Mimir
Neural circuit synthesis — learning to predict, generate, and repair computational circuits.
Neurosymbolic
Neural predictions are always verified symbolically via Asgard's simulator. No unchecked guesses.
Grammar-Constrained
Syntactically invalid circuits are impossible by construction. Every generated output is well-formed.
Curriculum Learning
Progressive difficulty training prevents mode collapse and builds robust generalization.
Behavioral Loss
Simulation-based loss compares predicted vs target circuit behavior during training.
Transformer Architecture
Sequence-to-sequence model over tokenized circuit ASTs with attention-based decoding.
Cloud Training
One-command training on GCP with GPU acceleration. Scale from laptop to cloud seamlessly.
Comprehensive Benchmarking
Validity, behavioral accuracy, diversity, and recovery metrics for thorough model evaluation.
Training Monitor
Real-time web dashboard for tracking training progress, loss curves, and curriculum stages.
Asgard Integration
Built on Gimle Asgard's circuit algebra and JAX runtime for symbolic verification.
How It Works
Generate
Create synthetic circuits with Asgard and simulate them to produce behavioral trajectories — input/output pairs that characterize each circuit's function.
Train
Train a transformer to predict circuit structure from behavioral trajectories, using curriculum learning and grammar-constrained decoding to ensure valid outputs.
Verify
Verify predictions symbolically — simulate the predicted circuit and compare its behavior against the target. Neural speed, symbolic correctness.
Getting Started
Install dependencies and launch a quick training run:
# Install with uv
uv sync --extra dev
# Train a small model with curriculum learning
uv run python scripts/train.py --model-size small --use-curriculum --curriculum-type fast