Research Loop (Coverage-Driven Refinement)

The research loop runs between relevance scoring and fulltext. It turns the one-shot pipeline into the assess–refine–stop cycle deep-research agents use: after filtering, the stage judges whether the kept papers actually answer the query; when coverage is thin it generates targeted follow-up queries, retrieves for them, and merges the new candidates through the same deduplication, ranking, and relevance machinery — repeating up to a configured budget.

Coverage assessment

Two assessors, chosen by the LLM feature resolution:

• LLM (when synthesis LLM mode is on and structured outputs are enabled) — a structured verdict: sufficient, missing_aspects, and follow_up_queries proposed as concise academic search phrases.
• Heuristic (always available) — coverage requires at least min_sufficient_papers kept papers and every core query concept matched by at least one paper; follow-up queries combine each missing concept with the covered ones.

The final assessment is stored in the coverage_assessment artifact, so debug dumps show why the loop did or did not fire.

Refinement round

Follow-up queries go through the standard retrieval fan-out, and the new candidates merge with the current set through deduplication (with cross-provider reconciliation), ranking, and the relevance filter — no special-case scoring path. Embedding-backend failures fall back to keyword ranking exactly as the ranking stage does.

Configuration

Key	Default	Meaning
research_loop.enabled	true	Toggle the stage
research_loop.max_iterations	1	Refinement rounds per run
research_loop.min_sufficient_papers	6	Heuristic floor for sufficiency
research_loop.max_follow_up_queries	3	Queries retrieved per round

Failure behavior

Retrieval failures during a refinement round pass the current ranking through with a warning; an assessment that never reaches sufficiency stops at the iteration budget. Stage metrics record iterations run, the follow-up queries used, and whether coverage ended sufficient.