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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Pith papers citing it
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2026 4representative citing papers
Knee-xRAI independently quantifies JSN, osteophytes, and sclerosis then fuses them into auditable classifiers reaching test QWK 0.8436 on 8260 radiographs.
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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Knee-xRAI: An Explainable AI Framework for Automatic Kellgren-Lawrence Grading of Knee Osteoarthritis
Knee-xRAI independently quantifies JSN, osteophytes, and sclerosis then fuses them into auditable classifiers reaching test QWK 0.8436 on 8260 radiographs.
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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.