arXiv:2606.10677v1 Announce Type: cross Abstract: Long-term LLM agents need persistent memory that can track changing facts and provide relevant evidence across sessions. Existing memory systems often store observations as isolated records, summaries, or indexed fragments, which makes evidence aggregation, fact revision, and memory maintenance difficult. We propose Infini Memory, a maintainable text-based persistent memory architecture that treats agent memory as topic-structured documents. Each topic document serves as a semantic unit for collecting related evidence, preserving metadata, and
The rapid advancement of LLM capabilities is pushing the boundary of agentic systems, necessitating more sophisticated memory architectures for practical application.
Improved long-term memory for LLM agents is critical for their sustained utility and autonomy, enabling them to handle complex, multi-session tasks efficiently.
This proposal offers a method for LLMs to manage and update their persistent memory more effectively, moving away from isolated records to structured, maintainable topic documents.
- · LLM developers
- · AI agent companies
- · Enterprises adopting AI agents
- · Legacy memory system providers
- · Companies with less sophisticated LLM agent architectures
LLM agents become more reliable and capable of handling complex, long-duration tasks.
Increased adoption of autonomous AI agents across various industries due to enhanced memory and reasoning over time.
Accelerated development of general intelligence agents capable of continuous learning and adaptation.
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Read at arXiv cs.CL