Meituan Longcat Team's open-source LongCat-Next is a multimodal model based on MoE architecture, integrating five capabilities including text, visual understanding, image generation, and speech. Its core design DiNA achieves unified task processing through discrete tokens, while the dNaViT used in the visual aspect enables excellent image generation performance. Compared with similar models, LongCat-Next demonstrates leading benchmark performance across various metrics, showcasing its advantages in multimodal understanding and generation.

Cursor 發布了 Composer 2 技術報告，介紹了其 Kimi K2.5 MoE 架構的完整訓練方案，包括兩階段訓練和自研基準 CursorBench。經過訓練，Composer 2 的表現顯著提升，並在推理成本上優於其他前沿模型。

Cursor於3月25日發布Composer 2技術報告，揭示了Kimi K2.5模型的訓練方案，採用MoE架構，參數量達到1.04萬億。訓練分為兩階段，使用真實場景模擬進行強化學習，最終在CursorBench基準上取得61.3分，提升70%，且推理成本低於其他大模型API。

Meituan's LongCat team open-sourced LongCat-Flash-Prover on March 21, a MoE model with 560 billion parameters, focused on Lean4 formal theorem proving. The model is divided into three capabilities: automatic formalization, sketch generation, and complete proof generation, combining reasoning tools with the Lean4 compiler for real-time verification. Training employs the Hybrid-Experts Iteration Framework and HisPO algorithm to prevent reward manipulation. Benchmark tests show that the model has set records for open-source weight models in automatic formalization and theorem proving.

Mistral AI 發佈開源代碼代理 Leanstral，專為 Lean 4 形式化驗證設計，能生成可自動校驗的代碼和證明。該模型採用稀疏 MoE 架構，表現優於其他頂級模型，並提供免費下載和 API 調用。