Founder, Coal Tiger Innovations · Safety-Critical Systems & AI Control Engineer
Independent engineering · Applied AI governance · Verifiable, scope-limited research
A hidden-cause inference perspective
What can we reliably infer about an unseen cause from the measurable effects it leaves behind?
A public research overview exploring residuals, scientific uncertainty, alternative explanations and falsifiable predictions, with a reproducible synthetic example. Exploratory research, not a claim of dark-matter detection.
NEW · 3 NOVEMBER 2026: Scientific research progress — real observational controls, uncertainty analysis and remaining falsification gates. No independent theory validation or particle-detection claim.
Also featured: NEOM — How the Bindu Method Could Make NEOM Work
I combine 18+ years of safety-critical railway signalling engineering with independent AI systems work focused on authorisation boundaries, operational assurance and evidence of system behaviour.
| Governance | Assurance | Engineering | Research |
|---|---|---|---|
| Control boundaries & consequence containment | Reproducible tests & stated limitations | Public integration contributions & failure analysis | Falsifiable hypotheses & synthetic demonstrations |
Evidence discipline: Public examples are intentionally scoped. A passing synthetic test is not an independent security certification, and an upstream contribution is not an accepted or merged feature unless the maintainer confirms it.
| Public work | What it demonstrates | Evidence status |
|---|---|---|
| Consequence-Bounded AI Control (CBAC) | A falsifiable systems-control hypothesis about containing the consequences of unsafe agent behaviour | Research hypothesis; external validation pending |
| Public adversarial evaluation — V1 | A reproducible 512-case synthetic input grid, explicit test contract, and three demonstrated limits | 27 locally passing unit tests; 0 in-grid expectation mismatches; not independent security validation |
| Synthetic AI control demonstration | Runnable Python example with mocked authority and completion checks | Public in-memory teaching example; no real authentication or effects |
| NEOM: How the Bindu Method Could Make NEOM Work | Infrastructure economics, capital staging, investment gates and downside modelling | Independent illustrative analysis; not an official forecast |
| Hidden Causes — Dark Matter Research Perspective | Indirect observation, residual-guided scientific inquiry and falsifiable hypotheses, with a reproducible synthetic demonstration | Exploratory research; neither dark-matter detection nor independent physics validation claimed |
| Public evidence register | Clear separation of hypothesis, implementation, verification and unproven claims | Live public disclosure record |
Automated public example checks: GitHub Actions — synthetic control tests. Results are limited to the published mock example; see the evidence register for scope.
The following changes were submitted to maintainers of established public projects. Acceptance and merge status are determined by the upstream projects, not this profile.
| Upstream contribution | Scope | Reproducible evidence |
|---|---|---|
| Palo Alto Networks — Prisma AIRS integrations PR #72 | Corrected correlation-identifier documentation and added black-box request-contract tests for Codex security hooks | Four tests passed on Node.js and Bash; PowerShell not run locally; no claimed production vulnerability |
| ServiceNow — AI Passport PR #1 | Independently authored Go revocation-caller example for the public draft specification, with fail-closed HTTP handling | 74/74 caller cases and 57/57 executed HTTP cases passed; 17 HTTP cases skipped by the published runner; no production certification |
| OpenAI — Recursive agent delegation budget proposal #5453 | Independently reproduced per-run vs nested-run budget behaviour and proposed an opt-in, inherited agent-tool recursion depth guard | Public research patch and offline tests: 1,188 local tests passed, 63 skipped; public CI added; upstream feature proposal open, not accepted |
| OpenAI — Agents Python SDK issue #5441 | Public source-path analysis of file-editing call ID propagation across approval planning and execution | Code inspection and targeted regression recommendations; no patch submitted or end-to-end test claimed |
| Anthropic — Sandbox Runtime issue #783 | Public source-grounded analysis of sandbox violation records, invocation attribution, policy updates and independent execution evidence | Four falsifiable synthetic test scenarios proposed; not a code patch or executed cross-platform evaluation |
| Anthropic — Sandbox Runtime PR #789 | Fix and regression tests for incorrect zero/negative violation-tail retrieval; originally reproduced against upstream | 47 targeted tests passed, one Linux-only test skipped on macOS; TypeScript type checks and formatting passed; submitted, not yet merged |
The three pull requests and two public source-inspection analyses use published interfaces and synthetic examples, without disclosing private Coal Tiger implementations. The 512-case evaluation above is a separate, fixed public synthetic grid, not a claim about present private engineering scale or maturity.
- AI control and agent safety: authorisation boundaries, tool-mediated actions and consequence containment.
- Assurance and verification: requirements, negative-path testing, provenance, reproducible evidence and regression discipline.
- Resilience and recovery: uncertainty handling, fail-safe behaviour, replay and recovery considerations.
- Systems engineering: constraint diagnosis, failure modes, dependency analysis and evidence-led design.
- Independent technical ownership: architecture decisions, AI-assisted software development, testing, critical review and documented limitations.
Working environment: Python, Go, Bash, Node.js, Git/GitHub, structured JSON contracts, test harnesses and safety-critical engineering practice. Public contributions above demonstrate bounded Go and Node.js work.
Through Coal Tiger, I am independently developing research and engineering work on AI governance and control for autonomous and tool-using systems.
The underlying engineering repositories are intentionally private. This profile does not imply that the work is commercially deployed, independently certified or safe for production use. Only deliberately public and scope-limited artefacts are linked above.
My NEOM Bindu revitalisation case study examines how a large interdependent infrastructure programme might be made more economically resilient through productive-asset-first sequencing.
The illustrative model compares a $46bn industrial core with $24bn retained as discretionary later development, tests financial stress conditions and requires independent investment gates. Its claim is that NEOM remains theoretically possible under revised sequencing and verified demand, not that the full original vision is proven bankable.
The work prompted public discussion on LinkedIn, which is professional engagement rather than independent financial validation.
London Underground / Transport for London — Signalling engineering (2008–present) IRSE-licensed signalling professional working in a safety-critical operational discipline involving technical diagnosis, controlled processes, safe-state reasoning and engineering evidence.
Mercedes-Benz — Technical service, parts and warranty operations (2000–2007)
Problem → Constraint → Architecture → Implementation → Adversarial test → Independent evidence → Regression
The key principle is evidence before authority. A system should not be treated as correct simply because it declares success.
© 2026 Leon Maurice Browne. Public descriptions and illustrative code are shared for examination and discussion; no general open-source licence or grant of proprietary rights is implied.