History Compressor Pocket
History Compressor Pocket is a controlled, modernized experiment inspired by the event-driven timescales of neural history compressors. A slow GRU level updates only when an informative control symbol arrives; a wider plain tanh RNN updates on every random distractor. Both models have exactly 3,464 parameters.
Training mixes distractor gaps from 8 to 32 steps. Evaluation extends the gap to 64 and 128 steps without further optimization. The interactive Space exposes the hidden-state trajectories and every boundary prediction.
Verified result
Across three independent seeds, the 3,464-parameter event-gated model achieved 100% boundary accuracy at gaps 32, 64, and 128. The exactly parameter-matched plain RNN averaged 58.17%, 58.40%, and 58.50%, respectively.
The control was notably unstable rather than uniformly incapable: its three gap-128 runs reached 50.33%, 100%, and 25.16%. Event gating therefore improved training reliability and guaranteed invariance to the distractor duration in this synthetic benchmark.
uv run python projects/history-compressor-pocket/train.py
uv run pytest tests/test_history_compressor_pocket.py
Hosted showcase
This free static Space preserves the complete original Gradio source, trained artifacts, evaluation files, and local launch requirements. Hugging Face now requires PRO for CPU-backed Gradio hosting, so the public landing page is static while the checked-in app.py remains the authoritative runnable demo.