Update architecture.md to reflect completed implementation and empirical findings

[Copilot]

The architecture document described a theoretical watermark detection system, but the project evolved into a source attribution framework and was completed/archived in January 2026. The document needed to reflect the final system state and validated empirical findings.

Key Changes

Version and Status

• Updated to v2.0 (Final), marked as archived January 2026
• Retitled to "Multi-Dimensional Text Analysis for Source Attribution"

Project Evolution Section (new)

• Documents shift from watermark detection hypothesis to source attribution reality
• Highlights counterintuitive finding: human writing shows lowest variance, making humans the most identifiable source (contradicts original AI-detection hypothesis)
• Explains multi-dimensional fingerprinting approach proved effective beyond original scope

Implementation Status (new)

• Tier 1 MVP complete: 32/32 tasks, 830 tests passing
• Real performance metrics: ~75 words/sec, 1-2s for short documents
• Validation on ~500 samples across human and AI sources

Component Descriptions

• Changed from future-tense design docs to past-tense completion summaries
• Added empirical validation results and test coverage per component
• Documented observed patterns: humans show high consistency + low magnitude echoes; AI models show variable consistency + model-specific signatures

Conclusion

• Reframed as "From Detection to Attribution"
• Emphasizes data collection (not algorithms) as limiting factor for accuracy scaling
• Notes philosophical implications: humans more "fingerprintable" than machines

Preserved Content

• Echo Rule theoretical foundation and methodology
• Five-component pipeline architecture and design rationale
• Three-dimensional analysis approach (phonetic, structural, semantic)
• Architectural principles and alternative approaches considered

The document now accurately represents a completed research project that discovered source attribution through linguistic fingerprinting, with humans paradoxically being the easiest source to identify.

Original prompt

Update architecture.md in johnzfitch/specHO to reflect the final system structure and findings. Rewrite the document to align with the project’s end-state architecture and include the validated conclusions (e.g., humans were easiest to identify due to lower variance, contradicting the original hypothesis that AI models would be easier to identify). Ensure the content matches the actual final pipeline/components and any changes in methodology or conclusions. Preserve the overall purpose of the document while updating versioning/date/summary as appropriate.

This pull request was created from Copilot chat.

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