Orgforge

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.