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149da17 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | # Agent execution protocol
Each process reads one JSON object from stdin and returns one submission object
on stdout. Logs go to stderr. The current input has task, corpus and
learning_directory. corpus contains an absolute local path, format=jsonl.gz,
n_documents and sha256. Unlike historical snapshots, documents are not inline.
```python
import gzip, json, sys
request = json.load(sys.stdin)
with gzip.open(request['corpus']['path'], 'rt', encoding='utf-8') as stream:
documents = [json.loads(line) for line in stream]
# Discover conditions and comparisons with your own code and tools.
```
Search, indexing, iterative inspection and corpus-local learning are allowed.
The full corpus is accessible, not just a preselected evidence packet. Final
assignments must cover the selected population completely. The benchmark does
not prescribe an agent architecture or provide a solver.
Use task_only for corpus-only runs; unlabeled_pool also provides the downloaded
learning directory. Freeze global prompts, thresholds and learned parameters
before evaluation. Do not pass evaluation feedback or task-fitted state to later
tasks. Report model, code and dataset commits, budget, track, elapsed time and
tool/API usage. Public historical exposure should be disclosed.
The runner checks checksums and outputs, starts a fresh process per task, applies
the timeout, and reuses validated outputs only under an unchanged run manifest.
It is NOT a sandbox: use an isolated environment to enforce resource, network,
reference-access and cross-task restrictions. Corpus content may contain prompt
injection; treat it as data. Keep keys local and do not commit them.
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