RRSI: Regularized Recursive Self-Improvement of Agent Harnesses

01 · The problem

Evolved harnesses overfit the benchmark they are scored on. RRSI transfers.

RRSI improves every out-of-distribution benchmark without overfitting the split it evolves on. Prior methods do the opposite: large evolve-set gains that shrink or vanish once the benchmark changes, two of them ending below the harness they started from.

(a) Evolve-split gain against out-of-distribution gain, one point per method. RRSI is the only method whose gain grows out of distribution.

(b–d) Held-out scores for the unevolved harness H₀, the average prior method and RRSI. RRSI beats the prior average by up to 22.9%.

02 · The idea

Regularize the search, not the harness

Every harness component stays editable. RRSI constrains the loop that edits it: how much one proposal may change, and which measured gains are allowed to stick.

One round of RRSI. The proposer spends a shrinking edit budget and reads the full ledger; the critic screens for leakage before anything is scored; the gate admits a candidate only if it clears the noise floor and pays for its tokens. Schematic; the real rounds are in the explorer below.

Proposal side

how search capacity is spent

Annealed edit budget

Early rounds may bundle a few coordinated edits to find a mechanism; late rounds get one attributable change.

Evidence-aware credit

Every candidate is logged with its hypothesis, diff, score and cost change, so the proposer builds on what worked and stops re-testing what failed.

Structured exploration

When progress stalls inside the noise band, budget is redirected to components the run has never touched.

Selection side

which gains become permanent

Leakage critic

Task names, entities, answers or benchmark-specific logic are rejected before a candidate is ever scored.

Noise-adjusted floor

A gain must clear the variance measured on the unchanged base harness.

Cost rule

Extra inference tokens have to be paid for by measured gain.

Pruning

Components that stop earning their place are flagged for deletion.

03 · Results

Every held-out benchmark improves

Evolve on one suite per domain, then run the harness unchanged everywhere else. Same tools, judge, trials and window as H₀; the policy is Claude Opus 4.8.

Pass rate for the coding suites, model-judged rubric scores for Harvey LAB, JobBench and APEX-Agents, win rate against the human expert for GDPval, frozen simulators for EngDesign and Frontier-Eng.

Cheaper, too. Policy tokens per trial of each final harness against its out-of-distribution average. RRSI is the lightest evolved harness and the only one that clears H₀ by more than a point.

WHY IT IS LIGHT

Two rules act on cost directly

The cost rule refuses growth that is not paid for when it is proposed; pruning removes growth that stopped paying for itself since. No prior method carries either.

04 · Evolution explorer

Watch the harness evolve, round by round

Four real runs, every candidate: what it proposed, what the critic said, why the gate kept or dropped it, and the exact diff. Click anything; use ← / → to step.

How the harness adapted, and what the regularizers blocked in between

05 · Citation

BibTeX

@article{xia2026rrsi,
  title   = {{RRSI}: Regularized Recursive Self-Improvement of Agent Harnesses},
  author  = {Xia, Peng and Han, Rujun and Wang, Zifeng and Chen, Yanfei and Zhang, Yufan and Lee, Yoonho and Huang, Chengsong and Yu, Han and CuiZhu, Zhongying and Ming, Yifei and Yao, Huaxiu and Gokturk, Burak and Pfister, Tomas and Lee, Chen-Yu},
  year    = {2026},
  url     = {https://regularized-rsi.com}
}