Multi-Agent Systems

TL;DR for operators DEoT is not “a smarter chatbot”. It is a structured analysis workflow for questions where there is no single correct answer: policy impact, market entry, geopolitical risk, crisis response, investment implications, technology disruption, and the usual executive swamp where every answer arrives with a footnote and a headache. The paper’s useful idea is simple: open-ended analysis needs two motions. First, go wide enough not to miss important dimensions. Then go deep enough not to produce a shallow consultant-flavoured smoothie. DEoT formalises this through a Breadth Engine, a Depth Engine, and an Engine Controller that decides when to branch, when to drill, and when to stop. ...

TL;DR for operators Memory is becoming an operations problem, not just a model feature. Once multiple AI agents maintain local context, update independently, and need to coordinate without a central brain, the usual “throw it into a vector database and pray politely” approach starts to creak. SHIMI, short for Semantic Hierarchical Memory Index, proposes a different memory layer for decentralized agent systems.1 Instead of storing knowledge as a flat set of embedding vectors, it organizes memory as a hierarchy of semantic concepts. Retrieval works by descending from broad concepts to specific entities. Synchronization works by exchanging only the parts of local memory trees that have diverged, using Merkle-DAG summaries, Bloom filters, and CRDT-style merging. ...