AWS simplifies AI agent customization with automated reinforcement learning - SiliconANGLE
<a href="https://news.google.com/rss/articles/CBMiqwFBVV95cUxQZUtMZHpuU1lKQ2JoY1c2YkVrdWoxRG1pd0tkNncwTUNPbjZwTXVOVWF3YWdLdG1vZkFLelR1RF9KY2lPNk5iTWkxVXJhTXV6QTNGV1paMlhtbjh3Zk5LcVBDTl80V2YyOWJYeHNEWDVnU2pJc044cHljMXVBMTdHRm5TQjczTTlSQzJqTDI4QmVqazlKTkV1eExxXzdZdkdobnl3WnVRZTA2ZFE?oc=5" target="_blank">AWS simplifies AI agent customization with automated reinforcement learning</a> <font color="#6f6f6f">SiliconANGLE</font>
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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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arXiv:2512.16081v2 Announce Type: replace-cross Abstract: Social interactions incorporate nonverbal signals to convey emotions alongside speech, including facial expressions and body gestures. Generative models have demonstrated promising results in creating full-body nonverbal animations synchronized with speech; however, evaluations using statistical metrics in 2D settings fail to fully capture user-perceived emotions, limiting our understanding of model effectiveness. To address this, we evaluate emotional 3D animation generative models within a Virtual Reality (VR) environment, emphasizing user-centric metrics emotional arousal realism, naturalness, enjoyment, diversity, and interaction quality in a real-time human-agent interaction scenario. Through a user study (N=48), we examine per
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