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Meta-Moderator is introduced, a learnable framework that dynamically regulates debate and decides when to finalize an answer, and outperforms widely used decision layers and transfers across tasks and system configurations.
The findings reveal that transformer-based large language models exhibit learning patterns similar to those of human learners, with a faster learning speed for simpler subtasks compared to more complex ones, which suggests that transformer-based LLMs may share cognitive processes with human learners in arithmetic.
The results show that chemical CoT is neither a faithful explanation nor merely a post-hoc rationalization, but a hallucination-prone molecular scratchpad, which cautions against treating CoT as direct evidence of faithful reasoning and motivates process-level supervision beyond answer-only evaluation.
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