Caltech researcher bridging robust reinforcement learning, Monte-Carlo planning, and self-improving LLM reasoning
2
papers here
2
reviewed by Evolve
0
first-author
0
corresponding
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works · OpenAlex
3
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—
Not yet scored: this snapshot lacks complete per-work output data, so overall publication volume and productivity cannot be computed reliably. This axis would measure total works and output consistency across the career once indexing is fuller.
Impact—
Not yet scored: citation-based influence metrics are incomplete in this record. Once available, this axis would measure citation counts, h-index trajectory, and uptake of the work relative to field norms.
Focus0/100
Scores 0 because the OpenAlex field distribution is an even 50/50 split between reinforcement learning and machine learning for LLM reasoning post-training, with no dominant field. Topics reinforce the breadth: reinforcement learning in robotics (30), adaptive dynamic programming (15), bandit algorithms (14), topic modeling (10), and domain adaptation and few-shot learning (8) — a two-front portfolio rather than one narrow lane.
Collaboration39/100
The collaboration score is 39, based on 6 distinct co-authors in the recent window — a compact, repeat-appointment network rather than a wide one. Recurring names include Tuan Dam, Brahim Driss, Adam Wierman, Zixun Huang, Haitao Mi, and Leowei Liang. On both reviewed 2026 papers Panaganti appears as a middle author without corresponding role, consistent with contributing inside small teams.
Activity94/100
The strongest axis at 94. The OpenAlex record shows steady early output — 4 works in 2020, 2 in 2021, 3 in 2022, 3 in 2023 and 4 in 2024 — followed by a sharp climb to 11 works in 2025 and 9 already in 2026, for 24 works in the most recent three years. Both Evolve-reviewed 2026 arXiv papers sit inside this acceleration.
Reproducibility55/100
Scores 55 (reproMean 55), computed from 2 Evolve-reviewed papers. Both carry a medium repro-readiness rating. Evolve's notes flag concrete friction points: the robust-MCTS paper hides constants and uses small, transition-only experiments, while FlowBalance reports consistent but small math gains with no significance tests. Theory and method design look sound; experimental scale and statistical reporting are the main gaps.
Research summary
Kishan Panaganti is a researcher at the California Institute of Technology whose OpenAlex-indexed profile splits evenly (0.5/0.5) between reinforcement learning and machine learning for LLM reasoning post-training. The bibliometric record is early-stage but clearly accelerating: 24 works in the most recent three years, rising from 4 works in 2024 to 11 in 2025 and 9 already in 2026, alongside an h-index of 3 and an i10-index of 2. Topic frequencies emphasize reinforcement learning in robotics (30), adaptive dynamic programming (15), bandit algorithms (14), topic modeling (10), and domain adaptation and few-shot learning (8). Evolve reviewed two of Panaganti's 2026 arXiv papers, on both of which Panaganti is a middle author. Online Robust Reinforcement Learning Through Monte-Carlo Planning presents what Evolve describes as the first robust MCTS, with an O(n^-1/2) root rate matching standard MCTS under robust-MDP ambiguity sets. FlowBalance targets verifier-grounded self-improvement for LLM reasoning, adding verifier-gated dense self-guidance to trajectory balance with GFlowNets, and shows consistent math gains over FlowRL and GRPO. Recurring co-authors include Tuan Dam, Brahim Driss, Adam Wierman, Zixun Huang, Haitao Mi, and Leowei Liang.
Collaboration pattern
Panaganti works in small, stable teams: 6 distinct co-authors in the recent OpenAlex window, with Tuan Dam, Brahim Driss, Adam Wierman, Zixun Huang, Haitao Mi and Leowei Liang recurring. On both reviewed 2026 papers he holds a middle-author position without corresponding role, suggesting contribution-driven collaboration rather than a lab-lead pattern; the collaboration dimension scores 39.
Research keywords
Highlights
Robust Monte-Carlo planning with standard-MCTS convergence
FlowBalance: verifier-gated self-improvement for LLM reasoning
Timeline
- 2020Earliest year in the OpenAlex record: 4 works indexed.
- 2024Output held steady at 4 works before the current climb.
- 2025Publication pace jumped to 11 works, the highest yearly count in the record.
- 20269 works so far, including both Evolve-reviewed papers: robust Monte-Carlo planning and FlowBalance.
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 figures (h-index 3, i10-index 2, per-year work counts, topic frequencies) come from an OpenAlex snapshot; the output and impact dimensions are not scored because the underlying data is insufficient.
- The reproducibility score (55) is computed from only 2 Evolve-reviewed papers, so it reflects a narrow sample.
- Both reviewed works are 2026 arXiv preprints; peer-review status and final published versions are not reflected.
- Identity is resolved from public bibliographic data (OpenAlex; ORCID 0000-0001-9746-698X); details beyond the indexed record are not covered.