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cs.CL2026
Select-then-Solve: Paradigm Routing as Inference-Time Optimization for LLM Agents
Heng Zhou, Zelin Tan, Zhemeng Zhang +15
When an LLM-based agent improves on a task, is the gain from the model itself or from the reasoning paradigm wrapped around it? We study this question by comparing six inference-ti…
cs.CL2026
TodoEvolve: Learning to Architect Agent Planning Systems
Jiaxi Liu, Yanzuo Jiang, Guibin Zhang +5
Planning has become a central capability for contemporary agent systems in navigating complex, long-horizon tasks, yet existing approaches predominantly rely on fixed, hand-crafted…