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.

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.

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.