\section{Introduction}

%\todo{Different example?}
Coreference is the phenomenon in which multiple expressions in a discourse refer to the same real-world entity. For example, in \emph{``Beethoven was a revolutionary artist. The German composer changed the course of music, and he continues to inspire musicians today.''}, the mentions \emph{``Beethoven''}, \emph{``the German composer''}, and \emph{``he''} all denote the same individual. The goal of \emph{coreference resolution} is to automatically identify such mentions and group them into entity clusters. In the multilingual setting, the task is complicated by the diversity of languages and their grammatical and discourse conventions.

This paper presents the shared task on multilingual coreference resolution based on the CorefUD collection. Building on previous editions, we aim to provide a common evaluation framework and a multilingual benchmark that supports fair comparison across modeling paradigms.

A particular focus of the most recent edition was the emergence of large language models (LLMs). While non-LLM approaches remained welcome in the \emph{Unconstrained track}, we introduced a dedicated \emph{LLM Track} to explicitly study the strengths and limitations of LLM-based coreference systems. The previous edition indicated that LLM-based submissions still lagged behind the best discriminative encoder-based systems; given the rapid pace of LLM research, we therefore repeated the LLM Track to reassess the current state of LLM-based coreference. It targets solutions that primarily rely on LLMs, including fine-tuning, in-context learning, prompt design, constrained decoding, and more complex agentic pipelines.

%To accommodate other modeling strategies and analyze their effects, we also organize the \emph{Unconstrained Track}, which is open to all other approaches, including non-LLM models and hybrid systems. It allows the use of additional pre-existing coreference systems, external tools, and extensive model modifications.

Beyond modeling, the shared task also addresses a long-standing limitation of coreference benchmarks. Widely used resources such as OntoNotes \citep{ontonotes5.0} predominantly consist of short-to-medium documents (often below 1k tokens), largely because coreference annotation becomes increasingly difficult and costly as context grows: annotators typically resolve references incrementally and must track entities across long spans. Consequently, several projects either partition long documents into smaller units (including OntoNotes, later re-merged in LongtoNotes \citep{shridhar-etal-2023-longtonotes}; similarly, PDTSC, newly added to CorefUD, limits its documents to 50 sentences \citep{mikulova-etal-2017-pdtsc}; see Section~\ref{sec:data-new}) or constrain the annotation scope (e.g., PROIEL datasets \citep{Haug2008CreatingAP} limit the window to 13 sentences).\footnote{See their Guidelines for annotation of givenness at \url{https://dev.syntacticus.org/proiel-givenness-annotation-v1.pdf}} Because of the latter, long documents do not necessarily imply long-range coreference. We therefore focus on the \emph{range} of an entity, defined as the number of words between the first and last mention heads.
% Martin: počítáme to přesně podle počtu "words", jak jsou definované v UD.
% Nikoli tokens. Třeba "don't" je 1 token, ale my to počítáme jako 2 words.

Despite these annotation complexities, new datasets targeting long-range coreference---often derived from literary texts---have emerged, partly driven by increased interest in NLP for literary domains \citep{bamman-etal-2014-bayesian,jcls3924}. Examples include
English: LitBank \citep{Bamman20Litbank}, FantasyCoref \citep{han-etal-2021-fantasycoref}, and BookCoref \citep{martinelli-etal-2025-bookcoref};
Dutch: RiddleCoref \citep{vanCranenburgh2019dutchcoref} and OpenBoek \citep{Cranenburgh_vanNoord_2022};
Korean: KoCoNovel \citep{kim-etal-2024-koconovel};
German: GerDraCor-Coref \citep{pagel-reiter-2020-gerdracor};
French: LitBank-fr \citep{MelanieBecquet2024BookNLPfr} and its extension Long-LitBank-fr \citep{bourgois-poibeau-2025-elephant}.
In parallel, there is growing work on coreference modeling in long contexts \citep{toshniwal-etal-2020-learning,toshniwal-etal-2021-generalization,guo-etal-2023-dual,gupta-etal-2024-coreference,martinelli-etal-2024-maverick,martinelli-etal-2025-xcore,bourgois-poibeau-2025-elephant}.

Most existing approaches and datasets, however, have been evaluated only monolingually. This motivated us to incorporate selected long-range entity resources into CorefUD, enabling multilingual evaluation of methods designed for long contexts. Because annotating long texts is challenging, many long-document datasets restrict the set of annotated entity types (e.g., LitBank distinguishes only six types, and some corpora focus exclusively on characters, such as BookCoref or KoCoNovel). For this shared task, we therefore prioritize datasets with a broader range of annotated entities; while character-only datasets are highly appealing (e.g., due to their length or coverage of underrepresented languages), including them would have required substantial changes to the evaluation framework that were not feasible within the available time.

For these reasons, we extend CorefUD with English FantasyCoref, French LitBank-fr, and Dutch OpenBoek.

In addition, we add two further datasets, introducing Latin and increasing the number of speech-domain datasets. Finally, for the Unconstrained track, we also support input data in a simplified JSON format to lower the barrier to participation.

The shared task was hosted on Codabench;\footnote{LLM Track: \url{https://www.codabench.org/competitions/13198}; Unconstrained Track: \url{https://www.codabench.org/competitions/13197}} the competition was re-opened a few months after the evaluation phase to enable future evaluation on the same test set.

%\formp{Chceme tu nastinit aj vysledky?
% The key takeaways from this year's shared task are threefold.
% First, LLM-based systems are narrowing the gap to encoder-based models, finishing only a few points behind the best system.
% Second, performance tends to drop on documents with the longest entity spans, but the magnitude of this degradation varies substantially across systems, and some models remain strong on long-range coreference.
% Finally, even as new datasets raise the bar, state-of-the-art systems continue to improve year after year.
% }
% Nemám na to silný názor. Vzhledem k celkové délce článku se ale spíš kloním k neprodlužování (když toto bude nakonec ještě párkrát zopakováno).

The remainder of the paper is structured as follows.
Section~\ref{sec:data} describes the data used in the shared task, while 
Section~\ref{sec:evaluation} presents the evaluation methodology.
Section~\ref{sec:systems} presents and compare baseline and participating systems.
We report and analyze the official results in Section~\ref{sec:results}, and conclude in Section~\ref{sec:conclusions}.