Context Intelligence Framework
Adaptive compression, drift detection, and threshold-gated validation for large language model context windows.
How can a threshold-triggered secondary agent detect context drift and selectively compress stale context to extend effective LLM context utilization — without degrading answer quality or increasing end-to-end latency beyond acceptable bounds?
The Core Problem
Modern LLMs have large but finite context windows. As a session grows, compounding issues emerge:
Token Budget Exhaustion
Every new token pushes an old one out. Models lose factual history they actually need.
Coherence Degradation
With no feedback loop, models silently drift — producing answers detached from original intent.
Cost Explosion
Long contexts scale quadratically (O(n²) attention). Intelligent compression cuts cost at scale.
Speed Penalty
Running full-context inference at 90%+ fill is measurably slower due to KV-cache pressure.
Existing approaches (truncation, RAG, sliding windows) solve parts of this. None provide a unified, model-agnostic, runtime-embeddable solution with anomaly detection. CIF closes that gap.
Our Approach
CIF introduces a threshold-triggered secondary agent that monitors context state, detects drift via anomaly signals, and selectively compresses stale segments — preserving semantic fidelity while reclaiming token budget.
- Drift detection — Continuous monitoring for coherence degradation
- Selective compression — Only compress what's safe to compress
- Threshold-gated validation — Trigger compression only when metrics cross defined bounds
Success Criteria
| Metric | Minimum Target | Stretch Target |
|---|---|---|
| Token reduction at 80% fill | 40% | 60% |
| Quality degradation (ROUGE-L, BLEU) | < 5% | < 2% |
| Drift detection precision | > 85% | > 92% |
| Drift detection recall | > 80% | > 88% |
| Compression latency (Rust kernel) | < 200ms / 100K tokens | < 80ms / 100K tokens |
| CLI tool integration | pip install | IDE plugin |
Stack: Python (research) + Rust (core compression runtime). Deliverable: Research paper draft + CIF SDK v0.1 (CLI/IDE plugin).