Coreference Resolution

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1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Latest papers 17

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CoRef-GS: Cooperative Referring Gaussian Splatting for Multi-Agent Scene Understanding
Sep 17, 2026Zhikun Zhou, Kunyu Peng, Runyi Yang +7Gaussian Splatting Simultaneous Localization And Mapping Systems Gaussian Splatting
A Circuit for Plural Reference: How LLMs Represent and Retrieve Singular and Plural Entities
Sep 3, 2026Anh Danh, Rick Nouwen, Massimo PoesioCoreference Resolution Entities
Utrecht University · Queen Mary University of London
MUSE: A Full-Text Cross-Domain Knowledge Base of Scientific Problems, Solutions, and Rationales
Aug 11, 2026Tsofia Cohen, Tom HopeScientific Discovery LLM Reasoning Strategies
The Hebrew University of Jerusalem · Allen Institute for AI (Ai2)
Schema-Constrained Document-Level Event Argument Extraction with Lightweight LLM Fine-Tuning
Jul 18, 2026Roberto Pietrantuono, Antonio Guerriero, Pouya SattariCoreference Resolution Large Language Model Fine-Tuning
University of Naples Federico II, Naples, Italy · University of Salerno, Fisciano, Italy
Introducing corpora Hlava Cor and Hlava AD: Human Label Variation in Coreference and Discourse Relations
Jun 24, 2026Anna Nedoluzhko, Šárka Zikánová, Jiří Mírovský +2Coreference Resolution Discourse
Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics, Prague, Czech Republic
Prague Dependency Treebank -- Consolidated 2.0: Enriching a Complex Annotation Scheme
Jun 23, 2026Marie Mikulová, Jiří Mírovský, Milan Straka +4Coreference Resolution Text Corpora
Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics Malostranské náměstí 25, 118 00 Prague 1, Czech Republic
Plug-and-Adapt: Multimodal Coreference Resolution at First Sight with a Pretrained Alignment Model
Jun 16, 2026Jinghan Wu, Jing Li, Ivor W. Tsang +1Vision-Language Alignment Coreference Resolution
State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, the Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an 710049, China · Centre for Frontier AI Research and the Institute of High-Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore 138634
Multilingual Coreference Resolution via Cycle-Consistent Machine Translation
Jun 3, 2026Adriana-Valentina Costache, Eduard Poesina, Silviu-Florin Gheorghe +2Coreference Resolution Machine Translation
Department of Computer Science, University of Bucharest, Romania
CorPipe at CRAC 2026: Empty Nodes and Cross-Lingual Transfer in Multilingual Coreference Resolution
May 28, 2026Milan StrakaCoreference Resolution Cross-Lingual Transfer
Charles University, Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Malostranské nám. 25, Prague, Czech Republic
LLMBridge: An LLM Pipeline for End-to-end Referential Bridging Resolution in English
May 27, 2026Lauren Levine, Amir ZeldesCoreference Resolution Large Language Model Pipelines
Georgetown University Department of Linguistics
Findings of the Fifth Shared Task on Multilingual Coreference Resolution: Expanding Datasets for Long-Range Entities
May 20, 2026Michal Novák, Miloslav Konopík, Anna Nedoluzhko +6Coreference Resolution Multilingual Language Models
Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics, Prague, Czechia · University of West Bohemia, Faculty of Applied Sciences, Department of Computer Science and Engineering, Pilsen, Czechia
Closing the Gap at CRAC 2026: Two-Stage Adaptation for LLM-Based Multilingual Coreference Resolution
May 16, 2026Antoine Bourgois, Olga Seminck, Thierry PoibeauCoreference Resolution Large Language Model Adaptation
Lattice (CNRS UMR 8094 & ENS-PSL & Universit´e Sorbonne Nouvelle), Montrouge, France
Interpretable Coreference Resolution Evaluation Using Explicit Semantics
May 11, 2026Bruno Gatti, Giuliano Martinelli, Roberto NavigliCoreference Resolution Multi-Dimensional Evaluation
Sapienza NLP Group, Sapienza University of Rome · 2Babelscape
Reasoning over Object Descriptions Improves Coreference Resolution in Task-Based Dialogue Systems
Apr 30, 2026Oier Ijurco, Oier Lopez de LacalleCoreference Resolution Dialogue
HiTZ Center - Ixa, University of the Basque Country UPV/EHU
One Identity, Many Roles: Multimodal Entity Coreference for Enhanced Video Situation Recognition
Apr 25, 2026Balaji Darur, Amanmeet Garg, Makarand TapaswiFine-Grained Video Understanding Video Dataset
CVIT, IIIT Hyderabad, India · Amazon Prime Video, Seattle
Beyond Word Boundaries: A Hebrew Coreference Benchmark and an Evaluation Protocol for Morphologically Complex Text
Apr 18, 2026Refael Shaked Greenfeld, Reut TsarfatyCoreference Resolution Natural Language Processing
Bar-Ilan University
CoReflect: A Reflective Co-Evolution Framework for Improving Conversational Evaluation
Jan 18, 2026Yunzhe Li, Richie Yueqi Feng, Tianxin Wei +1Dialogue Benchmarks Co-Evolution
Work done while at Google DeepMind · University of Illinois Urbana-Champaign · 2Google DeepMind

Sep 17, 2026, cs.ROJ/K move · Enter open · S save

CoRef-GS: Cooperative Referring Gaussian Splatting for Multi-Agent Scene Understanding

Zhikun Zhou, Kunyu Peng, Runyi Yang, Junhao Cai+6

Gaussian Splatting Simultaneous Localization And Mapping Systems Gaussian Splatting Spatial Grounding 3D Gaussian Primitives Coreference Resolution

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Referring scene understanding for embodied robots requires grounding object- and relation-centric language queries from a designated viewpoint. While a local semantic Gaussian map can support such grounding within one agent's observations, cooperative settings require this ability to remain effective after independently reconstructed maps are aligned and fused. In this setting, the referred target or its contextual landmark may come from another agent's observations, while spatial relations must still be interpreted from the querying robot's viewpoint. We formulate this problem as cooperative referring Gaussian grounding over fused maps, which requires geometric alignability, instance-level semantic comparability, and view-conditioned relation reasoning. Existing language-aware Gaussian methods mainly focus on single-map querying, whereas Gaussian registration methods optimize geometric or photometric alignment without preserving language-grounding-oriented semantic compatibility. We propose CoRef-GS, a cooperative referring Gaussian splatting framework. CoRef-GS constructs local open-vocabulary instance-aware Gaussian maps, then aligns partially overlapping maps with a cross-agent alignment module by geometric and semantic consistency, and grounds queries using a view-conditioned mask relation graph. We further introduce CoQuad-Ref, a dual-quadruped benchmark spanning both real-world and simulated indoor scenes. Experiments show that, on simulated scenes, CoRef-GS reduces the rotation error from 2.58° after coarse initialization to 0.15° after refinement, and improves real-world referring mIoU over ReferSplat from 52.6% to 68.8%. The established benchmark and source code will be publicly released at https://github.com/ruojiruoli17/CoRef-GS.git.

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