Computational learning systems

A research lab for AI and human learning.

The two-sigma benchmark Fig. 1 — Bloom (1984): two performance distributions. The one-to-one tutored cohort sits two standard deviations above the conventional classroom.

The forgetting curve Fig. 2 — Retrievability R = e^(−t/S) decays between reviews. A review at the recall threshold raises S and stretches the next interval.

Feedback latency Fig. 3 — A corrective signal only helps inside the window the brain can still act on — hundreds of milliseconds, not days.

Goodhart's law Fig. 4 — The feedback loop reinforces the test score, decoupled from the learning it was meant to stand for.

The factory model Fig. 5 — Age-graded batching advances students in lockstep; mastery routing advances from evidence of readiness.

Credential vs. competency Fig. 6 — A degree bundles many skills into one coarse proxy; verified competencies match precisely to what an employer needs.

Mereth Labs studies how modern AI can improve learning outcomes. We work on feedback, memory, assessment and skill verification — stated as measurable systems, evaluated against a published standard, and built into products once the evidence supports them.

Est. 2026 Bengaluru Methods: learner modelling, retrieval scheduling, LLM evaluation, mechanism design

The lab is looking for collaborators.

Partner classrooms and training programmes with measurable stakes, researchers in learning science and machine learning, and institutions that assess or hire around skill. Mereth Labs is based in Bengaluru.

hello@mereth.dev