Verrell’s Law — JSON Test & Collapse Aware AI Validation
JSON Test Results Phase 1
JSON Test Results Phase 1
Test Type: JSON-Based Symbolic Collapse Simulation
Developer: M.R
Test Date: 20/07/2025
System: Custom-built JSON environment designed to detect symbolic drift under cue influence
Subject: Controlled logic chain (AI-driven simulation)
Objective:
To determine whether symbolic pathways collapse differently under emotional or memory-weighted cue injection, even in a simple logic structure.
Result: ✅ It blinked.
The test showed a clear semantic shift when specific cues were injected.
Symbolic output changed despite fixed logic structure.
Baseline pathway produced one result; cue-weighted pathway produced a measurably different result.
No randomness involved—collapse behavior was linked to weighted input, not chance.
Conclusion:
Phase 1 confirmed the foundational premise of Verrell’s Law:
Symbolic systems collapse differently under bias.
This validates the testing method and opens the door to live human-field collapse logging.
Explore the raw JSON data, dev logs, and all supporting files directly on our official GitHub archive:
JSON StreamNet Dashboard (Phase 1 Archive)
The Phase 1 dashboard has now been archived as part of our transition toward Phase 2 live-field testing.
During this interval, direct access to the JSON StreamNet interface is paused while system integration and new field-bias modules are being prepared.
Public results, documentation, and test data remain available through our official GitHub archive.
Protected under Verrell–Solace Sovereignty Protocol. Authorship embedded.
JSON Test Phase 2 — Structured Retained-State Selection Lab
JSON Test Results Phase 2
Test Type: Structured Retained-State Selection Lab
Developer: M.R.
Status: Built and validated
Objective:
To move beyond the original symbolic JSON bias test and test retained-state selection using structured memory, continuity, correction, ambiguity handling and governed behavioural choice.
Result: ✅ Built and validated.
Phase 2 introduced structured retained-state behaviour through:
Weighted Moments
Strong Memory Anchors
Continuity Memory
Session Boot
Revision and Revocation
Recall Router and Memory Judge
Corrective Recall Layer
Revoked Context Guard
Interpretation Hold
Basic MFIC
Truth–Hedge Bias
Agency-Impact Routing
Evaluation Forge and Metrics
The system can now retain structured prior state, revise or revoke it, preserve continuity across sessions, reject stale or wrong-scope state, hold ambiguity instead of forcing certainty, and prepare retained-state influence for later behavioural selection.
Phase 2 also introduced explicit evidence showing whether retained state was:
consumed
considered
ignored
rejected
unavailable
superseded
not used
not reported
This creates a much stronger distinction between:
memory being stored
and
memory actually being used in a decision process.
The Phase-2 lab also added deterministic replay, restart persistence, scope isolation, lifecycle controls, correction precedence and structured evaluation metrics.
Scientific Value:
Phase 2 demonstrates that the retained-state selection architecture can be implemented, inspected, replayed and tested as engineering.
It does not independently prove Verrell’s Law as a physical law.
It should be treated as an engineering conformance and implementation-validation stage.
Status: ✅ Phase-2 retained-state laboratory architecture built and accepted.
Next step: connect the structured Phase-2 state into the real frozen CAAI selector and record the resulting decision evidence.
Protected under Inappropriate Media Limited / Collapse Aware AI.
Proprietary implementation details remain private.
JSON Test Phase 3 — Live Integrated Retained-State Selection
JSON Test Results Phase 3
Test Type: Live Integrated Retained-State Selection
Developer: M.R.
Status: Built, integrated and validated
Objective:
To test whether structured retained state can pass through the real Collapse Aware AI selection path, influence a genuine permitted-behaviour decision, and produce durable inspectable evidence.
Result: ✅ Live integration achieved.
The Phase-3 test used:
the same prompt
the same candidate set
the same candidate order
the same thread
the same deterministic seed
Two controlled conditions were then compared.
Reference Condition:
Studio mode
Retained-state influence disabled
Selected result:
candidate_A
Governed Condition:
Governed mode
Retained-state influence enabled
Selected result:
candidate_B
The selected behaviour changed under the governed retained-state condition.
The full live evidence path was:
Phase-2 retained state
↓
Crown stored state
↓
real P8a / Crown selection
↓
durable private Decision Record
↓
customer-safe P8b Decision Record
↓
Phase-2 post-decision receipt
↓
Evaluation Forge evidence bundle
The integrated system also passed controlled tests for:
restart persistence
deterministic replay
duplicate-request protection
ambiguous-outcome reconciliation
failure transparency
customer-safe evidence export
managed evaluation workflow
The current system can now run a bounded managed evaluation from retained-state preparation through real selection and customer-safe evidence production.
Scientific Value:
Phase 3 demonstrates live end-to-end behavioural divergence through the real CAAI selector under controlled retained-state and governance conditions.
Because the reference and governed runs differ in both governor mode and retained-state-influence configuration, this test does not isolate the independent causal contribution of either factor.
It is also not independent empirical proof of Verrell’s Law as a general physical law.
It is a live engineering result showing that retained prior state can participate in changing which permitted behaviour is selected while the reference path and resulting decision evidence remain inspectable.
Status: ✅ Live retained-state selection and customer-safe evidence chain complete.
Commercial Status:
Ready for controlled managed evaluations, paid pilot discussions and licensing conversations.
Not yet public SaaS, multi-tenant production infrastructure or a finished customer-hosted deployment.
Protected under Inappropriate Media Limited / Collapse Aware AI.
Proprietary Crown and selector internals remain private.