ChartNet is a million-scale multimodal dataset for chart understanding created via code-guided synthesis spanning 24 chart types with five aligned modalities per sample.
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Learn2Fold generates physically valid origami folding sequences from text prompts by decoupling LLM-based program proposals from verification in a learned graph-structured world model.
OmniZip introduces an audio-guided dynamic token compression framework that achieves 3.42X inference speedup and 1.4X memory reduction for omnimodal LLMs without any training.
citing papers explorer
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ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding
ChartNet is a million-scale multimodal dataset for chart understanding created via code-guided synthesis spanning 24 chart types with five aligned modalities per sample.
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Learn2Fold: Structured Origami Generation with World Model Planning
Learn2Fold generates physically valid origami folding sequences from text prompts by decoupling LLM-based program proposals from verification in a learned graph-structured world model.
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OmniZip: Audio-Guided Dynamic Token Compression for Fast Omnimodal Large Language Models
OmniZip introduces an audio-guided dynamic token compression framework that achieves 3.42X inference speedup and 1.4X memory reduction for omnimodal LLMs without any training.