### Summary
1. It changes the fallback semantics of the locate phase. When no chunks
are found, the system stays in locate. If the same claim has two
consecutive locate rounds with neither evidence chunks nor newly routed
document scope, web_search is admitted to the candidate tool set on the
next locate round as an external fallback.
2. It makes locate_empty_streak claim-scoped instead of shared in the
global context. This prevents one claim’s empty locate rounds from
affecting sibling claims running in parallel.
3. On the config side, it only raises max_parallel_agents for high /
ultra to 4, without changing max_agent_cycles. This increases parallel
claim execution without deepening per-claim search.
## 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).
