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Robustness Starts with the Objective: Designing and Certifying Robust LLM-Generated Heuristics — arXiv:2611.00504

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.00504
License (as archived): CC0-1.0
Archived: 2026-11-03T10:51:39.906Z
SHA-256 of the archived text: 925c89bfbbc614262c487eacc78cfd6a8b811ef7cd20a251275f42071a1cba7d
Stable URL of this page: https://metraipolis.com/documento/dc_aef1eb577a3f

How to cite

Robustness Starts with the Objective: Designing and Certifying Robust LLM-Generated Heuristics — arXiv:2611.00504 — arXiv (en), https://arxiv.org/abs/2611.00504 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_aef1eb577a3f (Metraipolis archive dc_aef1eb577a3f, sha256 925c89bfbbc614262c487eacc78cfd6a8b811ef7cd20a251275f42071a1cba7d)

BibTeX

@misc{dcaef1eb577a3f,
  title = {Robustness Starts with the Objective: Designing and Certifying Robust LLM-Generated Heuristics — arXiv:2611.00504},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.00504},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_aef1eb577a3f; sha256 925c89bfbbc614262c487eacc78cfd6a8b811ef7cd20a251275f42071a1cba7d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.00504. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

LLMs are increasingly used to design heuristics for computer systems. However, most synthesis frameworks evaluate candidates by their average performance, which can mask severe regressions on specific workloads. Even adding adversarial training inputs doesn't eliminate these regressions, and percentile objectives (e.g., optimizing for the 95th percentile) overlook how severe regressions can be beyond the selected threshold. We present TAILOR, a framework that guides LLM synthesis using the Conditional Value at Risk (CVaR) of the cost difference between each candidate and an existing baseline on the same workload. This paired CVaR measures the average difference over the worst fraction of workloads. We show that a negative paired CVaR guarantees improvements in both average performance and tail optimality gap relative to the unknown optimum, without ever computing that optimum. We find that TAILOR achieves the best mean--tail trade-off among the synthesis objectives we evaluate across three case studies: Azure GPU batch admission, Azure VM packing, and server placement. Tested across seven diverse optimization benchmarks, TAILOR produces heuristics that are certified with 99% confidence to improve both average performance and tail optimality gaps over the baseline in six of them. On these six benchmarks, TAILOR also outperforms known robust, human-designed alternatives in the tail while achieving better or comparable average-performance improvement over the baseline.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC); Performance (cs.PF).
Pagina del lavoro: https://arxiv.org/abs/2611.00504 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:49:38.249Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).