---
title: DarwinX
emoji: 🧬
colorFrom: blue
colorTo: indigo
sdk: static
app_file: index.html
pinned: false
license: cc-by-4.0
short_description: Evolving Agent Harnesses Through Natural Selection
---

# DarwinX: Evolving Agent Harnesses Through Natural Selection

Project page for [arXiv:2608.07545](https://arxiv.org/abs/2608.07545).

DarwinX treats agent self-evolution as selection over a *population* of harnesses with the base
model frozen. Across four benchmarks, one loop adds ~17 points on average:

| Benchmark | Result | Gain | What it isolates |
| --- | --- | --- | --- |
| Terminal-Bench 2.1 (avg@5) | 84.7% | +7.7 on matched base | in-domain evolution |
| TerminalWorld (held-out) | 68.3% | +7.3 | disjoint held-out task split |
| WebArena-Infinity (audit-clean pass@1) | 93.0% | +49.5 pp | synthetic → real intent shift |
| SWE-bench Verified (zero-shot transfer) | 84.2% | +3.4 | cross-benchmark transfer |

## Page sections

`Abstract` · `Method` · `Results at a glance` · `Benchmark detail` · `Ablation: what evolution
changes` · `Limitations` · `BibTeX`

The ablation section reproduces the paper's framing: it is an *exploratory attribution, not a
per-skill causal ablation*, since the skills were co-selected rather than independently randomized.
The limitations section is carried over from the paper's Discussion.

## Interactive figures

Eight figures are interactive, and each is driven by real run artifacts or the paper's own plotting
scripts rather than illustrative numbers. `tools/build_data.py` regenerates `assets/data.js` from the
original files, so nothing is hand-transcribed.

**1. TerminalWorld merge explorer.** Toggle any subset of the four evolved specialists and watch
which of the 41 held-out tasks the selection covers. Built from the `per_task_results` arrays of the
five evaluation runs, joined on `task_id` (the run files list the tasks in different orders, so
position-based joining would silently misalign them).

It surfaces something the paper states only in aggregate. The four specialists solve 24/25/26/27
tasks and their **union is 29**, but the harness recombination actually produced solves **28**:

| | tasks |
| --- | --- |
| best single specialist (D) | 27/41 |
| union of all four | 29/41 |
| realized merge | 28/41 |
| solved by merge, by no specialist | `tw_448247` |
| solved by some specialist, not by merge | `tw_449421`, `tw_498533` |
| solved by all four | 21/41 |
| solved by none | 12/41 |

So recombination is not a free set union: it adds a capability no parent had and loses two. These are
single attempts and one task is worth 2.4 points, so individual flips sit inside the noise band —
the page says so next to the widget.

**2. TB2.1 cluster explorer.** Per-cluster base vs. evolved avg@5, sortable by gain, base rate,
cluster size, or name, with exact rates on hover. Numbers from `notes/TB21_RESULTS.md` (paired
protocol, 88 tasks). Deltas are carried over as reported rather than recomputed, because the source
rounds them from unrounded rates.

**3. WebArena-Infinity evolution curve.** All 37 evaluated variants with a best-so-far envelope,
from `notes/tw_dynamics.json` → `wai_adaptive_scores`. Toggle either series.

**4. Headline four-benchmark panels.** Replaces the right half of the teaser. From
`scripts/gen_summary_figure.py`. The reason to make this one interactive is that the paper's figure
has to *state* its truncation in the caption: on a shared axis WebArena-Infinity's +49.5 flattens the
two terminal benchmarks into slivers, so each panel is scaled to its own range. Here the reader can
switch between per-panel and shared 0–100 axes and see both framings. The best-prior-agent bar can be
hidden, since those systems use different models and effort settings and are context rather than a
controlled comparison. Per-panel labels carry the exceptions: on SWE-bench Verified the grey bar is
the fix-skill reference, not an unevolved Monet, and there is no prior-agent bar.

**5. TerminalWorld specialist bars.** From `notes/tw_dynamics.json` → `tw_heldout`. Same axis
question in miniature: the six arms span 58.5–68.3, so a 0–100 track renders them near-identical.
Defaults to a 55–70 axis with the full axis one click away and the truncation named in both notes.
Spec. D and the Claude Code reference are both 27/41, so D's bar landing exactly on the dashed
reference line is a built-in check that the axis transform is right.

