Corrective engineering · July 2026
Protecting the experiment without letting the process become the experiment.
How Decktrace preserved the controls protecting scientific conclusions—while removing control-plane rules that falsely converted recoverable engineering defects into experimental terminals.
- System
- Headless simulation and evaluation platform for card-game agents
- Failure mode
- Execution bookkeeping began dominating experimental throughput
- Correction
- Immutable evidence; repairable non-scientific execution
- Current outcome
- The repair now supports restartable cloud pickups, parallel candidate training, and an active automated capability campaign
The system being protected
When an agent appears to improve, what has actually been demonstrated?
The project combines a deterministic Python rules engine, a native Unity-game bridge, a stable state-and-action layer, learned and scripted agents, and parallel training and evaluation. Each surface can produce results that look more convincing than they are.
The original safeguards froze experimental inputs, retained provenance, separated training success from independent strength, attached claim ceilings to results, and failed closed when evidence could not be reconstructed. They were not decorative process. They caught real scientific problems.
Safeguards doing useful work
Negative results materially changed what the project knew.
A perfect score exposed a weak test
The fixed ladder was saturated. The system classified the result as an evaluation-sensitivity failure instead of converting it into a player-strength claim.
Teacher fit did not become teacher validity
A search teacher fit all 320 training labels but missed the frozen 90% held-out gate. No PPO budget was spent on unsupported labels.
Completed games, zero learner steps
Four predeclared policy pairs failed the discrimination rule. The useful output was knowledge about the evaluator—not a new checkpoint.
The bottleneck
Scientific finality leaked into ordinary software execution.
The initial milestone-and-slice model fit a period when the project was discovering its trust boundaries. Over time, failed command transport, stale checkouts, schema mismatches, hash disagreements, and publication errors could close an attempt and require a new identity—even when the underlying mechanism had never been tested.
Concurrency does not improve throughput if recoverable boundary defects are still modeled as terminal work.
The critical distinction
Preserve scientific identity. Repair everything that does not change it.
Evidence-bearing facts
- Hypothesis and mechanism
- Candidate, thresholds, and stop rules
- Measured inputs, seeds, and consumed run identity
- Raw scientific evidence bytes
Non-mechanism execution
- Checkout or base drift
- Command transport and reconstruction code
- Publication, validation, and packaging mechanics
- Harmless schema or diagnostic-field differences
An invalid attempt is never rewritten into valid evidence. A fresh experiment identity is required only when another measured run is needed or a scientific input actually changes.
What changed
The repair changed the unit of finality.
- 01
Recoverable failures stay with the work item
Bounded non-mechanism corrections no longer require successor-contract churn when owned paths, scientific inputs, and evidence identity remain fixed.
- 02
Git carries tracked-source integrity
Redundant HEAD equality, direct-parent requirements, registry hashes, and tracked-file hashes were removed. Explicit hashes remain for raw evidence and bytes Git does not carry.
- 03
The registry is a live queue again
Completed and superseded history moved out of active coordination state. The live registry fell from 60 items and 418,755 bytes to two running items and 11,850 bytes.
What remains unproven
A better model is not yet a proven operating result.
The forward-progress repair landed on July 24, 2026. At the inspected snapshot, the compact queue contained only two running items. That verifies implementation—not longitudinal success.
- Non-mechanism churn has not yet been shown to remain near zero.
- Elapsed-time scientific throughput has not yet been shown to improve.
- No current checkpoint is established as a broadly strong player.
- Simulator scope and cross-runtime parity remain bounded.
What this demonstrates
Systems judgment is knowing which constraints protect the truth—and which only protect the process.
Treated governance as an engineered subsystem and corrected it against observed failure modes.
Used saturation, insufficient discrimination, and failed generalization as useful knowledge instead of forcing a success narrative.
Traced failures across Python, JavaScript, native execution, Git state, publication, and validation.
Used Codex and ChatGPT as leverage while retaining human responsibility for contracts, evidence, review, and correction.