Tag: deep-research

• Deep-Research Agent Architecture: Graph Runtime, Checkpointing, Governance, and Benchmarks

  A production deep-research agent can outperform human research teams when engineered around machine-native strengths: massive parallel fan-out, tireless tool-use loops, perfect recall, and deterministic replay. This is the architecture pillar — it owns the checkpointed graph runtime, the typed source/evidence/claim data model, role fan-out and fan-in, replay, adversarial source screening, human gates, and benchmarks — and cross-links to its companion for the retrieval and verification math.

• Deep-Research Agents: Retrieval, Fusion, Reranking, and Claim-Verification Algorithms

  A deep-research agent's core is a set of IR and verification algorithms: lexical (BM25), dense, and hybrid retrieval; reciprocal rank fusion and diversity-aware (MMR) selection; cross-encoder reranking; and atomic, NLI-based claim verification with counter-evidence. This guide owns the algorithm layer — retrieval architecture, fusion math, reranking, and verification — and cross-links to its companion architecture pillar for the graph runtime, data model, and governance.