English(EN) Algorithmic algorithm development with LLMs: A Case Study on LLM-Usage for Contraction Order Optimization in Tensor Networks

LLM 在算法开发方面展现出潜力，但人类监督仍然至关重要

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-02 04:00

研究人员探索了使用大型语言模型 (LLM) 来辅助算法开发，特别是用于优化张量网络中的收缩顺序。他们的案例研究利用 OpenEvolve，展示了验证器引导的进化编码代理在算法改进方面的潜力。然而，该研究也强调了人类科学家在结果评估、验证和解释方面至关重要且持续的作用。 AI

影响 LLM 驱动的算法开发可以加速科学发现和优化任务。

排序理由该集群包含一篇学术论文，详细介绍了使用 LLM 进行算法开发的案例研究。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇学术论文，详细介绍了使用 LLM 进行算法开发的案例研究。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 128 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

arXiv cs.AI TIER_1 English(EN) · Fabian Hoppe, Melven R\"ohrig-Z\"ollner, Philipp Knechtges · 2026-06-02 04:00

使用LLM进行算法开发：以张量网络收缩顺序优化为例

arXiv:2606.01975v1 Announce Type: new Abstract: We consider LLM-based algorithm development through a case study on contractionorder optimisation for tensor networks with OpenEvolve. We pay particular attention to the choice of the LLM as well as design choices such as evaluation…