Three code-specific uncertainty axes (lexical, algorithmic, functional) yield an ensemble that raises average AUROC from 0.696 to 0.776 across five code LLMs, with one single-pass signal matching multi-pass baselines at lower cost.
Grapheon RL: A Graph Neural Network and Reinforcement Learning Framework for Constraint and Data-Aware Workflow Mapping and Scheduling in Heterogeneous HPC Systems,
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Empirical tests show QUBO-SA and QAOA-inspired schedulers lose feasibility beyond 10-15 tasks while MILP, CP-SAT, GA and HEFT remain robust on the same instances.
DECICE delivers an Integrated AI Scheduler with RNN prediction and a Digital Twin for energy-aware workload scheduling in Kubernetes and Slurm environments as part of a European project.
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Code Is More Than Text: Uncertainty Estimation for Code Generation
Three code-specific uncertainty axes (lexical, algorithmic, functional) yield an ensemble that raises average AUROC from 0.696 to 0.776 across five code LLMs, with one single-pass signal matching multi-pass baselines at lower cost.
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DECICE: AI-Driven Scheduling and Digital Twin Integration for the Cloud-HPC-Edge Compute Continuum
DECICE delivers an Integrated AI Scheduler with RNN prediction and a Digital Twin for energy-aware workload scheduling in Kubernetes and Slurm environments as part of a European project.