LSTM-MAS: Multi-Agent System Using LSTM Architecture for Improved Long-Context Processing in LLMs
A newly created multi-agent system, known as LSTM-MAS, aims to tackle the ongoing issue of long-context comprehension in large language models. Inspired by the Long Short-Term Memory framework, this system features agents arranged in a chained format to replicate hierarchical information flow and memory gating. Each component consists of a worker agent for segment-level understanding, a filter agent to minimize redundancy, and a judge agent for ongoing error monitoring. This strategy seeks to address the drawbacks of current single-LLM methods, which often struggle with high computational demands or limited context length. Although multi-agent systems can alleviate some of these challenges, they are still prone to error accumulation and hallucination. The LSTM-MAS architecture specifically addresses these weaknesses. This research contributes to resolving a critical unresolved issue in LLM advancement regarding the effective handling of extended contexts. The methodology showcases an innovative application of neural network concepts within multi-agent system design. Detailed technical information is available in the arXiv preprint 2601.11913v2, which was released as a replacement cross submission.
Key facts
- LSTM-MAS is a multi-agent system designed for long-context understanding in large language models
- The system draws inspiration from Long Short-Term Memory architecture
- It organizes agents in a chained architecture with specialized nodes
- Each node contains worker, filter, and judge agents with distinct functions
- The approach addresses limitations of single-LLM-based methods
- Existing methods often encounter computational costs or constrained context length
- Multi-agent frameworks can mitigate limitations but remain susceptible to error accumulation
- The research is documented in arXiv preprint 2601.11913v2
Entities
Institutions
- arXiv