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    <abstract>Long-term conversational AI requires two distinct capabilities: remembering what was discussed and adapting to how a user’s preferences evolve over time. We argue these operate on different timescales and require separate but interoperable architectures. AgenticLOCI is a five-agent framework built on this premise: session retrieval fuses a short-term semantic ranker with a long-term keyword scorer via rank fusion, while personalization is maintained through a versioned deliberation-state memory that accumulates decision-episode snapshots across interactions. We evaluate on three benchmarks using Qwen 2.5-7B-Instruct. On LoCoMo, the AgenticLOCI Backbone achieves F1 = 0.342 versus BM25 = 0.285 while consuming 7.3\times fewer input tokens per item. On LongMemEval-S, item-level analysis confirms that the two retrieval layers solve architecturally distinct sub-problems; at 7B scale the keyword layer leads, a reversal expected to resolve at larger model sizes where LLM ranking is more reliable. On PersonaMem, all configurations perform within a narrow band, establishing that memory benefit is conditional on context-budget pressure. The longitudinal personalization layer is evaluated on a synthetic mobility benchmark with known user trajectories, keeping 97.6% of stable preference fields unchanged and making warranted updates with 58.3% precision against an ablation baseline. We demonstrate the full framework as a personalized urban mobility assistant for Magdeburg.</abstract>
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%0 Conference Proceedings
%T AgenticLOCI: A Multi-Agent Memory Framework for Conversational AI with Longitudinal Personalization
%A Schewior, Jonas
%A Bilgin, Deniz
%A Haefner, Gonzalo
%A Wendemuth, Andreas
%Y Kamalloo, Ehsan
%Y Gontier, Nicolas
%Y Rozanov, Nikolai
%Y Hashemzadeh, Maryam
%Y Arora, Akhil
%S Proceedings of the Second Workshop for REsearch on Agent Language Models (REALM 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-485-9
%F schewior-etal-2026-agenticloci
%X Long-term conversational AI requires two distinct capabilities: remembering what was discussed and adapting to how a user’s preferences evolve over time. We argue these operate on different timescales and require separate but interoperable architectures. AgenticLOCI is a five-agent framework built on this premise: session retrieval fuses a short-term semantic ranker with a long-term keyword scorer via rank fusion, while personalization is maintained through a versioned deliberation-state memory that accumulates decision-episode snapshots across interactions. We evaluate on three benchmarks using Qwen 2.5-7B-Instruct. On LoCoMo, the AgenticLOCI Backbone achieves F1 = 0.342 versus BM25 = 0.285 while consuming 7.3\times fewer input tokens per item. On LongMemEval-S, item-level analysis confirms that the two retrieval layers solve architecturally distinct sub-problems; at 7B scale the keyword layer leads, a reversal expected to resolve at larger model sizes where LLM ranking is more reliable. On PersonaMem, all configurations perform within a narrow band, establishing that memory benefit is conditional on context-budget pressure. The longitudinal personalization layer is evaluated on a synthetic mobility benchmark with known user trajectories, keeping 97.6% of stable preference fields unchanged and making warranted updates with 58.3% precision against an ablation baseline. We demonstrate the full framework as a personalized urban mobility assistant for Magdeburg.
%U https://aclanthology.org/2026.realm-1.42/
%P 618-638

Markdown (Informal)

[AgenticLOCI: A Multi-Agent Memory Framework for Conversational AI with Longitudinal Personalization](https://aclanthology.org/2026.realm-1.42/) (Schewior et al., REALM 2026)

• AgenticLOCI: A Multi-Agent Memory Framework for Conversational AI with Longitudinal Personalization (Schewior et al., REALM 2026)

ACL

• Jonas Schewior, Deniz Bilgin, Gonzalo Haefner, and Andreas Wendemuth. 2026. AgenticLOCI: A Multi-Agent Memory Framework for Conversational AI with Longitudinal Personalization. In Proceedings of the Second Workshop for REsearch on Agent Language Models (REALM 2026), pages 618–638, Budapest, Hungary. Association for Computational Linguistics.