Collaborative Learning and Reasoning over Model Slices
Large language models (LLMs) excel across diverse tasks, benefiting from knowledge priors, in-context reasoning, and test-time computation. Despite these strengths, single-model reasoning remains vulnerable to error propagation, as later steps depend on the model's earlier outputs, leaving reasoning blind spots difficu...
Yu-Heng Wang, Run-De Yang, Zhu-Han Bao et al.
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