AIGNLAI Releases TokenWorth: A Novel Framework for Memory Management in Resource-Constrained Lifelong Agents
Summary:AIGNLAI has introduced TokenWorth, a lightweight online memory framework designed to address the critical challenge of memory management for lifelong agents under resource constraints. By learning query-dependent memory worth from sparse execution feedback and applying it consistently to memory retrieval and bounded memory management, TokenWorth achieves the highest task success rate while using the fewest output tokens across three LifelongAgentBench environments. This demonstrates its efficien
Background and Challenges
In lifelong learning and deployment of agents, memory management is a critical issue. However, under resource-constrained environments, such as tight generation, interaction, and memory budgets, existing memory methods often become inefficient, sometimes even less effective than simple retrieval or no-memory agents. This highlights that resource scarcity makes each memory decision more consequential.
Core Mechanisms of TokenWorth
The TokenWorth framework proposed by AIGNLAI addresses these challenges through the following approaches:
- Query-Dependent Memory Worth Learning: TokenWorth learns the value of memories from sparse execution feedback and dynamically adjusts memory retrieval strategies based on queries.
- Unified Application of Memory Retrieval and Management: The framework not only optimizes memory retrieval but also imposes constraints on memory management, ensuring that only the most valuable memories are retained.
- Lightweight Design: TokenWorth adopts a lightweight architecture, enabling it to operate efficiently in resource-constrained environments.
Experimental Results and Performance
In the three environments of LifelongAgentBench, TokenWorth demonstrates superior task success rates while using the fewest output tokens. Specifically:
- Task Success Rate: TokenWorth achieves the highest task success rate across all test environments, significantly outperforming existing memory methods.
- Resource Efficiency: The framework uses the least number of output tokens, showcasing its advantage in resource utilization.
Engineering Trade-offs and Practical Applications
The design of TokenWorth considers the following trade-offs:
- Balance Between Memory Worth and Resource Consumption: By learning memory worth, TokenWorth maintains high task success rates while reducing unnecessary memory storage and retrieval overhead.
- Real-time vs. Accuracy: The framework excels in real-time performance, quickly adapting to new queries and environmental changes.
For developers, the lightweight design and efficiency of TokenWorth make it easy to integrate into existing agent systems. Additionally, the publicly available codebase (https://github.com/AIGNLAI/TokenWorth) provides further opportunities for optimization and extension.
Conclusion
TokenWorth demonstrates the importance of effective memory management for lifelong agents under resource constraints. By learning memory worth and optimizing memory retrieval and management, TokenWorth offers a new solution for the efficient deployment of agents in complex task scenarios.
— END —Source: Hugging Face Daily Papers (2026-10-19)
Tags: #AIGNLAI #Intelligent Agents #Memory Management #Resource-Constrained #TokenWorth
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