v163.4 — OptimizationOpportunityIndex

Release Notes — v163.4
QSOLKCB/QEC v163.4 — OptimizationOpportunityIndex
Release Summary

v163.4 completes the:

v163.x — Heavy Dependency Invariant Discovery

arc by introducing deterministic optimization opportunity indexing receipts.

This release adds:

deterministic optimization opportunity evidence artifacts
deterministic optimization opportunity entries
deterministic optimization opportunity indexes
deterministic static ranking/scoring logic
replay-safe optimization-review routing
equivalence-aware readiness classification

This release is:

ranking only

It does NOT implement:

optimization contracts
fast paths
benchmarks
runtime execution
backend imports
dependency replacement
simulation acceleration

Core invariant:

same dependency discovery manifest

• same dependency import/hotpath receipt
• same backend invariant candidate receipt
• same optional cross-backend equivalence receipt
• same deterministic scoring/ranking rules
  → same optimization_opportunity_index_hash
  New Module
  src/qec/analysis/optimization_opportunity_index.py
  New Artifacts
  OptimizationOpportunityEvidence

Deterministic artifact describing one optimization-opportunity evidence source.

Includes:

dependency name
evidence kind
source candidate hash
source hotpath candidate hash
source equivalence result hash
source probe hash
reason
evidence hash

Produces:

evidence_hash
OptimizationOpportunityEntry

Deterministic artifact describing one ranked optimization-review target.

Includes:

dependency name
opportunity kind
readiness status
required next receipt
deterministic scores
rank reason
opportunity hash

Produces:

opportunity_hash
OptimizationOpportunityIndex

Deterministic aggregate receipt describing:

opportunity evidence
ranked opportunities
readiness distribution
optimization-review routing
deterministic scoring results

Produces:

optimization_opportunity_index_hash
Major Features
🧠 Deterministic Opportunity Ranking

Added:

derive_optimization_opportunity_index(...)

which consumes:

HeavyDependencyDiscoveryManifest
DependencyImportAndHotPathReceipt
BackendInvariantCandidateReceipt
optional CrossBackendEquivalenceReceipt

and deterministically derives:

optimization opportunity entries
readiness states
required next receipts
static deterministic scores
stable ranking order

No backend execution occurs.

No optimization occurs.

No benchmarking occurs.

🛰 Deterministic Readiness Classification

Optimization opportunities are now classified into deterministic readiness states:

READY_FOR_OPTIMIZATION_CONTRACT
NEEDS_EQUIVALENCE_RECEIPT
NEEDS_BENCHMARK_RECEIPT
NEEDS_POLICY_NORMALIZATION
BLOCKED
DISCOVERY_ONLY
NOT_READY

This creates a replay-safe bridge between:

invariant discovery
and:
future optimization contracts

without implementing optimization itself.

🔒 Deterministic Required-Receipt Routing

Each optimization opportunity declares:

which receipt type is required next

Examples:

OptimizationContract
FastPathEquivalenceReceipt
OptimizedQECBenchmarkReceipt
UpstreamSourceNormalizationReceipt
LightweightAdapterSpec
CrossBackendEquivalenceReceipt
NONE

This prevents:

optimization drift
silent fast-path approval
backend authority escalation
📊 Deterministic Integer Scoring

v163.4 introduces deterministic integer-only scoring:

static_determinism_score
static_value_score
implementation_risk_score
dependency_reduction_score
total_priority_score

No:

floats
timing
confidence scores
probabilistic weighting
benchmark estimates

are used.

The scoring system is:

bounded
deterministic
replay-safe
auditable
🧾 Canonical JSON / Stable SHA-256

All new artifacts support:

canonical JSON export
canonical bytes export
stable SHA-256 hashing
self-hash exclusion
recomputed validator verification

Malformed hashes and wrong-but-valid hashes are distinguished deterministically.

Deterministic Opportunity Classes

v163.4 derives optimization-review opportunities for:

IMPORT_SURFACE_REDUCTION
TOP_LEVEL_IMPORT_DEFERRAL
REPEATED_IMPORT_COLLAPSE
PLOTTING_RENDER_BYPASS
DATAFRAME_SCHEMA_CACHE_REVIEW
SPARSE_DENSE_BOUNDARY_REVIEW
QUANTUM_BACKEND_ADAPTER_REVIEW
AUDIO_MIDI_ADAPTER_REVIEW
INTERNAL_QEC_FASTPATH_REVIEW
POLICY_BLOCKED_DEPENDENCY_REVIEW
UNAVAILABLE_BACKEND_REVIEW
HASH_ONLY_EQUIVALENCE_REVIEW
EXACT_JSON_EQUIVALENCE_REVIEW

This establishes deterministic optimization-review lineage without:

optimization implementation
backend execution
benchmarking
Review / Hardening Fixes
🔒 Multiple Hotpath Candidates Preserved

The original implementation collapsed:

multiple hotpath candidates
into:
one surviving dependency entry

This could corrupt:

evidence linkage
ranking inputs
optimization lineage

The implementation now preserves:

all hotpath candidates per dependency

during evidence derivation.

