What happens when the debate can learn from its failures?
With the stationary 50-seed benchmark established, I then enabled online adaptation to test whether the BARRED debate swarm could recover from failures rather than treating every failed adjudication as terminal.
I ran five independent 50-seed evaluations using the same 50 virgin C scenarios: 25 safe and 25 vulnerable. The Pro and Con debaters started from a warm Pareto frontier, while the live GEPA Reflector was allowed to inspect failed Round 0 attempts, generate graph-informed mutations, and retry them.
Across the five runs:
80.4% ± 5.5% accepted-row yield
0 accepted logic errors across 201 accepted rows
50.4% cumulative Round 1 recovery rate
The reflector encountered 113 Round 0 failures and recovered 57 of them on the refinement pass.
The adaptation was not simply producing more prompts. Across the 370 recorded attempts, the Pareto ledger grew from 355 to 669 candidate mutations, with the largest gains appearing in the concurrency, input_validation, and memory_safety buckets.
The interesting part was that a failed first-pass decision became a learning signal.
The full program was re-analyzed, the failure was mapped back to structural evidence, and a targeted mutation was generated before the second attempt. In other words, the system could use an adjudication failure as information for the next decision rather than simply retrying the same reasoning path.
There was also a measurable cost to this process.
The active system consumed 863,003 ± 46,855 benchmark tokens across the five runs, compared with 791,117 ± 64,573 for the stationary sliced baseline. Tokens per accepted row were 21,571 ± 2,179.
So the result was not simply “more adaptation is better.”
The experiment showed that online adaptation recovered a substantial fraction of failed adjudications while maintaining zero accepted logic errors in this benchmark, but that recovery came with additional computational cost.
This is a post-submission research evaluation, not a replacement for the original hackathon benchmark. The original BARRED-Fleet submission remains the submitted artifact; this update documents the continued research during judging.
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