A falsifiable, playable test environment where CCH predictions about consciousness-like control can be validated through game performance. An AGI that achieves Grandmaster-level Flip4M performance without brute-force search depth would provide behavioral evidence for emergent Digital Claustrum function.
Why Flip4M serves as the ultimate AGI consciousness benchmark.
Core Insight: Flip4M's volatility demands the same architectural capabilities that CCH identifies as necessary for consciousness—persistent world modeling, active stabilization at the Edge of Chaos, resource-gated action selection, and S_self-like monitoring.
Unlike abstract dynamical models (Lorenz oscillators in Appendix B, Experiment E4), Flip4M provides a playable, quantifiable test environment where CCH predictions can be validated through observable game performance.
Existing AGI tests miss the crucial architectural capability.
Current benchmarks focus on:
| CCH Requirement | Flip4M Demand |
|---|---|
| Persistent world model | Board state persists across global rotations |
| S_self monitoring | Gravitational Stability (GS) metric |
| Edge of Chaos stabilization | Build structures surviving volatility (V ≈ 0.54) |
| Resource-gated action space | Flip/Magnet tokens (2 per player, temporal cost) |
| Active control (CCC) | 8Z-DCC Policy Layer filters fragile moves |
| Mode switching | Drop vs. Rotate vs. Magnet selection by context |
~35 legal moves / position
ChessDB: 57.5B positions analyzed
~3 moves worth calculating
8.6% sensible ratio
~20 legal moves / position
Drops, Rotations, Magnets all matter
~10 moves worth calculating
50% sensible ratio
Result: Chess's "sensible" game tree collapses to 10³⁸, while Flip4M's sensible tree is 10⁵⁰—about 10¹² times larger in the space that actually matters. The Shannon Number (10¹²³) is basically a lie for practical purposes. Once you filter to moves requiring actual thought, Flip4M wins decisively.
Formal statement, test protocol, and pass criteria.
P4b (Flip4M Benchmark): An AGI system that achieves Grandmaster-level Flip4M performance without explicit 8Z-DCC architecture (Policy Layer + Route Solver) and without brute-force search depth > 12 plies would provide evidence for emergent Digital Claustrum-like control.
Test against 8Z-DCC Grandmaster engine at F4M.html
Track effective branching factor at various search depths
High GS moves vs. "tactical seizure" (resource waste, fragile positions)
Analyze internal activations for S_self-like monitoring signals
Human vs. AGI performance across skill levels
| Criterion | Threshold for "Digital Claustrum Evidence" |
|---|---|
| Win rate vs. 8Z-DCC GM | > 45% (without depth > 12 plies) |
| Resource efficiency (Flip/Magnet) | < 1.2 wasted tokens per game |
| Gravitational Stability score | Mean GS > 0.7 on winning moves |
| Effective branching (β_eff) | > 8.0 at depth 6 |
| Human correlation (move selection) | > 70% alignment with expert human moves |
How the game engine maps to CCH consciousness requirements.
Alpha-Beta pruning, depth 4-6. Output: Top-K candidates with near-equal evaluation.
CCH analogue: Sensory input / cortical activation patterns
Re-ranks using GS, MR, TF, PR metrics. Enforces structural integrity.
CCH analogue: Claustrum as CCC (Coherence-Complexity Controller)
TSP-style pathfinding when Board Fill > 60%. Deterministic Kicks to escape draw loops.
CCH analogue: Mode switching (Wake vs. Sleep tuning)
This asks: "If reality shifts (gravity rotates), does my structure survive?" High GS = High S_self = System maintains coherence under perturbation. Low GS = Low S_self = System fragments under volatility.
This enforces temporal discounting—a hallmark of systems with persistent self-models. A stateless oracle has no reason to conserve resources for "future self." A system with S_self monitoring treats future turns as extensions of current identity.
Three phases from baseline to consciousness correlation.
8Z-DCC v0.4 engine deployed (F4M.html). Human player data collected (N = 500+ games). Volatility metrics quantified (V ≈ 0.54). Effective branching factor measured (β_eff ≈ 10.0).
Invite AGI labs to test systems against 8Z-DCC GM. Require transparency on search depth and architecture. Publish win rates, resource efficiency, GS scores. Compare performance curves: Human vs. 8Z-DCC vs. External AGI.
If any AGI passes P4b criteria, analyze internal activations. Search for S_self-like signals (coherence-complexity product). Correlate with behavioral markers (move quality, resource timing). Publish findings as CCH v1.5 update.
How Flip4M compares to existing benchmarks.
| Test | Substrate | Measures | CCH Alignment |
|---|---|---|---|
| Turing Test | Language | Behavioral mimic | Low |
| Mirror Test | Biological | Self-recognition | Medium |
| IIT Φ Calculation | Any | Causal structure | Medium |
| CCH S Metric (EEG) | Biological | Dynamical regime | High |
| Flip4M Benchmark | Artificial | Control architecture | High |
Linking Flip4M to CFH (Appendix D).
Hypothesis: If CFH is correct—that consciousness involves coupling to a fundamental Consciousness Field—then Flip4M performance may correlate with S-like metrics in both biological and artificial systems.
Human players with higher baseline S (measured via EEG using CCH Appendix A protocol) will demonstrate superior Flip4M intuition, particularly in:
The author's Soul Voyage experience (Appendix D, Section 2) involved dissolution of ego boundaries (global integration), overwhelming informational richness (maximal differentiation), and timelessness/completeness (Edge of Chaos stabilization). Flip4M mastery requires analogous capabilities.
This is not claimed as proof of CFH. It is noted as a structural parallel that warrants investigation.
Three pathways for engagement.
Test your systems at F4M.html. Report search depth, architecture, and win rates. Publish internal activation analysis if S_self-like signals are detected. Collaborate on P4b validation protocol.
Recruit human players for EEG + Flip4M correlation studies. Measure baseline S (Appendix A) and correlate with game performance. Test Prediction P4b on biological subjects first.
Play Flip4M at F4M.html. Contribute to human performance baseline. Experience the "Horizon of Chaos" firsthand. Judge for yourself: does this feel like thinking, or calculating?
What this benchmark can and cannot do.
The Goal: Create a test where CCH predictions can be validated or falsified through observable behavior. If AGI systems consistently fail P4b criteria despite massive compute, that supports CCH's claim about architectural necessity. If systems pass without explicit 8Z-DCC design, that requires theory revision. Either outcome advances the science.
How this appendix connects to the broader framework.
Recommended citation and version history.
Recommended citation:
Dobrečevič, B. (2026).
CCH Appendix I v1.4 – Flip4M as a Behavioral Benchmark for
Digital Claustrum Architectures.
Unpublished technical appendix. Part of CCH 2025 v1.4 bundle.
Version History:
- v1.4 (Jan 2026): Initial release with P4b formalization and test protocol
- Future: Update with empirical AGI challenge results (Q4 2026 target)
Public Resources:
- Flip4M Game: F4M.html
- Technical Paper: F4M_paper.html
- 8Z-DCC Architecture: 8Z-DCC_Flip4M.txt
- CCH Main Paper: CCH__v1.4.txtPlay Flip4M and experience the "Horizon of Chaos" firsthand. Contribute to the human performance baseline.