.28597v1 Announce Type: cross Abstract: This position paper argues that the AI/ML community should stop overclaiming and retire the label 'positive backdoor,' and instead treat trigger-activ

arXiv:2504.12747v2 Announce Type: replace Abstract: The rapid advancement of diffusion models and personalization techniques has made it possible to recreate individual portraits from just a few publi

arXiv:2605.27942v1 Announce Type: cross Abstract: Principal component analysis (PCA) is traditionally implemented through a covariance or kernel matrix, leading-eigenvector extraction, and hard rank-k

arXiv:2508.09449v2 Announce Type: replace Abstract: Reference-based Super Resolution (RefSR) improves upon Single Image Super Resolution (SISR) by leveraging high-quality reference images to enhance t

arXiv:2512.12887v3 Announce Type: replace Abstract: 3D medical image classification is essential for modern clinical workflows. Medical foundation models (FMs) have emerged as a promising approach for

arXiv:2509.13177v2 Announce Type: replace Abstract: Continuum robots are advancing bronchoscopy procedures by accessing complex lung airways and enabling targeted interventions. However, their develop

arXiv:2509.25582v3 Announce Type: replace Abstract: In-context reinforcement learning (ICRL) is an emerging RL paradigm where an agent, after pretraining, can adapt to out-of-distribution test tasks w

arXiv:2510.02329v2 Announce Type: replace-cross Abstract: Speculative decoding accelerates LLM inference by verifying candidate tokens from a draft model against a larger target model. Recent judge de

arXiv:2605.28533v1 Announce Type: new Abstract: We introduce a testing-by-betting framework that leverages predictions on unlabeled data to enhance the power of sequential hypothesis testing. Given li

This episode of the Latent Space podcast features Walden Yan from Cognition and Cole Murray from OpenInspect discussing the emerging paradigm of asynchronous AI agents—autonomous systems that operate

arXiv:2508.14082v3 Announce Type: replace Abstract: Semi-supervised regression (SSR), which aims to predict continuous scores for samples while reducing the reliance on large-scale labeled data, has r

arXiv:2601.21309v4 Announce Type: replace Abstract: The increasing scale of graph datasets has significantly improved the performance of graph representation learning methods, but it has also introduc

arXiv:2605.28733v1 Announce Type: new Abstract: Product images strongly influence consumer decision-making in online marketplaces. Empowered by multimodal contrastive learning, generative AI can outpu

arXiv:2605.28364v1 Announce Type: cross Abstract: Reinforcement learning with multinomial logistic (MNL) function approximation has become an important framework due to its flexibility and broad appli

arXiv:2605.28422v1 Announce Type: cross Abstract: Latent reasoning enables reasoning over continuous hidden states rather than explicit tokens, avoiding the language bottleneck and inference overhead

arXiv:2605.21743v2 Announce Type: replace Abstract: Conversation logs from AI platforms are increasingly used to measure occupational exposure to artificial intelligence, but the users observed in the

arXiv:2605.27338v1 Announce Type: new Abstract: ASP(Q) extends Answer Set Programming (ASP) with Quantifiers over answer sets. In this paper we focus on the class of ASP(Q) programs with two quantifie

arXiv:2605.26936v1 Announce Type: new Abstract: Underwater robotics has advanced significantly over recent decades. however, the development of miniaturized underwater robots remains limited by low en

arXiv:2605.26747v1 Announce Type: new Abstract: Large Language Models (LLMs) have brought huge improvements to Artificial Intelligence (AI), which can be applied to general-purpose tasks. However, the

