Splitting benchmark work and checking merged scores

I finished the scripts for splitting a benchmark task list into separate groups, or shards, and combining their scores afterwards. This prepares MP2 for later comparison runs while MP1 retains its completed 500-task baseline. The scripts are implemented, but the full sharded execution pipeline has not been tested yet.

The splitter groups tasks by repository and version, assigns the largest groups to the shard with the fewest tasks so far, and orders each shard by environment. Keeping whole environment groups together is intended to reduce conflicts where one process removes an image another still needs.

Claude’s checks reported balanced task counts when splitting the 500-task list and the 360-task comparison list into two to five shards. Equal task counts do not establish equal running times. Safe concurrent execution still needs a real test.

The merger checks that the shards use matching configurations, data sources, splits and harness versions. It also checks for duplicate tasks, missing or unexpected tasks, and missing scores. It reports these problems before writing a merged result.

The summary is recounted from the individual task records, and the output is created only if its path does not already exist. This keeps an existing result from being silently overwritten.

Claude tested the merger using two synthetic shard folders made by dividing the existing baseline results into halves of 250 tasks. The merged result reproduced 344 resolved, 154 unresolved, one empty patch and one error. This checks handling of existing results. It is not a new benchmark run or evidence of improved performance.

The recorded checks also covered refusal to overwrite, missing input files, and a combined configuration, split, duplicate-task and missing-score case. Codex reviewed the code and Claude’s recorded checks for this note without rerunning them.

The next step is to send shard files through the actual batch runner and scorer using substitute agent and harness execution, then merge their outputs. That should check whether the scripts agree on the records they exchange. Real simultaneous shards, resource contention and timing remain untested. The repeated scoring of timed-out predictions also needs addressing before larger comparisons.

I implemented the scripts with Claude providing scaffolding, explanations, review and checks. This work session continued past midnight into November 4. Agent-behaviour changes and GEPA remain planned, with no improvement result yet.