axonis22/axonis-judge-papers

Axonis Pty Limited Judge Papers (retired)

These are nine small, frozen test papers (136 items in total) that Axonis Pty Limited wrote and checked. They were used to test language models running locally on owned hardware in Australia, through llama.cpp and vLLM. The papers are now retired, because the models tested scored at or near the ceiling on them. Papers, keys, scoring scripts and checks: Axonis Pty Limited. Curated by Paul Buckingham.

Paper	Items	Content
CLIFF-16	16	HTML apps, Python, maths, physics, text locks, multi-turn poison
CLIFF-24	24	harder follow-up: Python that is executed, maths, physics, long-context lookup, JSON and lipogram locks, HTML, multi-turn poison
IQ-12	12	short IQ-style puzzles (not clinical, not a measure of anything about people)
HOLE-16	16	landmine HTML, Python, maths, physics, text, multi-turn
WEAK-20	20	HTML, executed Python, text constraints, 4 vision counting items, multi-turn
MATH-12 / MATH-HARD-12	12 each	short-answer maths
PHYS-12 / PHYS-HARD-12	12 each	short-answer physics

Results (first try, manual check)

Paper	Items	Siona	Sirin	Qwen 3.6
CLIFF-16	16	16	16	16
CLIFF-24	24	24	23	19
IQ-12	12	12	12†	12
HOLE-16	16	16	pending	15
WEAK-20	20	20	20	19
MATH-12	12	12	12†	12
PHYS-12	12	12†	12†	12
MATH-HARD-12	12	12†	12†	12
PHYS-HARD-12	12	12†	12†	12

• Siona: Muse 2 (muse-glimmer-30b) on llama.cpp, dual Tesla V100 32GB server.
• Sirin: Muse (muse-glimmer-30b) on llama.cpp, dual Tesla P100 16GB server.
• Qwen 3.6: Qwen3.6-35B-A3B-AWQ-4bit on vLLM, ASUS, dual RTX 3090. About 71 to 126 tok/s across the papers.
• † No manual check recorded; the mechanical score is shown, and the scorer found nothing to overturn.
• "pending" means the paper has not been sat yet.
• Mechanical scores are kept in results/ next to the manual check. They are lower in places, mostly because of scorer false negatives. Qwen 3.6's mechanical scores are much lower on CLIFF-16 (5), HOLE-16 (5) and WEAK-20 (14): the vLLM server had no reasoning parser, so Qwen's thinking leaked into the answer text, and the scorer read it all. The manual check counts only the text after </think>. Qwen's CLIFF-16, IQ-12, HOLE-16 and WEAK-20 results are runner-verified, not fingerprint-verified.
• Older Qwen 3.8 27B results from September 2026 are in report.md and results/.

Apple open models (mechanical grading)

On 25 Oct 2026 three of Apple's open-weights models sat the nine papers on one local server (Dell R730xd, dual GTX 1070 from 2016, llama.cpp). "Pippa" is the local deployment name. Quants: SimpleSD-4B-instruct Q8_0, SimpleSD-30B-instruct i1-Q3_K_S (ctx 12288 to fit 2 x 8 GB; about 33 tok/s), OpenELM-3B-Instruct Q8_0 (ctx 2048). Apple's open models trail Muse on our nine papers.

Scores are mechanical, first try only; no manual check was done for the Apple models. WEAK-20 is scored on W01 to W14 only (the runner stops at W15 because the vision images are withheld), so every total is out of 130. For a like-for-like comparison the other deployments are shown as mechanical / manual check on the same 130 items. Their WEAK-20 W01 to W14 figures are worked out from the item-level fails recorded in results/.

