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Haocheng Wang

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Open access Sep 2026

Larger language models better align with neural representations of natural language

Recent research has used large language models (LLMs) to study the neural basis of naturalistic language processing in the human brain. LLMs have rapidly grown in complexity, leading to improved language processing capabilities. Here, we utilized several families of transformer-based LLMs to investigate the relationshi...

Zhuoqiao Hong, Hao-Cheng Wang, Zaid Zada et al. · 0 citations
Sep 2026

BPE-Level Visual-Textual Alignment for Multi-Scene Text Retrieval

Scene Text Retrieval (STR) aims to search images containing a given textual query within large-scale image collections. However, existing approaches are fundamentally constrained in two ways: 1) they are evaluated on narrow benchmarks that focus primarily on natural scenes; and 2) they rely on either error-prone multi-...

Tong-Kun Guan, Yu-Tong Cai, Hao-Cheng Wang et al. · 0 citations
Preprint Aug 2026

VaRS-Doc: Interpretation-Aware Variant Representations via Latent Self-Probing for Visual Document Retrieval

VaRS-Doc is proposed, a visual document retrieval framework that diversifies document representations by enabling the model to actively explore variant latent interpretations during document encoding, while preserving efficient late-interaction retrieval in which each query adaptively selects the best-fit representatio...

Hao-Cheng Wang, Tongkun Guan, Wei Shen et al. · 0 citations
Jul 2026

FormalRx: Rectify and eXamine Semantic Failures in Autoformalization

FormalRx is introduced, a comprehensive diagnostic evaluation framework that transforms autoformalization assessment from black-box judgments into actionable feedback, and enables systematic diagnosis and improvement of autoformalization systems.

Haocheng Wang, Baiyu Huang, Yingjia Wan et al. · 1 citation

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