Analogical Deep Research: Retrieving and Integrating Historical Analogies for Foresight Analysis

• URL: http://arxiv.org/abs/2607.13602v1
• Date: Wed, 15 Jul 2026 08:49:00 GMT
• Title: Analogical Deep Research: Retrieving and Integrating Historical Analogies for Foresight Analysis
• Abstract summary: We present an agentic framework called Causal Analogical Researcher (CANA) that guides LLMs to find and integrate historical analogies.<n>CANA brings up to 10% improvements in historical analogy generation, and surpasses the state-of-the-art deep research agents in the ADR-bench.
• Score: 20.493267933446692
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 underlying mechanisms. We argue that ADR is inherently a causal question as it requires understanding why the event occurred. Based on our theoretical analysis, we propose two principles required for ADR, including the mechanism alignment and cross-analogy confirmation. Built upon our theoretical results, we propose a new agentic framework called Causal Analogical Researcher (CANA) that guides LLMs to find and integrate historical analogies. CANA incorporates a simple yet effective structural decomposition representation, and integrates structural feedback for reflective improvements of historical analogy identification and integration. We show that CANA brings up to 10% improvements in historical analogy generation, and surpasses the state-of-the-art deep research agents in the ADR-bench. Case studies with the ongoing events confirm the effectiveness of CANA in leveraging historical analogies.

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