confidence-engine/docs/v0.6-release-notes.md

v0.6 Release Notes

Purpose

v0.6 turns the engine into a deterministic recursive reasoning system that keeps next questions, decomposition, propagation, and confidence updates explicitly grounded in the situation graph.

Capabilities added

• deterministic unknown selection explanations
• explicit ambiguity handling instead of silent tie-breaking
• comparability assessment before relationship reasoning
• relationship classification after comparability
• reasoning-stage progression after comparability answers
• graph-backed next questions via explicit unknown nodes
• investigation-strategy-based question formulation
• atomicity assessment for selected unknowns
• composite-unknown decomposition into child unknowns
• child-quality validation for decomposition outputs
• upward propagation from resolved children to parents and ancestors
• separation of evidence confidence, completeness, and conclusion confidence
• deterministic cross-branch corroboration, conflict, and duplicate-evidence handling
• developer-facing reasoning architecture documentation

Reasoning pipeline summary

Scenario
→ Reconstruction
→ Initial graph
→ Deterministic unknown selection
→ Question
→ Answer
→ Proposal
→ Proposal parsing / validation
→ Graph update
→ Reasoning-state rebuild
→ Comparability assessment
→ Relationship classification
→ Emergent unknown creation / reuse
→ Atomicity assessment
→ Optional decomposition
→ Propagation
→ Confidence / completeness / corroboration update
→ Next active unknown
→ Next question

Core invariants

• every asked question must originate from an explicit unresolved unknown
• unknown selection is deterministic
• ambiguity is preserved explicitly when no justified distinction exists
• relationship reasoning cannot precede comparability
• parent nodes cannot resolve before completion rules are met
• confidence cannot outrun completeness
• duplicate evidence cannot increase confidence
• conflicting evidence caps conclusion confidence
• cross-branch corroboration only counts for distinct branches with distinct evidence keys
• the LLM proposes updates but does not mutate the graph directly

What v0.6 proved

• graph-backed questioning works better when every justified next question maps to an explicit unresolved node
• broad unknowns can be decomposed deterministically before direct questioning
• resolved child evidence can be propagated upward without prematurely resolving parent reasoning
• confidence becomes easier to reason about when evidence quality, completeness, and conclusion strength are separated
• deterministic cross-branch corroboration can improve support without double-counting repeated evidence

Known limitations

• sibling selection still depends on the existing deterministic scorer and may choose a justified next branch that is not always the intuitively expected one
• cross-branch corroboration is limited to direct child branches of the same parent
• no multi-hop corroboration exists across unrelated subtrees
• reasoning remains bounded to explicitly represented graph structure and user-provided answers

Deliberate exclusions

• no persistence
• no autonomous exploration
• no probabilistic reasoning
• no Bayesian reasoning
• no semantic embeddings
• no expert mode
• no multi-hop corroboration across unrelated subtrees
• no heavy graph visualisation

Next experimental question

Can the engine preserve and reuse successful reasoning structures across separate cases without turning prior experience into unquestioned assumptions?