@inproceedings{sun-etal-2026-critics,
title = "Critic{'}s Eye: Review-Grounded Graph Reasoning for Long-Form Motif Discovery",
author = "Sun, Xinyu and
Yin, Zhangyue and
Wang, Xin",
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.470/",
pages = "9853--9869",
ISBN = "979-8-89176-462-0",
abstract = "Deep narrative understanding requires distinguishing what happens from how it is presented {---}moving beyond the chronological fabula to decode the sjuzet, the discursive organization through which themes are staged. We study this through motif recurrence, where symbolic significance emerges not from local cues, but through repetition-with-variation across the whole work. We operationalize this setting as Computational Motif Discovery, a transductive task that predicts missing line motif links using the full narrative structure of a play. We propose Critic{'}s Eye, which models the work as a heterogeneous Narrative Topology Graph and performs discriminative inference over global structural evidence. Critic{'}s Eye achieves 84.8{\%} Hit@5 on our benchmark, markedly outperforming state-of-the-art proprietary foundation models(22.6{\%}) by a factor of nearly four. Our analysis reveals that despite massive scaling, sequence-based models hit a ``ceiling'', struggling to resolve dispersed dependencies. These findings suggest that explicit structural priors are a far more effective inductive bias than parameter scaling for decoding the complex architecture of long-form literature."
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<abstract>Deep narrative understanding requires distinguishing what happens from how it is presented —moving beyond the chronological fabula to decode the sjuzet, the discursive organization through which themes are staged. We study this through motif recurrence, where symbolic significance emerges not from local cues, but through repetition-with-variation across the whole work. We operationalize this setting as Computational Motif Discovery, a transductive task that predicts missing line motif links using the full narrative structure of a play. We propose Critic’s Eye, which models the work as a heterogeneous Narrative Topology Graph and performs discriminative inference over global structural evidence. Critic’s Eye achieves 84.8% Hit@5 on our benchmark, markedly outperforming state-of-the-art proprietary foundation models(22.6%) by a factor of nearly four. Our analysis reveals that despite massive scaling, sequence-based models hit a “ceiling”, struggling to resolve dispersed dependencies. These findings suggest that explicit structural priors are a far more effective inductive bias than parameter scaling for decoding the complex architecture of long-form literature.</abstract>
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%0 Conference Proceedings
%T Critic’s Eye: Review-Grounded Graph Reasoning for Long-Form Motif Discovery
%A Sun, Xinyu
%A Yin, Zhangyue
%A Wang, Xin
%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 sun-etal-2026-critics
%X Deep narrative understanding requires distinguishing what happens from how it is presented —moving beyond the chronological fabula to decode the sjuzet, the discursive organization through which themes are staged. We study this through motif recurrence, where symbolic significance emerges not from local cues, but through repetition-with-variation across the whole work. We operationalize this setting as Computational Motif Discovery, a transductive task that predicts missing line motif links using the full narrative structure of a play. We propose Critic’s Eye, which models the work as a heterogeneous Narrative Topology Graph and performs discriminative inference over global structural evidence. Critic’s Eye achieves 84.8% Hit@5 on our benchmark, markedly outperforming state-of-the-art proprietary foundation models(22.6%) by a factor of nearly four. Our analysis reveals that despite massive scaling, sequence-based models hit a “ceiling”, struggling to resolve dispersed dependencies. These findings suggest that explicit structural priors are a far more effective inductive bias than parameter scaling for decoding the complex architecture of long-form literature.
%U https://aclanthology.org/2026.findings-emnlp.470/
%P 9853-9869
Markdown (Informal)
[Critic’s Eye: Review-Grounded Graph Reasoning for Long-Form Motif Discovery](https://aclanthology.org/2026.findings-emnlp.470/) (Sun et al., Findings 2026)
ACL