From Document Silos to Process Intelligence: A Multi-Layer Knowledge Graph for CMC Process Development — Paper

Abstract

Chemistry, Manufacturing and Controls (CMC) process development generates an enormous body of technical information across a multi-stage, knowledge-intensive continuum from drug discovery to commercial manufacturing. This knowledge is traditionally fragmented across functions and heterogeneous formats, causing traceability gaps and significant knowledge-management costs during technology transfer and regulatory filing. We present a modular agentic-AI platform that converts a heterogeneous corpus of process-development documents into a queryable, dual-layer knowledge graph. A base knowledge layer builds a lexical graph with a Document-Section-Chunk hierarchy through lossless ingestion of digital, scanned, handwritten, and multilingual documents, while an intelligence layer extracts ontology-aligned entities and bridges cross-document concepts through a provenance-anchored domain graph. LLM agents operate across both layers, selecting the retrieval path best suited to each question. We evaluate the lexical layer with a novel three-tier protocol measuring the deployment-fidelity of a retrieval-augmented generation (RAG) system on proprietary data, demonstrated on 505 questions curated from 38 development reports of a Sanofi small-molecule program. Tier-1 multiple-choice accuracy of 95% signals strong platform reliability; the stricter Tier-2 LLM-judge pass rate of 85%, which degrades on comparative and corpus-wide questions, reveals a failure taxonomy that Tier-1 accuracy alone fails to capture. A router agent selects between layers according to question type. We anticipate this protocol will enable future designers of agentic platforms to assess their systems against nonpublic databases, and that graph-based architectures will see broader adoption in pharma as a means of transforming fragmented document repositories into structured process intelligence.

Authors 3

Faryad Sahneh, Reza Amirmoshiri, Yasser Jangjou

Specification

Official page: arxiv.org/abs/2609.11493
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Arxiv announce type: new
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
arXiv id: 2609.11493
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Categories: cs.AI, cs.MA
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
PDF: arxiv.org/pdf/2609.11493
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Primary category: cs.AI
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Published: 12 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh

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9

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T19

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2 h ago

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Authors: Reza Amirmoshiri, Faryad Sahneh, Yasser Jangjou

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Value	Valid from → to	Status	Source	Confidence	Extractor
2609.11493	12 Sept 2026 04:12 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

12 Sept 2026
New paperPaperFrom Document Silos to Process Intelligence: A Multi-Layer Knowledge Graph for CMC Process Development
New paper: From Document Silos to Process Intelligence: A Multi-Layer Knowledge Graph for CMC Process Development
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Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.AI	feed	T1· Official	2 h ago	2

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