Fixed-length examinations force candidates through dozens of uninformative questions. Our adaptive engine dynamically routes evaluation items based on current posterior variance and Fisher information, converging to stable capability bounds in 42% fewer observations.
Active Bayesian Item Routing
Each interaction evaluates the candidate's current belief state P(θ | E) against the item characteristic curve. The item yielding the highest expected variance reduction is selected.
Figure 15.1 · Active inference loop: Belief informs next measurement · Measurement informs belief.
Interactive Adaptive Loop
Research Limitations: What this model does not know
A credible research programme makes its boundaries and assumptions explicit.
The current models are intentionally minimal baseline formulations. They operate under specific simplifying assumptions that will be expanded in subsequent research phases:
Binary Observations Only
Responses are strictly coded as y ∈ {0, 1}, ignoring partial credit or nuances.
No Task Difficulty Calibration
Currently treats solving an easy task as equivalent to solving a hard task.
Static Capability Assumption
Assumes latent θ remains constant during the assessment without fatigue or learning effects.
Synthetic & Unit-Test Scope
Evaluated on synthetic benchmark experiments, not yet calibrated on large-scale human cohort data.
What should we observe next? The Adaptive Loop
From passive observation to active information acquisition.
Once a system possesses both an estimate and an explicit measure of uncertainty, the fundamental research question changes:
"Suppose we currently believe θ ≈ 0.64. Which task should we administer next to learn the most about this person?"
Figure 15.1 · Active inference loop: Belief informs next measurement · Measurement informs belief.
The goal is not simply to administer more questions.
It is to select the observation expected to reduce uncertainty most.
Interactive Adaptive Engine
Manipulate current belief to see how the system reasons about optimal task selection.
ACTIVE INFERENCE SIMULATOR
Optimal Task: Task C (Medium)
CANDIDATE TASKS ACROSS DIFFICULTY SPECTRUM
Lab Notebook & Unit Test Suite
Exposing development logs, test assertions, and reproducibility checks.
DEVELOPMENT EVIDENCE
Experiment Log PS-002B & Pytest Verification
Boundary updates, step responses, learning rate scaling.
Conjugate updates, variance decay, Beta(7,4) reproduction.
100% test pass rate across core inference engine.
@techreport{systems2026adaptive,
author = {Systems Engineering Team and Mahandana, Daniel},
title = {Adaptive Measurement via Maximum Information-Gain Item Routing},
institution = {The Sota Lab},
year = {2026},
url = {https://sota-lab.lovable.app/research/cognitive-assessment}
}