What mattered
Structured-output enforcement and an explicit context hierarchy can reduce ambiguous agent actions and make tool boundaries easier to inspect.
What changed locally
PostTrainLLM retained the tool protocol shape and context hierarchy as learning assets for a future coding-agent lane.
Evidence limits
The research did not justify another agent layer or a coding product. Tool quality and a frozen task gate still come first.
Retained disposition
Revisit only when a concrete coding-agent target and evaluation exist.
The action is retained as study context rather than an active backlog item. Any implementation, download, training run, or benchmark requires a fresh scoped question under the repository's experiment gate.
How to read this dossier
This canonical page separates the retained repository decision from the material that informed it. A source may describe an external claim, historical measurement, local observation, or planned procedure; those evidence classes are not interchangeable. Follow the provenance links before reusing a number or method.
Status and disposition describe what PostTrainLLM retained when this record was indexed. They are not a live product promise, a newly run benchmark, or permission to restart historical work. Unknown or unmeasured fields remain unknown rather than being treated as zero.
To reuse the record, first name the exact claim you need and trace it to the linked source. Then check whether the original environment, model revision, data split, evaluator, hardware, and budget match the proposed use. If they do not, treat the record as a hypothesis or design reference and run the smallest fresh comparison that can falsify it. Preserve negative outcomes and regressions beside any improvement; a local win on one slice does not silently become a general capability claim.
Source provenance
The normalized record comes from docs/studies/registry.json. The links below are the tracked evidence and explanatory sources preserved with the record.