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Octopus Research Institute

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.

  • 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: Exploring
    Auslan 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 research
    Governed 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 research
    Evidence 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: Experimental
    Graph 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 research
    Privacy-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: Experimental
    Apple 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: Experimental
    DAWM — 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 research
    Local 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