Vinod Anbalagan

The Lab · 0 of 8 live

Learning, representations,
dynamics, computation.

Eight small experiments, each built from scratch, each asking one question, each running live in your browser. Together they are the ladder that leads to Reverie.

01

One question

Isolate a single mechanism and ask when it appears or breaks.

02

A scaling ladder

Three or four points along one axis: does the effect hold, grow, or vanish?

03

Honest limits

Every write-up ends with what would still need testing at scale.

04

Runs in your browser

Trained in Python, exported, and run live on this page. No server, no waiting.

  1. 02
    ComputationPlanned

    Hopfield Memory

    Why is attention a Hopfield network? Classical versus modern storage capacity.

    Scaling ladder · neurons × stored patterns

  2. 03
    DynamicsPlanned

    ODE vs NN

    Does a Hamiltonian inductive bias fix extrapolation and energy drift?

    Scaling ladder · training region, rollout length

  3. 04
    RepresentationsPlanned

    Latent Observatory

    Which training objective recovers the true generative factors?

    Scaling ladder · MNIST → dSprites → harder

  4. 05
    LearningPlanned

    Local Learning

    How fast does the gap to backprop grow with depth?

    Scaling ladder · depth 1 → 2 → 4 → 8

  5. 06
    DynamicsFlagshipPlanned

    Neural Cellular Automata

    Does growth trained by evolution strategies regenerate differently from growth trained by backprop?

    Scaling ladder · target complexity, grid size

  6. 07
    RepresentationsPlanned

    Grokking

    How do weight decay and data fraction change which circuit forms?

    Scaling ladder · width, data fraction

  7. 08
    DynamicsPlanned

    Tiny World Model

    How long does the latent keep an object it can no longer see?

    Scaling ladder · occlusion length, latent size

  8. →
    Research

    Reverie

    The full study: does a world model represent what it cannot see, and does it use it?