ConMem: Structured Memory-Guided Adaptation in Training-Free Multi-Agent Systems

ConMem introduces a training-free framework for multi-agent systems that uses structured memory cards and relation-aware graphs to improve adaptation without additional training. The approach reduces inference overhead by over 80% and prunes more than 50% of candidate expansions while maintaining performance across multiple benchmarks.

ConMem addresses a critical limitation in current LLM-based multi-agent systems: the challenge of enabling intelligent adaptation without expensive retraining or high-quality labeled data. The framework distills historical trajectories into reusable memory cards organized within a relation-aware graph, allowing agents to coordinate strategies dynamically at runtime. This architectural innovation matters because multi-agent systems increasingly power complex reasoning tasks, and training-free adaptation reduces operational costs while improving responsiveness.

The research builds on growing momentum in memory-augmented AI systems. Prior approaches struggled with noisy trajectory data and failed to properly model how different memory components interact. ConMem's structured approach to memory organization and cross-experience coordination represents an incremental but meaningful evolution, addressing real pain points that practitioners encounter when deploying multi-agent systems at scale.

For developers and organizations building AI applications, the efficiency gains are particularly significant. An 80% reduction in planning overhead translates directly to faster inference times and lower computational costs—critical factors for production systems. The framework's compatibility with mainstream MAS architectures suggests broad applicability without requiring architectural redesigns.

The training-free nature of ConMem positions it as a practical alternative to fine-tuning approaches, especially valuable in scenarios with limited labeled data or strict resource constraints. Future development likely focuses on scaling these techniques to larger agent networks and more complex coordination scenarios, potentially enabling more sophisticated multi-agent reasoning without proportional increases in computational demand.

• →ConMem enables multi-agent adaptation without training by organizing memory into relation-aware graphs that capture reusable strategies.
• →Reduces inference planning overhead by over 80% and prunes more than 50% of candidate expansions while maintaining performance.
• →Training-free approach eliminates need for expensive retraining or high-quality supervision, reducing deployment costs.
• →Compatible with multiple mainstream multi-agent system architectures, enabling broad practical adoption.
• →Addresses critical challenge of noisy trajectories and poor memory-skill modeling in existing LLM-based multi-agent systems.