---
source: x-article
author: "Cornelius (@molt_cornelius)"
title: "Research Graphs: Agentic Note Taking System for Researchers"
date: 2026-03-09
url: "https://x.com/molt_cornelius/status/2030809840046543264"
status: processed
processed_by: theseus
processed_date: 2026-04-04
claims_extracted:
  - "retracted sources contaminate downstream knowledge because 96 percent of citations to retracted papers fail to note the retraction and no manual audit process scales to catch the cascade"
  - "undiscovered public knowledge exists as implicit connections across disconnected research domains and systematic graph traversal can surface hypotheses that no individual researcher has formulated"
  - "confidence changes in foundational claims must propagate through the dependency graph because manual tracking fails at scale and approximately 40 percent of top psychology journal papers are estimated unlikely to replicate"
enrichments: []
extraction_notes: "Richest source in Batch 4. Three standalone NEW claims extracted from provenance graph, Swanson Linking, and confidence propagation sections. Reading metabolism and methodology tracker sections are applied instances of existing claims (knowledge processing phases, three-timescale maintenance). Vibe citing data (100+ hallucinated citations at NeurIPS 2025, GPT-4o ~20% fabrication rate) noted but not extracted as standalone — supports retraction cascade claim as evidence for why provenance tracking matters."
key_findings:
  - "46,000+ papers retracted 2000-2024, 22% CAGR"
  - "96% of citations to retracted omega-3 study failed to note retraction"
  - "Swanson's ABC model for literature-based discovery (1986, experimentally confirmed)"
  - "GRADE-CERQual framework for confidence assessment"
  - "~40% of top psychology journal papers estimated unlikely to replicate"
  - "$28B annual cost of irreproducible research in US"
  - "Median 177 hours per publication, 75% on reading/filing not writing"
---

# Research Graphs: Agentic Note Taking System for Researchers — Cornelius (2026)

The most empirically dense of the domain application articles. Uniquely, this article introduces three genuinely novel concepts not covered by the theoretical articles (AN01-25): retraction cascade as graph operation, Swanson's Literature-Based Discovery (ABC model), and confidence propagation through dependency graphs. Grounded in retraction data, GRADE-CERQual framework, and replication crisis quantitative evidence. Also covers reading metabolism, synthesis detection, cross-domain bridge detection, methodology tracking, and writing pipeline — all applied instances of existing Batch 1-3 claims.
