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510314e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | # Scripts
These are the scripts that produced the runs, copied as they were run (comments in Italian). Paths are absolute
paths on the author's machines (`/mnt/mneme-nvme/proxy-ideas/alb-bench-v2`, a Strata install under
`/mnt/mneme-nvme/strata-0140`): edit them before rerunning.
## What you need
- AgentLongBench code (commit `57e13e8` of https://github.com/euReKa025/AgentLongBench) and data
(https://huggingface.co/datasets/ign1s/AgentLongBench), laid out as `alb/src/AgentLongBench` and
`alb/data/benchmark/...`. Prompts and scorer are the official ones, imported unchanged by `common.py`.
- An OpenAI-compatible engine serving the model (here Strata 0.1.40 with Qwen3.8 Flash Next IQ3_XXS, KV int8,
10 experts), started once per window size with `--max-context` equal to the window.
- mnemonic-proxy at commit `b8d4920` (0.3.5) for the `proxy` method (`PROXY_SRC`), configured as in
`proxy-cfg-D.json`.
- The model's tokenizer (`PACK_TOK`), used to count prompt tokens exactly as the engine does.
## Files
| file | role |
|---|---|
| `make_main_lists.py` | writes the preregistered job lists for all 25 questions per type and length (100 questions), in `main-64k.txt`, `main-128k.txt` |
| `make_clar.py` | builds the clarified questions (`alb-clar/`, same as `questions-clarified/`) and `main-clar-*.txt` |
| `main-*.txt`, `main-clar-*.txt` | job lists: one line per job, `<policy> <window> <file> <qid>` (A = full window, D = proxy, B = summary, T = truncation) |
| `run_alb_v2.py` | runs one job: builds the official messages, applies the method, asks the question, scores it with the official scorer, writes `result.json` and the logs |
| `common.py` | official prompt/parser/scorer imports, token counter, HTTP client |
| `meter.py` | engine call counter (`engine_calls.jsonl`) and resource sampler (`resources.jsonl`) |
| `normfmt.py` | format-normalised score (secondary; the headline numbers use the raw official score) |
| `check_c2.py` | stop rules checked after every job |
| `gpu_block_c2.sh`, `gpu_block_clar.sh` | one GPU block: starts the engine per window, runs the jobs of a list in order, stops on a rule; `_c2` was used for the main run, `_clar` (same, with `ALB` pointing at the clarified data) for the confirmation run |
| `run_main.sh`, `run_clar.sh` | drivers: one block per length (64k then 128k) |
| `build_results.py` | builds `results/*.jsonl` of this dataset from the `result.json` files (extract `logs/*.tar.zst` into `raw/runs/` next to it first) |
## Rerunning
```
python make_main_lists.py # or use the lists shipped here
python make_clar.py # confirmation run only
bash run_main.sh 64k 128k # main run -> runs/main-64k, runs/main-128k
bash run_clar.sh 64k 128k # confirmation -> runs/main-clar-64k, runs/main-clar-128k
```
Sampling is seeded (seed 1), but GPU inference is not bit-for-bit deterministic: expect run-to-run noise (see the
dataset README, Limits).
|