A multi-agent binary reward system with unbiased GRPO post-training on ICLR-320 data outperforms baselines on expert-rated novelty, feasibility, and effectiveness for scientific idea generation.
Sci-idea: Context- aware scientific ideation using token and sentence embeddings
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The paper proposes a four-role framework for LLMs in scientific innovation and reviews methods, benchmarks, and limitations across Assistant, Collaborator, Scientist, and Evaluator roles.
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Debate as Reward: A Multi-Agent Reward System for Scientific Ideation via RL Post-Training
A multi-agent binary reward system with unbiased GRPO post-training on ICLR-320 data outperforms baselines on expert-rated novelty, feasibility, and effectiveness for scientific idea generation.
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Evolving Roles of LLMs in Scientific Innovation: Assistant, Collaborator, Scientist, and Evaluator
The paper proposes a four-role framework for LLMs in scientific innovation and reviews methods, benchmarks, and limitations across Assistant, Collaborator, Scientist, and Evaluator roles.