Recent work on recursive reasoning models like TRM demonstrates that tiny networks (7M parameters) can achieve strong performance on abstract reasoning tasks through latent recursion -- iterative refinement in hidden representation space without emitting intermediate tokens. This raises a natural question about operator choice: Mamba-2's state space recurrence is itself a form of iterative refinement, making it a natural candidate for recursive reasoning -- but does introducing Mamba-2 into the recursive scaffold preserve reasoning capability? We investigate this by replacing the Transformer blocks in TRM with Mamba-2 hybrid operators while maintaining parameter parity (6.83M vs 6.86M parameters). On ARC-AGI-1, we find that the hybrid improves pass@2 (the official metric) by +2.0\% (45.88\% vs 43.88\%) and consistently outperforms at higher K values (+4.75\% at pass@100), whilst maintaining pass@1 parity. This suggests improved candidate coverage -- the model generates correct solutions more reliably -- with similar top-1 selection. Our results validate that Mamba-2 hybrid operators preserve reasoning capability within the recursive scaffold, establishing SSM-based operators as viable candidates in the recursive operator design space and taking a first step towards understanding the best mixing strategies for recursive reasoning.

翻译：近期关于递归推理模型（如TRM）的研究表明，微型网络（7M参数）通过潜在递归——即在隐藏表示空间中进行迭代优化而无需生成中间标记——能够在抽象推理任务上实现强劲性能。这自然引出了一个关于算子选择的问题：Mamba-2的状态空间递归本身即是一种迭代优化形式，使其成为递归推理的天然候选方案。然而，将Mamba-2引入递归框架是否会保持推理能力？我们通过将TRM中的Transformer模块替换为Mamba-2混合算子（同时保持参数量基本相当：6.83M vs 6.86M参数）对此展开研究。在ARC-AGI-1数据集上，混合模型将官方评价指标pass@2提升了+2.0%（45.88% vs 43.88%），且在更高K值下持续优于原模型（pass@100提升+4.75%），同时保持pass@1指标持平。这表明模型在保持最优解选择能力相近的情况下，提升了候选解的覆盖质量——即能更可靠地生成正确解。我们的结果验证了Mamba-2混合算子在递归框架内能够保持推理能力，确立了基于状态空间模型（SSM）的算子作为递归算子设计空间中的可行候选方案，并为理解递归推理的最佳混合策略迈出了第一步。