Mihawk turns the physical infrastructure you already have into a persistent world model — one that remembers how spaces, people, vehicles, and events evolve, and can be queried, reasoned over, simulated, and acted on.
Vision sees. Intelligence remembers.
Today's systems do one — not both.
Alerts need speed; identity needs time — real understanding needs both.
Per-frame accuracy doesn't produce understanding.
Mihawk maintains a structured, time-indexed record of what actually happened — queryable, and constrained by recorded evidence.
Observations from existing sensors are fused into a shared, persistent model of the environment. Positions, relationships, and events are grounded in space and time — so the system knows where things are, even across viewpoints, occlusions, and changing layouts.
People and objects are tracked across views and over time into a searchable record of who was where, when.
Reasoning agents build and test hypotheses over the model, ruling out what physical and spatial constraints make impossible.
W[t₀, t₁] = { Entities, Geometry, Relations, Time }“To reason about the physical world at scale, a system must not only capture structure — it must think with it.”
Mihawk understands a space through how things relate and persist — not through perfect measurement — so it holds up on real, imperfect deployments.
Cameras move, sensors are added, layouts change, and real environments rarely stay clean. Mihawk folds new and imperfect signals into the same persistent world model without rebuilding the deployment from scratch.
Not a perfect simulator — just enough structure to constrain what's possible and compare candidate futures, degrading gracefully rather than collapsing.
The principles behind the product are formalized and benchmarked in our research — not asserted.
Structure-preserving dynamics — up to ~10× smaller and ~10⁴× more accurate than Transformers across 13 SciML benchmarks.
PaperA physical critic that flags unsafe actions before failure — one structured world model across robotics and DC microgrids: ~10× more stable under a robot-embodiment swap (graph baselines diverge), robust under topology and sensing change.
PreprintMihawk runs today on infrastructure already in place — under real constraints, across different environments.
Mihawk adds intelligence before it adds hardware.
It works with the cameras and systems you already have, and keeps working as those environments change.
From a single existing classroom camera, Mihawk maintains a persistent record of who was present and when — despite occlusion, natural movement, and imperfect camera placement.
Entry and exit decisions resolved with identity, policy, and event context on top of existing access infrastructure.
Vehicle movement, violations, and evidence reconstructed from existing roadside cameras — the same world model applied to vehicles.
Each deployment is a different query over the same underlying world model. Persistent situational understanding is the capability.
If you operate in environments where context, accountability, and time matter — or if you are building in physical-world intelligence — we'd like to hear from you.