用强化学习从海量文献中精准挑选关键参考文献，高效构建知识。
Learning to Construct Knowledge through Sparse Reference Selection with Reinforcement Learning
- 通过强化学习模拟人类选文策略，优先筛选高价值论文。
- 在仅读标题摘要条件下，成功发现药物-基因关联，准确率显著提升。
- 适合科研人员快速追踪领域进展，尤其适用于受限访问场景。
科学文献的快速增长使获取新知识愈发困难，尤其在专业领域中，推理复杂、全文访问受限，目标参考文献稀疏分布在大量候选中。我们提出一种深度强化学习框架，用于稀疏参考文献选择，模拟人类知识构建过程，在时间与成本受限下，优先决定阅读哪些论文。在仅能访问标题和摘要的条件下，针对药物-基因关系发现任务进行评估，结果表明，无论是人类还是机器，均可基于部分信息有效构建知识。
原文摘要 · Abstract (English)
The rapid expansion of scientific literature makes it increasingly difficult to acquire new knowledge, particularly in specialized domains where reasoning is complex, full-text access is restricted, and target references are sparse among a large set of candidates. We present a Deep Reinforcement Learning framework for sparse reference selection that emulates human knowledge construction, prioritizing which papers to read under limited time and cost. Evaluated on drug--gene relation discovery with access restricted to titles and abstracts, our approach demonstrates that both humans and machines can construct knowledge effectively from partial information.
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