CONTROL
Frozen baseline
A non-learning reference that tells us whether adaptation is doing real work.
OPEN RESEARCH PROGRAM 2026
Beyond reflexes.
Toward a mind.
We are evolving a fly brain from simple collision avoidance into robust, adaptive embodied intelligence—one challenge, one learner, and one honest experiment at a time.
SENSORY INPUTADAPTIVE POLICY↗ EVOLUTIONARY SEARCH
08learner families
05embodied challenges
600FlyWire proxy neurons
∞hypotheses to test
01 / THE MISSION
A tiny nervous system is a
serious research question.
The fruit fly has a compact brain, rich behavior, and a body that has to make decisions in the real world. That makes it a sharp starting point for asking what intelligence needs—and what it can learn.
Learning To Fly is a reproducible research workspace for evolving controllers in progressively harder environments. The primary track stays grounded in the fly’s immediate needs: avoid walls, find resources, recover from damage, and adapt when the world changes.
02 / THE SEARCH SPACE
One world.
Many ways to learn.
Every approach gets the same sensors, actions, lifetime, online updates, compute budget, and held-out shifts. The question is not which name sounds smartest—it is which mechanism survives contact with the world.
ONLINE LEARNING
CLRM
A compact continual learner with matched observation, action, and update budgets.
ONLINE LEARNING
IntuiKit-CLRM
A parallel CLRM formulation kept separate so intuitions can be tested fairly.
PREDICTIVE LATENTS
JEPA-style proxy
Predicts future latent state and learns useful representations without a full external JEPA.
CAUSAL MEMORY
LLM-style proxy
A bounded causal token-memory controller that tests sequence prediction under tight budgets.
SELF-REVISION
Gödel-style proxy
A constrained self-rewriting controller exploring whether useful policy changes can be discovered online.
CONTINUOUS TIME
Liquid LTC
Sparse liquid time-constant dynamics with adaptive rates and local plasticity.
CONTINUOUS TIME
Liquid CfC
A closed-form continuous-time sibling probing fast, bounded recurrent adaptation.
03 / THE METHOD
Evolve.
Stress-test.
Learn.
Evolution proposes the learner and its parameters. The environment supplies the pressure. Held-out challenges decide whether an apparent improvement is real.
01
Compose the world
Food, water, procedural mazes, relocation, sensor damage, and multi-challenge carryover create a richer behavioral substrate.
02
Search across mechanisms
Evolution mutates families, memory, rates, gains, and structure under bounded, matched budgets.
03
Separate signal from story
Controls, ablations, disjoint seeds, and honest negative results keep the primary claim gated.
THE FLIGHT PLAN
From avoiding walls
to finding a way home.
01Collision avoidance
02Homeostatic foraging
03Relocation adaptation
04Damage recovery
05Multi-challenge carryover
CURRENT READOUT
Promising signals. No shortcuts.
Our proxy JEPA-style learner currently leads the broad family matrix. Functional FlyWire motifs improve evolved liquid topologies over degree-only selection, but the frozen motif winners did not generalize uniformly to held-out seeds. Matched random-dynamics controls still win the aggregate topology comparison. Three search replicates suggest family-specific preferences, not a universal topology winner. A 16-member random ensemble also led both liquid families on mean score, with wide variance—an effect worth studying, not a shortcut to a conclusion. We keep those caveats visible because the goal is knowledge, not a premature victory lap.