research-scout

To detect novel research without overwriting existing knowledge, you need a system that fetches new information, compares it against stored learnings, and appends only validated non-redundant entries. This process stages confirmed novelties safely into long-term memory with timestamps and source links.

Verifying if new web search findings contradict documented learnings requires comparing fetched content against stored decision rules to classify hits as actually-new, contradicting, or redundant. This novelty detection ensures only validated updates challenge your existing knowledge base without data loss.

Consolidating information retrieval from Reddit, Hacker News, and arXiv into a single memory file involves bounded multi-source retrieval with per-source caps. The system fetches content, validates it against existing corpora, and appends non-overlapping items into long-term-memory.md under new_learnings.

You can run context-driven web searches for project updates by deriving targeted search terms from routing config and recent activity. The system stages only confirmed novelties with source URLs and rationale into long-term memory, preventing overwrites until later consolidation.

Automated knowledge management works with ongoing project contexts by applying context-derived query construction to find tool and process announcements. It performs bounded multi-source retrieval across web communities and research sources, appending only non-overlapping entries into your knowledge base.

A limitation of using novelty detection for content verification is that it only appends validated items as new entries without overwriting existing memory. This means outdated or contradicted information remains in the knowledge base, requiring separate manual or downstream consolidation to resolve conflicts.