# RRSI: Regularized Recursive Self-Improvement of Agent Harnesses

arXiv: https://arxiv.org/abs/2609.24972 · HTML: https://mlsanity.com/paper/2609.24972/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in Agent skill learning and evolution, one of 16 directions in LLM agents and tool use.
- Builds on 32 of its 61 references that are in the map.
- First posted on arXiv 21 Sep 2026.

## Placed in

- LLM agents and tool use / [Agent skill learning and evolution](https://mlsanity.com/agents/agent-skill-learning-and-evolution/)
- Synthetic data and self-improvement for LLMs / [Self-evolving LLM agents](https://mlsanity.com/synth/self-evolving-llm-agents/)

## Builds on (most cited first, 30 of 32)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning](https://mlsanity.com/paper/2602.08234/) (2026)
- [SkillOpt: Executive Strategy for Self-Evolving Agent Skills](https://mlsanity.com/paper/2605.23904/) (2026)
- [Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses](https://mlsanity.com/paper/2604.25850/) (2026)
- [Self-Harness: Harnesses That Improve Themselves](https://mlsanity.com/paper/2606.09498/) (2026)
- [Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving](https://mlsanity.com/paper/2507.06229/) (2025)
- [AutoHarness: improving LLM agents by automatically synthesizing a code harness](https://mlsanity.com/paper/2603.03329/) (2026)
- [Hyperagents](https://mlsanity.com/paper/2603.19461/) (2026)
- [Huxley-G"odel Machine: Human-Level Coding Agent Development by an Approximation of the Optimal Self-Improving Machine](https://mlsanity.com/paper/2510.21614/) (2025)
- [HarnessX: A Composable, Adaptive, and Evolvable Agent Harness Foundry](https://mlsanity.com/paper/2606.14249/) (2026)
- [Continual Harness: Online Adaptation for Self-Improving Foundation Agents](https://mlsanity.com/paper/2605.09998/) (2026)
- [MetaClaw: Just Talk -- An Agent That Meta-Learns and Evolves in the Wild](https://mlsanity.com/paper/2603.17187/) (2026)
- [Harness Updating Is Not Harness Benefit: Disentangling Evolution Capabilities in Self-Evolving LLM Agents](https://mlsanity.com/paper/2605.30621/) (2026)
- [APEX-Agents](https://mlsanity.com/paper/2601.14242/) (2026)
- [Recursive Harness Self-Improvement](https://mlsanity.com/paper/2607.15524/) (2026)
- [Adaptive Auto-Harness: Sustained Self-Improvement for Agentic System Deployment on Open-Ended Task Streams](https://mlsanity.com/paper/2606.01770/) (2026)
- [Evolving Agents in the Dark: Retrospective Harness Optimization via Self-Preference](https://mlsanity.com/paper/2606.05922/) (2026)
- [G-Zero: Self-Play for Open-Ended Generation from Zero Data](https://mlsanity.com/paper/2605.09959/) (2026)
- [HarnessCompass: Guiding Automatic Harness Evolution toward Generalizable and Effective Agent Harnesses](https://mlsanity.com/paper/2608.01918/) (2026)
- [Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimization](https://mlsanity.com/paper/2604.12290/) (2026)
- [TTHE: Test-Time Harness Evolution](https://mlsanity.com/paper/2607.08124/) (2026)
- [EvolveMem:Self-Evolving Memory Architecture via AutoResearch for LLM Agents](https://mlsanity.com/paper/2605.13941/) (2026)
- [HarnessBank: Semantic Gene-Bank Search with Gated Verification for Agent-Harness Self-Evolution](https://mlsanity.com/paper/2607.13683/) (2026)
- [Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable](https://mlsanity.com/paper/2607.13285/) (2026)
- [DarwinX: Evolving Agent Harnesses Through Natural Selection](https://mlsanity.com/paper/2608.07545/) (2026)
- [EnvHarness: Awakening Static Worlds for Agent Learning](https://mlsanity.com/paper/2608.19880/) (2026)
- [Evo-Bench: Can Language Models Improve Agent Harness?](https://mlsanity.com/paper/2608.09096/) (2026)
- [JobBench: Aligning Agent Work With Human Will](https://mlsanity.com/paper/2605.26329/) (2026)
- [Better Harnesses, Smaller Models: Building 90% Cheaper Agents via Automated Harness Adaptation](https://mlsanity.com/paper/2607.08938/) (2026)
- [AutoMem: Automated Learning of Memory as a Cognitive Skill](https://mlsanity.com/paper/2607.01224/) (2026)

