A single chronological feed of technical reports, lab notes, and open research questions from Helios Brain. Every artefact is dated and traceable. Categorised by mission area, not by content type.
Ontologies now compile to a typed graph with version pins on every entity-relation. Reasoning runs always cite the schema version. Customers can roll a substrate back to any prior schema and replay decisions deterministically.
Internal preprint. We argue partial-ontology uncertainty is a first-class quantity, distinguishable from aleatoric and epistemic uncertainty over data, and composable across all eight reasoning layers without leakage.
We ran a six-week adversarial campaign against the governance layer of a banking substrate. The team probed ontology gaps, permission boundaries, and policy staleness. Eighteen findings, all reproducible. Mitigations land in v1.6.
We are formalising how a recommendation that rests on a logically provable regulatory constraint and a probabilistic posterior over outcomes can compose without losing the type signature of each guarantee.
Three new hires from European universities. A first quarter of joint engineering with a sovereign wealth fund and a tier-one bank. Long evenings, short emails. The work feels right.
A short note on why our lab focuses on organisational intelligence rather than autoregressive assistants. The world does not need a smarter answerer. It needs a substrate that remembers, governs, and learns from outcomes.
Helios Brain now records every fact with two timestamps: when it was true in the world, and when we learned about it. Audits can replay any prior state. Bitemporal is no longer a research property, it is a customer guarantee.
Customer ontologies are always partial. When the substrate encounters an entity outside its declared schema, current practice silently coerces it. We are formalising partial-ontology uncertainty as a first-class quantity.
Most decision systems extrapolate beyond their support and emit a softened warning. We are researching a refusal calculus where the substrate can certify a query lies outside its evidence, and return a citable bound.
A single recommendation may rest on a logically provable regulatory constraint AND a probabilistic posterior over outcomes. Today these compose unsoundly. We are exploring lifted probabilistic logic.
Forecast-accuracy backtests reward the wrong objective. A decision system should be evaluated on the decisions it enabled and the regret it accrued, not the RMSE of intermediate predictions.
When the action space is high-dimensional and partially confounded, causal identification under do-calculus is rarely possible. We are researching practical relaxations: partial identification, sensitivity-bounded effects.
A world model that learns hourly, weekly and quarterly must detect distribution shift at each scale independently. Standard drift detectors collapse the scales.
Domain experts encode knowledge that data alone never captures. Turning that tacit knowledge into versioned, citable, governable evidence, without flattening it into brittle rules, remains an unsolved problem.
Compliance is the last line. We red-team it internally with multi-agent adversaries that probe rule boundaries, jailbreak permissions, exploit ontology gaps, and trace data exfiltration paths.
For collaborators, due-diligence analysts, and technical contributors. A member of the research team will reply.