| --- |
| 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 |
| ``` |
|
|
