[2603.14997] OrgForge: A Multi-Agent Simulation Framework for Verifiable Synthetic Corporate Corpora

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arXiv:2603.14997 (cs)

[Submitted on 16 Mar 2026 (v1), last revised 8 Apr 2026 (this version, v2)]

Title:OrgForge: A Multi-Agent Simulation Framework for Verifiable Synthetic Corporate Corpora

Authors:Jeffrey Flynt<br>View a PDF of the paper titled OrgForge: A Multi-Agent Simulation Framework for Verifiable Synthetic Corporate Corpora, by Jeffrey Flynt

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Abstract:Building and evaluating enterprise AI systems requires synthetic organizational corpora that are internally consistent, temporally structured, and cross-artifact traceable. Existing corpora either carry legal constraints or inherit hallucination artifacts from the generating LLMs, silently corrupting results when timestamps or facts contradict across documents and reinforcing those errors during training. We present OrgForge, an open-source multi-agent simulation framework that enforces a strict physics-cognition boundary: a deterministic Python engine maintains a SimEvent ground-truth bus while LLMs generate only surface prose. OrgForge simulates the organizational processes that produce documents, not the documents themselves. Engineers leave mid-sprint, triggering incident handoffs and CRM ownership lapses. Knowledge gaps emerge when under-documented systems break and recover through organic documentation and incident resolution. Customer emails fire only when simulation state warrants contact; silence is verifiable ground truth. A live CRM state machine extends the physics-cognition boundary to the customer boundary, producing cross-system causal cascades spanning engineering incidents, support escalation, deal risk flagging, and SLA-adjusted invoices. The framework generates fifteen interleaved artifact categories traceable to a shared immutable event log. Four graph-dynamic subsystems govern organizational behavior independently of any LLM. An embedding-based ticket assignment system using the Hungarian algorithm makes the simulation domain-agnostic. An empirical evaluation across ten incidents demonstrates a 0.46 absolute improvement in prose-to-ground-truth fidelity over chained LLM baselines, and isolates a consistent hallucination failure mode in which chaining propagates fabricated facts faithfully across documents without correcting them.

Comments:<br>v2: Major revision. Recenters the paper on the simulation framework as the primary contribution. System Architecture substantially expanded (CRM state machine, Knowledge Recovery Arc, multi-pathway knowledge gap detection, embedding-based ticket assignment). Introduction restructured for broader framing. RAG retrieval baselines replaced by cross-document consistency evaluation

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)

Cite as:<br>arXiv:2603.14997 [cs.CL]

(or<br>arXiv:2603.14997v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2603.14997

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arXiv-issued DOI via DataCite

Submission history<br>From: Jeffrey Flynt [view email]<br>[v1]<br>Mon, 16 Mar 2026 09:02:24 UTC (23 KB)

[v2]<br>Wed, 8 Apr 2026 22:43:39 UTC (34 KB)

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