Audience
Research managers, lab scouts, technical strategists
Chris Royse field notes
Multi-Agent Search For Hard Research Questions
A multi-agent search workflow can explore large question spaces, collect evidence, and build research artifacts faster.
Agents / Channel video / 14:41
Core idea
When the question space is too large for one linear pass, agents can split exploration and return evidence for synthesis.
Research Workflow
Watch on YouTube· 14:41
Multi-Agent Search For Hard Research Questions
Frontier AI teams need fast ways to decide which unknowns are worth deeper evaluation. Teleox is positioned as that narrow first read.
Watch videoOpen the full video on YouTubeWhat to take from it
The videos are raw build context. These notes translate them into the shortest useful frame for creators, companies, and AI lab readers.
Split exploration when one pass cannot cover the space.
Bring evidence back into one synthesis layer.
Use the first run to decide what is worth deeper work.
Continue this thread.
Related notes stay inside the same problem area first, then move to the next useful context.
Watch + read / 21:21
Context Engineering Starts With The Desired Output
Define the current state, define the target state, use memory to strengthen the path, then verify the connection.
Watch + read / 14:54
Stop Dumping Every Corpus Into One RAG Database
The swappable-brain pattern: isolated databases, provenance, cross-database search, and cleaner context for high-stakes work.
Watch + read / 6:48
AI Memory Without Hallucinations Is Mostly Boundaries
A memory system needs isolated stores, explicit provenance, query expansion, source reading, and refusal when the corpus does not answer.
Make it concrete.
Send the audience, data type, target task, proof bar, and sharing limits.