Analogical Deep Research: Retrieving and Integrating Historical Analogies for Foresight Analysis
Systematic comparisons between current situations and structurally similar past events in the historical, i.e., historical analogies, is among the most powerful tools for foresight analysis. In this work, we present a new task called Analogical Deep Research (ADR) to Large Language Model (LLM) agents and construct the first ADR benchmark ADR-bench to study whether LLM agents are able to find and leverage historical analogies when doing foresight analysis. Our investigation reveals a key obstacle: LLM agents are poor at finding analogies because they match on surface features rather than underl
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzySimilar name plus overlapping authors · 67%google-research/google-research →
“Title similarity 0.73; shared authors: sun”
- LinkedLinked via arxiv author · 85%Yongqiang Chen →
“Analogical Deep Research: Retrieving and Integrating Historical Analogies for Foresight Analysis”
- LinkedLinked via arxiv author · 85%Guangyi Chen →
“Analogical Deep Research: Retrieving and Integrating Historical Analogies for Foresight Analysis”
- LinkedLinked via arxiv author · 85%Yuewen Sun →
“Analogical Deep Research: Retrieving and Integrating Historical Analogies for Foresight Analysis”
- LinkedLinked via arxiv author · 85%Yukun Zhang →
“Analogical Deep Research: Retrieving and Integrating Historical Analogies for Foresight Analysis”