<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chen-etal-2026-macro">
    <titleInfo>
        <title>MACRO-LLM: LLM-Empowered Multi-Agent Collaborative Reasoning under Spatiotemporal Partial Observability</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Handi</namePart>
        <namePart type="family">Chen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Running</namePart>
        <namePart type="family">Zhao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xiuzhe</namePart>
        <namePart type="family">Wu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zhanfeng</namePart>
        <namePart type="family">Xu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Edith</namePart>
        <namePart type="given">Cheuk-Han</namePart>
        <namePart type="family">Ngai</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>Large Language Model (LLM) agents in distributed decision-making scenarios often operate as spatially separated entities and coordinate from fragmented local information. However, this physical dispersion constrains agents to limited local perception and finite temporal horizons. We characterize this bottleneck as spatiotemporal partial observability, in which spatial and temporal limitations are fundamentally coupled. Resolving spatial conflicts requires temporal reasoning about neighbors’ future actions, while temporal planning requires spatial context beyond local perception. To bridge this gap, we introduce **MACRO-LLM**, LLM-empowered multi-agent collaborative reasoning under spatiotemporal partial observability. The architecture integrates spatial and temporal reasoning within each decision cycle via three interdependent modules: (1) the CoProposer mitigates *temporal uncertainty* by screening candidate actions via LLM-predicted rollouts; (2) the Negotiator overcomes *spatial myopia* by resolving conflicts through mean-field statistical aggregation, grounded in the CoProposer’s LLM-estimated rollout rewards; and (3) the Introspector closes the reasoning loop by analyzing environmental drift and attributing performance changes to refine strategies. Extensive evaluations on two complex long-horizon tasks, cooperative platoon planning and pandemic control, demonstrate that our framework enables robust coordination under spatiotemporal partial observability.</abstract>
    <identifier type="citekey">chen-etal-2026-macro</identifier>
    <location>
        <url>https://aclanthology.org/2026.findings-emnlp.979/</url>
    </location>
    <part>
        <date>2026-10</date>
        <extent unit="page">
            <start>20685</start>
            <end>20717</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T MACRO-LLM: LLM-Empowered Multi-Agent Collaborative Reasoning under Spatiotemporal Partial Observability
%A Chen, Handi
%A Zhao, Running
%A Wu, Xiuzhe
%A Xu, Zhanfeng
%A Ngai, Edith Cheuk-Han
%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 chen-etal-2026-macro
%X Large Language Model (LLM) agents in distributed decision-making scenarios often operate as spatially separated entities and coordinate from fragmented local information. However, this physical dispersion constrains agents to limited local perception and finite temporal horizons. We characterize this bottleneck as spatiotemporal partial observability, in which spatial and temporal limitations are fundamentally coupled. Resolving spatial conflicts requires temporal reasoning about neighbors’ future actions, while temporal planning requires spatial context beyond local perception. To bridge this gap, we introduce **MACRO-LLM**, LLM-empowered multi-agent collaborative reasoning under spatiotemporal partial observability. The architecture integrates spatial and temporal reasoning within each decision cycle via three interdependent modules: (1) the CoProposer mitigates *temporal uncertainty* by screening candidate actions via LLM-predicted rollouts; (2) the Negotiator overcomes *spatial myopia* by resolving conflicts through mean-field statistical aggregation, grounded in the CoProposer’s LLM-estimated rollout rewards; and (3) the Introspector closes the reasoning loop by analyzing environmental drift and attributing performance changes to refine strategies. Extensive evaluations on two complex long-horizon tasks, cooperative platoon planning and pandemic control, demonstrate that our framework enables robust coordination under spatiotemporal partial observability.
%U https://aclanthology.org/2026.findings-emnlp.979/
%P 20685-20717

Markdown (Informal)

[MACRO-LLM: LLM-Empowered Multi-Agent Collaborative Reasoning under Spatiotemporal Partial Observability](https://aclanthology.org/2026.findings-emnlp.979/) (Chen et al., Findings 2026)

• MACRO-LLM: LLM-Empowered Multi-Agent Collaborative Reasoning under Spatiotemporal Partial Observability (Chen et al., Findings 2026)

ACL

• Handi Chen, Running Zhao, Xiuzhe Wu, Zhanfeng Xu, and Edith Cheuk-Han Ngai. 2026. MACRO-LLM: LLM-Empowered Multi-Agent Collaborative Reasoning under Spatiotemporal Partial Observability. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 20685–20717, Budapest, Hungary. Association for Computational Linguistics.