| Challenge: | Contemporary automated scientific discovery systems focus on generating experiments, but higher-level activities such as theory building remain underexplored. |
| Approach: | They propose to synthesize theories from scientific literature using literature-grounding versus parametric knowledge. |
| Outcome: | The proposed method matches existing evidence better than parametric LLM memory generation. |
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| Challenge: | Existing methods for hypothesis generation are theory-driven and data-driven, but they lack the computational power to complement each other. |
| Approach: | They develop a method that combines literature-based insights with data to perform LLM-powered hypothesis generation. |
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ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models (2024.emnlp-main)
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Benjamin Newman, Yoonjoo Lee, Aakanksha Naik, Pao Siangliulue, Raymond Fok, Juho Kim, Daniel Weld, Joseph Chee Chang, Kyle Lo
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ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models (2025.naacl-long)
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| Challenge: | a new system that leverages the encyclopedic knowledge and linguistic reasoning capabilities of Large Language Models (LLMs) is proposed to enhance the productivity of researchers . a researcher's research idea generation process involves problem identification, method development, experiment design and iterative revision . |
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HypER: Literature-grounded Hypothesis Generation and Distillation with Provenance (2025.emnlp-main)
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| Challenge: | Existing approaches focus on retrieval augmentation and focus on the quality of the output . Existing methods focus on generating a highly specific declarative statement ignoring the underlying reasoning process behind ideation. |
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WildSci: Advancing Scientific Reasoning from In-the-Wild Literature (2026.findings-acl)
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| Challenge: | Recent advances in large language model reasoning focus on mathematics and coding domains, but scientific reasoning remains limited in other domains due to limited dataset coverage. |
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SciText2Eq: Assessing LLMs for Explainable Equation Generation for Scientific Creativity (2026.findings-acl)
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| Challenge: | Prior work has addressed problems in unstructured grounding, multi-equation dependency, and human-aligned evaluation. |
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SciReviewGen: A Large-scale Dataset for Automatic Literature Review Generation (2023.findings-acl)
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| Challenge: | Existing literature review models have addressed literature review generation, but lack of large-scale datasets has been a stumbling block. |
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SurveyGen: Quality-Aware Scientific Survey Generation with Large Language Models (2025.emnlp-main)
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EvoNarrator: Modeling Scientific Evolution for Feasible Hypothesis Generation (2026.acl-long)
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A Survey on LLMs for Story Generation (2025.findings-emnlp)
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Maria Teleki, Vedangi Bengali, Xiangjue Dong, Sai Tejas Janjur, Haoran Liu, Tian Liu, Cong Wang, Ting Liu, Yin Zhang, Frank Shipman, James Caverlee
| Challenge: | Methods for story generation with Large Language Models (LLMs) have come into the spotlight recently. |
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