TBNAI
Memory-Aware Multi-Agent Architecture with LLM-based Orchestration for Conversational Recommendation
Publication: cs.AI updates on arXiv.org
Author: Emilly Pitman de Castro, Vanessa Silva, Gustavo Kanno, Marcus Santos, Bruno Ferraz do Amaral, Marcelo Nogueira, Lucas Francisco Pellicer, Lucas Lanaro, Pedro Augusto Luiz, Jo\~ao Renato Ribeiro Manesc
Source published: 09/11/2026, 05:00:00 UTC
Briefing created: 09/11/2026, 07:22:26 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 09/11/2026, 05:03:25 UTC.
Briefings
A memory-aware multi-agent architecture using LLMs as orchestration layers was tested on the Amazon Reviews dataset. The system integrated long-term memory to recover up to 63% of the performance gap between implicit and explicit queries, preventing semantic degradation in extended dialogues. Supervisory orchestration increased user acceptance by 28.9 percentage points. The study also identified an exposure bottleneck between retrieved candidates and displayed recommendations, noting that LLM-reranking improved semantic consistency and diversity despite not improving Hit@K metrics.
Key points
- The system integrates long-term memory as a first-class component influencing routing decisions and semantic query formation.
- Memory-enabled interactions recovered up to 63% of the gap toward explicit-query scenarios in cross-session experiments.
Context
Conversational recommender systems typically treat user sessions in isolation, limiting their ability to capture evolving user intent. This research addresses that limitation by proposing a memory-aware multi-agent architecture where LLMs coordinate intent refinement and interaction flow, integrating memory as a core design principle.
Limitations
Evaluation was conducted on the Amazon Reviews dataset using cross-session memory experiments and supervisor-driven interaction studies. Results are based on simulated or limited conversational simulations rather than real-world deployment data. The study does not report real user acceptance rates or long-term retention metrics.
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