Eliminating catastrophic forgetting in on-device continual learning. Our ICRL framework allows models to learn from new environments and threats in real-time without degrading performance on previously learned tasks targeting <100ms adaptation cycles.

Research into extreme compression of model update payloads reducing the data required to transmit a full capability update from megabytes to kilobytes. Enabling model updates over low-bandwidth tactical comms links including HF radio.

CV models in contested environments face physical adversarial attacks, camouflage, thermal masking, retroreflective materials. AlpEye's robustness research develops multi-spectral fusion and invariant feature representations resilient to deliberate counter-AI measures.

Distilling capabilities from massive vision-language foundation models (GPT-4o Vision, Gemini Ultra Vision) into sub-10M parameter models. Targeted knowledge transfer preserving semantic reasoning at <5W compute budgets.

Enabling drone and robot systems to autonomously understand and execute novel mission types described in natural language, without retraining. The system decomposes mission instructions into perception tasks and assembles the requisite model pipeline on-the-fly.

A fleet of drones each seeing different parts of a mission environment collectively trains a shared model with no central server. Federated learning over tactical mesh enables collective intelligence with zero data exfiltration from the operational theater.

Moving beyond correlation-based pattern matching to causal reasoning: systems that understand why an event occurred, not just that it occurred. Critical for distinguishing genuine threats from spoofed scenarios in adversarial environments.