arXiv cs.AI· Burak Susam, Ruiyuan Kang, Tingting Mu·· 10 小时前AI 评分39
AutoManifold：用多智能体 LLM 按用户偏好自动生成流形嵌入可视化算法
AutoManifold: Agentic Design of Data Visualisation Algorithms via Manifold Embedding
AI 导读
AutoManifold 是一套由多智能体 LLM 编排的算法生成流水线，可从亲和度、代价与优化原语组成的受限词汇表中组合出新的流形嵌入算法，并按用户指定的结构保留偏好逐阶段条件化、迭代精炼。
正文 · 原文
Abstract:Manifold embedding for data visualisation is dominated by a small set of hand-designed state-of-the-art (SOTA) algorithms. Their design targets are fixed at development time, resulting in inflexible algorithmic behaviour that cannot be readily steered towards user-specified structural priorities. Hand-designing new embedding algorithms to address user priority requires highly specialised expertise and is time consuming. To address autonomous algorithm design tailored to user preference, we introduce an agentic algorithm generation pipeline AutoManifold. It composes new manifold embedding algorithms from a constrained vocabulary of affinity, cost, and optimisation primitives, supported by multi-agent large-language-model (LLM) orchestration. AutoManifold conditions every stage of its design on user-specified structural preservation preferences, and iteratively refines the algorithm configuration through an LLM-guided, metric-grounded iterative loop. We compare the generated algorithms against the three strongest and most frequently used SOTA methods (t-SNE, UMAP, and PaCMAP) on real-world datasets spanning different difficulty regimes under identical evaluation infrastructure. The results demonstrate that LLM-driven, priority-conditioned algorithm synthesis can move beyond hyperparameter tuning, producing genuinely new algorithms that outperform traditional hand-designed methods in their ability to steer towards user-specified properties without compromising the intrinsic structure in the original data. The code of AutoManifold is available at this https URL.
| Comments: | Accepted at NeurIPS 2026 (40th Conference on Neural Information Processing Systems). 37 pages, 5 figures. Code: this https URL |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.13710 [cs.AI] |
| (or arXiv:2611.13710v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13710 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Burak Susam [view email]
[v1]
Tue, 10 Nov 2026 08:03:15 UTC (3,345 KB)
来源：arXiv cs.AI · arxiv.org