The Local Loop: Refinement through Deterministic Control
Refining the Multiplier
If the local model provides the 80% baseline, the Harness is the multiplier that gets us to 100%. To make the Infinite Feedback Loop efficient, we must refine the interface so the model can focus its reasoning budget where it matters most.
I am now focusing on three engineering pillars to move from “contaminated proofs” to “predictable outcomes.”
The Interconnected Machine
These three pillars form a secondary feedback loop for the harness itself:
- Quantitative Forensics identifies that a high percentage of our failures are trivial noise.
- This data justifies ACI Healing, which automatically filters that noise.
- With trivialities removed, Rule-Based Recovery can focus on catching complex “logic loops” in real-time.
1. Rule-Based Recovery (Event-Driven Hooks)
Most systems rely on passive timeouts. I’m moving toward pattern-based recovery. The harness monitors logs for repetitive reasoning cycles or malformed tool calls and triggers an immediate hook. We don’t wait for a timeout; we relaunch the process the moment the “loop” becomes unproductive, preserving the purity of the context.
2. Healing the Interface (Fixing the “Dumb” Stuff)
The Agent-Computer Interface (ACI) is a waste of reasoning budget if the model is stuck fixing commas. The harness should act as a Semantic Buffer. If a linter finds a trivial syntax error, the harness fixes it automatically. We should only ask the model to reason about logic, letting the harness handle deterministic safety.
3. Quantitative Forensics (Making Failure Measurable)
To make the system predictable, we must categorize failure patterns (Syntax vs. Logic vs. Context Drift). This turns “MISTAKES.md” into actionable data, allowing us to see exactly where the Infinite Feedback Loop is hitting friction and how our engineering is reducing that friction week over week.
Building the Predictable Machine
The goal is a machine that is 100% predictable, even when its core is probabilistic. By treating local models as powerful but fallible components, we are building an engineering foundation that turns the “80% baseline” into a 100% reliable production system.