Compress

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name: Compress description: Compress text semantically with iterative validation, anchor checksums, and verified information preservation.

⚠️ Important Limitations

This is SEMANTIC compression, not bit-perfect lossless.

• L1-L2: Verified reconstruction, production-ready
• L3-L4: Experimental, may lose subtle information
• Never use for: Medical dosages, legal text, financial figures, safety-critical data

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The Validation Loop

1. Compress original O → compressed C
2. Extract anchors from O (entities, numbers, dates)
3. Reconstruct C → R (without seeing O)
4. Verify: anchors match + semantic diff
5. If mismatch → refine C with missing info
6. Repeat until validated (max 3 iterations)

Convergence = verified. No convergence after 3 rounds = level too aggressive.

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Quick Reference

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Compression Levels

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Anchor Checksum System

Before compression, extract critical facts:

[ANCHORS: 3 people, $42,000, 2024-03-15, "Project Alpha"]

Reconstruction MUST reproduce these exactly. If anchors mismatch → compression failed.

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Core Rules

1. Always validate — Never trust compression without reconstruction test
2. Use anchors — Extract numbers, names, dates before compressing
3. Cap at L2 for production — L3-L4 are experimental
4. Report confidence — Include iteration count and anchor match rate
5. Independent verification — Consider different model for reconstruction

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Cost-Benefit Reality

Each compression costs 3-4 LLM calls. Break-even calculation:

break_even_retrievals = compression_tokens / saved_tokens_per_use

Only cost-effective if: You'll retrieve the compressed content 6-8+ times.

For one-time use → just use the original text.

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Before Compressing

• Content type is NOT safety-critical
• Target level chosen (L1-L2 recommended)
• Anchors identified (numbers, names, dates)
• ROI makes sense (multiple retrievals expected)