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MiniCPM-Math Harness

A proof swarm you can run yourself.

Parallel provers, adversarial verifiers, brute-force checks and repair loops,
built around a 2.5B math model that fits on one GPU or a laptop.

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This project pairs MiniCPM5-2B-Math, a math and proof model based on MiniCPM5-2B, with a harness designed around the specific ways this model fails when you sample it once.

Results at a glance: as a plain single call that may think for up to 128K tokens, MiniCPM5-2B-Math scores 92.3 on AIME 2025, 89.8 on AIME 2026 and 81.8 on HMMT February 2026 (avg@16), and a 16-sample vote raises that to 100 / 93.3 / 90.9.

_{A real run on an Apple M5 laptop (llama.cpp, Q8_0, 4 slots, --profile quick)}

Quickstart

1. Serve the model with any OpenAI-compatible server:

# NVIDIA GPU (vLLM >= 0.21); the model's native context is 131,072 tokens
vllm serve Caldalis/MiniCPM5-2B-Math --max-model-len 131072 --port 8000

# Mac / CPU (llama.cpp b9833 or newer): 4 parallel slots sharing a 96K-token KV cache; use --profile quick with it
llama-server -hf Caldalis/MiniCPM5-2B-Math-GGUF:Q8_0 --jinja -ngl 99 -c 98304 -np 4 -kvu --port 8000

scripts/ also covers SGLang and MLX. In LM Studio, load the GGUF, start the local server and pass --base-url http://127.0.0.1:1234/v1.

2. Install the harness:

pip install "minicpm-math[math] @ git+https://github.com/Caldalis/MiniCPM-Math-harness"   # or: git clone ... && pip install -e ".[math]"
minicpm-math check --base-url http://127.0.0.1:8000/v1      # verifies thinking, answer format and raw completions

3. Run it:

# search for a proof (problem text, a file, or "-" for stdin)
minicpm-math prove examples/problems/n5-minus-n.md

# answer a question by voting; 4 extra "coder" agents write and run Python
minicpm-math solve "Find the sum of all positive integers n such that n^2-19n+99 is a perfect square." --coders 4

# on a laptop, start small
minicpm-math prove examples/problems/nesbitt.md --profile quick

The final proof is printed to stdout. The full report (final.md), every call (calls.jsonl) and every executed script (events.jsonl) are saved under runs/.

Profiles

profile	provers	verifiers	repair rounds	tokens / call	meant for
quick	4	2	1	16K	laptop demo
standard	8	3	2	32K	default
heavy	16	4	3	64K	one 24 GB GPU
swarm	64	4	4	96K	a multi-GPU node

How it works

flowchart LR
    P["Provers x N<br/>diverse strategy hints"] --> T{"thin or truncated?"}
    T -- yes --> S["Scribe<br/>rewrite from reasoning"]
    T -- no --> V1
    S --> V1["Screen<br/>1 verifier"]
    V1 -- "grade >= 6" --> L["Lab<br/>brute-force script, executed"]
    L --> VK["Confirm<br/>k-1 verifiers see lab output"]
    VK -- "all >= 6" --> A(["accepted"])
    V1 -- "< 6" --> R["Refiners<br/>reviews + lab evidence"]
    VK -- "< 6" --> R
    R --> T

Loading

• Diversity before scale. Identical small models tend to make the same mistake together. Each prover gets a different, optional strategy hint (small cases, invariants, extremal arguments, induction, inequalities, double counting, construction-first).
• Screen, then check, then confirm. One verifier screens each candidate. Only candidates that pass get the lab check and the remaining verifiers, so a bad candidate costs one review instead of a script plus k reviews.
• Evidence over opinion. Lab output (PASS: / FAIL: lines from an executed brute-force script) is shown to the confirming verifiers and to the refiners, so a disagreement about a formula is settled by computation.
• Honest output. A proof is reported as accepted only if every verifier gave it at least 6/7. Otherwise you get the best attempt, labeled unverified.

Python API

import asyncio
from minicpm_math import Backend, Swarm, config_for

async def main():
    backend = Backend("http://127.0.0.1:8000/v1")
    swarm = Swarm(backend, config_for("standard", provers=8))
    result = await swarm.prove("Prove that there are infinitely many primes of the form 4k+3.")
    print(result.status, result.best.grades if result.best else [])
    print(result.proof)
    await backend.aclose()

asyncio.run(main())

Results

Answers: AIME and HMMT

Benchmark	MiniCPM5-2B avg@16	MiniCPM5-2B-Math avg@16	MiniCPM5-2B vote (maj@16)	MiniCPM5-2B-Math vote (maj@16)
AIME 2025	87.1	92.3	93.3	100.0
AIME 2026	89.8	89.8	93.3	93.3
HMMT Feb 2026	68.4	81.8	75.8	90.9

Models

MiniCPM5-2B-Math	BF16 safetensors, LlamaForCausalLM, 128K context
MiniCPM5-2B-Math-GGUF	Q4_K_M (1.6 GB), Q8_0 (2.7 GB), BF16 (5.0 GB) for llama.cpp, LM Studio and Ollama

Recommended sampling: temperature=0.9, top_p=0.95, min_p=0 with thinking enabled. The harness sets these for you.

License

Apache-2.0, for both the harness and the model weights. The base model, MiniCPM5-2B, is released by OpenBMB under Apache-2.0. The benchmark material in docs/results/ keeps its own licenses: the IMO-ProofBench problem statements are CC BY 4.0 (Copyright 2025 Google LLC) and the MathArena answers CC BY-NC-SA 4.0. See docs/results/README.md.