Abhay Anand
University of California San Diego · United States
UC San Diego researcher bridging world-model reinforcement learning and autonomous LLM research agents
2
papers here
2
reviewed by Evolve
0
first-author
0
corresponding
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works · OpenAlex
0
h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-16) 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—
Output cannot be scored yet: OpenAlex shows only 3 works recorded in 2026 and no reliable lifetime publication volume for this name, so the axis — which measures total scholarly production and its growth trajectory — remains without sufficient data.
Impact—
Impact is not scored: the record shows an h-index of 0 and an i10-index of 0, meaning citation-based influence cannot yet be measured meaningfully. This axis would capture citation accumulation, uptake by other researchers, and scholarly reach over time.
Focus0/100
The focus score is 0, reflecting a field distribution split exactly 0.5/0.5 between two distinct areas: machine learning (world models and model-based reinforcement learning) and artificial intelligence (autonomous LLM research agents). Topic counts reinforce this even split — reinforcement learning in robotics and AI-based problem solving and planning (2 papers each) sit alongside single-paper topics such as GANs and image synthesis and multimodal applications. No single specialization yet dominates the record.
Collaboration48/100
The collaboration score is 48, computed from 10 distinct coauthors on record. Frequent collaborators include Akash Anand, Yash Vishe, Xinle Yu, Fan Bai, Kaiser Sun, Hengshuo Miao, Zhongyan Luo, Kun Zhou, and Zhen Wang, indicating a compact, recurring circle rather than a broad rotating network. Both reviewed 2026 papers list the researcher in a middle author position without corresponding-author status.
Activity40/100
The activity score is 40, based on 3 works recorded in the last three years, all dated 2026 in the OpenAlex counts-by-year data. Both Evolve-reviewed papers appeared as arXiv preprints in 2026, marking the visible start of this researcher's publication record. With only a single active year on file, the current pace cannot yet be judged as sustained; the score reflects a short but recent burst of output rather than an established multi-year cadence.
Reproducibility55/100
The reproducibility score is 55, the mean across 2 Evolve-reviewed papers, both rated medium reproducibility readiness. Reviewer verdicts are specific: PrimeScientist shows solid attempt-efficiency gains, but headline reward gains rest on single runs; World Models Under Asynchronous Sensor Observations offers a rigorous capacity-matched three-part study with a positive control, yet has narrow scope and few seeds. The picture is methodologically careful work whose evidentiary breadth is still thin.
Research summary
Abhay Anand is an early-stage researcher at the University of California San Diego whose recorded output to date centers on two 2026 arXiv preprints reviewed by Evolve: PrimeScientist, on strategic allocation of research effort in autonomous LLM research agents, and World Models Under Asynchronous Sensor Observations, on time-to-refresh signals in model-based reinforcement learning. The bibliometric field distribution splits evenly (0.5/0.5) between autonomous LLM research agents and world models / model-based RL, so the profile currently reads as two-front rather than narrowly specialized. OpenAlex records 3 works in 2026 (3 in the last three years), 10 distinct coauthors, and an h-index of 0 — consistent with a record that has just started accumulating citations. Evolve's reproducibility assessment averages 55 across the two reviewed papers, both rated medium readiness, with reviewer notes flagging single-run results in one case and few seeds in the other. Collaboration (48) and activity (40) scores sit in the lower-middle range, reflecting a compact co-author circle and a very short publication window. Output and impact axes are not yet scored because citation and volume data remain insufficient.
Collaboration pattern
OpenAlex attributes 10 distinct coauthors to this record, and the frequent-collaborator list — Akash Anand, Yash Vishe, Xinle Yu, Fan Bai, Kaiser Sun, Hengshuo Miao, Zhongyan Luo, Kun Zhou, and Zhen Wang — forms a small, stable circle. Both 2026 reviewed papers place the researcher in a middle author slot with no corresponding-author role, suggesting contribution within team projects rather than solo-led efforts; the collaboration score of 48 sits in the lower-middle range.
Research keywords
Highlights
Strategic effort allocation for autonomous research agents
Evidence on when time-to-refresh helps world models
Timeline
- 2026World Models Under Asynchronous Sensor Observations posted to arXiv — a capacity-matched, three-part study of when time-to-refresh helps world models in model-based RL.
- 2026PrimeScientist posted to arXiv — budget-adaptive Monte Carlo tree search over executable plan trees to allocate autonomous-research effort.
- 2026OpenAlex records 3 works in 2026, all within this single active year; the last-three-year count is also 3.
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 metrics are derived from OpenAlex records only (identity: openalex); no ORCID is linked, so this profile is name-resolved from public bibliographic data.
- Both cataloged papers are arXiv preprints rather than peer-reviewed venues, and the researcher is not listed as corresponding author on either.
- Citation-based output and impact dimensions are unscored: the reported h-index and i10-index are both 0, and no multi-year publication counts exist yet.
- Reproducibility assessment and verdicts come from Evolve's review of only 2 papers, so they may not generalize to the researcher's broader work.