Meeting recorders, AI wearables, smart glasses, memory assistants, digital companions, enterprise copilots, passive audio devices, and lifelogging systems ship as separate categories with separate metrics. Their trajectories are not separate. Each accumulates longitudinal context and advertises the same destination: a persistent model of a particular person. TargetSpace is the shared measurement layer that tests whether any of them is actually building one.
Version 1.0 — pre-pilot protocol proposal. Synthetic harness only. No human-subject results.
The internal representation is unconstrained: a personal world model is one architectural hypothesis, and TargetSpace requires no world model, memory graph, or simulator — only sealed probabilistic forecasts under the protocol. The placement of categories is illustrative, not a ranking. The convergence claim is observational: evidence that the capability class is arriving, not validation of any method and not a claim that any vendor misuses data.
Interaction comes after understanding, not before. The sequence is observe → infer → forecast → validate → interact: observation produces evidence, inference maintains a belief over the latent target-state, forecasting commits to a distribution before the outcome exists, validation scores it against the sealed outcome, and interaction then acts on a model that has earned its claims. Today's products largely run observe-and-interact, with understanding asserted in between rather than measured. Interaction quality is a legitimate product metric; it is not evidence of a target-specific model — which is why the benchmark scores the sealed forecast and nothing else. Here, as everywhere in TargetSpace, “understanding” means calibrated predictive skill about future observable states and nothing more.
Recorders and notetakers. Does meeting audio forecast commitment follow-through beyond the calendar?
Wearable memory layers. Retrieval or modeling? An enable/disable A–B over sealed tasks answers it.
Always-on capture. How much audio is sufficient, and does sparse sampling match continuous?
Egocentric audiovisual context. Does vision add calibrated lift over audio alone?
Physiology, mobility, location. Marginal lift beyond the routine the R2 baseline already captures?
Copilots over organizational exhaust, for consenting individuals. TS-Enterprise itself is research-status.
Accessibility, memory support, care coordination — under the strictest consent architecture.