arxiv:2610.20431

SwarmRSI: Recursive Self-Improvement through Evolving Scientific Agent Swarm

Published on Oct 14

· Submitted by

youganglyu on Oct 15

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Authors:

Xi Zhang ,

Konstantinos Papakostas ,

Abstract

SwarmRSI evolves scientific agent swarms through feedback-guided mutation and complementary recombination to enable recursive self-improvement across research tasks.

Generated by thinkingmachines/Inkling-Small

Recursive self-improvement (RSI) in scientific research aims to improve agents' research capabilities through their own experimental experience. Scientific agent swarms can combine evidence and implementations from different research directions to guide their agents' subsequent experiments. However, poor experimental results can leave unclear whether to revise a hypothesis or its implementation, while overall program scores can hide components useful to other agents. To address these challenges, we introduce SwarmRSI, a framework that evolves scientific agent swarms for recursive self-improvement. We devise Feedback-Guided Swarm Mutation to refine hypotheses and their implementations using experimental feedback from other agents. We also propose Complementary Swarm Recombination to reuse retained hypotheses and complementary program components across agents, allowing partial results to support new research. Recursive Swarm Evolution integrates both mechanisms into a feedback loop, using new experimental evidence to guide subsequent research. Experiments on OpenProblems, NatureBench and MLE-Bench Lite demonstrate SwarmRSI's state-of-the-art (SOTA) performance. Further evaluations show that accumulated research experience improves performance on held-out tasks. Biologists also reproduce its programs for follow-up biological analyses.

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youganglyu

Paper submitter about 20 hours ago

Something I keep noticing with AI scientist agents: running more of them in parallel doesn’t make them smarter by default. Mostly they throw away each other’s work. 🧪

Picture a lab where a student has a promising idea but botches the implementation, and the idea gets crossed off. Down the hall, another student’s “failed” project holds the one trick a colleague needed, and nobody ever opens it.

Agent swarms run into the same two walls:
• A failed experiment doesn’t tell you whether the hypothesis was wrong or the code was
• A program with no overall gain can still hold a component another agent needs
• So good ideas get abandoned and useful pieces get buried

That’s the problem behind our new paper:
🚀 SwarmRSI: Recursive Self-Improvement through Evolving Scientific Agent Swarm

The idea: let the swarm evolve what its agents inherit, not just keep its best scores.
• A shared graph records each research state: hypotheses, programs, experimental evidence and open needs
• Feedback-Guided Swarm Mutation uses other agents’ feedback to decide whether to revise the hypothesis or repair the program
• Complementary Swarm Recombination re-tests kept hypotheses on other agents’ programs (rebase) and merges components that complement each other (crossover)
• Recursive Swarm Evolution sends every outcome back into the graph, whether it succeeded, failed or made no gain, so it shapes where the next agent starts

Results on 118 scientific coding and ML engineering tasks:
📈 76.2 average on OpenProblems (single-cell), vs 57.2 for human experts and 67.1 for TusoAI
🧬 Matches published SOTA on 44.4% of NatureBench’s 90 tasks, the highest of all compared methods (the best Claude Code setup reaches 35.6%)
🏅 Medals on 81.8% of MLE-Bench Lite competitions, above Arbor and MLEvolve
🔁 About 77% of final best NatureBench programs come from a rebase, a crossover or a feedback-triggered update
🔬 Biologists reproduced two of its programs and used them in follow-up studies of intestinal and splenic immunity

I think the next progress in AI for science will come less from adding agents or scaling models, and more from making sure what one agent learns changes where the next one begins.

📝 Paper: https://arxiv.org/abs/2610.20431