@inproceedings{gao-etal-2026-graph,
title = "Graph of Trace: Visualizing Execution Traces of Scientific Agents",
author = "Gao, Tianci and
Li, Haoxuan and
Li, Jian He and
Zhao, Tianxiang and
Runze, Shi and
Wang, Weiran and
Wu, Zezhao and
Mi, Lu",
editor = "Durrett, Greg and
Jian, Ping",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-demo.29/",
pages = "297--306",
ISBN = "979-8-89176-392-0",
abstract = "Scientific AI agents can autonomously carry out complex research workflows, yet these unfolded workflows often remains difficult for humans to inspect and review, limiting interpretable, controllable and effective human{--}AI collaboration. To address this challenge, we present a monitoring and visualization framework that records fine-grained execution events and organizes them into a directed graph that make agent workflows explicit as they proceed. The system records intermediate steps (e.g. tool calls and code executions), and renders them as real-time updated visual traces that expose workflow structure. This allows users to examine how results are produced, identify where failures emerge, and better understand agent behavior across different stages of the research process.We conduct an evaluation on complex research tasks with domain experts of interdisciplinary background in AI, neuroscience and biology. Experts report that structured traces visualization improves understanding of agent workflows, perceived interpretability, and usability for analysis and further interaction."
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%0 Conference Proceedings
%T Graph of Trace: Visualizing Execution Traces of Scientific Agents
%A Gao, Tianci
%A Li, Haoxuan
%A Li, Jian He
%A Zhao, Tianxiang
%A Runze, Shi
%A Wang, Weiran
%A Wu, Zezhao
%A Mi, Lu
%Y Durrett, Greg
%Y Jian, Ping
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-392-0
%F gao-etal-2026-graph
%X Scientific AI agents can autonomously carry out complex research workflows, yet these unfolded workflows often remains difficult for humans to inspect and review, limiting interpretable, controllable and effective human–AI collaboration. To address this challenge, we present a monitoring and visualization framework that records fine-grained execution events and organizes them into a directed graph that make agent workflows explicit as they proceed. The system records intermediate steps (e.g. tool calls and code executions), and renders them as real-time updated visual traces that expose workflow structure. This allows users to examine how results are produced, identify where failures emerge, and better understand agent behavior across different stages of the research process.We conduct an evaluation on complex research tasks with domain experts of interdisciplinary background in AI, neuroscience and biology. Experts report that structured traces visualization improves understanding of agent workflows, perceived interpretability, and usability for analysis and further interaction.
%U https://aclanthology.org/2026.acl-demo.29/
%P 297-306
Markdown (Informal)
[Graph of Trace: Visualizing Execution Traces of Scientific Agents](https://aclanthology.org/2026.acl-demo.29/) (Gao et al., ACL 2026)
ACL
- Tianci Gao, Haoxuan Li, Jian He Li, Tianxiang Zhao, Shi Runze, Weiran Wang, Zezhao Wu, and Lu Mi. 2026. Graph of Trace: Visualizing Execution Traces of Scientific Agents. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 297–306, San Diego, California, United States. Association for Computational Linguistics.