History

Diane Blackwood 88208d0fa4 Updated use of gellerman sequence features.		2025-09-20 21:00:54 -04:00
..
README.md	Adding LLM evaluation code for faster processing.	2025-09-20 20:33:55 -04:00
plot_curiosity.py	Adding LLM evaluation code for faster processing.	2025-09-20 20:33:55 -04:00
run_bench.py	Adding LLM evaluation code for faster processing.	2025-09-20 20:33:55 -04:00
run_curiosity_demo.py	Updated use of gellerman sequence features.	2025-09-20 21:00:54 -04:00

README.md

Bench

Runs a synthetic finite-state “puzzle belt” over a batch of boxes.

Run

python -m pip install -r requirements.txt
. scripts/bench_env.sh
python bench/run_bench.py

# Bench

- `run_bench.py`: pure speed micro-benchmark (synthetic FSM)
- `run_curiosity_demo.py`: demonstrates **non-advancing PEEK** and **k-ary sequences**
  with two puzzle families:
  - **Informative**: `EAT` is valuable *after* `PEEK`, costly otherwise
  - **Uninformative**: `PEEK` yields cost but no benefit

Expect higher peek rates in the informative segments only.

# Bench

- `run_bench.py`: pure speed micro-benchmark (synthetic FSM)
- `run_curiosity_demo.py`: demonstrates **non-advancing PEEK** with **k-ary sequences**,
  logs a CSV of results per segment
- `plot_curiosity.py`: reads CSV and renders summary figures into an output directory

## Typical usage

```bash
python -m pip install -r requirements.txt
. scripts/bench_env.sh
python bench/run_curiosity_demo.py --out results/curiosity_demo.csv
python bench/plot_curiosity.py --in results/curiosity_demo.csv --outdir results/figs