arXiv：cs.AI· Nathaniel Gorski, Shusen Liu, Bei Wang·· 20 小时前
TopoPilot：面向拓扑数据分析与可视化的可靠对话式工作流自动化框架
TopoPilot: Reliable Conversational Workflow Automation for Topological Data Analysis and Visualization
AI 导读
TopoPilot 是一个可靠且可扩展的智能体框架，用于自动完成拓扑数据分析与可视化工作流，采用编排器与验证器双智能体架构，并配合确定性护栏。在覆盖 100 条提示词的 1,000 次模拟多轮对话中，其成功率超过 95%，而无完整护栏与验证的基线不足 50%；在相似提示下其准确率、完成时间和 token 用量均优于 Claude Code 与 Codex。
正文
Abstract:Recent agentic systems demonstrate that large language models can generate complex codebases from natural language prompts. Despite this progress, they present reliability concerns for scientific visualization due to invalid operations, subtle errors, and failure to resolve underspecified inputs. These challenges are compounded in real-world workflows that exceed the complexity of standard benchmarks. In this work, we introduce TopoPilot, a reliable and extensible agentic framework for automating complex scientific visualization workflows. We focus on topological data analysis and visualization as a primary use case, while designing the framework to generalize to broader visualization domains. TopoPilot employs a two-agent architecture: an orchestrator translates user prompts into workflows composed of atomic backend actions, while a verifier evaluates workflows prior to execution. Its modular design isolates components and supports new topological descriptors and domain-specific workflows without modifying the core system. It also employs numerous deterministic guardrails to ensure semantic consistency and accurate execution. To systematically improve reliability, we develop a taxonomy of failure modes and employ targeted safeguards for each class of error. Across 1,000 simulated multi-turn conversations spanning 100 prompts, including adversarial and infeasible requests, TopoPilot achieves a success rate exceeding 95%, compared with under 50% for a baseline without comprehensive guardrails and verification. We also show that TopoPilot outperforms Claude Code and Codex on similar prompts in terms of accuracy, completion time, and token usage.
| Comments: | 21 pages, 10 figures |
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG) |
| Cite as: | arXiv:2603.25063 [cs.HC] |
| (or arXiv:2603.25063v2 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2603.25063 arXiv-issued DOI via DataCite |
Submission history
From: Nathaniel Gorski [view email]
[v1]
Thu, 26 Mar 2026 05:56:53 UTC (8,973 KB)
[v2]
Fri, 9 Oct 2026 22:53:42 UTC (9,257 KB)
来源：arXiv：cs.AI · arxiv.org