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20222026
most citedSummary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models

765 citations · 1k across the 25 of their papers we have counts for

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5 papers · 1 filter

eess.IV2026

FSDBN: Foreground-Aware EEG-Visual Alignment via Dynamic Brain Networks

Yiheng Liu, Chuhang Zheng, Peiliang Gong +3

EEG-based visual decoding provides a non-invasive pathway for interpreting visual semantics. However, existing methods often overlook the perceptual asymmetry between foreground an…

cs.CV2026

Multi-Level Bidirectional Biomimetic Learning for EEG-Based Visual Decoding

Jingtao Liu, Peiliang Gong, Chuhang Zheng +2

EEG-based visual neural decoding aims to align neural responses with visual stimuli for tasks such as image retrieval. However, limited paired data and a fundamental mismatch betwe…

cs.CL2026

FNF: Functional Network Fingerprint for Large Language Models

Yiheng Liu, Junhao Ning, Sichen Xia +8

The development of large language models (LLMs) is costly and has significant commercial value. Consequently, preventing unauthorized appropriation of open-source LLMs and protecti…

cs.CV2026

VAR RL Done Right: Tackling Asynchronous Policy Conflicts in Visual Autoregressive Generation

Shikun Sun, Liao Qu, Huichao Zhang +8

Visual generation is dominated by three paradigms: AutoRegressive (AR), diffusion, and Visual AutoRegressive (VAR) models. Unlike AR and diffusion, VARs operate on heterogeneous in…

cs.CV2026

NextFlow: Unified Sequential Modeling Activates Multimodal Understanding and Generation

Huichao Zhang, Liao Qu, Yiheng Liu +33

We present NextFlow, a unified decoder-only autoregressive transformer trained on 6 trillion interleaved text-image discrete tokens. By leveraging a unified vision representation w…