Search Results for author:
Found 7 papers, 5 papers with code
Proving Theorems Recursively
This approach allows the theorem to be tackled incrementally by outlining the overall theorem at the first level and then solving the intermediate conjectures at deeper levels.
DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data
Proof assistants like Lean have revolutionized mathematical proof verification, ensuring high accuracy and reliability.
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation.
MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data
Recent large language models (LLMs) have witnessed significant advancement in various tasks, including mathematical reasoning and theorem proving.
LEGO-Prover: Neural Theorem Proving with Growing Libraries
Our ablation study indicates that these newly added skills are indeed helpful for proving theorems, resulting in an improvement from a success rate of 47. 1% to 50. 4%.
Ranked #1 on
Automated Theorem Proving
on miniF2F-test
(Pass@100 metric)
Lyra: Orchestrating Dual Correction in Automated Theorem Proving
In addition, we introduce Conjecture Correction, an error feedback mechanism designed to interact with prover to refine formal proof conjectures with prover error messages.
Ranked #1 on
Automated Theorem Proving
on miniF2F-test
(Pass@100 metric)
FIMO: A Challenge Formal Dataset for Automated Theorem Proving
We present FIMO, an innovative dataset comprising formal mathematical problem statements sourced from the International Mathematical Olympiad (IMO) Shortlisted Problems.
