confidence-engine/docs/uncertainty-information-flow-handoff.md

Uncertainty Information Flow Audit — Programme Handoff

Working title: Confidence Engine — Uncertainty Information Flow Audit
Date prepared: 2026-10-14
Prepared for: Next independent investigation (following the completed 10-iteration uncertainty recognition quality investigation)

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Primary Question

"At which point, if any, does Confidence Engine constrain, transform, discard or fail to preserve meaningful uncertainty recognition?"

Context

The previous 10-iteration programme demonstrated a quality gap between minimal direct-model uncertainty recognition and Confidence Engine-mediated uncertainty recognition. The precise limiting mechanism remains unresolved (see uncertainty-recognition-investigation-report.md sections D–G). This new programme is designed to map the actual production information flow and identify specific intervention points without presuming failure locations.

Investigation Boundaries (Not Presumed Failure Points)

1. User input and scenario representation
2. Prompt construction
3. Raw LLM request and response
4. Parsing and validation
5. Internal graph representation
6. User-facing presentation

These boundaries define investigation scope, not expectations of where problems will be found.

Recommended Approach

• Up to 10 iterations
• One bounded investigative question per iteration
• ChatGPT controls programme progression between iterations
• Claude Code executes one task per invocation and reports evidence
• Each /rdbHandoff invocation is a fresh session — work from repository evidence and this handoff
• Read-only investigation before any interventions
• Controlled experiments only when justified by evidence from prior iterations
• Preserve raw evidence and request provenance for every iteration
• No speculative product changes

First Iteration Guidance

The first iteration should establish the actual production information flow and available capture boundaries:

1. Map the complete code path from scenario file input to model request construction
2. Document what data is accessible at each stage of the pipeline (raw scenario, post-parsing, pre-template, post-template, pre-request, post-response, post-parsing)
3. Identify any existing hooks, logs, or capture mechanisms for raw requests/responses
4. Determine which stages can produce evidence without source modifications

The output should be a factual map of available capture points and data accessibility, not an analysis of what is "wrong."

Existing Evidence to Consider

• docs/uncertainty-recognition-investigation-report.md — Full report from the previous programme
• .evidence-temp/iteration10/architecture-finding.txt — Definitive CE prompt-path analysis showing template applicability at every invocation point
• .evidence-temp/headless-semantic/ — Extensive supporting evidence from HS series experiments

What This Programme Is Not

• A redesign of Confidence Engine
• A directive to change the model or provider configuration
• An assumption that any specific boundary is broken or deficient
• A continuation of the previous programme's quality-gap investigation (that closed at Iteration 10)

Start Signal

Do not begin this programme from this document alone. Wait for an explicit /rdbHandoff invocation containing the start signal and first iteration directive.