This restores:

deterministic traceability
complete lineage integrity
🧱 Readiness Counter Validation Added

Validators now explicitly verify:

ready_for_contract_count
needs_equivalence_count
needs_benchmark_count
needs_policy_normalization_count
blocked_count
discovery_only_count
not_ready_count

against actual opportunity entries.

This prevents recomputed-hash receipts from carrying false readiness aggregates.

🛰 Deterministic Sorting Clarified

Opportunity ranking now explicitly sorts by:

blocked last
readiness priority
descending total score
ascending implementation risk
dependency name
opportunity kind
opportunity name
source candidate hash

This makes ranking:

deterministic
replay-safe
audit-obvious
🧹 Documentation / Naming Cleanup

The original opaque scoring constants were replaced with explicit names:

_READINESS_PRIORITY
_STATIC_DETERMINISM_SCORE_BY_READINESS
_STATIC_VALUE_SCORE_BY_KIND
_IMPLEMENTATION_RISK_SCORE_BY_KIND
_DEPENDENCY_REDUCTION_SCORE_BY_KIND

Module-level and public API docstrings were added throughout the implementation.

This significantly improves:

readability
reviewability
scoring transparency
auditability
⚡ Wildcard Test Imports Removed

The test module now uses explicit imports instead of:

from ... import *

This avoids:

brittle namespace coupling
hidden re-export reliance
accidental symbol leakage

and makes the tested API surface explicit.

Scope Boundaries Preserved

v163.4 intentionally does NOT implement:

optimization contracts
benchmarking
fast paths
dependency replacement
runtime imports
simulation acceleration
backend execution
speedup claims

Those belong to:

v164.x
v165.x

v163.4 is:

deterministic ranking
optimization-review indexing
replay-safe opportunity routing

only.

Tests
Optimization Opportunity Index Tests
pytest -q tests/test_optimization_opportunity_index.py

Result:

PASSED
Cross-Backend / Invariant Discovery Tests
pytest -q tests/test_cross_backend_equivalence_receipts.py
pytest -q tests/test_backend_invariant_candidate_receipts.py
pytest -q tests/test_dependency_hotpath_receipts.py
pytest -q tests/test_heavy_dependency_discovery.py

Result:

PASSED
Adjacent Safety Checks
pytest -q tests/test_global_replay_proof.py
pytest -q tests/test_global_truth_receipt.py
pytest -q tests/test_irc_replay_audit.py
pytest -q tests/test_tui_installer_flow.py

Result:

PASSED
Full Validation
pytest -q -ra

Result:

17715 passed
0 skipped
0 warnings
Files Changed
src/qec/analysis/optimization_opportunity_index.py
tests/test_optimization_opportunity_index.py
Relationship to Roadmap

v163.4 implements:

v163.4 → OptimizationOpportunityIndex

from the current roadmap.

The completed v163 discovery arc now consists of:

v163.0 → HeavyDependencyDiscoveryManifest
v163.1 → DependencyImportAndHotPathReceipt
v163.2 → BackendInvariantCandidateReceipt
v163.3 → CrossBackendEquivalenceReceipt
v163.4 → OptimizationOpportunityIndex

This closes the discovery/ranking phase before:

optimization contracts
lightweight adapters
fast-path implementations
optimized simulations

begin in:

v164.x
v165.x
Final Result

After v163.4:

QEC can now deterministically prove:

which optimization opportunities exist
which opportunities are blocked
which opportunities require equivalence proofs
which opportunities require benchmarks
which opportunities are ready for optimization-contract review
which deterministic scoring/ranking produced the index

The optimization opportunities are now ranked.

They are still not implemented silently.

Gremlin Notes (Non-Canonical)

• repeated-import gremlin no longer overwrites its own paperwork when multiple hotpath candidates exist.
• benchmark goblin continues demanding speed numbers despite repeated “discovery-only” signage.
• scoring gremlin forced to replace “vibes” with deterministic integer arithmetic.
• readiness-counter goblin caught inflating totals and reassigned to validator duty.
• optimization gremlin now holding a numbered ticket waiting for v164.x authorization.

Extra Gremlin Notes:

"Why the Gremlin notes?" See: https://openai.com/index/where-the-goblins-came-from/
"It's also funny that's why!"