[
    {
        "id": "osp-20945",
        "type": "article-journal",
        "title": "AdaEva: Accelerating LLM-Driven Algorithm Design with Adaptive Partial Evaluation",
        "author": [
            {
                "family": "Nguyen",
                "given": "Tai"
            },
            {
                "family": "Liu",
                "given": "Fei"
            },
            {
                "family": "Le",
                "given": "Phong"
            },
            {
                "family": "Doerr",
                "given": "Carola"
            },
            {
                "family": "Dang",
                "given": "Nguyen"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/adaeva-accelerating-llm-driven-algorithm-design-with-adaptive-partial-evaluation",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Large Language Models (LLMs) are increasingly used for automated algorithm design. However the computational cost of evaluating the generated algorithms can be excessive. We consider the common LLM-driven automated algorithm design (LLM4AD) setting in which a candidate algorithm is evaluated by aggregating its performance over a shared set of training instances. This instance-wise structure raises a natural question: must every candidate be evaluated on the entire instance set before deciding whether it remains competitive? Taking inspiration from algorithm configuration, we introduce AdaEva, a drop-in adaptive partial-evaluation framework that progressively evaluates candidates on larger subsets of the same instance pool and eliminates unpromising candidates as evidence accumulates. Importantly, AdaEva leaves the underlying LLM4AD procedure and per-instance evaluator unchanged and requires no prior knowledge about instance difficulty. We instantiate this idea using successive halving (AdaEva-S) and statistical racing (AdaEva-R), and evaluate both mechanisms across three representative LLM4AD frameworks, multiple LLM backbones, and optimization domains spanning combinatorial and continuous black-box optimization. Under matched evaluation budgets, AdaEva more reliably balances evaluation effort across candidates than fixed partial-evaluation strategies, yielding strong search efficiency and anytime performance together with improved held-out generalization across the evaluated settings."
    }
]