Publications.

2026

1. CHI WS

   

   Short form Workshop First author 0 cites

   What Happened and Why? Trace-Guided Micro-Episodes with Elicited User Explanations for Product Iteration

   Sirui Tao, William P. McCarthy, and Steven P. Dow

   In Herding CATs: Making Sense of Creative Activity Traces (CHI 2026 Workshop), Apr 2026

   Workshop Position Paper

   What Happened and Why? Trace-Guided Micro-Episodes with Elicited User Explanations for Product Iteration: When this paper helps. You can see what someone did in an AI tool but still cannot tell what that action meant to them.

   This paper helps when you can see what someone did in an AI tool but still cannot tell what that action meant to them.

   Look for

   • You are designing lightweight feedback at the moment a creative workflow becomes ambiguous.
   • You need a unit of analysis that joins a short trace, interface state, and a user's optional explanation.

   It adds

   A proposal for trace-guided micro-episodes and a utility-for-rationale pattern that turns useful recovery controls into chances to learn why something happened.

   Its limit

   It is a position paper and research agenda, not an empirical validation of the interventions or their downstream effects.

   I checked this against the paper and its official record on 2026-07-13.

   Read the full evidence and citation context

   Bib PDF Website

   @inproceedings{tao2026whw,
     title = {What Happened and Why? Trace-Guided Micro-Episodes with Elicited User Explanations for Product Iteration},
     author = {Tao, Sirui and McCarthy, William P. and Dow, Steven P.},
     booktitle = {Herding CATs: Making Sense of Creative Activity Traces (CHI 2026 Workshop)},
     url = {https://herding-cats-ws.github.io/2026/papers/p02.pdf},
     note = {Workshop Position Paper},
     month = {apr},
     year = {2026}
   }

2025

1. CVPR

   

   Full paper Coauthor 20 cites

   HotSpot: Signed Distance Function Optimization with an Asymptotically Sufficient Condition

   Zimo Wang, Cheng Wang, Taiki Yoshino, Sirui Tao, Ziyang Fu, and Tzu-Mao Li

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2025

   CVPR 2025 Highlight

   HotSpot: Signed Distance Function Optimization with an Asymptotically Sufficient Condition: When this paper helps. An implicit-surface argument needs more than the usual eikonal condition—especially around convergence, stability, topology, or surface area.

   This paper helps when an implicit-surface argument needs more than the usual eikonal condition—especially around convergence, stability, topology, or surface area.

   Look for

   • You are comparing objectives for reconstructing signed distance functions from unoriented points.
   • You need theoretical and experimental evidence about a screened-Poisson alternative.

   It adds

   A heat-loss objective with convergence and stability analysis, plus 2D and 3D reconstruction evidence and a natural surface-area penalty.

   Its limit

   The reported gains belong to the evaluated reconstruction settings; sparse boundaries and parameter choices can still produce failure modes.

   I checked this against the paper and its official record on 2026-07-13.

   Read the full evidence and citation context

   DOI arXiv Bib PDF Video Code Website

   @inproceedings{wang2025hotspot,
     title = {HotSpot: Signed Distance Function Optimization with an Asymptotically Sufficient Condition},
     author = {Wang, Zimo and Wang, Cheng and Yoshino, Taiki and Tao, Sirui and Fu, Ziyang and Li, Tzu-Mao},
     booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
     pages = {1276--1286},
     publisher = {IEEE},
     doi = {10.1109/CVPR52734.2025.00127},
     arxiv = {2411.14628},
     url = {https://openaccess.thecvf.com/content/CVPR2025/html/Wang_HotSpot_Signed_Distance_Function_Optimization_with_an_Asymptotically_Sufficient_Condition_CVPR_2025_paper.html},
     note = {Highlight},
     month = {jun},
     year = {2025}
   }

2. CHI

   

   Full paper First author 50 cites

   DesignWeaver: Dimensional Scaffolding for Text-to-Image Product Design

   Sirui Tao, Ivan Liang, Cindy Peng, Zhiqing Wang, Srishti Palani, and Steven P. Dow

   In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, Apr 2025

   DesignWeaver: Dimensional Scaffolding for Text-to-Image Product Design: When this paper helps. A blank prompt box is asking novices to supply design vocabulary they have not had a chance to learn.

   This paper helps when a blank prompt box is asking novices to supply design vocabulary they have not had a chance to learn.

   Look for

   • You are designing dimensional scaffolds for text-to-image exploration.
   • You want evidence about prompt language, visual diversity, novelty, and rising expectations.

   It adds

   A palette that externalizes product-design dimensions, grounded in expert practice and evaluated with 52 novice designers.

   Its limit

   The study used one chair-design task with novices, and richer prompts did not reliably improve satisfaction or expectation alignment.

   I checked this against the paper and its official record on 2026-07-13.

