KDD 2026 Workshop: Geometric Space, Architecture and Learning Objective for Large Pre-Trained Models

The Geometric Space, Architecture and Learning Objective for Large Pre-Trained Models (GALOP) workshop at KDD 2026 brings together researchers working on geometric representation spaces, geometry-aware architectures, and learning objectives for large pre-trained models, spanning natural language processing, computer vision, graph learning, knowledge discovery, and scientific AI.

Geometric Space, Architecture and Learning Objective for Large Pre-Trained Models @KDD 2026

News

• 2026-03-22: Call for paper, Submission website
• 2026-03-11: The workshop proposal was accepted by KDD 2026 as a half day workshop.

Introduction

Foundation models now drive progress across language, vision, graphs, recommendation, and scientific discovery, yet most of them are still built around Euclidean representations and objectives. In many real world settings, however, the underlying data contain hierarchy, relational structure, multi scale organization, or nonuniform geometry that is not naturally captured by standard design choices.

This workshop focuses on how geometric spaces, geometric neural networks, and geometric objectives can improve foundation models by introducing more appropriate inductive bias for representation, reasoning, and adaptation. It highlights work on hyperbolic, spherical, mixed curvature, and other geometry aware approaches that can better align model structure with the structure of data.

The goal is to bring together researchers from machine learning, data mining, natural language processing, computer vision, graph learning, knowledge discovery, and scientific AI. By connecting theory, methods, systems, and applications, the workshop aims to create a shared forum for understanding when geometric modeling matters, how it should be integrated into large models, and how its benefits should be evaluated in practice.

Important Dates

Time: 11:59 PM Anywhere on Earth unless otherwise specified.

• Workshop paper submission: April 30, 2026 extended to May 31, 2026
• Workshop paper notification: June 10, 2026
• Camera ready: June 15, 2026
• Final workshop program, materials, and full website online: June 22, 2026
• Workshop date: TBA
• Conference dates: August 9 to 13, 2026
• Venue: Jeju, Korea

Topics of Interest

We welcome submissions on topics including, but not limited to, the following directions from the proposal:

• Hyperbolic, spherical, and mixed curvature embeddings for large pre-trained models
• Non Euclidean word, sentence, document, and multimodal representations
• Geometric transformers and manifold aware attention mechanisms
• Equivariant and invariant architectures for foundation models
• Metric learning and contrastive objectives with geometric constraints
• Curvature aware optimization on Riemannian manifolds
• Alignment and fusion across different geometric spaces
• Theory for geometric large pre-trained models, including expressiveness and generalization
• Applications in natural language processing, computer vision, graph learning, knowledge discovery, and scientific discovery
• Benchmarks, evaluation protocols, open source tools, visualization, and reproducibility resources

Submission

• We welcome short research papers of up to 4 pages and full research papers of up to 9 pages, excluding references and supplementary materials.
• All accepted papers are planned to be presented as posters.
• Approximately 4 papers will be selected for oral presentations and 2 papers for outstanding paper awards.
• The workshop follows the current KDD 2026 workshop policy and is planned as an in person event.

GALOP Workshop @ KDD 2026 Submission

Tentative Program

The accepted workshop is half day. Based on the proposal, the program will include the following components:

• Opening remarks and workshop overview
• Invited talks on geometric learning and large pre-trained models
• Contributed paper spotlight presentations
• Poster and discussion session
• Panel discussion on the future of geometric AI
• Best paper recognition and closing remarks

Invited Speakers

TBA

Organizers

Menglin Yang HKUST (GZ)

Jiahong Liu CUHK

Lucas Vinh Tran JPMorgan Chase

Rex Ying Yale University