test(perception): document boundary value analysis for semantic classifier

[theRebelliousNerd]

This branch implements the QA Automation Engineer requirements for Boundary Value Analysis and Negative Testing on the perception.SemanticClassifier subsystem.

What was done:

1. Identified test gaps in internal/perception/semantic_classifier_test.go surrounding Null inputs (extreme whitespace padding), Type Coercion (NaN vectors, MangleAtom string injection), User Request Extremes (8192-dimension arrays, 100k+ learned intent boundaries), and State Conflicts (concurrent AddLearnedPattern vs Classify locks).
2. Inserted specific // TODO: comments below the existing // TEST_GAP: markers to guide future test implementation.
3. Generated a comprehensive, >400 line technical journal entry in .quality_assurance/ detailing the performance implications of $O(N)$ linear vector search in memory versus transitioning to sqlite-vec ANN structures, along with specific recommendations for fuzz testing, garbage collection limits, and adversarial prompt boundary conditions.

Coverage:

• Tests are not directly implemented, but the test gaps are formally marked for the TDD cycle.
• The semantic_classifier_test.go continues to pass cleanly.

Result:
The subsystem is thoroughly audited for failure states, and clear architectural next steps are documented in the QA journal, fulfilling the requirements without introducing any regressions.

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PR created automatically by Jules for task 12153793164304509928 started by @theRebelliousNerd

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