SAP Pumps Up Agentic AI and Data Chops with Reltio Acquisition - Cloud Wars
<a href="https://news.google.com/rss/articles/CBMiqwFBVV95cUxOdkRNb2tjVzBIZnAxSERzV3FlNEpfRXlUcVlDSzR4Z0Y2V1VVNG41SFJZSnJUV3cyNUNoNFJldU84VWM0WlpqVmMxS0NtdWdFcGNxSzJsYWY4TUJfQm1GQllrMnByeVltUWkwVEhWaUl0ZmtvSkxGNF9zQURaVWVidkNtTUlNNUV0ZThzVno2ZFgtbGIwblBMamUwNVZnUHJ0Y2RDWUpZcmtidTA?oc=5" target="_blank">SAP Pumps Up Agentic AI and Data Chops with Reltio Acquisition</a> <font color="#6f6f6f">Cloud Wars</font>
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Multi-Layered Memory Architectures for LLM Agents: An Experimental Evaluation of Long-Term Context Retention
arXiv:2603.29194v1 Announce Type: new Abstract: Long-horizon dialogue systems suffer from semanticdrift and unstable memory retention across extended sessions. This paper presents a Multi-Layer Memory Framework that decomposes dialogue history into working, episodic, and semantic layers with adaptive retrieval gating and retention regularization. The architecture controls cross-session drift while maintaining bounded context growth and computational efficiency. Experiments on LOCOMO, LOCCO, and LoCoMo show improved performance, achieving 46.85 Success Rate, 0.618 overall F1 with 0.594 multi-hop F1, and 56.90% six-period retention while reducing false memory rate to 5.1% and context usage to 58.40%. Results confirm enhanced long-term retention and reasoning stability under constrained conte
Quality-Controlled Active Learning via Gaussian Processes for Robust Structure-Property Learning in Autonomous Microscopy
arXiv:2603.29135v1 Announce Type: new Abstract: Autonomous experimental systems are increasingly used in materials research to accelerate scientific discovery, but their performance is often limited by low-quality, noisy data. This issue is especially problematic in data-intensive structure-property learning tasks such as Image-to-Spectrum (Im2Spec) and Spectrum-to-Image (Spec2Im) translations, where standard active learning strategies can mistakenly prioritize poor-quality measurements. We introduce a gated active learning framework that combines curiosity-driven sampling with a physics-informed quality control filter based on the Simple Harmonic Oscillator model fits, allowing the system to automatically exclude low-fidelity data during acquisition. Evaluations on a pre-acquired dataset
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