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## Summary This PR improves the RAGFlow agentic-search path in three areas: it stops the outer agent from re-looping over the same rag call, lets the medium thinking mode discover and follow new sub-claims mid-loop, and strengthens retrieval by having the LLM emit synonym-rich queries with time/date/number terms boosted. 1. Avoid the outer re-loop — keep all multi-hop cycles inside agentic RAG 2. Dynamic claims in medium mode — keep querying newly discovered sub-questions medium now enables allows_dynamic_claims. During orchestration, when claim analysis discovers a new required sub-question (discovered_claims), the loop spawns it as a new ClaimTarget and continues searching it in subsequent cycles (bounded by the dynamic-claim budget) instead of stopping. Also added: 3. Stronger query strategy — synonym-rich queries + time/date/number weighting LLM-generated synonyms: the claim-analysis prompt now instructs the model to write each next_queries entry as a retrieval-boosted query that actively folds in entity aliases, DATE/TIME synonyms (e.g. 1994 → 1994, 66th Academy Awards), and number/unit variants (e.g. 1.95 m → 6 ft 5 in). Time/date/number boosting: query.py boosts numeric/date tokens to a high weight (_NUM_DATE_TOKEN_RE).