Unverified50% confidenceFactExact time
Query caching, result caching, and materialized views all presuppose that "the same question" produces "the same bytes", and that presupposition collapses when queries are generated by an LLM.
1
Sources
50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedIn multi-turn conversations, elliptical queries with anaphora perform poorly when used directly for vector retrieval, so query rewriting using an LLM to rewrite them into self-contained queries is a common approach70% similarUnverifiedSemantic retrieval (vector search over stored knowledge) and state recovery (loading a checkpointed workflow) solve different problems; vector search can surface relevant context, but should never be the sole mechanism for restoring the exact state of an in-progress task.68% similarUnverifiedRAG suffers from a scattered-answer problem because the current retrieval approach scores relevance between a query and individual chunks, making it difficult to guarantee that multiple complementary chunks are retrieved simultaneously.67% similarUnverifiedThe reason V1 self-testing failed was data leakage: the query images and index images came from the same rendering pipeline and the same text content, measuring near-duplicate retrieval rather than font identity.66% similarUnverifiedosquery's built-in scheduler periodically executes queries at preset intervals and sends result differences to the logging pipeline, a pattern known as differential queries.66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1045779API
curl https://kongchang.com/api/v1/knowledge/claims/1045779MCP
get_claim(id=1045779)