85 lines
2.3 KiB
Markdown
85 lines
2.3 KiB
Markdown
# Context Compression
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Strategies for long-running sessions exceeding context windows.
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## Core Insight
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Optimize **tokens-per-task** (total to completion), not tokens-per-request.
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Aggressive compression causing re-fetching costs more than better retention.
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## Compression Methods
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| Method | Compression | Quality | Best For |
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|--------|-------------|---------|----------|
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| **Anchored Iterative** | 98.6% | 3.70/5 | Best balance |
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| **Regenerative Full** | 98.7% | 3.44/5 | Readability |
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| **Opaque** | 99.3% | 3.35/5 | Max compression |
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## Anchored Iterative Summary Template
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```markdown
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## Session Intent
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Original goal: [preserved]
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## Files Modified
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- file.py: Changes made
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## Decisions Made
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- Key decisions with rationale
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## Current State
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Progress summary
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## Next Steps
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1. Next action items
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```
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**On compression**: Merge new content into existing sections, don't regenerate.
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## Compression Triggers
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| Strategy | Trigger | Use Case |
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|----------|---------|----------|
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| Fixed threshold | 70-80% utilization | General purpose |
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| Sliding window | Keep last N turns + summary | Conversations |
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| Task-boundary | At logical completion | Multi-step workflows |
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## Artifact Trail Problem
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Weakest dimension (2.2-2.5/5.0). Coding agents need explicit tracking of:
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- Files created/modified/read
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- Function/variable names, error messages
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**Solution**: Dedicated artifact section in summary.
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## Probe-Based Evaluation
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| Probe Type | Tests | Example |
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|------------|-------|---------|
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| Recall | Factual retention | "What was the error?" |
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| Artifact | File tracking | "Which files modified?" |
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| Continuation | Task planning | "What next?" |
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| Decision | Reasoning chains | "Why chose X?" |
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## Six Evaluation Dimensions
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1. **Accuracy** - Technical correctness
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2. **Context Awareness** - Conversation state
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3. **Artifact Trail** - File tracking (universally weak)
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4. **Completeness** - Coverage depth
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5. **Continuity** - Work continuation
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6. **Instruction Following** - Constraints
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## Guidelines
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1. Use anchored iterative for best quality/compression
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2. Maintain explicit artifact tracking section
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3. Trigger compression at 70% utilization
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4. Merge into sections, don't regenerate
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5. Evaluate with probes, not lexical metrics
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## Related
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- [Context Optimization](./context-optimization.md)
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- [Evaluation](./evaluation.md)
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