Hyperbolic geometry has recently emerged as a powerful representation space for multimodal learning, as it naturally captures hierarchical semantic structure across modalities. Despite this progress, how such representations behave under continual learning poses fundamentally different challenges that remain underexplo...
Jia-Hong Liu, Ming Shen, Xiao-Hao Liu et al.· 2 citations
Across a unified 16-task classification-retrieval stream with three hyperbolic backbones, HMCL improves final performance and backward transfer over sequential fine-tuning and four continual-learning baselines; HMCL-CA gives the highest Overall score on every backbone.
Jia-Hong Liu, Ming Shen, Xiao-Hao Liu et al.· 0 citations
Across multi-hop QA and long-horizon web reasoning tasks, StepKV sustains accuracy under low KV budgets where token-level baselines degrade sharply, offering a more robust efficiency-accuracy trade-off for multi-step agent inference.
Bo-Yu Feng, Jia-Hong Liu, Yi-Fan Li et al.· 0 citations
It is argued that long-horizon personalization should instead model memory as a user-specific dynamical state space with locally heterogeneous geometry, which captures stable versus volatile regions, variable-rate drift, heterogeneous neighborhoods, and uncertainty about current user state.
Jia-Hong Liu, Wenhao Yu, Ze-Xuan Qiu et al.· 1 citation
This lecture-style tutorial covers hyperbolic methods for data organization, retrieval, and memory layers in foundation-model systems: manifold operations, scalable neural primitives, retrieval-aware pipelines, recommendation and knowledge systems, agent memory, multimodal and scientific data modeling, and lifecycle op...
Jiahong Liu, Menglin Yang, Irwin King· Proceedings of the 32nd ACM...· 0 citations
The Geometric Space, Architecture and Learning Objective for Large Pre-Trained Models (GALOP) workshop is an accepted half-day KDD 2026 workshop that examines how geometric principles can make large pretrained models more expressive, robust, interpretable, and efficient.
Meng-Lin Yang, Jia-Hong Liu, Lucas Vinh Tran et al.· Proceedings of the 32nd ACM...· 1 citation
Large pretrained models have reshaped artificial intelligence, yet their Euclidean design assumptions often limit their ability to model hierarchy, curvature, symmetry, and heterogeneous relations in real-world data. The Geometric Space, Architecture and Learning Objective for Large Pre-Trained Models (GALOP) workshop...
Menglin Yang, Jiahong Liu, Lucas Vinh Tran et al.· Proceedings of the 32nd ACM...· 0 citations
Foundation models are increasingly deployed as agentic data-and-memory systems built on pretrained parameters, retrieval corpora, external knowledge stores, and persistent interaction histories. For the Knowledge Discovery and Data Mining (KDD) community, this matters because recommendation, search, temporal modeling,...
Jiahong Liu, Menglin Yang, Irwin King· Proceedings of the 32nd ACM...· 0 citations
This work proposes FlatLand, a novel personalized federated learning method that embeds different clients'data in tailored Lorentz space of hyperbolic geometry, and develops a parameter decoupling strategy that separates heterogeneous information from common knowledge, enabling direct aggregation without requiring clie...
Jia-Hong Liu, B. RamSamarthB, Xinyu Fu et al.· 2 citations
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