I saw a developer asking on Reddit if there was any “sane way” to manage Cloud Run cold starts for AI across multiple regions. They were experiencing startup latencies of up to 20 seconds, a frustrati

arXiv:2509.21167v2 Announce Type: replace-cross Abstract: Most existing methods for concept unlearning in text-to-image diffusion models minimize a mean squared error (MSE) loss between the denoiser o

arXiv:2605.26925v1 Announce Type: cross Abstract: We present a Multi-task Soft Actor-Critic (SAC) Reinforcement Learning framework designed for open-system quantum control across diverse Hamiltonians,

arXiv:2605.27174v1 Announce Type: cross Abstract: Artificial intelligence is increasingly being integrated into professional audio production workflows, yet a gap persists between the tools developers

arXiv:2605.26149v1 Announce Type: cross Abstract: Open surface components prevail in real industrial 3D content and support rendering, physical simulation and geometric editing. Garments serve as a ty

arXiv:2601.05028v2 Announce Type: replace Abstract: Equivariance is a powerful inductive bias in neural networks, improving generalisation and physical consistency. Recently, however, non-equivariant

arXiv:2502.14321v3 Announce Type: replace-cross Abstract: Large language model-based multi-agent systems have recently gained significant attention due to their potential for complex, collaborative, a

arXiv:2605.26654v1 Announce Type: cross Abstract: Reinforcement learning (RL) often has a hierarchical structure, where an upper-level (UL) learner selects model parameters and a lower-level (LL) deci

arXiv:2605.26577v1 Announce Type: cross Abstract: Learning-based methods for synthesizing controllers have gained popularity due to their high expressiveness and strong empirical performance. However,

arXiv:2410.00995v3 Announce Type: replace Abstract: The automatic synthesis of analog circuits presents significant challenges. Most existing approaches formulate the problem as a single-objective opt

arXiv:2601.21845v2 Announce Type: replace Abstract: Meta reinforcement learning (RL) allows agents to leverage experience across a distribution of tasks on which the agent can train at will, enabling

arXiv:2605.26440v1 Announce Type: new Abstract: The rapid advancement of Large Language Models (LLMs) has outpaced the scalability of traditional evaluation benchmarks, which remain heavily dependent

This post highlights an open-source project built using LangChain and Langgraph that implements thoughtful memory management and organization features. The project demonstrates best practices for stru

arXiv:2605.26385v1 Announce Type: cross Abstract: Large-scale search, recommendation, and retrieval-augmented generation (RAG) systems typically employ a two-stage architecture: an early-stage ranker

arXiv:2605.26569v1 Announce Type: new Abstract: We present Distribution-aware Conformal Prediction (DCP), a unified framework integrating probabilistic predictors like Monte Carlo dropout, deep ensemb

arXiv:2505.18603v2 Announce Type: replace Abstract: Document understanding aims to perform question answering and information extraction over document images, where the visual content is highly inform

arXiv:2605.26656v1 Announce Type: new Abstract: Multimodal large language models are typically trained end-to-end to predict ground-truth answers, yet supervision signals are applied exclusively to te

arXiv:2605.26119v1 Announce Type: cross Abstract: Industrial Edge AI programs often begin with the model and only later confront the platform. That sequencing is attractive because it allows early dem

arXiv:2605.27195v1 Announce Type: new Abstract: Chart-to-data extraction with vision-language models (VLMs) is increasingly evaluated on benchmarks that show diminishing headroom (frontier VLMs exceed

arXiv:2605.26305v1 Announce Type: new Abstract: This paper details two novel frameworks for developing autonomous, agentic AI in scientific workflows. Both systems leverage a hybrid Local Body, Remote

arXiv:2605.27360v1 Announce Type: cross Abstract: Cellular research and development (R&D) is throttled by six structural processes that each consume months of manual engineering work per iteration: (i

arXiv:2605.26620v1 Announce Type: new Abstract: Natural language conveys information at varying levels of granularity, from fine-grained references to broad descriptions. While granularity is fundamen

arXiv:2605.27249v1 Announce Type: new Abstract: An effective method of teaching across disciplines is to provide examples of high-quality work. However, an example may be significantly different from

arXiv:2512.17090v2 Announce Type: replace-cross Abstract: Squared tensor networks (TNs) and their extension as computational graphs--squared circuits--have been used as expressive distribution estimat

arXiv:2605.26520v1 Announce Type: cross Abstract: While vision-language models (VLMs) have exhibited multi-turn visual reasoning capabilities, their reasoning trajectories remain relatively shallow an

It's crazy that this is even possible today. It inspired me to build my own self-improving coding agent with simple read, write, bash,... I already used the coding agent to build an entire production-