Search: “tools”

arXiv:2605.20635v1 Announce Type: new Abstract: This paper proposes a general machine learning framework called the localization method, which is fundamentally built on two core concepts: localization

arXiv:2605.20461v1 Announce Type: new Abstract: Accurate polyp size stratification guides surveillance decisions, with lesions larger than 5 mm typically requiring closer follow-up. However, monocular

arXiv:2605.19988v1 Announce Type: cross Abstract: Documentation has long guided computer system tuning by distilling expert knowledge into per-parameter recommendations. Yet such guides capture only w

arXiv:2605.18880v1 Announce Type: cross Abstract: Rare diseases such as inborn errors of immunity (IEI) require early diagnosis to prevent end organ damage and improve quality of life. Hurdles in acce

arXiv:2605.19595v1 Announce Type: cross Abstract: The inspection of electrical power line insulators is essential for ensuring grid reliability and preventing failures caused by damaged or degraded in

arXiv:2605.19366v1 Announce Type: new Abstract: Environmental science plays a pivotal role in safeguarding ecosystems, a domain driven by large-scale, heterogeneous data. In the big data era, artifici

arXiv:2603.14918v2 Announce Type: replace-cross Abstract: Model-based approaches for (bio)process systems often suffer from incomplete knowledge of the underlying physical, chemical, or biological law

arXiv:2605.20176v1 Announce Type: new Abstract: Large language models (LLMs) and agentic systems have shown promise for clinical decision support, but existing works largely assume that evidence has a

arXiv:2605.18827v1 Announce Type: cross Abstract: Multiple-choice QA benchmarks usually evaluate small language models (SLMs) as direct answerers, but deployed language-model systems increasingly rely

arXiv:2605.19099v1 Announce Type: new Abstract: We introduce DecisionBench, a benchmark substrate for emergent delegation in long-horizon agentic workflows. The substrate fixes a task suite (GAIA, tau

arXiv:2512.16856v2 Announce Type: replace Abstract: AI safety and alignment research has predominantly been focused on methods for safeguarding individual AI systems, resting on the assumption of an e

arXiv:2602.23622v2 Announce Type: replace-cross Abstract: Significant progress has been made in the field of Instruction-based Image Editing Models (IIEMs). However, while these models demonstrate pla

arXiv:2602.04381v2 Announce Type: replace-cross Abstract: Real-time polyp segmentation is essential for early colorectal cancer detection, yet clinical deployment remains blocked by GPU dependency. We

arXiv:2605.19743v1 Announce Type: new Abstract: Large Language Model (LLM) agents are increasingly applied to engineering design tasks, yet existing evaluation frameworks do not adequately address mul

arXiv:2605.18918v1 Announce Type: cross Abstract: Modern AI assistants are agentic. To answer a single user request, the underlying language model pulls in information from many sources, such as web s

arXiv:2605.18854v1 Announce Type: new Abstract: Coding agents accumulate extensive context during long-running tasks, yet fixed context windows force practitioners to choose between truncation and tas

arXiv:2605.20005v1 Announce Type: new Abstract: Fine-tuning large language models on new data improves task performance but degrades capabilities learned during pretraining, a phenomenon known as cata

arXiv:2605.20006v1 Announce Type: new Abstract: Geospatial reasoning requires solving image-grounded problems over the complex spatial structure of a scene. However, developing this capability is hind

arXiv:2605.19733v1 Announce Type: cross Abstract: Community detection is a central problem in graph analysis, with applications ranging from network science to graph signal processing. In recent years

arXiv:2605.19038v1 Announce Type: cross Abstract: The rapid advancement of autonomous driving (AD) technologies has outpaced the development of robust safety evaluation methods. Conventional testing r

arXiv:2605.18838v1 Announce Type: cross Abstract: Scaling laws predict loss from compute but not how capabilities interact. We measure the coupling between reasoning and truthfulness across 63 base mo

arXiv:2605.19723v1 Announce Type: cross Abstract: Mathematical reasoning is essential for problem-solving in education, science, and industry, serving as a crucial benchmark for evaluating artificial

arXiv:2605.19792v1 Announce Type: new Abstract: Visually-grounded language models (VLMs) are highly effective in linking visual and textual information, yet they often struggle with basic classificati

