<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhang-etal-2026-video">
    <titleInfo>
        <title>Can Video Large Multimodal Models Think Like Doubters—or Double-Down: A Study on Defeasible Video Entailment</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yue</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jilei</namePart>
        <namePart type="family">Sun</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yunhui</namePart>
        <namePart type="family">Guo</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Vibhav</namePart>
        <namePart type="given">Giridhar</namePart>
        <namePart type="family">Gogate</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EMNLP 2026</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Hung-Yi</namePart>
            <namePart type="family">Lee</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sunipa</namePart>
            <namePart type="family">Dev</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jessy</namePart>
            <namePart type="family">Li</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Desmond</namePart>
            <namePart type="family">Elliott</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Budapest, Hungary</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-462-0</identifier>
    </relatedItem>
    <abstract>Video Large Multimodal Models (VLMMs) have achieved impressive progress in video understanding, yet they remain limited in adaptive reasoning—the ability to revise conclusions when new evidence emerges. In reality, conclusions are rarely set in stone; additional context can strengthen or weaken an initial inference. To capture this capability, we introduce Defeasible Video Entailment (DVidE), a new task that challenges models to think like doubters, requiring them to dynamically update entailment judgments as evidence evolves. In DVidE, given a video premise and a textual hypothesis, the models must determine whether a new update strengthens or weakens the hypothesis (classification task) or generates a coherent update that modifies the entailment relationship (generation task). For solving the classification task, we propose the Chain of Counterfactual Thought (CoCT) framework, utilizing counterfactual reasoning, ASR-enhanced video content, and rationale refinement to reduce inference bias. For the generation task, we develop a framework that combines ASR output with a Large Language Model (LLM) to produce coherent, contextually relevant updates aligned with the intended strengthener or weakener goals. We also introduce a benchmark dataset with human annotations and an LLM-based evaluation metric for assessing generative performance. Experimental results show substantial improvements across multiple VLMM baselines, demonstrating that CoCT significantly enhances both classification and generation performance and strengthens the dynamic reasoning capabilities of video-language models.</abstract>
    <identifier type="citekey">zhang-etal-2026-video</identifier>
    <location>
        <url>https://aclanthology.org/2026.findings-emnlp.116/</url>
    </location>
    <part>
        <date>2026-10</date>
        <extent unit="page">
            <start>2379</start>
            <end>2398</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Can Video Large Multimodal Models Think Like Doubters—or Double-Down: A Study on Defeasible Video Entailment
%A Zhang, Yue
%A Sun, Jilei
%A Guo, Yunhui
%A Gogate, Vibhav Giridhar
%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 zhang-etal-2026-video
%X Video Large Multimodal Models (VLMMs) have achieved impressive progress in video understanding, yet they remain limited in adaptive reasoning—the ability to revise conclusions when new evidence emerges. In reality, conclusions are rarely set in stone; additional context can strengthen or weaken an initial inference. To capture this capability, we introduce Defeasible Video Entailment (DVidE), a new task that challenges models to think like doubters, requiring them to dynamically update entailment judgments as evidence evolves. In DVidE, given a video premise and a textual hypothesis, the models must determine whether a new update strengthens or weakens the hypothesis (classification task) or generates a coherent update that modifies the entailment relationship (generation task). For solving the classification task, we propose the Chain of Counterfactual Thought (CoCT) framework, utilizing counterfactual reasoning, ASR-enhanced video content, and rationale refinement to reduce inference bias. For the generation task, we develop a framework that combines ASR output with a Large Language Model (LLM) to produce coherent, contextually relevant updates aligned with the intended strengthener or weakener goals. We also introduce a benchmark dataset with human annotations and an LLM-based evaluation metric for assessing generative performance. Experimental results show substantial improvements across multiple VLMM baselines, demonstrating that CoCT significantly enhances both classification and generation performance and strengthens the dynamic reasoning capabilities of video-language models.
%U https://aclanthology.org/2026.findings-emnlp.116/
%P 2379-2398

Markdown (Informal)

[Can Video Large Multimodal Models Think Like Doubters—or Double-Down: A Study on Defeasible Video Entailment](https://aclanthology.org/2026.findings-emnlp.116/) (Zhang et al., Findings 2026)

• Can Video Large Multimodal Models Think Like Doubters—or Double-Down: A Study on Defeasible Video Entailment (Zhang et al., Findings 2026)

ACL

• Yue Zhang, Jilei Sun, Yunhui Guo, and Vibhav Giridhar Gogate. 2026. Can Video Large Multimodal Models Think Like Doubters—or Double-Down: A Study on Defeasible Video Entailment. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 2379–2398, Budapest, Hungary. Association for Computational Linguistics.