⚡ Bolt: Vectorize novelty curve computation with np.diagonal

[seonghobae]

💡 What:
Vectorized the inner loop within the _checkerboard_novelty function using np.diagonal.

🎯 Why:
The previous implementation iterated over a Python for loop across valid frames (which scaled with length $N$ where $N$ can easily reach 15,500 frames), executing array allocations and summations in an inner loop for each kernel offset. This resulted in significant memory overhead and slow execution times.

📊 Impact:
Massive performance improvement in calculating novelty metrics from self-similarity matrices. It prevents blocking operations in Python scripts and pushes the mathematical heavy-lifting down to C-level arrays via standard numpy np.diagonal.

🔬 Measurement:
Run uv run pytest tests/test_segmenter.py and ./scripts/harness/quickcheck.sh locally to ensure no functionality is broken. Tests correctly pass, proving that the exact same boundary times are extracted.

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PR created automatically by Jules for task 101184407488519959 started by @seonghobae

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OpenCode Review Overview

• Head SHA: 739cc3d73a7e3bd7eb5c37a824f959057b81a2e4
• Workflow run: 28916382937
• Workflow attempt: 1
• Gate result: APPROVE (exit 0)

Changed-File Evidence Map

flowchart LR
  PR["PR changed files"] --> Evidence["OpenCode bounded evidence"]
  Evidence --> S1["Workflow: trivy.yml"]
  S1 --> I1["GitHub Actions review job"]
  I1 --> R1["Review risk: Workflow: trivy.yml"]
  R1 --> V1["actionlint plus required checks"]
  Evidence --> S2["Changed file (2 files)"]
  S2 --> I2["repository behavior"]
  I2 --> R2["Review risk: Changed file (2 files)"]
  R2 --> V2["required checks"]

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