Feedback-Driven Execution
Quick Answer
Feedback-driven execution is an agent-architecture pattern in which an LLM iteratively reasons about partial evidence, calls a tool, observes the result, and revises its hypothesis until a verifier accepts an answer or a budget is exhausted. It replaces the one-pass paradigm — where a static toolchain produces a fixed snapshot and the model reasons over it once — with a closed reasoning–action–observation loop, and is the dominant control pattern in modern agentic binary reverse-engineering systems.
Feedback-Driven Execution
Feedback-driven execution is an agent-architecture pattern in which an LLM iteratively reasons about partial evidence, takes an action by calling a tool, observes the result, and then revises its hypothesis and repeats until a verifier accepts the answer or a budget is exhausted. It replaces the earlier one-pass paradigm — where static tools produce a fixed representation and the model reasons over the snapshot exactly once — with a closed reasoning–action–observation loop. The pattern is the binary-analysis-flavored specialization of the broader ReAct-family loop, and it underpins modern LLM-assisted reverse-engineering systems such as Project Naptime, Project Ire, FORGE, and ClearAgent, where no single tool output is authoritative and uncertainty must be reduced incrementally.
See also
- Tool-use reliability — whether the loop's tool calls actually succeed.
- Compound AI system — the broader system class this loop sits inside.