The frontier model
that benefits
humanity.

We are compiling diagnostic criteria into probabilistic temporal logic: symptoms, duration, impairment, exclusions, episode boundaries, and relationships between diagnoses. Multimodal encoders supply uncertain observations, with absent, unknown, and contradictory evidence kept distinct.

We are investigating differentiable inference through the diagnostic program so training improves interpretation while retaining explicit clinical constraints. Backward reasoning is part of the architecture we are designing to identify the smallest set of additional observations that distinguishes competing explanations.

RESEARCH FOUNDATIONMentalKG, introduced in MentalBench, supplies existing work on DSM knowledge graphs. Our research focus is jointly trained, temporally precise, inspectable inference.

We are designing cognitive programs for belief formation, attention, memory retrieval, threat interpretation, reward learning, and decision-making. The architecture combines Bayesian program induction, inverse planning, and differentiable cognitive modules.

We are developing a foundation of reusable cognitive primitives across people, with personal evidence updating the distribution over their composition and parameters. The design retains multiple plausible mechanisms and generates testable predictions about how a person responds to new evidence, experiences, and uncertainty.

RESEARCH FOUNDATIONCentaur demonstrates broad behavioral prediction across experimental tasks. Our research focus is persistent, intervention-tested cognitive programs across tasks and real interactions.

We are designing a hierarchical generative architecture linking measured biological processes, neural circuit dynamics, cognitive computations, behavior, and reported experience. The central challenge is causal abstraction across scales: an intervention at one level needs a mathematically explicit, testable relationship to changes at others.

We are investigating mechanistic models, neural operators, and multiscale state estimation with explicit uncertainty about unobserved mechanisms. Biological claims require measured biological data; conversation alone cannot establish receptor or circuit states.

RESEARCH FOUNDATIONVirtual Brain Twin pursues personalized psychiatric brain modeling. Our research focus is a validated connection between biological dynamics and executable personal cognition.

We are investigating causal representation learning, Bayesian nonparametric models, and intervention-based model selection to discover latent disease structure. Candidate groupings need to explain longitudinal dynamics and predict intervention responses.

We are designing an engine to generate falsifiable hypotheses about mechanisms within and across diagnoses, along with the evidence needed to distinguish them. Conventional diagnostic categories remain an interpretable output as the underlying scientific representation evolves.

RESEARCH FOUNDATIONNIMH’s RDoC framework studies biological and psychological dimensions across diagnostic categories. Our research focus is computational discovery coupled to prospective testing.

We are developing a person-specific stochastic dynamical system that connects psychological state, interventions, context, and individual dynamics.

We are investigating attractors, stability boundaries, hysteresis, and transition probabilities where the evidence supports them. The architecture is designed to distinguish temporary disturbances from patterns that are becoming harder to reverse, with calibrated uncertainty about which transition mechanisms apply to each person.

RESEARCH FOUNDATIONResearch on critical slowing down in depression investigates early-warning signals. Our research focus is a calibrated model of individual transitions conditioned on interventions.

We are combining program synthesis, causal world models, and robust planning to represent strategies as actions, timing, observations, decision branches, stopping conditions, and constraints. The design evaluates sequence, carryover, and interaction effects across plausible models of the person.

We are designing each strategy with an explicit domain of validity: supporting assumptions, evidence for each component, and uncertainty introduced by their combination. Clinical strategies remain candidates for clinician review and evaluation.

RESEARCH FOUNDATIONStructured Learning of Compositional Sequential Interventions provides groundwork for modeling sequences. Our research focus is adaptive program synthesis with uncertainty propagated through the full strategy.

We are designing a person-conditioned model of how response policies affect beliefs, distress, autonomy, dependence, and behavior over time. The architecture connects cognitive programs, psychiatric dynamics, and a long-horizon outcome critic.

We are investigating outcome-sensitive training signals and inference-time evaluation of interaction strategies across multiple plausible personal models. The design accounts for how AI changes subsequent disclosure and preferences, with uncertainty and goals endorsed by the person kept explicit.

RESEARCH FOUNDATIONPerformative prediction studies systems that change the outcomes they predict. Our research focus is measuring and constraining those effects in personalized, repeated AI interaction.