A Multi-Agent Human-LLM Collaborative Framework for Closed-Loop Scientific Literature Summarization
Scientific discovery is slowed by fragmented literature that requires excessive human effort to gather, analyze, and understand. AI tools, including autonomous summarization and question answering, have been developed to aid in understanding scientific literature. However, these tools lack the structured, multi-step approach necessary for extracting deep insights from scientific literature. Large Language Models (LLMs) offer new possibilities for literature analysis, but remain unreliable due to hallucinations and incomplete extraction. We introduce Elhuyar, a multi-agent, human-in-the-loop system that integrates LLMs, structured AI, and human scientists to extract, analyze, and iterative...
| Authors | Maxwell J. Jacobson, Daniel Xie, Jackson Shen et al. |
|---|---|
| Primary category | cs.AI |
| Published | 1 April 2026 |
| Last updated at source | 31 August 2026 |
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