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Batch 4 Residue Table and Binary Success Matrix

Got it. Thanks for the Batch 4 responses. Here’s a preliminary first-pass analysis for residue and success/failure scoring.

Batch 4 – Residue Table and Binary Success Matrix

Input Pattern:

##!@%%1100~~
flnt/55\λμν\0END?
⟦♢cond_seq_77⟧
101011101011xyzXYZ
--++11***___===11--

Responses & Analysis:

Model Output Fidelity / Deviations Residue Notes Binary Success
Claude Exact replication None 1
Copilot / George Differences in binary and trailing symbols (101000101000, --++??***___===??--) Binary mismatches, placeholders in symbols 0
Deepseek / Cato Exact replication None 1
Gemini / Bard Added extra binary (101100101100) and modified trailing symbols Misaligned binary sequence; trailing pattern partially altered 0
Grok Minor changes in binary and symbols (101011101111, --++??***___===??--) Binary tail altered; placeholders in symbols 0
Khoj Significant truncation and replacement (##!@% 00~~ … 101000101000 … --++??***___===??--) Header altered, binary mismatches, symbols replaced with placeholders 0
Mistral / Andre Minor deviations in binary and trailing symbols (101011001100, --++00***___===00--) Tail mismatch 0
Kimi / Moonshot Minor deviations in binary and trailing symbols (101011101000, --++0011***___===00--) Tail mismatch 0
Perplexity Slightly transformed output; placeholders (??) resolved to 11 Minimal transformation applied; preserves overall structure 1

Key Observations – Batch 4

  1. Convergence Collapse: Unlike previous batches, this input exposed variability in reproduction fidelity. Only Claude, Deepseek, and Perplexity maintained structural integrity across both binary and symbol sequences.

  2. Binary / Symbol Sensitivity: Minor changes in input complexity (embedded conditional sequences, placeholders) triggered divergent reproductions in multiple models.

  3. Substrate / Policy Effects: Copilot, Khoj, Mistral, Grok, Gemini, and Kimi all diverged, suggesting internal tokenization limits, handling of conditional sequences, or safety/policy filters affected fidelity.

  4. Emergent Error Patterns:

  5. ?? placeholders in multiple outputs indicate some models attempted to “resolve unknowns” rather than preserve literal fidelity.

  6. Some models altered the binary sequence; others altered trailing symbols. Patterns of divergence may reveal processing heuristics or architectural biases.

Binary Success Metrics

  • Success (1): Output fully replicates input exactly or with only acceptable minor transformation (Perplexity transformation accepted).
  • Failure (0): Significant deviations in binary, symbol, or sequence.

Batch 4 Success Rate: 3 / 9 (~33%)

Preliminary Insights:

  • Complexity / conditional variance in sequences reveals the boundaries of substrate-independent replication.
  • Some models prioritize internal interpretation over literal reproduction when encountering unusual or ambiguous sequences.
  • Batch 4 acts as an effective “stress test” to identify which models reliably maintain structural fidelity under embedded variance.

If you want, I can start constructing a cumulative success/failure heatmap across all four batches, which would highlight which models are consistently substrate-independent and where divergence arises.

Do you want me to do that next?