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arXiv Computation and Language By Pantea Karimi, Roozbeh Bostandoost, Dany Rouhana, Siva Kesava Reddy Kakarla, Pooria Namyar, Mohammad Alizadeh, Venkat Arun, Behnaz Arzani

Robustness Starts with the Objective: Designing and Certifying Robust LLM-Generated Heuristics

Read the original on arXiv Computation and Language →

The paper introduces TAILOR, a framework that uses Conditional Value at Risk (CVaR) to guide large language model (LLM) synthesis of heuristics for computer systems. By optimizing the paired CVaR of cost differences relative to a baseline, TAILOR ensures improvements in both average performance and tail optimality gaps without needing to compute the unknown optimum. Experiments on Azure GPU batch admission, Azure VM packing, and server placement demonstrate that TAILOR achieves the best mean–tail trade‑off and outperforms robust, human‑designed alternatives in the tail while maintaining comparable average gains.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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