cs.AI updates on arXiv.org 07月30日 12:46
AGORA: Incentivizing Group Emergence Capability in LLMs via Group Distillation
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文章提出通过结构化交互超越模型参数增加的常规方法,提出AGORA框架,通过协作集成在数学基准测试中提升推理性能,验证交互作为智能可扩展驱动的重要性。

arXiv:2507.21166v1 Announce Type: cross Abstract: Progress in complex reasoning is constrained by the static nature of the current training datasets. We propose structured interaction as a new scaling axis, moving beyond the prevailing paradigm of increasing model parameters. Our self-evolving framework, AGORA, enables a collaborative ensemble to achieve reasoning performance exceeding state-of-the-art monolithic systems by up to 4.45 percentage points on challenging mathematical benchmarks. This gain stems from group emergent ability-the synthesis of collective capabilities unattainable by isolated models, validating interaction as a scalable driver of intelligence. Our results position the engineering of collaborative ecosystems as a vital frontier for capability emergence.

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结构化交互 推理能力 AGORA框架 智能提升 协作集成
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