Practical time-series forecasting requires domain knowledge, constraints, plausibility checks, and human intervention. We present CastClaw, an interactive forecasting system centered on an autonomous forecasting loop and supported by a forecasting-oriented harness. Users create and refine tasks in natural language thro...
Xiao-Yu Tao, Ming-Yue Cheng, Ze Guo et al.· 0 citations
Proximal Sequence Policy Optimization (PSPO), a process-aware, sequence-level policy optimization method that aligns optimization with agent--environment interaction, is introduced, validating the effectiveness of the adaptive agentic framework and optimization strategy.
Ting-Yue Pan, Jie Ouyang, Mingyue Cheng et al.· arXiv.org· 6 citations
CastFSR is proposed, an agentic framework that formulates context-aware forecasting as a Fast--Slow--Reflect workflow that supports both training-free inference with off-the-shelf LLMs and efficient deployment through a two-stage SFT and reinforcement learning strategy that transfers its orchestration capability to com...
Xiao-Yu Tao, Mingyue Cheng, Bo-Kai Pan et al.· 0 citations
The Dreamer-SAC framework, which integrates a recurrent state-space world model with an off-policy soft actor-critic algorithm trained directly in latent space, uses a combination of real interactions and short-horizon generated trajectories with n-step target estimation and multi-objective supervision.
Jia-Zhuo Li, Lin-Jiang Cao, Qi Liu et al.· 0 citations
CastClaw is presented, a human-in-the-loop autonomous forecasting system built through forecasting-oriented harness engineering that connects data, specialized models, analytical tools, user input, and a versioned execution record in one runtime.
Xiao-Yu Tao, Ming-Yue Cheng, Ze Guo et al.· 0 citations
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