@inproceedings{liang-etal-2026-puzzles,
title = "Puzzles to Paradigms: A Survey of Human{--}Agent Collaboration in Scientific Innovation",
author = "Liang, Zhenni and
Zhang, Haoxuan and
Chen, Haihua and
Xiao, Ting and
Ding, Junhua and
Zhang, Yang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1645/",
pages = "35017--35041",
ISBN = "979-8-89176-462-0",
abstract = "Agentic AI systems are increasingly embedded in scientific workflows, including literature synthesis, hypothesis generation, tool use, writing, and peer review. However, existing surveys and evaluations often emphasize task performance while underexamining how humans and agents coordinate under different epistemic conditions of scientific work. This survey introduces **CSCW**$\times$**Kuhn**, a coordination-aware framework that connects computer-supported cooperative work dimensions with distinct scientific contexts. Using this framework, we synthesize recent human{--}agent systems and benchmarks to examine how coordination demands and failure risks may vary across contexts. Our synthesis identifies five recurring failure modes: procedural drift, grounding substitution, evaluative flattening, proxy optimization, and epistemic black-boxing, and argues for evaluations that move beyond task accuracy and toward grounding, provenance, disagreement preservation, and long-horizon scientific reliability. Our corpus and coding artifact are available at https://github.com/jenlia024/CSCWxKuhn-artifact."
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%0 Conference Proceedings
%T Puzzles to Paradigms: A Survey of Human–Agent Collaboration in Scientific Innovation
%A Liang, Zhenni
%A Zhang, Haoxuan
%A Chen, Haihua
%A Xiao, Ting
%A Ding, Junhua
%A Zhang, Yang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F liang-etal-2026-puzzles
%X Agentic AI systems are increasingly embedded in scientific workflows, including literature synthesis, hypothesis generation, tool use, writing, and peer review. However, existing surveys and evaluations often emphasize task performance while underexamining how humans and agents coordinate under different epistemic conditions of scientific work. This survey introduces **CSCW**\times**Kuhn**, a coordination-aware framework that connects computer-supported cooperative work dimensions with distinct scientific contexts. Using this framework, we synthesize recent human–agent systems and benchmarks to examine how coordination demands and failure risks may vary across contexts. Our synthesis identifies five recurring failure modes: procedural drift, grounding substitution, evaluative flattening, proxy optimization, and epistemic black-boxing, and argues for evaluations that move beyond task accuracy and toward grounding, provenance, disagreement preservation, and long-horizon scientific reliability. Our corpus and coding artifact are available at https://github.com/jenlia024/CSCWxKuhn-artifact.
%U https://aclanthology.org/2026.findings-emnlp.1645/
%P 35017-35041
Markdown (Informal)
[Puzzles to Paradigms: A Survey of Human–Agent Collaboration in Scientific Innovation](https://aclanthology.org/2026.findings-emnlp.1645/) (Liang et al., Findings 2026)
ACL