Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.32781

Every Edge Needs a Quote: Building a Quote-Grounded Knowledge Graph of Healthcare Software Adoption with Language-Model Agents

Juan Felipe Duran·Arthur Tobler·Martin Vallières
View original source ↗Read source document ↗Export metadata ↓

Abstract

Hospitals regularly buy software that clinicians and patients depend on. Because these purchases are costly, hard to reverse and can affect patient care, buyers look for evidence of which peer institutions already use a product and which systems it connects to. This evidence is public but scattered across vendor websites, press releases and third-party pages. Language-model agents can now collect it at low cost, but the correctness of the extracted claims is difficult to establish, and a wrongly listed customer can mislead buyers and misrepresent an organisation. We present a pipeline that builds a knowledge graph of customer and integration relations from the open web under one rule: every edge must be supported by a verbatim quote from its source page. Pages come from a deterministic crawl of each vendor's site and a budgeted ReAct agent that searches the rest of the web. A permissive extractor proposes relations with quotes, and a layered grounding stage decides which to keep, using a referent step that reads the whole page when a quote names only one side, deterministic checks, and a panel of five model calls voting on nine criteria. On 61 vendors, the pipeline reaches 0.94 precision and 0.71 recall against a page-level key covering the 6,428 candidate relations proposed by its extractor, whose verdicts were drafted by one model and re-checked with a second. Against the golden reference, the marketplace's own curated list, it scores lower, but 89% of the relations it keeps that the reference lacks are stated on a page. A confidence score built from the panel's votes keeps more correct relations than the fixed rule at the same precision, and reaches 0.90 precision at 0.90 recall. We also discuss how narrow, bounded model calls, string-matched evidence and voting address long-context degradation and run-to-run variance.

Explore related subjects

cs.CLcs.AIcs.IR

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Juan Felipe Duran, Arthur Tobler, Martin Vallières. 2026-10-23. Every Edge Needs a Quote: Building a Quote-Grounded Knowledge Graph of Healthcare Software Adoption with Language-Model Agents. https://arxiv.org/abs/2610.32781

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

A survey of diversity quantification in natural language processing: The why, what, where and how

The concept of diversity received increasing attention in natural language processing (NLP) in recent years. It became an advocated property of datasets and systems, with many measures used to quantify it, but also many cross-paper inconsistencies. To address this fragmentation, we take inspiration from other scientific fields where diversity has been more thoroughly conceptualized. We build upon (Sarkar, 2010) and (Stirling, 2007), two unified cross-domain frameworks inspired from ecology, economy and other domains. The first one advocates explicit justification of diversity endorsement. The latter distinguishes three dimensions of diversity: variety, balance, and disparity. Surveying over 300 recent diversity-related papers from ACL Anthology, we propose an NLP-specific framework with 4 perspectives: why diversity is important, what diversity is measured on, where it is measured, and how. We find that in NLP richness (the simplest variety) and distance averaging (the simplest disparity) are the most prevalent measure families, and that pure balance measures are nearly absent. Moreover, half of the measures used in NLP do not conform to Stirling's framework. Our analysis increases comparability of approaches to diversity in NLP, allowing us to formulate recommendations for the field. We recommend that the field systematically use a unified terminology, and that it engage in debates about the desirable properties of diversity measures as well as about the normative assumptions behind their use.

cs.CL↗
arXiv2026

What MLLMs Learn about When they Learn about Multimodal Reasoning

Evaluation of multimodal reasoning models is typically reduced to a single accuracy score, implicitly treating reasoning as a unitary capability. We introduce MathLens, a benchmark of textbook-style geometry problems that exposes this assumption by operationally decomposing performance into perception, reasoning, and multimodal-specific components. Each problem is derived from a symbolic specification and accompanied by visual diagrams, text-only variants, multimodal questions, and targeted perceptual probes, enabling controlled measurement of each component. Using this decomposition, we show that common training strategies are associated with systematically different capability profiles that are invisible under aggregate accuracy. Across the models we study, reinforcement learning primarily improves perceptual grounding and robustness to diagram variation, while our analysis suggests that textual SFT yields gains through reflective reasoning. In contrast, as perception and reasoning improve, a growing fraction of remaining errors fall outside these components and are categorized as multimodal-specific. These results suggest that apparent progress in multimodal reasoning reflects shifting balances among subskills rather than uniform advancement, motivating evaluation beyond scalar accuracy.

cs.CL↗
arXiv2026

EvalCards: A Framework for Standardized Evaluation Reporting

Evaluation reports guide the use of general-purpose AI (GPAI) models, yet inconsistent reporting often leaves essential questions unanswered. Through a review of prior work, we identify three central issues in evaluation reporting practices today, reproducibility, accessibility, and accountability, and examine how existing reporting artifacts fall short in addressing them. We introduce the EvalCards framework, outlining its key dimensions and presenting a preliminary format for concise evaluation summaries. We conclude by discussing how the framework can guide the future development of evaluation reporting standards.

cs.CL↗