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.
XGBoost: A scalable tree boosting system
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4representative citing papers
Foundation model embeddings provide no advantage over traditional spectral features for cross-country maize yield generalization in Africa, with all methods yielding negative R² under leave-one-country-out testing due to distribution shifts.
A quantile-regression ensemble with safety factor reduces under-allocated jobs from 4.17% to 2.89% and average overallocation from 148% to 44.51% on SAP build data.
citing papers explorer
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CastFlow: Learning Role-Specialized Agentic Workflows for Time Series Forecasting
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.
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Do Foundation Model Embeddings Improve Cross-Country Crop Yield Generalisation? A Leave-One-Country-Out Evaluation in Sub-Saharan Africa
Foundation model embeddings provide no advantage over traditional spectral features for cross-country maize yield generalization in Africa, with all methods yielding negative R² under leave-one-country-out testing due to distribution shifts.
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Optimizing Memory Allocation in Distributed Clusters with Predictive Modeling
A quantile-regression ensemble with safety factor reduces under-allocated jobs from 4.17% to 2.89% and average overallocation from 148% to 44.51% on SAP build data.
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