New SAP Learning Journey: Discovering High-Value Use Cases for Agentic AI - SAP News Center
<a href="https://news.google.com/rss/articles/CBMigAFBVV95cUxPcDFNRFpSbk1lTVVHZDBZNXVDQkJJOV93N3VpTkRIUFNad3ExMnV4Yy1yWDJNYzBvenZtcmFwWFc4UWpVZnVfUWdjbTAzcjZmWW0wa3d4eXJxMGd4YkJrTjZkSjhESU5ScDY5c2NtcTdOcVY4bEFNZ0FZT1JBaEZRbQ?oc=5" target="_blank">New SAP Learning Journey: Discovering High-Value Use Cases for Agentic AI</a> <font color="#6f6f6f">SAP News Center</font>
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MemRerank: Preference Memory for Personalized Product Reranking
arXiv:2603.29247v1 Announce Type: new Abstract: LLM-based shopping agents increasingly rely on long purchase histories and multi-turn interactions for personalization, yet naively appending raw history to prompts is often ineffective due to noise, length, and relevance mismatch. We propose MemRerank, a preference memory framework that distills user purchase history into concise, query-independent signals for personalized product reranking. To study this problem, we build an end-to-end benchmark and evaluation framework centered on an LLM-based \textbf{1-in-5} selection task, which measures both memory quality and downstream reranking utility. We further train the memory extractor with reinforcement learning (RL), using downstream reranking performance as supervision. Experiments with two L
Multi-Agent Memory from a Computer Architecture Perspective: Visions and Challenges Ahead
arXiv:2603.10062v2 Announce Type: replace-cross Abstract: As LLM agents evolve into collaborative multi-agent systems, their memory requirements grow rapidly in complexity. This position paper frames multi-agent memory as a computer architecture problem. We distinguish shared and distributed memory paradigms, propose a three-layer memory hierarchy (I/O, cache, and memory), and identify two critical protocol gaps: cache sharing across agents and structured memory access control. We argue that the most pressing open challenge is multi-agent memory consistency. Our architectural framing provides a foundation for building reliable, scalable multi-agent systems.
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