Enhancing Conversational Agents via Task-Oriented Adversarial Memory Adaptation

TL;DR

This paper introduces AMA, a novel adversarial memory adaptation method that aligns memory construction and updating processes with task objectives, significantly improving long dialogue handling in conversational agents.

Contribution

The paper proposes AMA, an innovative adversarial framework that enhances memory systems by incorporating task-aware supervision during offline memory construction and update phases.

Findings

AMA improves long dialogue performance on LoCoMo benchmark

Memory adaptation aligns offline memory with downstream tasks

Enhanced memory systems lead to better conversational understanding

Abstract

Conversational agents struggle to handle long conversations due to context window limitations. Therefore, memory systems are developed to leverage essential historical information. Existing memory systems typically follow a pipeline of offline memory construction and update, and online retrieval. Despite the flexible online phase, the offline phase remains fixed and task-independent. In this phase, memory construction operates under a predefined workflow and fails to emphasize task relevant information. Meanwhile, memory updates are guided by generic metrics rather than task specific supervision. This leads to a misalignment between offline memory preparation and task requirements, which undermines downstream task performance. To this end, we propose an Adversarial Memory Adaptation mechanism (AMA) that aligns memory construction and update with task objectives by simulating task execution. Specifically, first, a challenger agent generates question answer pairs based on the original dialogues. The constructed memory is then used to answer these questions, simulating downstream inference. Subsequently, an evaluator agent assesses the responses and performs error analysis. Finally, an adapter agent analyzes the error cases and performs dual level updates on both the construction strategy and the content. Through this process, the memory system receives task aware supervision signals in advance during the offline phase, enhancing its adaptability to downstream tasks. AMA can be integrated into various existing memory systems, and extensive experiments on long dialogue benchmark LoCoMo demonstrate its effectiveness.