compress

⚠️ 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

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.

Quick Reference

Task	Load
Compression levels (L1-L4)	`levels.md`
Validation algorithm details	`validation.md`
Format-specific strategies	`formats.md`
Token budgeting and metrics	`metrics.md`

Compression Levels

Level	Ratio	Reliability	Use Case
L1	~0.8x	✅ High	Production, human-readable
L2	~0.5x	✅ Good	System prompts, repeated use
L3	~0.3x	⚠️ Moderate	Experimental, review output
L4	~0.15x	⚠️ Low	Research only, expect losses

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.

Core Rules

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

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.

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)