Problem Solving Through Human–AI Preference-based Cooperation

Subhabrata Dutta; Timo Kaufmann; Goran Glavaš; Ivan Habernal; Kristian Kersting; Frauke Kreuter; Mira Mezini; Iryna Gurevych; Eyke Hüllermeier; Hinrich Schütze

doi:10.1162/coli.a.19

Problem Solving Through Human–AI Preference-based Cooperation

Subhabrata Dutta, Timo Kaufmann, Goran Glavaš, Ivan Habernal, Kristian Kersting, Frauke Kreuter, Mira Mezini, Iryna Gurevych, Eyke Hüllermeier, Hinrich Schütze

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Abstract

While there is a widespread belief that artificial general intelligence—or even superhuman AI—is imminent, complex problems in expert domains are far from being solved. We argue that such problems require human–AI cooperation and that the current state of the art in generative AI is unable to play the role of a reliable partner due to a multitude of shortcomings, including difficulty in keeping track of a complex solution artifact (e.g., a software program), limited support for versatile human preference expression, and lack of adapting to human preference in an interactive setting. To address these challenges, we propose HAI-Co2, a novel human–AI co-construction framework. We take first steps towards a formalization of HAI-Co2 and discuss the difficult open research problems that it faces.

Anthology ID:: 2025.cl-4.8
Volume:: Computational Linguistics, Volume 51, Issue 4 - December 2025
Month:: December
Year:: 2025
Address:: Cambridge, MA
Venue:: CL
SIG:
Publisher:: MIT Press
Note:
Pages:: 1337–1372
Language:
URL:: https://aclanthology.org/2025.cl-4.8/
DOI:: 10.1162/coli.a.19
Bibkey:
Cite (ACL):: Subhabrata Dutta, Timo Kaufmann, Goran Glavaš, Ivan Habernal, Kristian Kersting, Frauke Kreuter, Mira Mezini, Iryna Gurevych, Eyke Hüllermeier, and Hinrich Schütze. 2025. Problem Solving Through Human–AI Preference-based Cooperation. Computational Linguistics, 51(4):1337–1372.
Cite (Informal):: Problem Solving Through Human–AI Preference-based Cooperation (Dutta et al., CL 2025)
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PDF:: https://aclanthology.org/2025.cl-4.8.pdf

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@article{dutta-etal-2025-problem,
    title = "Problem Solving Through Human{--}{AI} Preference-based Cooperation",
    author = {Dutta, Subhabrata  and
      Kaufmann, Timo  and
      Glava{\v{s}}, Goran  and
      Habernal, Ivan  and
      Kersting, Kristian  and
      Kreuter, Frauke  and
      Mezini, Mira  and
      Gurevych, Iryna  and
      H{\"u}llermeier, Eyke  and
      Sch{\"u}tze, Hinrich},
    journal = "Computational Linguistics",
    volume = "51",
    number = "4",
    month = dec,
    year = "2025",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://aclanthology.org/2025.cl-4.8/",
    doi = "10.1162/coli.a.19",
    pages = "1337--1372",
    abstract = "While there is a widespread belief that artificial general intelligence{---}or even superhuman AI{---}is imminent, complex problems in expert domains are far from being solved. We argue that such problems require human{--}AI cooperation and that the current state of the art in generative AI is unable to play the role of a reliable partner due to a multitude of shortcomings, including difficulty in keeping track of a complex solution artifact (e.g., a software program), limited support for versatile human preference expression, and lack of adapting to human preference in an interactive setting. To address these challenges, we propose HAI-Co2, a novel human{--}AI co-construction framework. We take first steps towards a formalization of HAI-Co2 and discuss the difficult open research problems that it faces."
}

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%0 Journal Article
%T Problem Solving Through Human–AI Preference-based Cooperation
%A Dutta, Subhabrata
%A Kaufmann, Timo
%A Glavaš, Goran
%A Habernal, Ivan
%A Kersting, Kristian
%A Kreuter, Frauke
%A Mezini, Mira
%A Gurevych, Iryna
%A Hüllermeier, Eyke
%A Schütze, Hinrich
%J Computational Linguistics
%D 2025
%8 December
%V 51
%N 4
%I MIT Press
%C Cambridge, MA
%F dutta-etal-2025-problem
%X While there is a widespread belief that artificial general intelligence—or even superhuman AI—is imminent, complex problems in expert domains are far from being solved. We argue that such problems require human–AI cooperation and that the current state of the art in generative AI is unable to play the role of a reliable partner due to a multitude of shortcomings, including difficulty in keeping track of a complex solution artifact (e.g., a software program), limited support for versatile human preference expression, and lack of adapting to human preference in an interactive setting. To address these challenges, we propose HAI-Co2, a novel human–AI co-construction framework. We take first steps towards a formalization of HAI-Co2 and discuss the difficult open research problems that it faces.
%R 10.1162/coli.a.19
%U https://aclanthology.org/2025.cl-4.8/
%U https://doi.org/10.1162/coli.a.19
%P 1337-1372

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Markdown (Informal)

[Problem Solving Through Human–AI Preference-based Cooperation](https://aclanthology.org/2025.cl-4.8/) (Dutta et al., CL 2025)

Problem Solving Through Human–AI Preference-based Cooperation (Dutta et al., CL 2025)

ACL

Subhabrata Dutta, Timo Kaufmann, Goran Glavaš, Ivan Habernal, Kristian Kersting, Frauke Kreuter, Mira Mezini, Iryna Gurevych, Eyke Hüllermeier, and Hinrich Schütze. 2025. Problem Solving Through Human–AI Preference-based Cooperation. Computational Linguistics, 51(4):1337–1372.