AI researcher mapping strategic reasoning across chess studies of humans, engines, and language models
1
papers here
1
reviewed by Evolve
1
first-author
0
corresponding
—
works · OpenAlex
—
h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-09-21) 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 snapshot indexes only one paper, so output-volume signals such as publication count, venues, and yearly productivity are insufficient. This axis would measure total research output and how it is distributed across time and publication venues.
Impact—
Not yet scored: no citation counts, h-index, or other influence indicators appear in the provided data. This axis would measure how widely the work is cited and taken up by other researchers, fields, and follow-up studies.
Focus100/100
Focus scores a perfect 100 because the field distribution of indexed output contains exactly one field, artificial intelligence. With a single indexed paper, concentration is trivially maximal; the score confirms thematic unity of the current record — strategic reasoning in chess spanning humans, engines, and language models — rather than proven long-term breadth, and may diversify or consolidate as output accumulates.
Collaboration14/100
The collaboration score of 14 comes from real bibliometric data showing just one distinct co-author in the indexed snapshot — Remo Pareschi, listed as the frequent collaborator — attached to a single first-authored paper. A one-person co-author pool yields a minimal collaboration footprint; this reflects the narrow evidence base rather than a demonstrated preference for solo work, and the score should be re-read once more papers enter the catalog.
Activity—
Not yet scored: only a single 2026 paper exists in this snapshot, so a year-by-year publishing rhythm cannot be established. This axis would measure ongoing research activity and output cadence across recent years.
Reproducibility55/100
Reproducibility averages 55 across the one paper assessed (reproPapers: 1). Evolve rated the 2026 mapping's reproducibility readiness as medium: the systematic method and its defined corpus of 84 studies support replication, but the review flags single-coder coding as a reliability limitation — a classic reproducibility weak point that multi-coder validation would directly address.
Research summary
Paolo Ciancarini is an artificial-intelligence researcher whose Evolve profile currently rests on a single, substantial first-authored output: a 2026 arXiv systematic mapping study asking what counts as strategic reasoning. The paper maps 84 chess studies published between 2023 and 2026, organizing a fast-moving literature into strands on human players, classical chess engines, reinforcement-learning agents, large language models, and hybrid human-AI systems. Evolve's review judges the map valuable for the research gaps it surfaces, while flagging that single-coder coding limits the reliability of its classifications; the paper's reproducibility readiness is rated medium. Bibliometrically the picture is narrow but sharply concentrated: the indexed output falls entirely under artificial intelligence, giving a perfect focus score of 100, while the collaboration score of 14 reflects just one distinct co-author in the snapshot, with Remo Pareschi the recurring partner. No reliable output-volume, citation-impact, or year-by-year activity metrics are available yet, so those radar axes are left unscored. The profile is name-resolved from public bibliographic data linked to his ORCID and should be read as an early-stage, evidence-thin record rather than a settled career summary.
Collaboration pattern
Bibliometric evidence shows a compact collaboration pattern: one distinct co-author (score 14) in the indexed snapshot, with Remo Pareschi listed as the frequent collaborator on the 2026 mapping, where Ciancarini is first author. Two-person authorship suits a synthesis paper but offers no signal about larger team networks, supervision, or cross-institutional work; those remain unmeasured until additional indexed output appears.
Research keywords
Highlights
Systematic map of 84 chess studies (2023–26)
Surfaces gaps between humans, engines, and LLMs
Methodological caution: single-coder coding
Timeline
- 2023Start of the evidence window: per the paper's stated 2023–26 corpus span, the earliest of the 84 chess studies covered by his 2026 mapping date from this period.
- 2026Publishes, as first author on arXiv, the systematic mapping 'What Counts as Strategic Reasoning?', covering 84 chess studies across humans, engines, RL, LLMs, and hybrids.
- 2026Evolve reviews the mapping: valued for its gap analysis, reproducibility readiness rated medium (reproMean 55), with single-coder coding noted as the main reliability limitation.
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 rest on a very small base — one indexed paper and one distinct co-author — so every radar value is fragile and likely to move.
- Output, impact, and activity dimensions are unscored because citation and productivity data are absent from the provided snapshot.
- Institution and country are not part of the input data; identity is name-resolved from public bibliographic records linked to ORCID 0000-0002-7958-9924.
- Field classification derives from a single paper tagged 'artificial intelligence', so the perfect focus score reflects data scarcity, not verified long-term specialization.