Self-Evolving LLM Memory Extraction Across Heterogeneous Tasks

TL;DR

This paper introduces BEHEMOTH, a benchmark for heterogeneous memory extraction in LLMs, and proposes CluE, a cluster-based method that improves prompt optimization across diverse tasks.

Contribution

It formalizes the heterogeneous memory extraction task, creates a new benchmark, and develops CluE, a novel clustering approach that enhances prompt evolution in varied scenarios.

Findings

CluE achieves a 9.04% relative gain over prior methods.

No single static prompt dominates across all task types.

Existing self-evolving frameworks degrade with heterogeneous data.

Abstract

As LLM-based assistants become persistent and personalized, they must extract and retain useful information from past conversations as memory. However, the types of information worth remembering vary considerably across tasks. We formalize the \textit{heterogeneous memory extraction} task and introduce \textbf{BEHEMOTH}, a benchmark that repurposes 18 existing datasets spanning personalization, problem-solving, and agentic tasks, using a downstream utility-driven metric for systematic evaluation. Our empirical analysis confirms that no single static extraction prompt dominates across all task categories, and that existing self-evolving prompt optimization frameworks, originally designed for homogeneous distributions, degrade when training tasks are heterogeneous. To address this, we propose \textbf{CluE}, a cluster-based self-evolving strategy that groups training examples into clusters by extraction scenarios, analyzes each cluster independently, and synthesizes cross-cluster insights to update the extraction prompt. Experiments on BEHEMOTH show that CluE generalizes effectively across heterogeneous tasks ($+$9.04\% relative gain), consistently outperforming prior self-evolving frameworks.