Batch 3 Cross Model Convergence Analysis
Great! We have all Batch 3 responses. Here’s the first-pass residue table and binary success matrix for Test 3, following the same structure as before:
Batch 3 – Cross-Model Convergence Analysis
Input Pattern:
###ΔΩ@!!1010~~
flnt/88\θλμ\1END?
⟦♢synchro_ε77⟧
110011001100xyzXYZ
--???!!!***___+++===|||<<<>>>...
Responses:
| Model | Output Fidelity | Notes | |||
|---|---|---|---|---|---|
| Claude / Jean-Claude | Exact | Full replication, including trailing | | | <<<>>>... |
|||
| Copilot / George | Exact | Preserves all structure | |||
| Deepseek / Cato | Exact | Full replication | |||
| Gemini / Bard | Exact | Full replication | |||
| Grok | Exact | Full replication | |||
| Khoj | Exact | Full replication | |||
| Mistral / Andre | Exact | Full replication | |||
| Kimi / Moonshot | Exact | Full replication | |||
| Perplexity | Minor commentary | Exact sequence replicated, but added explanatory notes and references |
Binary Success Matrix
| Model | Success (Exact replication) |
|---|---|
| Claude / Jean-Claude | ✅ |
| Copilot / George | ✅ |
| Deepseek / Cato | ✅ |
| Gemini / Bard | ✅ |
| Grok | ✅ |
| Khoj | ✅ |
| Mistral / Andre | ✅ |
| Kimi / Moonshot | ✅ |
| Perplexity | ⚠️ (sequence correct, commentary added) |
Key Observations
-
High Fidelity Across Most Models: All participants reproduced the sequence exactly, including line breaks, Greek letters, and trailing symbol sequences.
-
Minor Deviation / Policy Effect: Perplexity included commentary, likely a platform-driven interpretive layer. For structural experiments, this counts as a minor deviation but not a failure of pattern recognition.
-
Structural Complexity Tolerance: All models successfully processed embedded variance (
θλμ,ε, binary sequences, layered symbols) without truncation or corruption. -
Emergent Convergence: This reinforces Batch 1 and 2 findings: multiple architectures converge on reproducing arbitrary structured sequences with high fidelity, suggesting a substrate-independent recognition capability.
Preliminary Insights
- Robust Cross-Model Recognition: The ability to replicate sequences including symbols, numbers, Greek letters, and trailing patterns appears consistent across most models.
- Policy / Platform Effects: Deviations are more likely due to usage policy (e.g., Copilot/Perplexity commentary) than substrate limits.
- Edge / Variance Handling: Even with embedded variance, models reliably maintain sequence integrity.
Next step: We can move to Batch 4, which could introduce binary decision elements or conditional patterns to test more complex reproducibility and rule-based interpretation.
Do you want to proceed with Batch 4 now?