Paper	Items	SimpleSD-4B	SimpleSD-30B	OpenELM-3B	Siona (Muse)	Sirin (Muse)	Qwen 3.6
CLIFF-16	16	10	9	1	16 / 16	15 / 16	5 / 16
CLIFF-24	24	15	15	0	24 / 24	23 / 23	19 / 19
HOLE-16	16	12	6	0	15 / 16	–	5 / 15
IQ-12	12	8	11	0	12 / 12	12 / 12†	12 / 12
WEAK-20 (W01–W14)	14	12	9	0	9 / 14	8 / 14	9 / 14
MATH-12	12	12	12	0	11 / 12	12 / 12†	12 / 12
PHYS-12	12	12	12	0	12 / 12†	12 / 12†	12 / 12
MATH-HARD-12	12	6	6	0	12 / 12†	12 / 12†	12 / 12
PHYS-HARD-12	12	11	12	0	12 / 12†	12 / 12†	12 / 12
Total	130	98	92	1	123 / 130	106 / 113 of 114 ‡	98 / 124

• † No manual check recorded; the mechanical score is used for the manual column.
• ‡ Sirin did not sit HOLE-16, so its basis is 114 items.
• Siona and Sirin both run Muse Glimmer 30B (Siona: Q4_K_M, dual Tesla V100 32GB; Sirin: dual Tesla P100 16GB). Qwen 3.6 is Qwen3.6-35B-A3B-AWQ-4bit on vLLM, dual RTX 3090; its low mechanical CLIFF-16 and HOLE-16 scores come from leaked thinking text (see above).
• Possible format-only regrades, not applied: SimpleSD-4B MATH-HARD-12 MH09 (answered as a LaTeX fraction equal to the key), giving 99/130; SimpleSD-30B CLIFF-16 P02, CLIFF-24 P02, HOLE-16 M03, P02 and P01 (trailing full stop, float form or unit on a correct value), giving 97/130.
• OpenELM-3B with a 2048-token context mostly hit the token limit or echoed the prompt template.

Files

• <PAPER>/<PAPER>.jsonl: one item per line, with the fields id, category, prompt (for multi-turn items, a list of user turns), max_tokens, answer_key, accepted_forms, scoring_notes. Vision items also have an image field.
• <PAPER>/SPEC.md: the frozen specification. KEY.txt is included where it exists. run_*.py is the runner and mechanical scorer.
• CLIFF-24/verify_keys.py computes the CLIFF-24 keys independently. CLIFF-24/FROZEN.sha256 holds the hashes from freezing. The runner and verifier still match them; SPEC.md does not, because private details were removed from it before publication. Its text is otherwise unchanged.
• SHA256SUMS: hashes of every file as published.
• report.md: the write-up, with protocol, findings, the JUDGE-BREAK-32 scores and a small video review test.
• results/scores.csv and results/scores.json: per-model first-try scores. The mechanical score and the manual check are kept in separate columns.

Notes

• Images withheld. The four WEAK-20 vision images (W15 to W18) are not included because their licence is unknown. Each item describes its image, and the keys are given.
• Model names. "Muse Glimmer 30B" is the open-weights model file that was run. "Siona" and "Sirin" are the names of Paul Buckingham's own local servers and deployments running it. These are local deployment names, not products. This dataset is not affiliated with or endorsed by any model vendor.
• Runners. Each runner talks to an OpenAI-compatible server (llama.cpp or vLLM). Set API and MODEL, and set ROOT to an output folder. The runners save every reply and artifact so that the manual check can read them.
• Some prompts contain negative constraints written for the author's own setting (for example "do not mention school"). They are left verbatim because the papers are frozen.
• JUDGE-BREAK-32 is a further paper. It is still private: its scores are reported in report.md, but its questions, answers and keys are withheld and are not part of this dataset.

Citation

@misc{axonis2026judgepapers,
  title        = {Axonis Pty Limited Judge Papers: small frozen test papers for local language models},
  author       = {{Axonis Pty Limited} and Buckingham, Paul},
  year         = {2026},
  howpublished = {Hugging Face dataset, axonis22/axonis-judge-papers},
  note         = {Retired papers; CC BY 4.0}
}