Engineering for clarity, not for hype.
TASC is being built around a small number of disciplined ideas. Where capability is not yet validated, we say so.
What we are building, and why.
Semantic compression
Reduce repeated, stale, and low-value reporting burden while preserving the changes that matter to a commander or S2 reader.
Entity / event normalization
Align entities, locations, times, and event types across structured and unstructured sources into a consistent tactical state representation.
Changed-state detection
Compare incoming traffic against current state, surface material deltas, and ignore traffic that does not change the picture.
Source traceability
Maintain the link from every conclusion back to its underlying reports, sensors, and chat fragments.
Confidence and uncertainty modeling
Track confidence per claim, surface contradictions, and expose uncertainty alongside the result rather than hiding it.
Evaluation harness
Reproducible evaluation around time-to-understanding, duplicate suppression, traceability, latency, and user trust on representative data.
Edge / DDIL design considerations
Designed toward intermittent connectivity, bandwidth-aware sync, and resilient evidence caching at the edge.
No algorithm claims we cannot substantiate.
Specific model architectures, benchmarks, and performance numbers will be published only when measured on representative tasks with the evaluation harness described above.
- Time-to-understanding[Insert metric]
- Duplicate / stale suppression[Insert metric]
- Traceability coverage[Insert metric]
- Latency on edge profile[Insert metric]
- User trust and workload[Insert metric]