cl33-opLM — selective reproducibility release (preprint v1.1)
Paper: One Object: Memory, Navigation, and Reportability in an Operator-Only Language Model — https://t3atlas.dev/cl33/paper/ · Live demo: https://cl33.t3atlas.dev Author: Garret Sutherland, MirrorEthic LLC.
This bundle contains what is necessary to independently test the published claims on frozen artifacts. It is deliberately not the training stack: the paper's §12 program is ongoing and its machinery is not included. Reproducibility surface ≠ complete source disclosure.
Contents
| file | what | sha256 |
|---|---|---|
cl33_oplm_prose_236m.pt |
prose base, 236.5M, step 189307 (Table 1b checkpoint) | fe407328…6a1fbf |
cl33_oplm_chat_236m.pt |
chat/serving model, step 13996 (the cl33.t3atlas.dev model) | ddd042a6…559535 |
invert_probe_chat.pt |
reverse-readout probe (held-out top-1 0.860, card inside) | 91201342…ca79ef |
model_v2.py + model.py + so33.py + wedge.py + t3v3_wedge_memory.py + tape_memory.py |
model definition (load-only) | — |
repro_bottleneck.py |
Claim 1: the mandatory operator bottleneck | — |
repro_reverse_readout.py |
Claim 2: the operator stream is a transcript | — |
SHA256SUMS |
full hashes | — |
Full hashes in SHA256SUMS. Deps: torch, transformers, datasets (Python ≥3.10).
Claim 1 — the operator bottleneck is mandatory
Zero the emitted operators; the model loses its only path to output.
python repro_bottleneck.py --ckpt cl33_oplm_prose_236m.pt
python repro_bottleneck.py --ckpt cl33_oplm_chat_236m.pt
Expected (WikiText-103 test, public data, seq 1024 — measured on this exact bundle):
| ckpt | native PPL | ops-off PPL | ratio |
|---|---|---|---|
| prose | ≈61 | ≈6,500 | ≈106× |
| chat | ≈186 | ≈21,000 | ≈112× |
(The paper's 314× is the chat checkpoint on its in-domain validation mix; the claim is the order of magnitude, and it holds off-domain.)
Claim 2 — the operator stream is a readable transcript
A probe that sees ONLY the emitted operators (no token input) decodes the text:
python repro_reverse_readout.py --text "any sentence you like"
Expected: ~0.86 top-1 on typical English (the probe's held-out card prints on load; rare words fail toward semantic neighbors — that is the paper's §7 claim, not a bug).
What is NOT here, and why
Training orchestration, data pipelines, the §12 memory-organ program (labeled ongoing in the paper), and downstream control/steering machinery. The claims those support are either reported with their own dated work-log provenance (paper, Appendix R) or not yet published. This bundle is scoped to verify what the preprint asserts about these frozen artifacts.
Checksums / provenance
Both checkpoints are weights-only exports (optimizer state stripped) of the exact
training checkpoints named in the paper's Appendix R. Verify with:
sha256sum -c SHA256SUMS