Learning to Construct Knowledge through Sparse Reference Selection with Reinforcement Learning
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908522556424192 |
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| author | Yin, Shao-An |
| author_facet | Yin, Shao-An |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05874 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning to Construct Knowledge through Sparse Reference Selection with Reinforcement Learning Yin, Shao-An Machine Learning Artificial Intelligence Information Retrieval I.2.6 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. |
| title | Learning to Construct Knowledge through Sparse Reference Selection with Reinforcement Learning |
| topic | Machine Learning Artificial Intelligence Information Retrieval I.2.6 |
| url | https://arxiv.org/abs/2509.05874 |