Beyond Symmetric Agents: Cognitive Diversity and Multi-Agent Debate in Small Language Models
Unlocking enhanced reasoning in compact models through the strategic integration of diverse agent perspectives.
Multi-agent debate (MAD) reportedly improves reasoning and factuality over single-model inference, but prior work treats agents as symmetric peers, leaving open what drives the gains. We test the hypothesis that cognitive diversity among agents is the driver, in the setting where...
ArXiv CS.AI
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This research suggests that the efficacy of multi-agent systems stems from intentional cognitive variance rather than simple iterative reinforcement. By diversifying the personas within small language models, developers can unlock superior reasoning capabilities without needing massive compute resources.
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