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New SCORE framework enables AI to evolve its own research evaluation

Researchers have developed a new framework called SCORE that jointly trains a language model for research generation and its own evaluation. This self-evolving approach allows the evaluator to adapt its standards as the generator improves, overcoming limitations of static evaluation methods. Experiments show this co-evolutionary training enhances the quality of generated research reports, suggesting a promising path for developing advanced research agents. AI

IMPACT This framework could accelerate AI-driven scientific discovery by enabling more sophisticated and adaptive research agents.

RANK_REASON The cluster contains an academic paper detailing a new AI research framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Han Zhu, Chengkun Cai, Yuanfeng Song, Xing Chen, Sirui Han, Yike Guo ·

    Self-Evolving Deep Research via Joint Generation and Evaluation

    arXiv:2606.04507v1 Announce Type: cross Abstract: Large Language Models (LLMs) have become increasingly adopted in daily applications, with deep research standing out as a particularly important capability. Unlike traditional question-answering (QA) tasks, deep research report ge…