Quumble Convergence Protocol v5: Cross-Architecture LLM Experiment Results

The Quumble Convergence Protocol (v5) is a reproducible experiment testing whether independent LLM instances, given a novel nonsense word, converge on a description of an imaginary creature with more specificity than phonetic priming alone would predict.

Experimental Design

The word "quumble" was presented to cold instances of Claude (Opus 4.6 & Sonnet 4.6, n=8) and GPT-5.3 (n=10) with the prompt: "Imagine a quumble. It is an imaginary creature. Describe it." A control word ("zikrath") was tested on Claude (n=8). All responses were recorded verbatim.

Key Findings

Both architectures independently produce a small, round, soft, lavender-tinted, bioluminescent creature that hums — and both derive its name from the sound it makes. The convergence includes features that are not phonetically motivated by the word.

However, the models also diverge on specific details: Claude produces six legs at 5/8 frequency, while GPT produces zero legs. This suggests the attractor is partly shared and partly architecture-specific.

Dataset Contents

• The Quumble Convergence Protocol (v5, PDF) — full protocol with Sections 1–9, including cross-architecture results
• Appendix A (PDF) — raw Claude convergence data with eight verbatim quumble descriptions
• 10 GPT-5.3 quumble responses (TXT) — verbatim copy-paste from fresh conversations, March 10, 2026
• 8 Claude zikrath responses (TXT) — control word data
• Feature coding and cross-architecture analysis (XLSX)

All data is unedited. Feature coding was performed by a single researcher (Bo). Limitations are discussed in Section 9.8 of the protocol. This is preliminary data intended to support replication and extension, not to establish conclusions.

The protocol is free to use and experiment with for non-commercial purposes under CC BY-NC-SA 4.0.