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This paper introduces HYMELL, a hybrid analytical-machine-learning framework for estimating LLM inference latency and energy across prefill and decode phases, validated on NVIDIA H100 with under 5% error for LLaMA 3 8B.
This paper introduces the Hybrid-to-NeSy (H2N) framework, which systematically translates hybrid mechanistic-data-driven models into neuro-symbolic AI designs, enabling the derivation of metrics for structural violation and belief dispersion as measures of epistemic uncertainty in the mechanistic part.
The paper describes a metric-aware hybrid forecasting system for the CTF4Science Lorenz challenge, combining neural denoisers, ODE fitting, and histogram-tail substitution to optimize different metrics across nine task pairs, achieving a public leaderboard score of 83.85529.