Kainan Zhou
Google (United States)
Google researcher bridging LLM-based RTL verification and retrieval-augmented generation with an audit-first style.
2
papers here
2
reviewed by Evolve
1
first-author
0
corresponding
—
works · OpenAlex
—
h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-15) 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 scored: cumulative publication-volume and venue-type data are insufficient in this snapshot. This axis would measure how prolific the researcher is over time; the evidence here covers only two 2026 arXiv preprints.
Impact—
Not scored: citation counts, h-index-style metrics, and downstream uptake are unavailable, so influence cannot be estimated. This axis would measure how widely the work is cited and adopted; with 2026 preprints only, no citation record exists yet.
Focus0/100
Scores 0/100, reflecting an even 0.5/0.5 field distribution between hardware security and AI for EDA (RTL verification) and retrieval-augmented generation (information retrieval / NLP). The two 2026 preprints sit in unrelated areas with little methodological overlap, so this snapshot shows no consolidated specialization yet; the split is consistent across authorship positions, with the first-author RAG paper on one side and the middle-author RTL audit on the other.
Collaboration44/100
Scores 44/100 with 8 distinct coauthors recorded. The recurring partner list centers on Chuhong Xu, Gangzhen Qian, and Lu Yi, each appearing twice, plus Hang Xiao; the list also contains the researcher's own name, a likely name-resolution artifact. Two papers, two fields, and a modest partner set suggest small, field-specific teams rather than a large standing collaboration network.
Activity—
Not scored: the underlying publication-frequency and year-over-year activity data are insufficient in this snapshot. This axis would measure how consistently the researcher publishes over time; only two same-year (2026) entries are available here.
Reproducibility38/100
Scores 38/100, computed as a mean over the 2 reviewed papers. The RTL verification audit is rated medium readiness, helped by its preregistered design and released benchmark-plus-baseline even though its AI runs produced no valid analysis; the RAG triage paper is rated low, with a single-coded pilot and a synthetic scenario as its only evidence. Both papers are upfront about these limits.
Research summary
Kainan Zhou is a researcher affiliated with Google (United States) whose Evolve catalog profile spans two distinct fields in near-equal measure: hardware security and AI for EDA (RTL verification), and retrieval-augmented generation (information retrieval / NLP). The profile rests on two 2026 arXiv preprints. The first, “Query-Aware Source-Risk Triage for Retrieval-Augmented Generation,” is a first-author proposal introducing query-aware triage of retrieval sources with auditable scores; Evolve's review notes the evidence is a single-coded pilot plus a synthetic scenario only. The second, “Trust, but Validate the Instrument: Auditing AI-Generated RTL Verification Plans on Authored Security-Regression Proxies,” lists Zhou in a middle authorship position; it is a preregistered audit of LLM-generated RTL verification plans whose AI runs yielded no valid analysis, assessed as a valuable fail-closed benchmark and baseline without model-performance evidence. Bibliometrically, the profile records 8 distinct coauthors (collaboration 44/100), an even 0.5/0.5 field distribution (focus 0/100), and a reproducibility mean of 38 across the 2 reviewed papers. Output, impact, and activity dimensions lack sufficient data and are left unscored, making this an early-stage, preprint-based evidence base rather than an established publication record.
Collaboration pattern
The collaboration footprint is compact: 8 distinct coauthors, with Chuhong Xu, Gangzhen Qian, and Lu Yi recurring (each twice in the record) and Hang Xiao appearing once; collaboration scores 44/100. The pattern reads as small, per-project teams — one cluster around the RTL verification audit and another around the RAG triage paper — rather than one persistent lab. Note the coauthor list also includes the researcher's own name, a likely name-resolution artifact.
Research keywords
Highlights
First-author RAG triage proposal with auditable source-risk scores
Fail-closed audit of AI-generated RTL verification plans
A shared audit-and-provenance methodology across two fields
Across both 2026 papers the same instinct recurs: validate the instrument before trusting the output. The RTL work preregisters an audit of LLM-generated verification plans (mutation testing, CWE), while the RAG work pushes auditable source-risk scores and source-provenance concerns; Evolve's verdicts reward this transparency while flagging thin evidence on both sides.
Timeline
- 2026First-author arXiv preprint “Query-Aware Source-Risk Triage for Retrieval-Augmented Generation” opens the RAG line, proposing query-aware triage with auditable source-risk scores.
- 2026Middle-author preregistered audit “Trust, but Validate the Instrument” appears on arXiv, auditing AI-generated RTL verification plans against authored security-regression proxies.
- 2026Both preprints enter the Evolve catalog with review verdicts, and the profile's field distribution settles at an even 0.5/0.5 split across the two areas.
Papers in the Evolve catalog
Frequent co-authors
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 scores are computed from public bibliometric data only; output, impact, and activity axes lack sufficient data and are intentionally unscored.
- No ORCID is provided and the name may be shared by multiple researchers, so this profile is name-resolved from public bibliographic data and may mix or split identities.
- Both cataloged papers are 2026 arXiv preprints; no peer-reviewed venue, citation, funding, or career-history information is in scope.
- The frequent-coauthor list contains the researcher's own name, so recurring-partner inference should be treated as provisional.