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DeepSeek has quietly open‑sourced a new mathematics‑focused large language model, DeepSeek‑Prover‑V2 (available in 671B and 7B variants), achieving 88.9% on MiniF2F and strong results on PutnamBench, alongside the high‑quality ProverBench dataset and a novel recursive theorem‑proving pipeline.
Researchers from Nanjing University of Aeronautics, Nanjing University of Technology and Oxford show that high‑instruction prompts dramatically boost large language models' mathematical reasoning, enabling DeepSeek‑R1 and Qwen2.5 to solve complex polynomial tasks and even produce a new counterexample to Hilbert's 17th problem.
Recent research by Apple’s Iman Mirzadeh team introduces the GSM‑Symbolic benchmark, revealing that large language models, despite high scores on GSM8K, exhibit significant performance drops when problem numbers, names, or extra clauses change, indicating a lack of true formal reasoning ability.