arXiv:2605.20183v1 Announce Type: new Abstract: Video generation is rapidly evolving from single-shot synthesis to complex multi-shot audio-video (MSAV) narratives to meet real-world demands. However,

arXiv:2605.18825v1 Announce Type: new Abstract: Prefix caching is a key optimization in Large Language Model (LLM) serving, reusing attention Key-Value (KV) states across requests with shared prompt p

arXiv:2605.19276v1 Announce Type: new Abstract: In recent years, the field of artificial intelligence has undergone a paradigm shift from task-specific small-scale models to general-purpose large lang

arXiv:2605.19769v1 Announce Type: new Abstract: We present OpenComputer, a verifier-grounded framework for constructing verifiable software worlds for computer-use agents. OpenComputer integrates four

arXiv:2605.18812v1 Announce Type: cross Abstract: Modern NLP and LLM systems are pipelines: named entity recognition (NER) -> entity disambiguation (NED) -> entity typing, retrieval-augmented generati

arXiv:2605.19077v1 Announce Type: cross Abstract: Task-oriented dialogue systems -- handling transactions, reservations, and service requests -- require predictable behavior, yet the moderately-sized

arXiv:2605.18794v1 Announce Type: cross Abstract: Decoupling is a powerful modeling paradigm for representing multivariate functions as compositions of linear transformations and univariate nonlinear

arXiv:2605.18857v1 Announce Type: cross Abstract: For most of the history of information retrieval (IR), search results were designed for human consumers who could scan, filter, and discard irrelevant

arXiv:2605.19064v1 Announce Type: cross Abstract: Workforce transformations are difficult to forecast and costly to mismanage. In particular, the integration of artificial intelligence into knowledge

arXiv:2605.18831v1 Announce Type: cross Abstract: Cold-chain storage limits access to insulin for hundreds of millions of people; a thermally protective patch polymer could help, but the design space

arXiv:2605.18847v1 Announce Type: cross Abstract: Do transformers, when trained on sequential reasoning traces, build internal models of the underlying task? And if so, does the structure of those int

arXiv:2602.09023v4 Announce Type: replace Abstract: Despite strong generalization capabilities, Vision-Language-Action (VLA) models remain constrained by the high cost of expert demonstrations and lim

arXiv:2603.06740v2 Announce Type: replace-cross Abstract: Protein language models (pLMs) have shown strong potential for zero-shot prediction of missense variant effects, yet systematic benchmarking o

arXiv:2605.19908v1 Announce Type: new Abstract: Authorship attribution models fine-tuned with the same pretrained encoder, data, and loss can differ four-fold in performance depending only on their sc

arXiv:2603.22453v2 Announce Type: replace Abstract: Community Notes have emerged as an effective crowd-sourced mechanism for combating online deception on social media platforms. However, its reliance

arXiv:2503.14346v3 Announce Type: replace Abstract: Multi-map Sparse Monocular visual Simultaneous Localization and Mapping applied to monocular endoscopic sequences has proven efficient to robustly r

arXiv:2512.04329v2 Announce Type: replace Abstract: Reusing existing neural-network components is central to research efficiency, yet discovering, extracting, and validating such modules across thousa

arXiv:2605.18250v1 Announce Type: cross Abstract: Many dynamical systems can be described in terms of structured flows combining source/sink behavior, cyclic dynamics, and topology-constrained transpo

arXiv:2605.17380v1 Announce Type: new Abstract: We present the Agentic AI Detection and Response (ADR) system, the first large-scale, production-proven enterprise framework for securing AI agents oper

arXiv:2510.03879v3 Announce Type: replace-cross Abstract: Translating C to memory-safe languages, like Rust, prevents critical memory safety vulnerabilities that are prevalent in legacy C software. Ev

arXiv:2601.15630v2 Announce Type: replace Abstract: Healthcare organizations are beginning to embed agentic AI into routine workflows, including clinical documentation support and early-warning monito

arXiv:2511.06316v3 Announce Type: replace Abstract: In low- and middle-income countries, public safety and urban planning initiatives frequently face a critical shortage of accurate, location-specific

arXiv:2605.18529v1 Announce Type: new Abstract: The alignment of Large Language Models (LLMs) for complex reasoning heavily relies on Reinforcement Learning with Verifiable Rewards (RLVR). However, st