---
pretty_name: RAP E1 Luna Six-Way Evaluation
language:
- en
- zh
tags:
- pcb
- agent-evaluation
- rap
- tool-use
size_categories:
- n<1K
configs:
- config_name: e1_luna_six_way
  data_files:
  - split: test
    path: e1-luna-six-way-20261003/results.jsonl
---
# RAP evaluation data

Current release: **e1-luna-six-way-20261003**. It contains **98 frozen simulated PCB test tasks × 6 conditions = 588 episodes**, all with GPT-5.6 Luna, seed 1, and at most 5 model API calls per episode. No models were called and no results were regraded for publication.

| Condition | Clean pass | Total tokens | Mean wall seconds / episode |
|---|---:|---:|---:|
| Historical R2 | 24/98 | 966,897 | 41.77 |
| Historical RAP / R4b | 94/98 | 591,725 | 9.13 |
| Native V2 + R2 | 29/98 | 2,244,091 | 22.53 |
| Native V2 + RAP | 91/98 | 1,577,621 | 15.19 |
| Harbor Codex + R2 | 24/98 | 6,992,722 | 71.78 |
| Harbor Codex + RAP | 90/98 | 5,211,733 | 62.44 |

## Browse and download

- [Results and latency HTML](e1-luna-six-way-20261003/report.html) — download and open locally.
- [Setup summary HTML](e1-luna-six-way-20261003/setup-summary.html) — each row's tools, history, settings, and comparison limits.
- [Per-episode metrics](e1-luna-six-way-20261003/results.jsonl), [summary](e1-luna-six-way-20261003/summary.json), [98 complete tasks](e1-luna-six-way-20261003/tasks/tasks.jsonl).
- [Raw evidence archives](e1-luna-six-way-20261003/raw): one compressed archive per condition, retaining per-task prompts/requests, visible responses, tool calls/feedback, artifacts, grades, usage, and available run configuration.
- [Provenance and redaction manifest](e1-luna-six-way-20261003/manifest.json), [SHA256SUMS](SHA256SUMS), [offline validator](verify_release.py).

The historical two arms have recorded actions and usage ledgers, but not all original HTTP request/response bodies. Current four arms include the stored request/response records. A file named `private/grade.json` inside an archive is a formerly host-private grader artifact, now deliberately included for audit; it is not a credential file. No hidden checker was exposed as an agent tool during the runs.

## Scope and limitations

This is **not** all E1–E5, all nine models, or 196 tasks across six seeds. The full frozen manifest includes 223 task entries; only the 98 IDs in `selected-task-ids.json` belong to this release. Other historical baseline summary values may appear in provenance metadata, but their trajectories are not included.

Historical Luna used output cap 1,200 and omitted reasoning effort; current runs use 8,192 and medium. Their prompts, history handling and tool serialization differ. Native and Harbor use the same RAP backend and checker, but Harbor adds a general coding-agent loop, tool discovery and scratch tools. R2 is a single-object/net geometry interface, while RAP realizes whole bundles. The 5-call constraint creates a necessary-action bottleneck for at least 47 tasks under R2. Thus these are interface/pipeline results, not an isolated proof of superior general reasoning or a pure native-vs-text/harness ablation.

Latency includes both passing and failing episodes. Harbor includes job and environment setup; Native records its loop and final grading. Timing also depends on request load and worker conditions. Tokens are input + output per provider call, including cached input and reasoning output exactly once. Costs are reference estimates, not verified invoices.

## Sanitization

Public copies remove credential fields and recognizable credential strings, replace local path prefixes, and remove opaque encrypted reasoning values (including JSON embedded in strings). Duplicate SSE chunks and runtime/authentication directories are excluded. Visible tool decisions, artifacts and numeric outcomes are retained. Local originals are unchanged. The manifest contains original and published SHA256 values; selected JSONL records are hashed without their line terminator. These sanitized files are not a byte-identical full transport replay package.

## Reproduce the reported aggregates

Download the dataset repository, then run `python verify_release.py`. It verifies every published file checksum, all archive member checksums, 588 task/condition records, token arithmetic, and the success/token/call aggregates directly from detailed episode records. This validates the stored results without new paid API calls. Re-running stochastic model inference is a separate procedure and need not reproduce the exact numbers. Selected implementation files and run configurations are included for inspection, not advertised as a complete standalone environment image.

No new license is asserted for upstream software or task sources by this data card; existing ownership and notices remain applicable.