## Built on by (most cited first, 13 of 13)

- [Control the Harness, Control the Cost: Routing and Governing AI Coding Agents in the Enterprise](https://mlsanity.com/paper/2609.28919/) (2026)
- [VERSE: Verified Self-Evolving Optimizer for Agent Harnesses](https://mlsanity.com/paper/2610.02616/) (2026)
- [Self-Evolving Harness on Multiple Tasks with the Agent as Its Own Optimizer](https://mlsanity.com/paper/2609.38372/) (2026)
- [PhysEvo: Astra Can Act, Let It](https://mlsanity.com/paper/2610.08995/) (2026)
- [Harness Evolution Hits a Ceiling: When Weight Training Should Begin](https://mlsanity.com/paper/2610.11655/) (2026)
- [Cortex: Measurement-Gated Evolution and Pruning of Coding-Agent Skills and Rules](https://mlsanity.com/paper/2610.14216/) (2026)
- [From Existing Methods to New Research Ideas: Two Perspectives on Recursive Self-Improvement](https://mlsanity.com/paper/2610.14625/) (2026)
- [Trace-to-Architecture: Capability Bottleneck Diagnosis for Harness Revision](https://mlsanity.com/paper/2610.16725/) (2026)
- [D-World: An Explicit World-Language-Action Model with Recursive Self-Improvement under Dual Control](https://mlsanity.com/paper/2610.17114/) (2026)
- [ECG-RSI: Recursive Self-Improvement through Targeted Electrocardiogram Waveform Evolution](https://mlsanity.com/paper/2610.17718/) (2026)
- [Self-Evolving Agent Harness](https://mlsanity.com/paper/2610.18813/) (2026)
- [VideoHarness-SRSI: Structured Recursive Self-Improvement for Video Understanding Harness](https://mlsanity.com/paper/2610.19424/) (2026)
- [MMLA-RSI: Recursive Self-Improvement through RTT and RTMA](https://mlsanity.com/paper/2610.20294/) (2026)

## Abstract (arXiv)

An LLM agent's capability is largely magnified by its harness, namely the prompts, control flow, tooling, memory, and context management surrounding the frozen backbone model. Recent methods increasingly automate this process by iteratively proposing and selecting component-wise edits of an agent harness, practically establishing a form of recursive self-improvement (RSI) at the agent-system level. However, such recursive evolution may overfit by memorizing the training tasks, showing large in-distribution gains that shrink or even vanish on out-of-distribution benchmarks. We introduce Regularized Recursive Self-Improvement of Agent Harnesses (RRSI), which incorporates the principles of regularizations into harness self-improvement by constraining the evolution candidate proposal and selection. The proposer operates with a temporally annealed budget, limiting how many edits a candidate can bundle, and it encourages unexplored trajectories based on evolution history. The selector is equipped with a critic and a pruner: the critic screens benchmark-specific proposals, while the pruner, removes changes that are too small, too expensive, or no longer useful. Together these constraints favor reusable agent mechanisms over benchmark-specific ones or even noises. Across eight benchmarks spanning coding, agentic workspace and engineering design tasks, RRSI gains up to 6.0 points on the split it evolves against and up to 4.3 points on the five out-of-distribution benchmarks, while producing a harness that runs on 30% fewer policy tokens than the unregularized evolution. Code is available at https://github.com/google-research/rrsi and project page is https://regularized-rsi.com/.