arXiv:2605.28732v1 Announce Type: cross Abstract: Memory is essential for enabling large language models to support long-horizon reasoning, yet existing memory systems remain unreliable and difficult to debug. Tracing memory's dynamic evolution is crucial to understand how information is synthesized, propagated, or corrupted over time. In this work, we study the new problem of error tracing and attribution in LLM memory systems. We propose a novel framework that transforms memory pipelines into executable memory evolution graphs, enabling fine-grained tracing of operational information flow. W
As LLMs become more complex and integrated into critical applications, the need for robust debugging and error attribution in their memory systems becomes paramount to ensure reliability and trust.
This work directly addresses a core challenge in scaling LLM capabilities, enabling more reliable AI agents and long-horizon reasoning systems by providing tools to diagnose and fix systemic errors.
The ability to 'MemTrace' changes the LLM development paradigm by introducing systematic methods for understanding and improving memory behavior, moving beyond black-box debugging.
- · AI developers
- · LLM deployment platforms
- · AI safety researchers
- · Enterprises adopting AI
- · Companies with unreliable AI products
- · Traditional debugging toolkit providers
Improved reliability and performance of advanced LLM applications will accelerate their adoption across various industries.
Reduced operational risks associated with AI will lead to greater investment in developing more autonomous and complex AI systems.
The enhanced transparency and debuggability of LLMs could accelerate public and regulatory acceptance of AI in sensitive domains, potentially influencing standards for AI accountability.
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Read at arXiv cs.LG