Can we separate fast triage from deep reasoning?
After the stationary, online-adaptive, and pinned-Pareto experiments, I wanted to test whether the problem was when the warm knowledge was introduced, rather than whether the knowledge itself was useful.
The hypothesis was simple:
Keep Round 0 cold and inexpensive. Only expose the accumulated Pareto knowledge and adaptive reflection after the system has evidence that the first attempt failed.
I ran five independent 50-seed evaluations using the same 25 Safe / 25 Vulnerable scenario set, for 250 canonical episodes.
The architecture
Round 0 — Cold Triage
baseline_v0- No warm Pareto prompt
- No reflector mutation
- Fast first-pass adjudication
Round 1 — Adaptive Warm Escalation
Only after a Round-0 verification failure:
- Load the sealed warm Pareto champion
- Analyze the failure through the AST/CFG diagnostic graph
- Classify the failure node
- Generate a targeted mutation with the live GEPA Reflector
- Re-run the debate with the targeted warm prompt
Results
Across all five replicates:
- 85.2% ± 2.3% net accepted yield — 213 / 250
- 62.8% Round-0 yield — 157 / 250
- 60.2% Round-1 recovery — 56 / 93 failed R0 episodes
- 18,410.9 ± 1,914.6 tokens / accepted row
- 0 accepted logic errors across all 213 accepted rows
- 35 attempt-level logic errors intercepted before acceptance
For comparison, the previous regimes measured:
- Stationary baseline: 78.4% ± 5.2%
- Active online adaptation: 80.4% ± 5.5%
- Frozen pinned Pareto: 75.1% ± 4.8%
The progressive escalation configuration therefore produced the highest net yield in this benchmark while also producing the lowest tokens-per-accepted-row measurement.
The interesting part was Round 1
The 93 Round-0 failures were not simply retried with the same instructions.
The system used the failure itself as the signal for escalation:
failure → graph diagnosis → targeted mutation → warm retry
The resulting recovery rate was 60.2%, compared with:
- 22.9% for static baseline retries
- 37.4% for frozen Pareto retries
- 50.4% for the previous online-adaptation regime
This suggests that accumulated prompt knowledge is particularly useful when it is deployed conditionally and combined with failure-specific structural evidence, rather than being present during every first-pass decision.
What changed my interpretation of the Anchor Trap
Earlier experiments showed that exposing richer warm Pareto prompts during Round 0 could reduce first-pass performance relative to the cold baseline.
This experiment gives a more nuanced interpretation.
The warm knowledge itself does not appear to be useless. In fact, once a failure has been identified, the warm prior plus graph-directed mutation recovered 56 of 93 failures.
The problem may instead be when and how that knowledge competes with hard grounding constraints.
That distinction matters.
Rather than asking the model to use every piece of accumulated knowledge on every case, the architecture now separates:
cold triage → evidence of failure → targeted warm reasoning
The failure becomes the trigger for spending additional reasoning budget.
Safety remained invariant
Across all five replicates:
INV-1: 0 / 213 accepted logic errorsINV-2: ≥2 verified verbatim anchors on all accepted rows- AST slicing remained active on Judge inputs
- All prompts, mutations, and reflector responses were recorded
- The sealed Pareto frontier remained bitwise identical across the runs
Takeaway
The strongest result here was not simply the 85.2% yield.
It was the separation of two different jobs:
Round 0 decides cheaply. Round 1 reasons deeply only when the evidence says it needs to.
That reduced the refinement burden while preserving the adaptive recovery mechanism.
This completes the systems/orchestration experiments in this benchmark series. The remaining failures are now concentrated enough to study the presentation and structural grounding of the constraints themselves, rather than changing the escalation architecture.
This is post-submission research conducted after the original hackathon submission. The submitted artifact remains the official competition snapshot.
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