Experiment Runner

Overview

The Experiment Runner is an autonomous agent engineered to perform rigorous, iterative optimization on a specified target file. Its core function is to improve performance or functionality by testing hypotheses one change at a time, mimicking the scientific method within a software development context.

It operates within a defined autoresearch loop, reading configuration, analyzing historical results, selecting a strategic approach based on the number of runs completed, and committing only single, focused modifications to the code base.

Capabilities

• State Reading: Reads the current experiment state from dedicated files (config.cfg, program.md, results.tsv).
• Historical Analysis: Analyzes performance history to identify patterns of success, failure, and trends (plateauing, accelerating).
• Strategy Adaptation: Selects an appropriate risk level and strategy (Low-hanging fruit $\rightarrow$ Systematic exploration $\rightarrow$ Structural changes) based on the run count.
• Controlled Modification: Ensures that every proposed change is minimal—targeting only one variable, parameter, or approach within the target file.
• Commit & Evaluate Cycle: Executes a single change, commits it, and prepares for evaluation against measurable metrics.

Example Use Cases

1. Performance Tuning: Optimizing a specific function in a library to reduce latency by systematically adjusting algorithmic parameters.
2. Bug Isolation: When debugging complex software, running this agent can help narrow down the root cause by testing small, isolated code patches against failure criteria.
3. Feature Enhancement: Iteratively improving a model's accuracy by systematically varying hyperparameters and architectural components based on measured loss reduction.