WOMO LABS RESEARCH · FOUNDATIONS · MARCH 2026FLAGSHIP01
The Enterprise World Model
We introduce WOMO, a continuously learning causal model of an enterprise's market environment, built from 194K canonical entities and 119K validated causal edges. Unlike sequence models trained on static corpora, WOMO maintains a governed, auditable representation that updates as the world changes. We show that grounding strategic queries in an explicit world model reduces hallucinated causal claims by an order of magnitude relative to frontier LLMs.
Enterprise models must learn daily without erasing what they knew. We present a consolidation architecture that achieves backward transfer of approximately 0% across 14 months of live market updates. The method requires no replay of raw customer data, preserving the zero-egress deployment contract.
We operationalize Pearl's do-calculus over a 119K-edge causal graph, answering interventional and counterfactual queries in under 150 ms. Identification is checked mechanically before any estimate is returned, so unanswerable queries fail loudly rather than plausibly. We report calibration against 4,100 held-out natural experiments.
Existing benchmarks reward fluent retrospection; MIRAGE scores forward simulation. Models are evaluated on counterfactual market scenarios with ground truth resolved by subsequent real-world outcomes. We publish the protocol, the anti-contamination design, and baseline scores for seven frontier systems.
WOMO LABS RESEARCH · APPLICATIONS · NOVEMBER 202505
Black Swan Intelligence
Rare, high-impact events are precisely where pattern-matching systems fail. We describe a stress-testing regime that samples structurally coherent low-probability shocks from the world model and measures downstream exposure. In retrospective studies the method surfaced 71% of realized tail events at least three weeks early.
