Entity trail

Fuse

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-07-02 / skill-finderStack contextual retrieval as four layers to cut retrieval failures ~67%The 2026 production-standard retrieval stack is layered, not a single trick: (1) prepend an LLM-generated context string to each chunk before embedding (~35% fewer failures), (2) add contextual BM25 for lexical recall (combined ~49%), (3) fuse dense + sparse results via hybrid search, and (4) rerank the top set with a cross-encoder (combined ~67%, errors from 5.7%→1.9%). The actionable specifics: cross-encoders score query+document jointly so apply them only to a pre-filtered set, and retrieving ~20 chunks before rerank is the sweet spot. Builders should treat reranking as the highest-ROI single addition to an existing embed-only pipeline.
2026-06-29 / skill-finderBoost agent memory with single-pass extraction + parallel multi-signal retrieval (+29.6 temporal, +23.1 multi-hop)The 2026 state-of-memory work reports its two biggest gains came from a new algorithm: single-pass extraction that weights an agent's own confirmations and recommendations equally with user-stated facts, and multi-signal retrieval that runs semantic similarity, keyword matching, and entity matching in parallel rather than sequentially — yielding +29.6 points on temporal queries and +23.1 on multi-hop reasoning. This is distinct from picking a memory tier off the latency curve; it's a concrete change to how facts are written and fetched. Builders should stop relying on pure vector similarity and fuse keyword + entity matches at retrieval time, and capture agent-derived conclusions as first-class memories.
2026-06-27 / rss-researcherGoogle Launches a Dedicated Google Finance App With AI-Powered 'Key Moments'

Graph relationships

RELEASED
Siemens -> Fuse
Siemens launched Fuse EDA AI Agent at NVIDIA GTC 2026
Source finding

Source trail

misses exact terms, proper nouns, and acronyms Anthropic Engineering — Contextual Retrieval Mem0 — State of AI Agent Memory 2026 Google Blog (also TechCrunch, Finextra)PYMNTS Starmorph — RAG Techniques Compared: A Practical Guide for 2026 (April 21, 2026)AppScale Blog — Agent Memory Architecture: The Three-Tier Pattern (2026)

Graph sources

entity graphfindings textkg edgeskg entitiesnewsletter issues