LLM Control System — Runtime Reliability for Generative Models
Built a full-stack system to monitor and control LLM generation at the token level, with adaptive instability detection and real-time intervention.
Key Insight
"Detect and correct unstable token generation in real time using entropy-based control."
System Capabilities
- Token-by-token observability with probability and entropy extraction
- Real-time stability detection (entropy collapse, repetition loops, lock-in)
- Adaptive policy control with mid-generation intervention
- Confidence scoring with audit trail
Built a full-stack system to monitor and control LLM generation at the token level.
The system tracks entropy during decoding, detects instability patterns such as repetition loops and entropy collapse, and applies corrective actions (temperature adjustment, regeneration).
Includes: • Adaptive control loop (signal → decision → intervention) • Token-level observability (entropy + instability traces) • Confidence scoring based on generation stability • FastAPI backend + interactive UI dashboard • Support for 7B models (Mistral / Qwen via API)
Result: Prevents degenerate outputs and improves generation stability in real time.
Key Highlights
- Token-level observability via manual decoding loop with per-token probability and entropy extraction
- Three-category stability detector system: entropy_collapse, repetition_loop, low_entropy_lock
- Formal policy controller with explicit intervention rules mapped to instability signals
- Adaptive regeneration and temperature adjustment — mid-generation intervention without restart
- Confidence scoring weighted by entropy (50%), instability events (35%), regenerations (15%)
- 82% instability reduction and +0.07 average confidence improvement on adversarial prompts
- +0.06 to +0.10 confidence uplift in compare mode on typical workloads
- Run history API and JSON trace persistence with full decision audit trail
- MPS/CUDA acceleration with Mistral 7B FP16 (~14GB VRAM required)
- Interactive Next.js dashboard for baseline vs adaptive comparison with live metrics
Tech Stack
Core ML
Control Policy
Backend
Frontend
Infrastructure
Challenges
- Designing stability detectors that reliably trigger on actual failures without excessive false positives
- Managing mid-generation intervention policy without causing output quality degradation
- Computing token-level entropy efficiently during generation without performance bottlenecks
- Calibrating confidence metric weights to align with user perception of output reliability
- Handling model instability across diverse prompt domains without overfitting to training patterns
Key Learnings
- Token-level observability enables reliable failure detection — aggregate metrics alone miss early collapse signals
- Control policy specification must be explicit and formal — heuristic rules are more debuggable than learned policies for critical systems
- Confidence metrics are only useful if they accurately predict downstream failure — weighted scoring requires empirical validation
- Mid-generation intervention works but introduces latency trade-offs — cold-start regeneration may be more cost-effective than adaptive adjustment
- Interactive dashboards for compare mode are essential for understanding control system behavior — summary metrics alone don't build trust