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OrgForge v1.0.0 — Preprint Release

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@jeffreyflynt jeffreyflynt released this 15 Mar 15:32
v1.0.0-preprint
7ff4e2e

This release corresponds to the codebase used to produce the results in:

OrgForge: A Multi-Agent Simulation Framework for Verifiable Synthetic Corporate Corpora
Jeffrey Flynt
Preprint forthcoming on arXiv

What's included

  • Full simulation engine (flow.py) with domain-routed incident assignment
  • LLM-driven department planning with cross-signal filtering (day_planner.py)
  • Post-simulation eval dataset generator with TEMPORAL, CAUSAL, KNOWLEDGE_GAP,
    RETRIEVAL, ROUTING, GAP_DETECTION, PLAN, and ESCALATION question types (eval_harness.py)
  • BM25 and dense retrieval baselines over a 2,715-document synthetic enterprise corpus
  • HuggingFace export pipeline (export_to_hf.py)
  • 22-day simulation of a 20-person sports-wearables company (Apex Athletics)

Benchmark dataset

The eval dataset generated by this release is available on HuggingFace: [link]

Reproducibility note

Results were produced using Losspost/stella_en_1.5b_v5 via Ollama for dense retrieval and BM25Okapi (rank-bm25) for sparse retrieval. Simulation non-determinism arises from LLM sampling — exact corpus content will vary across runs, but benchmark structure and question types are stable.

Assets 2
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