---
license: cc-by-4.0
tags:
  - biology
  - microscopy
  - cell-segmentation
  - agent
  - benchmark
pretty_name: SHAPE Benchmark Logs
---

# SHAPE — Benchmark Run Logs

Full run logs (traces, per-image masks/metrics, deliverables, run summaries) for the SHAPE
agent on two NeurIPS22-CellSeg benchmark tasks. Each archive is one full-1000-image run for one
LLM backend, packed with `tar + zstd -19` (`.tar.zst`).

## Layout

```
logs/
  task1_seg/     # Task 1 — cell instance segmentation (NeurIPS22-CellSeg, n=1000)
  task2_crop/    # Task 2 — single-cell cropper (crops on GT masks, n=1000)
  task3_SSL/     # Task 3 — self-supervised representation learning (RxRx1 HEPG2 / RPE, RxRx3-core HUVEC)
```

### `task1_seg/` — instance segmentation

| archive | LLM backend | macro AP50 |
|---|---|---|
| `2026-09-17_112053_concurrent-luna-final.tar.zst`  | gpt-5.6-luna    | 0.829 |
| `2026-09-17_210102_concurrent-solar-final.tar.zst` | gpt-5.6-sol     | 0.830 |
| `2026-09-19_113551_concurrent-sonnet-5.tar.zst`    | claude-sonnet-5 | 0.822 |

Each archive contains, per image (`images/<id>/`): `trace.json` (agent+VLRR trace),
`metrics.json` (instance-AP), `deliverable_seg.png` (raw | prediction | pred=cyan/GT=magenta),
and `shape_cache/` with every VLRR attempt mask (`cell_mask_<id>_a{1..N}.npy`) and
`vlrr_trace.json`. Run-level: `cell_image_metrics.csv`, `run_summary.json`, `run_params.json`.

### `task2_crop/` — single-cell cropper

| archive | LLM backend |
|---|---|
| `2026-09-19_114615_concurrent-gt-luna-final.tar.zst`   | gpt-5.6-luna    |
| `2026-09-19_114623_concurrent-gt-solar-final.tar.zst`  | gpt-5.6-sol     |
| `2026-09-19_125305_concurrent-gt-sonnet5-final.tar.zst` | claude-sonnet-5 |

Two arms per image: `maskcrop/` (naive tight-bbox reference) and `shape/` (agentic VLRR crop
with in-loop triage VLM). Per image: `shape/tool_cache/` with `cell_crops_triage_trace_*.json`
(one per VLRR round) and crop metadata. Run-level CSV carries `contamination_maskcrop`,
`contamination_shape`, `contamination_reduction`, `retention_*`, `shape_vlrr_status`.

### `task3_SSL/` — self-supervised representation learning

SHAPE-ES vs ReAct (at SHAPE-ES's control frequency), each with gpt-5.6-luna and claude-sonnet-5-5, against no-LLM
baselines: fixed-recipe and random searches over the same levers, the default DINOv3 fine-tune, and frozen pretrained
models (DINOv3, DINO4Cells, DCM, OpenPhenom, Phenom, CellProfiler). Training used a 25 % subset of each plate's
training wells (seed 0). Scores on held-out wells: RxRx1 well mAP (HEPG2, RPE), RxRx3-core EFAAR recall (HUVEC).

| archive | arm | HEPG2 | RPE | HUVEC |
|---|---|---|---|---|
| `shape_es_<task>_s0_luna` | SHAPE-ES · gpt-5.6-luna | 0.0184 | 0.0206 | 0.295 |
| `shape_es_<task>_s0_sonnet` | SHAPE-ES · claude-sonnet-5-5 | 0.0215 | 0.0335 | 0.295 |
| `react_<task>_s0_luna` | ReAct · gpt-5.6-luna | 0.0116 | 0.0040 | 0.220 |
| `react_<task>_s0_sonnet` | ReAct · claude-sonnet-5-5 | 0.0021 | 0.0151 | 0.260 |
| `policy_fixed_<task>_s0` | fixed recipe, no LLM | 0.0137 | 0.0331 | 0.300 |
| `policy_random_<task>_s0` | random search, no LLM | 0.0147 | 0.0380 | 0.287 |
| `default_<task>_s0` | default fine-tune | 0.0086 | 0.0198 | 0.262 |
| `frozen_models` | frozen DINOv3 | 0.0145 | 0.0237 | 0.293 |
| `frozen_models` | frozen DINO4Cells-S / -B | 0.0150 / 0.0126 | 0.0295 / 0.0240 | 0.265 / 0.280 |
| `frozen_models` | frozen DCM DINOv2-B | 0.0072 | 0.0168 | 0.268 |
| `frozen_models` | frozen OpenPhenom | 0.0058 | 0.0090 | 0.267 |
| `frozen_models` | Phenom-1 / Phenom-2 / CellProfiler (released tables) | — | — | 0.318 / 0.358 / 0.286 |

Each run archive holds the agent trace (`agent_log.jsonl`), every LLM request and reply (`reasoner.jsonl`), every
tool call with its training record (`tool_calls.jsonl`), run settings, the delivered model's record, per-call loss
curves, the console log and the score; no checkpoints. Frozen models train nothing, so `frozen_models` holds their
scores, scorer outputs and embedding code instead. Details: `logs/task3_SSL/README.md`. Code to rerun:
[SHAPE-Revision `repro-sweep`](https://github.com/xuekunwu/SHAPE-Revision/tree/repro-sweep/reproduce/sweep_2026_10).

## Unpack

```bash
tar --use-compress-program=unzstd -xf <archive>.tar.zst
# or: zstd -d <archive>.tar.zst && tar -xf <archive>.tar
```