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Towards Robust Visual Continual Learning with Multi-Prototype Supervision
Xiwei Liu, Yulong Li, Yichen Li, Xinlin Zhuang, Haolin Yang, Huifa Li, Imran Razzak
https://arxiv.org/abs/2509.16011

Towards Robust Visual Continual Learning with Multi-Prototype Supervision
Language-guided supervision, which utilizes a frozen semantic target from a Pretrained Language Model (PLM), has emerged as a promising paradigm for visual Continual Learning (CL). However, relying on a single target introduces two critical limitations: 1) semantic ambiguity, where a polysemous category name results in conflicting visual representations, and 2) intra-class visual diversity, where a single prototype fails to capture the rich variety of visual appearances within a class. To this …

TransGAT: Transformer-Based Graph Neural Networks for Multi-Dimensional Automated Essay Scoring
Hind Aljuaid, Areej Alhothali, Ohoud Al-Zamzami, Hussein Assalahi
https://arxiv.org/abs/2509.01640

TransGAT: Transformer-Based Graph Neural Networks for Multi-Dimensional Automated Essay Scoring
Essay writing is a critical component of student assessment, yet manual scoring is labor-intensive and inconsistent. Automated Essay Scoring (AES) offers a promising alternative, but current approaches face limitations. Recent studies have incorporated Graph Neural Networks (GNNs) into AES using static word embeddings that fail to capture contextual meaning, especially for polysemous words. Additionally, many methods rely on holistic scoring, overlooking specific writing aspects such as grammar…

TokenBlowUp: Resolving Representational Singularities in LLM Token Spaces via Monoidal Transformations
Dongfang Zhao
https://arxiv.org/abs/2507.19747 https://

TokenBlowUp: Resolving Representational Singularities in LLM Token Spaces via Monoidal Transformations
Recent work has provided compelling evidence challenging the foundational manifold hypothesis for the token embedding spaces of Large Language Models (LLMs). These findings reveal the presence of geometric singularities around polysemous tokens, which can lead to representational instability. Existing methodologies, which presuppose a smooth data manifold, are ill-equipped to address such intrinsic structural flaws. In this paper, we formalize this problem in the language of scheme theory and p…