The field of large language models (LLMs) is moving towards more efficient and adaptive reasoning capabilities. Researchers are exploring hybrid approaches that allocate subtasks across models of varying capacities, enabling collaborative reasoning and reducing computational costs. Novel frameworks and systems are being proposed to address the challenges of task decomposition, difficulty-aware subtask allocation, and dynamic adaptation to varying task complexities. These innovations have the potential to significantly improve the performance and efficiency of LLMs. Noteworthy papers include:

• R2-Reasoner, which proposes a reinforced model router for collaborative reasoning across heterogeneous LLMs, reducing API costs by 86.85% while maintaining or surpassing baseline accuracy.
• DynamicMind, which introduces a tri-mode thinking system that enables LLMs to autonomously select between fast, normal, and slow thinking modes for zero-shot question answering tasks.
• Reasoning-Search, which presents a single-LLM search framework that unifies multi-step planning, multi-source search execution, and answer synthesis within one coherent inference process.
• Router-R1, which formulates multi-LLM routing and aggregation as a sequential decision process using reinforcement learning.