AgentIR: A Workload-Adaptive Cascade Retrieval Substrate for Long-Term Conversational Memory
arXiv:2605.25092v1 Announce Type: cross Abstract: Long-term conversational memory is a retrieval workload classical IR was not built for: the index grows during the query stream, query types shift intra-session, and the latency budget per retrieval is sub-10 ms. Lucene-class engines treat the index as static and the query as stateless, leaving the workload's structure unexploited. AgentIR treats fusion as a per-query decision along two axes: which fusion to apply (BM25, Dense, RRF, or agent-aware RRF), and whether the ~52 ms dense channel is worth running at all. The second axis is a confidenc
The proliferation of advanced AI models and agentic systems is pushing the limits of current retrieval architectures, necessitating new approaches for conversational memory at scale.
Improving long-term conversational memory directly enhances the capabilities and reliability of AI agents, making them more effective in persistent, complex tasks.
This research introduces a workload-adaptive retrieval system that dynamically optimizes for the unique demands of conversational AI, moving beyond static, stateless index assumptions.
- · AI Agent developers
- · Conversational AI platforms
- · Large language model providers
- · Enterprise AI
- · Legacy search engine architectures
- · Static information retrieval systems
More sophisticated and context-aware AI agents become feasible for deployment in complex problem-solving scenarios.
Reduced latency and improved accuracy in agentic systems could accelerate their adoption across various white-collar workflows.
The enhanced performance of AI agents, powered by better memory, could lead to a ' Cambrian explosion' of novel applications previously constrained by memory limitations.
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