Author

Ruijin Hua

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#machine learning Preprint Oct 2026

SplitJEPA: Learning Invariant and Variant Latent Worlds without Reconstruction

Understanding a dynamical world calls for more than a latent state that summarizes its observations: the state should also be organized into the factors that stay shared across related observations and the factors that vary between them. For example, a robot pushing a cube to a goal should take the same action when the...

Rui-Jin Hua, Zichuan Liu, Zhuokai Zhao et al. · 0 citations

Jul 2026

Tool-Adaptive LLM Reranker

TALRanker is a novel framework that formalizes pointwise relevance scoring as an agentic Markov decision process that achieves state-of-the-art performance across standard and reasoning-intensive retrieval benchmarks, matching throughput with pointwise rerankers while outperforming parameter-heavy reasoning models.

Zichuan Liu, Ruijin Hua · arXiv.org · 0 citations