Unverified50% confidenceFactExact time
Agentic Search tools like Tavily first understand the question, decompose it into sub-questions, select the best data source for each sub-question, then extract, perform vector retrieval, score and filter, and finally return structured JSON.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/7/2026
First Seen
Valid until: 1/5/2027
Sources
Related Entities
Related Claims
UnverifiedThe solution employs agentic retrieval to give the retrieval process active reasoning capabilities, and uses the Cypher query language to perform structured queries on the graph.70% similarUnverifiedBeam search is a decoding algorithm commonly used in natural language generation that retains the k highest-probability candidate sequences at each step.70% similarUnverifiedTool Retrieval uses vector search to first filter out the 3–5 most relevant tools before providing them to the model.69% similarVerifiedMainstream ANN search implementations include HNSW and IVF, which reduce retrieval time complexity from linear to logarithmic by constructing index structures.69% similarVerifiedPerplexity's Pro Search implements a multi-step reasoning-retrieval loop that decomposes complex questions into sub-questions, executes searches independently, evaluates result relevance, automatically generates follow-up queries when necessary, and finally synthesizes a structured answer.68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/985752API
curl https://kongchang.com/api/v1/knowledge/claims/985752MCP
get_claim(id=985752)