arXiv:2603.19595v2 Announce Type: replace-cross Abstract: Lifelong interactive agents are expected to assist users over months or years, which requires continually writing long term memories while retrieving the right evidence for each new query under fixed context and latency budgets. Existing memory systems often degrade as histories grow, yielding redundant, outdated, or noisy retrieved contexts. We present \textbf{All-Mem}, an online/offline lifelong memory framework that maintains a topology structured memory bank via explicit, non destructive consolidation, avoiding the irreversible info
The proliferation of advanced AI models necessitates more efficient and robust memory systems to support long-term, interactive agentic operations without performance degradation.
Improving AI agent memory is critical for developing truly autonomous and persistent intelligent systems, expanding their utility and scope across various industries.
Existing memory systems often degrade as histories grow, yielding redundant, outdated, or noisy retrieved contexts. This research introduces a framework that directly addresses this problem through dynamic topology evolution and non-destructive consolidation.
- · AI developers
- · Cloud computing providers
- · Enterprises adopting AI agents
- · Inefficient AI memory solutions
- · Systems with high retrieval latency
AI agents become more reliable and persistent, capable of longer-term, more complex interactions.
This enables broader deployment of autonomous agents in critical applications across industries, reducing human oversight requirements.
The increased sophistication and reliability of AI agents could accelerate the automation of white-collar tasks, leading to significant shifts in workforce demands and economic structures.
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Read at arXiv cs.CL