Research
Our research areas and programmes. Every area and programme carries an explicit status, and separates motivation, questions, methods, observations and limitations.
Research areas
- Governed AI SystemsStatus: Active research
Agent governance, approval boundaries, human-in-the-loop control, policy enforcement and reversible, fail-closed execution.
- Evidence, Audit and ReplayStatus: Active research
Evidence ledgers, tamper-evident records, decision replay, provenance and runtime observability for AI execution.
- Graph ReasoningStatus: Experimental
Graph-based reasoning runtimes: planners, transaction logs, evidence-aware inference and constraint enforcement over structured domain knowledge.
- Sovereign and Local AIStatus: Active research
Local and on-device inference, model optimisation, edge deployment, privacy-aware inference and multi-provider portability.
- Privacy-Preserving AIStatus: Active research
Data minimisation, sensitive-data detection and redaction, controlled model access, data residency and residual-risk evaluation.
- Healthcare AIStatus: Exploring
Non-diagnostic, operational clinical-documentation support with human oversight, evidence and governance. No clinical validation is claimed.
- Accessibility TechnologiesStatus: Exploring
Accessible interaction, communication support, vision and hearing accessibility, and inclusive AI design.
- Auslan and Multimodal AIStatus: Exploring
Camera-based recognition of Auslan (Australian Sign Language) explored as multimodal representation of hands, body, face and timing — not gesture classification. Community-informed and early-stage.
- Model EvaluationStatus: Active research
Accuracy, robustness, failure analysis, domain transfer, generalisation, reproducibility and transparent reporting of limitations.
- Responsible AIStatus: Active research
Human accountability, governance, safety boundaries, community impact, responsible data use and evidence-backed claims.
- World ModelsStatus: Experimental
Deep-perception world models (DAWM): world-model experiments, phase diagnostics, failure analysis and current-belief updates.
Research programmes
- Status: ExploringAuslan and Multimodal Accessibility
Early-stage, community-informed exploration of camera-based Auslan recognition as multimodal representation — not gesture classification, not interpreter replacement.
- Auslan and Multimodal AI
- Accessibility Technologies
- Responsible AI
- Status: Active researchGoverned Agent Systems
A single controlled execution boundary for AI agents, with human approval for irreversible actions and policy-based access to tools.
- Governed AI Systems
- Evidence, Audit and Replay
- Responsible AI
- Status: Active researchEvidence and Workstate
Store-untrusting, fail-closed design where verdicts are captured as evidence, contracts are pinned, and work state is replayable and tamper-evident.
- Evidence, Audit and Replay
- Responsible AI
- Status: ExperimentalGraph Reasoning Runtime
An experimental reasoning runtime separating a planner, a transaction log and an evidence ledger, with constraint enforcement over structured domain knowledge.
- Graph Reasoning
- Evidence, Audit and Replay
- Status: Active researchPrivacy-Preserving AI Pipelines
Sensitive-data detection, redaction and transformation with data-residency controls and residual-risk evaluation before controlled model access.
- Privacy-Preserving AI
- Responsible AI
- Status: ExperimentalApple Silicon Inference Runtimes
Kernel design, memory movement and bandwidth ceilings for local inference on Apple Silicon and other accelerators — consolidated from earlier Octoryn Research work.
- Sovereign and Local AI
- Model Evaluation
- Status: ExperimentalDAWM — Deep-Perception World Models
World-model experiments, phase diagnostics and failure analysis, treating regressions as a chain of rejected hypotheses rather than a single training failure.
- World Models
- Model Evaluation
- Status: Active researchLocal and Sovereign Inference
On-device and edge inference, model optimisation for constrained hardware, and multi-provider portability to reduce single-vendor dependence.
- Sovereign and Local AI
