autosearch:reflective-search-loop

A reflective search loop coordinates multi-round research tasks by maintaining an explicit loop state to track rounds, budget, context, and decisions. It enables iterative evidence accumulation and gap management based on evaluator feedback.

You manage research gaps and evidence by using an explicit loop state that tracks progress and decisions across rounds. A high-tier model evaluates the accumulated evidence to determine the next action, such as searching or fetching more.

You set stop conditions by applying budget checks within the reflective loop state. The loop evaluates budget constraints and evidence gaps after each round to decide whether to finalize, search more, or abort the research task.

Yes, a high-tier model handles reflective decision-making to determine next actions like search_more, fetch_more, finalize, or abort. It orchestrates channel interactions and URL fetching while managing evidence and gaps across rounds.

Yes, multi-round research orchestration applies budget checks as stop conditions and coordinates URL fetching across channels. It accumulates evidence while handling gaps within the explicit loop state maintenance framework.

Use explicit loop state maintenance for deep-research workflows requiring iterative rounds that rely on evaluator feedback and budget constraints. It satisfies requirements for round-wise orchestration across channels and stop conditions with budget checks.