TimeStack's AI is grounded in peer-reviewed behavioral science, computational neuroscience, and state-of-the-art machine learning research. We publish our methodological innovations and collaborate with academic institutions.
Our research agenda spans the full stack of behavioral intelligence — from foundational ML methods to applied behavioral science.
How do you train LLMs to reason about human behavior with the nuance of expert coaches? Our research explores domain-adaptive pre-training on behavioral corpora, temporal position encodings that enable models to understand time-dependent context, and RLHF alignment using expert coach evaluations as reward signals.
Human behavior operates on multiple timescales simultaneously — circadian rhythms, weekly patterns, seasonal trends, and multi-year trajectories. We develop temporal fusion architectures that jointly model these scales, with custom attention mechanisms handling the characteristic irregularity of real-world behavioral data.
Our graph neural network research focuses on learning personalized causal structures from observational behavioral data. Unlike correlation-based approaches, we develop methods that distinguish causal direction (does stress cause poor sleep, or does poor sleep cause stress?) using Granger causality tests integrated with GNN message passing.
The intervention optimization problem — when, how, and how much to nudge a user — is fundamentally a sequential decision problem under uncertainty. We develop RL agents that balance short-term engagement with long-term behavioral change, explicitly modeling notification fatigue and the diminishing returns of repetitive interventions.
Behavioral data is deeply personal. Our federated learning research develops methods to train effective shared models without centralizing sensitive data. We combine federated averaging with differential privacy guarantees (epsilon-DP) and investigate secure aggregation protocols for multi-tenant enterprise deployments.
Detecting behavioral anomalies (burnout, disengagement, mental health decline) requires models that understand individual baselines and can distinguish normal variation from concerning trends. Our VAE-based approach learns personalized behavioral distributions and calibrates detection thresholds per user to minimize false positives while catching genuine signals.
Our AI doesn't operate in a scientific vacuum. Every model is designed around established behavioral science frameworks.
Our intervention optimizer is constrained to support autonomy, competence, and relatedness — the three fundamental psychological needs for sustained motivation. The RL reward function penalizes paternalistic interventions that undermine user autonomy.
The Chronos temporal model explicitly encodes cue-routine-reward cycles in its feature representation. Habit formation probability is predicted as a function of consistency, environmental cues, and reward immediacy — aligning with the Atomic Habits framework.
The Goal Decomposition LLM generates goals that are specific, measurable, achievable, relevant, and time-bound (SMART). It also maintains optimal difficulty levels — Locke's research shows that moderately difficult goals produce the highest performance.
The core mathematical model: 1.01^365 = 37.78x. TimeStack's scoring system, goal hierarchies, and temporal prediction all model compound growth — small daily improvements cascading into transformative long-term outcomes.
The DomainGraph model limits simultaneous domain recommendations to prevent cognitive overload. The intervention optimizer caps daily nudges based on the user's current cognitive load estimate, derived from their active goal count and recent task complexity.
Positive emotions broaden awareness and build lasting resources. The Wellbeing Sentinel model tracks positive affect indicators and the LLM coaching engine uses upward spirals — leveraging small wins to build momentum for larger behavioral changes.
Behavioral AI is a nascent field. These are the hard problems we're actively working to solve.
New users have no behavioral history. How do you provide meaningful predictions from day one? We're developing meta-learning approaches that transfer knowledge from similar user cohorts, bootstrapping personalization from minimal demographic and preference signals.
Observational behavioral data contains both causal and correlational relationships. Distinguishing them matters: "exercise improves mood" (causal, actionable) vs. "happy people exercise more" (correlational, not actionable the same way). We're integrating causal inference methods with our GNN architecture.
Did today's 30-minute workout contribute to the career promotion 6 months later? The causal chain is long and noisy. Our temporal models struggle with credit assignment beyond 90-day horizons, which is a fundamental challenge for modeling compound growth.
Behavioral patterns vary significantly across cultures. "Work-life balance" means different things in different societies. We're researching culture-adaptive model variants that respect diverse behavioral norms while maintaining universal psychological foundations.
When should an AI system intervene in someone's life? Our research explores formal frameworks for intervention ethics — distinguishing between helpful nudges and manipulative persuasion, with constitutional AI constraints applied to our coaching LLM.
Users can game gamification systems. Our models need to distinguish genuine behavioral improvement from reward-hacking (e.g., completing trivial tasks for points). We're developing adversarial robustness for our scoring and prediction models.
TimeStack's research program ensures that every AI model we deploy is grounded in science, rigorously evaluated, and continuously improved. We're building the foundational AI for human behavioral intelligence.