# experiments/

Throwaway scripts used to probe capabilities of SSM models with memba.
Not part of the library API — kept in the repo as reference and reproducible
evidence for product decisions.

Each script is self-contained and prints what it finds; read the source for
the test's claim and run it yourself if you want to verify on different
models or hardware.

## Scripts

| File | What it measures |
|------|------------------|
| `recall_poc.py` | Can a memba state, built from N days of git activity, answer "what did I work on last month" in a fresh process? |
| `mood_poc.py` | Batch sentiment-trajectory test (single prompt with full chat log). Compares Falcon-Mamba vs a Transformer. |
| `mood_stream_poc.py` | The same trajectory but fed turn-by-turn through `Session.chat()`, then queried cross-process. |
| `mood_batch_poc.py` | Batch ingest in build process, save, then query in a fresh process. |
| `diag_saveload.py` | Minimal diagnostic: tell the model one fact, ask it back before save, after save, after cross-process load. |

## Headline finding (2026-05-16, Falcon-Mamba-7B-Instruct Q4_K_M)

- **Batch single-prompt analysis** (all input + question in one call): works
  for both sentiment and recall.
- **Multi-turn fact recall** (ingest in turn 1, ask in turn 2): fails even
  in the *same process*. The model does not preserve specific facts in its
  hidden state across conversation turns.
- **Save/load roundtrip**: byte-identical, no information loss attributable
  to memba's file format. The persistence layer works correctly; the
  trained model just doesn't use the state for cross-turn recall.

See the script outputs (or rerun) for the raw evidence.