Reasoning-intensive information retrieval uses large language models to solve complex queries via multi-step reasoning.However, existing methods have critical limitations.Chain-of-Thought (CoT) approaches suffer from inefficiency, while state-based methods, despite better token efficiency, often fall into reasoning cycles that trap the query refinement process.To address these issues, we propose Episodic Memory for Retrieval (EMR), which enhances the state-based framework with an episodic memory.This module stores the full history of prior states for a query, allowing the model to avoid repetition of such cycles.Experiments on the BRIGHT benchmark show that EMR consistently outperforms both CoT and state-based baselines.Moreover, it is highly token-efficient, reducing token usage by 72% on average.Our results show that episodic memory is an effective and tokenefficient mechanism for reasoning-intensive retrieval.The gains also generalize across different base models and stay efficient in terms of end-to-end latency.The code is available in https://github.com/ldilab/EMR.

Large Language Models (LLMs) have demonstrated near-human performance in summarization tasks based on traditional metrics such as ROUGE and BERTScore.However, these metrics do not adequately capture critical aspects of summarization quality, such as factual accuracy, particularly for long narratives (>100K tokens).Recent advances, such as LLM-as-a-Judge, address the limitations of metrics based on lexical similarity but still exhibit factual inconsistencies, especially in understanding character relationships and states.In this work, we introduce NARRATIVEFACTSCORE (NFS), the first "Agent-as-a-Judge" framework that evaluates and refines factuality in narrative summarization.By leveraging a Character Knowledge Graph (CKG) extracted from input narrative, NARRATIVEFACTSCORE evaluates the factuality and provides actionable guidance for refinement, such as identifying missing or erroneous facts.Our experimental results demonstrate that constructing the CKG enables reasoning with 1/3 of the factuality computation used in the prior approach, and achieve three times higher correlation with human judgments.Furthermore, refinement with actionable guidance improves the quality of the summary. 1