---
license: cc-by-4.0
task_categories:
  - reinforcement-learning
tags:
  - arc-agi
  - world-models
  - agents
  - ablation
pretty_name: ARC-AGI-3 executable-world-model ablation runs
---

# ARC-AGI-3 ablation runs

Fifty complete agent runs on ARC-AGI-3: five harness configurations across the ten games with
the highest human baseline action counts. Each archive is one run — every action, every model
revision, and the full LLM transcript that produced them.

## Configurations

| arm | what is removed |
|---|---|
| `ctrl2` | the full harness |
| `nobt` | `--no-backtest`: cannot replay history to check a model revision |
| `nobfs` | `--no-planning`: cannot search inside the model for a route |
| `nompc` | `--no-mpc`: no rolling re-planning |
| `text` | `--text-worldmodeling`: the world model is prose in notes.md; no code at all |

## Files

`<game>_<arm>.tar.zst` unpacks to two directories:

- the run workdir — `events.jsonl` (every action, tool call and model revision), `history.jsonl`,
  `world_model_v5.py` (the final model), `notes.md`, `snapshots/` (the model at each level
  cleared), `.git/` (one commit per turn, so every intermediate model is recoverable), and
  `sessions/` (the complete LLM transcripts);
- `.rt/` — the sandbox sidecar: `logs/` and `src/`, the harness source as it stood inside the jail.

`__pycache__/` and `session_live/` (an in-flight duplicate of `sessions/`) are excluded.

`manifest.csv` carries the results: per run the job id, step and turn counts, level reached,
status, score, per-level action counts, and a `censored` flag for runs cut short.