# Show HN: A Lego-like composable Framework for RSI

> Source: <https://rsi-gear.github.io/>
> Published: 2026-10-28 07:25:08+00:00

# GEAR: An Open General Evolutionary Architecture

 for Recursive Self-Improvement

 of LLM Agent Harness

<sup>1</sup>Imperial College London

<sup>2, 5</sup>Independent Researcher

<sup>3</sup>Duke University

<sup>4</sup>The Chinese University of Hong Kong

<sup>6</sup>Brown University

## Learn from experience.

Improve across tasks.

GEAR organizes recursive self-improvement as a **two-level meta-learning process.** In the **inner loop**, a rollout agent uses task feedback to refine its reasoning, planning, and actions within a task.

In the **outer loop**, a Meta Agent identifies common patterns across task experiences, including recurring failures and effective strategies, and proposes reusable improvements to enhance **cross-task performance.** GEAR evaluates and promotes these changes for subsequent tasks. The two loops can be optimized independently and are connected by experience.

## Modular algorithms for

evolutionary search.

GEAR organizes evolutionary search into replaceable algorithm components,

 providing a common framework for studying different search strategies.

 Staged GEPA is one concrete instantiation, illustrated below.

### Open the search, component by component.

#### Parent selection

Choose which archived harness to improve next. Staged GEPA uses task-level fronts to retain useful specialists and sample parents from the frozen archive.

- Reads
- Frozen archive, champion, task-level evidence
- Produces
- A parent harness for candidate generation

Policy modules express the framework’s design boundaries. The current staged implementation exposes parent selection as a public policy hook; evidence validation, evaluation stages, and final promotion remain controlled by the runtime.

## Better harnesses.

More completed tasks.

Improvements across coding, marketing, and support.

Same-model comparisons, with reasoning effort made explicit.

```
Marketing · task pass rate27% → 40%GPT 5.6 Luna · medium · DSH Base → OursSupport · task pass rate17% → 25%GPT 5.6 Luna · medium · DSH Base → OursCoding · task pass rate52.8% → 62.9%GPT 5.6 Luna · medium · DSH Base → Ours
```

## Full benchmark comparison

| Harness comparison · same model and reasoning effort within each group |  |  |  |  |  |  | 
|---|---|---|---|---|---|---|
| Agent | Harness | AutomationBench · Marketing ↑ |  | AutomationBench · Support ↑ |  | Terminal-Bench 2.1 ↑ | 
|---|---|---|---|---|---|---|
|  |  | Task pass rate | Obj. completion | Task pass rate | Obj. completion | Task pass rate | 
| GPT 5.6 Luna medium | DSH (Base) | 27.0% | 75.4% | 17.0% | 72.9% | 52.8% | 
|  | DSH (Ours) | 40.0% | 83.9% | 25.0% | 81.3% | 62.9% | 
|  | Δ (pp) | +13.0 | +8.5 | +8.0 | +8.4 | +10.1 | 
| GPT 6 Astra max | Codex | 57.0% | 84.1% | 35.0% | 88.4% | 86.7% | 
|  | DSH (Ours) | 61.0% | 86.3% | 39.0% | 88.7% | 87.6% | 
|  | Δ (pp) | +4.0 | +2.2 | +4.0 | +0.3 | +0.9 | 

Δ is the absolute improvement in percentage points.

| Additional evaluation · GPT 5.6 Luna (max) · DSH (Ours) |  |  | 
|---|---|---|
| Benchmark | Task pass rate | Objective completion | 
|---|---|---|
| AutomationBench · Marketing | 53.0% | 88.9% | 
| AutomationBench · Support | 27.0% | 82.4% | 
| Terminal-Bench 2.1 | 84.3% | — | 

AutomationBench reports task pass rate and objective completion; Terminal-Bench 2.1 reports task pass rate. Luna max is a separate evaluation at a higher reasoning effort. [Result data ↗](assets/paper-results.json)

## Optimize. Execute. Understand.

Separate responsibilities, connected by versioned evidence.

## Explore GEAR.

Code, experiment records, and the tools to start your own research.

[01 / Source ↗](https://github.com/rsi-gear/gear)

### Get the code

Explore the framework, search components, and examples.

github.com/rsi-gear/gear
[02 / Documentation ↗](https://rsigear.xyz/docs/gear)

### Run your first experiment

Set up a task benchmark and connect the Refine Skill.

Read the guide
[03 / Experiment ↗](https://github.com/rsi-gear/gear/blob/main/docs/guide/en/example-algorithm.md)

### Follow the search

Inspect the staged Marketing experiment and its evaluation evidence.

View the experiment
## Cite GEAR.

Preprint · October 2026. arXiv link will be added when available.

```
@unpublished{tang2026gear,
  title  = {{GEAR}: An Open General Evolutionary Architecture for
            Recursive Self-Improvement of {LLM} Agent Harness},
  author = {Tang, Yehui and Zhu, Guanqi and Xia, Shao-Jun and
            Chen, Xin and Geng, Mengzhe and Du, Hongyang and Tang, Yuhao},
  year   = {2026},
  note   = {Preprint},
  url    = {https://github.com/rsi-gear/gear}
}
```