   Read the full evidence and citation context

   DOI arXiv Bib PDF Video Code Website

   @inproceedings{tao2024designweaver,
     title = {DesignWeaver: Dimensional Scaffolding for Text-to-Image Product Design},
     author = {Tao, Sirui and Liang, Ivan and Peng, Cindy and Wang, Zhiqing and Palani, Srishti and Dow, Steven P.},
     booktitle = {Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems},
     series = {CHI '25},
     pages = {1--26},
     publisher = {ACM},
     doi = {10.1145/3706598.3714211},
     arxiv = {2502.09867},
     month = {apr},
     year = {2025}
   }

2023

1. CogSci

   

   Play the animation

   Short form Poster + abstract Coauthor 0 cites

   Physion++: Evaluating Physical Scene Understanding with Objects Consisting of Different Physical Attributes in Humans and Machines

   Hsiao-Yu Tung, Mingyu Ding, Zhenfang Chen, Sirui Tao, Vedang Lad, Daniel Bear, Chuang Gan, Josh Tenenbaum, Daniel Yamins, Judith Fan, and Kevin Smith

   In Proceedings of the Annual Meeting of the Cognitive Science Society, Jul 2023

   Poster with abstract

   Physion++: Evaluating Physical Scene Understanding with Objects Consisting of Different Physical Attributes in Humans and Machines: When this paper helps. Physical prediction depends on inferring hidden material properties from motion rather than receiving those properties directly.

   This paper helps when physical prediction depends on inferring hidden material properties from motion rather than receiving those properties directly.

   Look for

   • You are comparing human and model adaptation to mass, friction, elasticity, or deformability.
   • You need the exact CogSci record for Physion++ rather than the separate NeurIPS technical paper.

   It adds

   A benchmark framing for latent-property inference and matched human–model prediction under changing mechanical properties.

   Its limit

   The public CogSci record supports qualitative claims from the abstract, not detailed quantitative claims from the companion paper.

   I checked this against the paper and its official record on 2026-07-13.

   Read the full evidence and citation context

   Bib PDF Website

   @inproceedings{tung2023physion++,
     title = {Physion++: Evaluating Physical Scene Understanding with Objects Consisting of Different Physical Attributes in Humans and Machines},
     author = {Tung, Hsiao-Yu and Ding, Mingyu and Chen, Zhenfang and Tao, Sirui and Lad, Vedang and Bear, Daniel and Gan, Chuang and Tenenbaum, Josh and Yamins, Daniel and Fan, Judith and Smith, Kevin},
     booktitle = {Proceedings of the Annual Meeting of the Cognitive Science Society},
     url = {https://escholarship.org/uc/item/3x9960zn},
     note = {Poster with abstract},
     month = {jul},
     year = {2023}
   }

2021

1. NeurIPS

   

   Play the animation

   Full paper Coauthor 220 cites

   Physion: Evaluating Physical Prediction from Vision in Humans and Machines

   Daniel Bear, Elias Wang, Damian Mrowca, Felix Binder, Hsiao-Yu Tung, Pramod RT, Cameron Holdaway, Sirui Tao, Kevin Smith, Fan-Yun Sun, Fei-Fei Li, Nancy Kanwisher, Josh Tenenbaum, Dan Yamins, and Judith Fan

   In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, Dec 2021

   Physion: Evaluating Physical Prediction from Vision in Humans and Machines: When this paper helps. You need a reproducible benchmark for asking whether a model predicts physical outcomes in human-like ways.

   This paper helps when you need a reproducible benchmark for asking whether a model predicts physical outcomes in human-like ways.

   Look for

   • You are comparing object-centric and non-object-centric visual models with human judgments.
   • You need a common contact-prediction task across several simulated physical scenarios.

   It adds

   Eight scenario families, matched human and model evaluation, and released data and code for systematic comparison.

   Its limit

   Synthetic scenes and binary contact prediction do not establish broad real-world physical understanding.

   I checked this against the paper and its official record on 2026-07-13.

   Read the full evidence and citation context

   arXiv Bib PDF Video Code Website

   @inproceedings{bear2021physion,
     title = {Physion: Evaluating Physical Prediction from Vision in Humans and Machines},
     author = {Bear, Daniel and Wang, Elias and Mrowca, Damian and Binder, Felix and Tung, Hsiao-Yu and RT, Pramod and Holdaway, Cameron and Tao, Sirui and Smith, Kevin and Sun, Fan-Yun and Li, Fei-Fei and Kanwisher, Nancy and Tenenbaum, Josh and Yamins, Dan and Fan, Judith},
     booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks},
     editor = {Vanschoren, J. and Yeung, S.},
     volume = {1},
     arxiv = {2106.08261},
     url = {https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/d09bf41544a3365a46c9077ebb5e35c3-Abstract-round1.html},
     month = {dec},
     year = {2021}
   }