CastFlow introduces a role-specialized agentic workflow with memory retrieval and multi-view toolkit for iterative ensemble time series forecasting, using two-stage SFT+RLVR training on a domain-specific LLM to outperform static baselines.
Time-Series Representation Learning via Temporal and Contextual Contrasting
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QoEReasoner is an agentic framework using LLMs with deterministic KPI tools, a domain knowledge base, and a historical case bank orchestrated by a stateful planner for automated, explainable QoE diagnosis in RANs, claiming 18-40% accuracy gains and reduction to 3-minute sessions on real datasets.
ADAPT is a new pre-training paradigm that aligns physical properties of time-series data to allow simultaneous training on 162 diverse classification datasets, achieving new state-of-the-art performance.
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