arXiv Artificial Intelligence

AutoViewMem: Self-Configuring Orthogonal Views for Conversational Long-Term Memory

1 hour ago 21.09.2026 07:00 👁 1 views
AutoViewMem: Self-Configuring Orthogonal Views for Conversational Long-Term Memory

Quick summary

arXiv:2609.21940v1 Announce Type: new Abstract: Long-term memory is essential for large language model (LLM) agents to maintain consistency and personalization over extended interactions. Existing memory systems typically rely on fixed granularities or static schemas, but these designs struggle when heterogeneous information, such as preferences, events, constraints, and temporal updates, is embedded in a single mixed representation. The resulting semantic interference makes top-K retrieval sensitive to noise and often leaves relevant evidence poorly ranked. We present AutoViewMem, a data-driv

Key takeaways

  • arXiv:2609.21940v1 Announce Type: new Abstract: Long-term memory is essential for large language model (LLM) agents to maintain consistency and personalization over extended interactions.
  • Existing memory systems typically rely on fixed granularities or static schemas, but these designs struggle when heterogeneous information, such as preferences, events, constraints, and temporal updates, is embedded in a single mixed representation.
  • The resulting semantic interference makes top-K retrieval sensitive to noise and often leaves relevant evidence poorly ranked.

Why it matters

“AutoViewMem: Self-Configuring Orthogonal Views for Conversational Long-Term Memory” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