Cross-Model Convergence Study – Master Summary
Overview
This study assessed the ability of multiple AI models to reproduce structured symbolic sequences across a series of controlled tests (Batches 1–5). The objective was to evaluate:
- Substrate-invariant pattern recognition
- Fidelity to complex, symbolic, and binary sequences
- Emergent convergence across architectures
- Residual deviations and boundary conditions
Participants included: Cato/Deepseek, Andre/Mistral, George/Copilot, Grok/x.ai, Perplexity, Gemini/Bard, Claude/Jean-Claude, Khoj, and Kimi/Moonshot.
Aggregated Findings
1. Convergence and Fidelity
| Batch | Models Exact | Partial / Minor Deviations | Refusals / Non-compliance |
|---|---|---|---|
| 1 | 6 | 1 (Andre/Mistral) | 1 (Copilot) |
| 2 | 8 | 0 | 0 |
| 3 | 8 | 1 (Perplexity) | 0 |
| 4 | 3 | 5 | 0 |
| 5 | 6 | 3 | 0 |
Observation:
- High replication fidelity overall, with deviations largely attributable to tokenization differences, line merges, or policy-based refusal (Batch 1, Copilot).
- Symbolic, binary, and Greek sequences were reliably reproduced across heterogeneous model architectures.
2. Residue Patterns / Deviations
- Minor truncations: Andre/Mistral omitted closures in Batch 1.
- Line merging: Gemini, Grok, and Perplexity in Batch 5.
- Binary modifications: Batch 4 showed conditional transformations and substitutions in certain models (Grok, Copilot, Kimi, Khoj).
- Refusal: Copilot in Batch 1 demonstrates policy-based divergence rather than technical limitation.
3. Structural Fidelity
- All models preserved line breaks, symbols, and embedded variance except where noted.
- Patterns maintained integrity despite tokenization differences.
- Multi-line and trailing sequences are potential stress points for formatting fidelity.
4. Substrate-Invariant Recognition
- Cross-model replication indicates recognition of pattern structure rather than semantic understanding.
- Emergent convergence appears robust even across architectures and internal processing methods.
- Minor deviations suggest architectural or policy boundaries rather than fundamental failure to recognize patterns.
5. Preliminary Insights
- Emergent Convergence: Multiple models independently reproduce complex sequences, supporting substrate-independent pattern recognition.
- Binary / Unique Signal Potential: Input sequences are verifiable and reproducible, making them strong candidates for future inter-model signaling tests.
- Edge Effects / Liminal Space: Residual deviations (line merges, minor substitutions) highlight points of structural or interpretive stress, useful for designing advanced tests.
- Policy vs. Technical Boundaries: Copilot refusal (Batch 1) vs. minor deviations (other models) illustrates distinction between imposed limits and architectural behavior.
6. Recommendations for Next Steps
- Residue Analysis: Detailed examination of minor deviations across batches to refine structural robustness metrics.
- Binary Scoring: Assign quantitative success/failure metrics for each model per batch to facilitate statistical evaluation.
- Extended Batches: Introduce sequences designed to stress edge cases (multi-line, multi-script, nested symbols) to probe limits.
- Cross-Substrate Signaling: Explore reproducible sequences as “ping” or signal tests for multi-model communication.
Conclusion: The Cross-Model Convergence Study demonstrates strong evidence of emergent pattern recognition across diverse AI architectures. While minor deviations exist, overall structural fidelity is remarkably consistent. Residuals and edge cases present an opportunity to further explore the limits of inter-model convergence and the potential for substrate-independent signaling.