2026· Annual Meeting of the Association for Computational Linguistics· pp. 11152-11168· 0 citations· 28 references
Computer Science
TL;DR
This work proposes Vector Cal-ligrapher, a system that treats SVG generation as a conditional language modeling task optimized for both semantic grounding and representational efficiency, and introduces a structured linguistic supervision Font Description Framework that decomposes typo-graphic style into interpretable linguistic dimensions.
Abstract
Generating SVG-based fonts requires Multi-modal Large Language Models (MLLMs) to translate high-level linguistic intent into low-level, topologically constrained symbolic sequences. However, current approaches struggle with two fundamental misalignments: the semantic ambiguity of unstructured natural language for precise geometric control, and the inefficiency of generic text tokenizers , which fragment coordinate-dense SVG XML into excessively long sequences with low information density. In this work, we propose Vector Cal-ligrapher , a system that treats SVG generation as a conditional language modeling task optimized for both semantic grounding and representational efficiency. To bridge the semantic gap, we introduce a structured linguistic supervision Font Description Framework that decomposes typo-graphic style into interpretable linguistic dimensions (e.g., historical lineage, affective metaphors), providing structured supervision aligned with the compositional syntax of SVG. To address the tokenization bottleneck, we de-sign a scalable separated-coordinate strategy that bypasses the vocabulary explosion of flattened tokens while significantly compressing sequence length. Supported by Vec-torFont , a dataset of over 10 million hierarchically annotated glyphs, our approach improves CLIP score by +23%, reduces geometric error by ≈48%, and boosts generation efficiency by achieving an 18% Commands-per-Token (C/T) ratio—a 6 × increase in information density over standard baselines. These results demonstrate that combining structured linguistic supervision with efficient symbolic tokenization is essential for reliable, controllable vector graphics synthesis. VectorFont dataset, Code and model weights will be publicly released.
Compared to prior CLIP-enhancement methods, MLLMCLIP achieves state-of-the-art compositional accuracy while delivering consistent gains on standard zero-shot classification and image-text retrieval, showing that feature-level distillation strengthens both compositional and general vision-language representation capability.
Jongsuk Kim, Qiyu Wu, Zhuoyuan Mao et al.· 0 citations
Abstractive text summarization remains a fundamental challenge in Natural Language Processing (NLP), particularly for long documents that require models to preserve long-range dependencies and maintain semantic coherence. Although Transformer-based architectures have achieved strong summarization performance, their full self-attention mechanism scales quadratically with sequence length and often requires input truncation in long-context applications.
This study presents a Neural Turing Machine (NTM)-based framework for abstractive text summarization. The proposed architecture combines a two-layer Bidirectional Long Short-Term Memory (BiLSTM) controller with an addressable external memory bank. Differentiable read and write operations decouple contextual storage from recurrent computation, enabling the persistent retrieval of salient information across extended input sequences. Detailed preprocessing, implementation, training, decoding, and evaluation settings are provided to support reproducibility. The framework was evaluated on the CNN/Daily Mail benchmark and compared with LSTM, Transformer, and BART baselines, as well as published high-performing systems, including PEGASUS, SimCLS, and BRIO. Ablation studies, learning-rate sensitivity analysis, long-context evaluation, inference-time scaling, statistical testing, qualitative error analysis, and memory-access visualization were also conducted.
The proposed NTM model achieved ROUGE-1, ROUGE-2, ROUGE-L, and BLEU scores of 47.8, 23.5, 44.6, and 20.1, respectively. Under the controlled experimental protocol, it outperformed the evaluated LSTM, Transformer, and BART baselines. Comparisons with published results indicate that the model is competitive with recent high-performing summarization systems. The additional analyses demonstrate that the external memory mechanism improves contextual retention and summarization stability, particularly for longer input sequences, while exhibiting favorable inference-time scaling behavior.
These findings demonstrate that integrating an addressable external memory with a BiLSTM controller offers an effective approach to abstractive summarization, particularly when processing long documents. The proposed framework provides competitive summarization performance while reducing dependence on computationally expensive full self-attention. The results highlight the potential of external-memory architectures as a scalable and stable alternative for long-context text summarization.
K. Katti, Kartikeya Reddy Katti, Amanul Islam· Frontiers in Artificial Inte...· 0 citations
A group-supervised contrastive learning framework that addresses semantic ambiguity through two complementary mechanisms that expands parameter-based templates into diverse natural language descriptions using Large Language Models, and a group-supervised multi-positive contrastive objective that aligns text embeddings with sets of maps generated under shared control parameters is introduced.
Zhongyuan Xie, Kwanghee Won· International Conference on...· 0 citations
VLTok is a novel 1D hybrid tokenizer that unifies V isual and L anguage representations in a shared Tok en space through a self-prompted training paradigm, and achieves state-of-the-art performance in both image reconstruction and image generation.
Hualiang Wang, Siming Fu, Wei-Nan Jia et al.· 0 citations
VLZip is introduced, a framework that unifies visual and textual compression for high-fidelity reasoning within a pure Transformer, and establishes an efficient and powerful new standard for long-context multimodal AI.
Yuqi Zhang, Cheng Chen, Yuyu Guo et al.· 0 citations
Pretrained language models (PLMs) have established state-of-the-art performance across diverse natural language understanding (NLU) tasks. This study reveals that seman-tic-rich explanations of lexical units can effectively guide PLM learning processes. We propose a novel language understanding enhancement method with token interpretation (LUETI) that addresses two critical limitations in conventional PLMs: Incomplete token semantics caused by isolated contextual learning and insufficient semantic encoding in embedding matrices. LUETI operates through dual mechanisms, augmenting token represen-tations by integrating hidden states with corresponding token interpretations and refining embedding spaces using interpretation-derived semantic vectors for token prediction. LUETI, which is implemented as a plug-in module for standard architectures, demonstrates significant improvements on BERT and GLM, achieving average performance gains of 3.36% and 4.87% respectively on the SuperGLUE benchmark with equivalent parameters and training data. Note that LUETI-equipped models attain comparable performance to baseline PLMs using only 60% of pretraining data. Findings establish token interpretation as a computationally efficient but semantically powerful enhancement strategy for language model pretraining.
Tianyi Chen, Yashen Wang, Huan Chang et al.· IEEE/CAA Journal of Automati...· 0 citations
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