Enhancing Conversational Agents via Task-Oriented Adversarial Memory Adaptation

2026-01-29

AI Summary

Existing conversational agents are constrained by limited context windows and struggle with long dialogues, while their memory systems often lack task-oriented design during offline phases, leading to misalignment between stored memories and downstream objectives. This work proposes Adversarial Memory Adaptation (AMA), the first framework to introduce task-aware adversarial adaptation into the offline memory construction phase. AMA employs a tri-agent architecture—comprising a challenger, an evaluator, and an adapter—to generate question-answer pairs that simulate reasoning, assess response quality, and perform dual-level memory updates, thereby actively aligning memory content with task goals. Experiments demonstrate that AMA seamlessly integrates with diverse memory systems and significantly enhances agent performance on long-horizon tasks, as evidenced by substantial gains on the LoCoMo long-context dialogue benchmark.

Technology Category

• Multiagent Systems: Adversarial Agents
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• conversational agents
• memory systems
• task alignment
• offline memory
• context window limitations

Innovation

• Adversarial Memory Adaptation
• Task-Oriented Memory
• Conversational Agents
• Memory Adaptation
• Long Dialogue Understanding