Enhancing Conversational Agents via Task-Oriented Adversarial Memory Adaptation

TL;DR

This work addresses the misalignment between offline memory preparation and downstream task needs in long-context conversational agents. It introduces Adversarial Memory Adaptation (AMA), a Challenger–Evaluator–Adapter loop that simulates task execution during memory construction to furnish task-aware supervision and updates both the memory content and the extraction strategy. AMA is designed as a plug-in module compatible with existing memory systems and achieves consistent gains on the LoCoMo benchmark across multiple baselines and LLM backbones. The results demonstrate improved memory quality, enhanced downstream QA performance, and robust behavior across task types, validating AMA’s potential to advance long-context dialogue systems. The approach provides a practical pathway toward task-focused memory systems in real-world applications.

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.

Figures (4)

• Figure 1: A typical memory system pipeline.
• Figure 2: The overall architecture of our model.
• Figure 3: The Evolutionary Process with AMA, with Nemori as the baseline and GPT-4o-mini as the backbone.
• Figure 4: LLM Evaluation Results on LightMEM and Nemori with GPT-4o-mini.