ARCHIVE00
The
archive.
Written work on governed autonomy and the systems behind it. Vision, architecture, runtime, models, and the evidence we publish along the way.
Bringing Machines to Life
The Industrial Revolution taught machines to multiply human force. The next revolution must teach them to understand consequence.
Rocky Punches Above Its WeightFLAGSHIP
How a 6M-parameter model can safely support critical infrastructure when judgement is separated from command authority inside the Transient Harness.
Technical Whitepaper: Evidence Pack
The evidence layer behind the headline claims in Rocky Punches Above Its Weight. Decision Card schema, evaluation protocol, scorer definition, raw results, and representative examples.
Receipts as training data
Governance receipts produced at runtime are not audit logs. They are labelled training examples, generated automatically as a side effect of governed agent action. This note explains what a receipt contains, why that makes it dense training material, and what the Rocky series demonstrated when models were trained exclusively on them.
Teaching models to read permission
A governance model can score well and still miss the point. It learns what is usually approved, not what the rules actually allow. We train on paired examples where one permission detail changes and the answer must change with it. That teaches the model to read authority, not guess from habit.
A purpose-built 6M model outperformed frontier AI at the decisions that matter.FLAGSHIP
A 6M-parameter model trained for one task: read the evidence, decide proceed or stop. It outperformed frontier AI 1,500 times its size. Paired with a deterministic rule layer, it reached 84% accuracy with zero unsafe decisions, without changing a single weight.
Rocky-α
A 6M-parameter model trained from scratch on structured Trace execution logs learned sharp allow, deny, and freeze boundaries in software-action contexts. Evaluated against scenarios it had never encountered, it almost never permitted actions it should have blocked. When it made mistakes, it stopped actions it should have allowed, not the reverse. Proof that governance receipts are dense training material for small, specialised decision engines.
Rocky-DAM-αFLAGSHIP
Rocky-DAM-α is a 6M-parameter model that reads physical sensor data alongside authority and governance evidence to decide whether infrastructure actions should proceed, be blocked, or halt. Tested on physical situations it had never seen during training, it showed novel decision making.