**6. TB2.1 compute.** From `scripts/gen_tb21_compute.py` (medians over clean attempts). Switch
between turns and tokens; the bars share one scale across both task groups so the newly-solved vs.
already-solved contrast is not rescaled away.

**7. WAI invalid-trajectory composition.** From `scripts/gen_wai_invalid_composition.py`. Break the
293 → 17 collapse down by application or by mechanism. Both rows sit on a shared absolute scale by
default, which makes the "after" row a near-invisible sliver — that *is* the finding; normalizing
each row to its own total then shows what the remainder consists of. `build_data.py` asserts both
decompositions still total 293 and 17.

**8. WAI audit dumbbells.** From `scripts/gen_wai_audit_by_app.py`. One line per application from raw
pass@1 to audited pass@1, so line length reads directly as how much a harness was leaning on
trajectories the audit rejects. Sortable by audit loss. `build_data.py` cross-checks the audited
DarwinX column against the per-application table rendered elsewhere on the page.

Three figures stay static because they are conceptual diagrams with no underlying data: the teaser's
selection schematic, the method overview, and the per-generation operators. The archive lineage tree
also stays static for a different reason — its node/edge data lives in a `state.db` on the cluster,
not on this machine, so there is nothing truthful to make interactive yet.

Tables are click-to-sort. Numeric columns open descending, text columns A–Z, and `Overall` rows stay
pinned to the bottom.

## Local preview

```bash
python3 -m http.server 8000
# open http://localhost:8000
```

## Regenerate and test

```bash
python3 tools/build_data.py          # rebuild assets/data.js from the run artifacts
# then, with the server running, open:
#   http://localhost:8000/tools/interaction_test.html
# it drives every widget with real click events and writes PASS/FAIL into the page title
```

Headless run:

```bash
python3 -m http.server 8000 &
"/Applications/Google Chrome.app/Contents/MacOS/Google Chrome" --headless --disable-gpu \
  --virtual-time-budget=6000 --dump-dom http://localhost:8000/tools/interaction_test.html \
  | grep -o '<title>.*</title>'
```

## Layout

```
index.html                          the page: inline CSS + vanilla JS, no build step, no external
                                    requests, no web fonts, no chart library
assets/data.js                      generated — real numbers behind the interactive figures
assets/*.png                        web-sized figures (max 2000px wide, 256-color) — 736 KB total
assets/full/*.png                   full-resolution originals for the three dense diagrams,
                                    linked from the page via click-to-zoom
tools/build_data.py                 regenerates assets/data.js from the original run artifacts
tools/interaction_test.html         click-driven test suite for the interactive figures (49 checks)
```

| Figure | Content |
| --- | --- |
| `teaser.png` | four-benchmark teaser (full figure, click-to-zoom target) |
| `teaser_schematic.png` | teaser's left half — the selection schematic shown on the page |
| `overview.png` | method overview |
| `population.png` | per-generation operators (mutation, classification, merge) |
| `tb21_category_gain.png` | per-cluster TB2.1 gain |
| `tb21_compute.png` | per-task compute: newly-solved vs already-solved |
| `tw_heldout_specialists.png` | held-out generalization vs archive diversity |
| `wai_evolution_curve.png` | best-so-far screening score |
| `wai_tree.png` | archive lineage tree |
| `wai_invalid_composition.png` | invalid trajectories, 293 → 17 |
| `wai_audit_by_app.png` | raw vs audited pass@1 per application |

Result figures are rendered from the paper's PDF sources at 200 DPI, then palette-reduced without
dithering (RMSE vs. truecolor < 0.5%, which is imperceptible for line art but 4.7x smaller).

## Hosting

The frontmatter above configures this directory as a Hugging Face **static Space**. The same files
work unchanged as a GitHub Pages site or behind any static file server.

One thing to change when hosting: `og:image` and `twitter:image` are relative paths, which most
crawlers resolve but X/Twitter wants absolute. Replace them with the deployed absolute URL to get a
large social card.