README.md

---
license: apache-2.0
tags:
- lstm
- recurrent-neural-network
- long-term-dependencies
- sequence-modeling
- gradio
---

# LSTM Time Capsule

LSTM Time Capsule is a modern small-compute retest of long-lag credit assignment.
Every sequence contains random values and two distant markers; the model must add
only the marked values after processing the entire sequence. A parameter-matched
vanilla tanh RNN, an LSTM, and a GRU train on identical data and are evaluated at
the training length and at longer unseen lengths.

The historical anchor is Hochreiter and Schmidhuber's 1997
[Long Short-Term Memory](https://doi.org/10.1162/neco.1997.9.8.1735), which
motivated LSTM by the difficulty of decaying error flow over extended intervals.
This project uses current PyTorch implementations and a new synthetic benchmark; it
does not claim to reproduce the paper's original code, exact cells, or tables.

## Verified results

All cells trained on the same 16,000 length-100 sequences. The test sets contained
4,000 independently generated sequences at each length.

| Cell | Parameters | Length 100 RMSE | Length 200 RMSE | Length 400 RMSE |
| --- | ---: | ---: | ---: | ---: |
| Vanilla tanh RNN | 4,825 | 0.3855 | 0.4010 | 0.4056 |
| LSTM | 4,641 | 0.0105 | 0.0381 | 0.1050 |
| GRU | 4,357 | 0.0129 | 0.1980 | 0.4264 |

At the training length, LSTM placed 100% of predictions within 0.1 of the target;
the vanilla RNN reached 19.03%. At four times the training length, LSTM retained
56.83% within 0.1 while GRU reached 11.50%. This is evidence for these saved
checkpoints on this adding task, not a universal architecture ranking.

## Reproduce

```powershell
uv run python projects/lstm-time-capsule/train.py
```