Lingyao Li

University of Arizona

Unclaimed · AI-structured profileOpenAlex

University of Arizona researcher bridging construction robotics and HCI studies of everyday AI agents

papers here

reviewed by Evolve

first-author

corresponding

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works · OpenAlex

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h-index · OpenAlex

Bibliometric figures come from OpenAlex (2026-10-13) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.

Research profile

Output—

Not yet scored: this profile has no reliable publication-count or venue-volume data for Lingyao Li beyond the two reviewed papers. The output axis would measure total publication volume and its distribution over venues and years.

Impact—

Not yet scored: no citation counts or h-index data are available in the input. The impact axis would measure citations, citing works, and influence within construction robotics and HCI/AI agent research.

Focus0/100

Focus scores 0 on the 0-100 scale, and the raw field distribution explains why: work splits exactly evenly, 0.5 to construction robotics and robot learning and 0.5 to HCI/AI agent studies. The two catalogued papers sit one in each field, so the current evidence shows a dual-track portfolio rather than concentration; more catalogued papers would be needed to see whether one track comes to dominate.

Collaboration48/100

Collaboration scores 48, drawn from a raw count of 10 distinct coauthors in the bibliometric data. The frequent-coauthor list includes Lin He, Yanyi Chen, Haofei Sun, Min Deng, Renkai Ma, Ruyuan Wan, Xuan Lu, Fan Yang and Chen Chen, and the two catalogued 2026 papers place Lingyao Li in middle and last authorship positions respectively, consistent with moderate but genuine team-based work across both fields.

Activity—

Not yet scored: beyond two arXiv postings dated 2026, the data does not include a full publication history or yearly cadence. The activity axis would measure recent publication rate and sustained research engagement over time.

Reproducibility55/100

Reproducibility scores 55, a mean derived from 2 catalogued papers, both rated medium readiness. The robotic insertion paper is limited by thin hardware and gate validation despite a large reported error reduction (80.65% to 10.74%); the Reddit-based agent study uses careful statistics but carries a possible circularity in its fulfillment-outcome link. The mid-range score reflects this mixed, medium-readiness evidence rather than either strong or weak replication support.

Research summary

Lingyao Li is a researcher at the University of Arizona whose catalogued work spans two fields in roughly equal measure: construction robotics and robot learning on one side, human-computer interaction and AI agent studies on the other (field distribution 0.5/0.5). The Evolve catalog includes two 2026 arXiv papers. The first, a middle-author contribution, presents SAGE, a material-informed robotic insertion pipeline that couples LLM-based information extraction and retrieval-augmented generation with a finite element response model to set force limits for peg-in-hole assembly; per Evolve's review it reduces mean capacity error from 80.65% to 10.74%, though hardware and gate validation remain thin. The second, a last-author study, applies Value Sensitive Design to roughly 73,000 Reddit posts about the OpenClaw AI agent, using LLM-assisted content analysis to argue that user values attach to delegation conditions rather than outputs, while flagging a possible circularity in the fulfillment-outcome link. Bibliometrically, the profile records 10 distinct coauthors (collaboration score 48) and a reproducibility mean of 55 across two papers rated medium readiness; output, impact and activity axes lack sufficient data to score.

Collaboration pattern

The raw data counts 10 distinct coauthors, giving a collaboration score of 48. The frequent-coauthor list spans Lin He, Yanyi Chen, Haofei Sun, Min Deng, Renkai Ma, Ruyuan Wan, Xuan Lu, Fan Yang and Chen Chen. The two catalogued 2026 papers place Lingyao Li in different authorship roles — middle author on the robotics paper and last author on the HCI paper — suggesting contribution across team structures in both fields rather than a single fixed lab role.

Research keywords

robotic insertionconstruction roboticspeg-in-hole assemblyLLM information extractionretrieval-augmented generationfinite element response modelValue Sensitive Designautonomous AI agentsLLM-assisted content analysishuman-agent interactionOpenClawReddit

Highlights

SAGE: LLM-extracted material strengths set force limits for robotic insertion
In this middle-author 2026 arXiv paper, the SAGE pipeline couples LLM-based information extraction and retrieval-augmented generation with a finite element response model to turn documented material strengths into insertion force limits for peg-in-hole assembly. Evolve's review reports mean capacity error falling from 80.65% to 10.74%, while noting that hardware and gate validation remain thin; reproducibility readiness is medium.
Value-sensitive analysis of 73k Reddit posts on everyday AI agent delegation
This last-author 2026 arXiv study applies Value Sensitive Design and LLM-assisted content analysis to 73k Reddit posts about OpenClaw, finding that user values attach to the conditions under which everyday tasks are delegated to autonomous AI agents rather than to their outputs. Evolve's review credits careful statistics while flagging a risk of circularity in the fulfillment-outcome link; readiness is medium.

Timeline

2026First catalogued construction robotics paper: SAGE applies LLM extraction and a finite element response model to set force limits for robotic peg-in-hole insertion (middle author, arXiv).
2026Last-author HCI study of 73k Reddit posts on OpenClaw finds that values around everyday AI-agent delegation center on delegation conditions rather than outputs.
2026Bibliometric snapshot: work splits evenly between construction robotics/robot learning and HCI/AI agent studies, with 10 distinct coauthors and a reproducibility mean of 55.

Papers in the Evolve catalog

Frequent co-authors

Lin He×1 Yanyi Chen×1 Haofei Sun×1 Min Deng×1 Renkai Ma×1 Ruyuan Wan×1 Xuan Lu×1 Fan Yang×1 Chen Chen×1 Lingyao Li×1

About this profile

This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.

Bibliometric dimensions are computed from OpenAlex-based public bibliometric data; output, impact and activity axes lack sufficient data and are left unscored.
No ORCID or country is provided, and 'Lingyao Li' is a common name; this profile is name-resolved from public bibliographic data and may merge or split identities.
Only the two Evolve-reviewed papers are covered here; other publications by this author are not reflected in the scores or narratives.
The reproducibility score rests on only 2 papers, both rated medium readiness, so the 55 mean is a thin estimate.