Omni-R1 unifies multimodal reasoning by generating intermediate images during the process in a SFT-plus-RL framework, with an Omni-R1-Zero variant that matches or exceeds it using only text data.
naacl-main.191
5 Pith papers cite this work, alongside 457 external citations. Polarity classification is still indexing.
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SMMD loss combines MMD with numeric distance kernels and smoothness to improve accuracy on mathematical reasoning, arithmetic, clock recognition, and chart QA across LLMs and VLMs.
EvidenceLens is a visual analytics system that decomposes LLM financial answers into atomic claims and visualizes their multimodal evidence alignment, support gaps, and contradictions through a claim-evidence matrix and review-priority ranking.
PaliGemma is an open 3B VLM based on SigLIP and Gemma that achieves strong performance on nearly 40 diverse open-world tasks including benchmarks, remote-sensing, and segmentation.
ACSESS automatically combines 23 sample selection strategies to outperform individual strategies in few-shot learning on text and image datasets.
citing papers explorer
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Omni-R1: Towards the Unified Generative Paradigm for Multimodal Reasoning
Omni-R1 unifies multimodal reasoning by generating intermediate images during the process in a SFT-plus-RL framework, with an Omni-R1-Zero variant that matches or exceeds it using only text data.
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Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment
SMMD loss combines MMD with numeric distance kernels and smoothness to improve accuracy on mathematical reasoning, arithmetic, clock recognition, and chart QA across LLMs and VLMs.
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EvidenceLens: A Claim-Evidence Matrix for Auditing Financial Question Answering
EvidenceLens is a visual analytics system that decomposes LLM financial answers into atomic claims and visualizes their multimodal evidence alignment, support gaps, and contradictions through a claim-evidence matrix and review-priority ranking.
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PaliGemma: A versatile 3B VLM for transfer
PaliGemma is an open 3B VLM based on SigLIP and Gemma that achieves strong performance on nearly 40 diverse open-world tasks including benchmarks, remote-sensing, and segmentation.
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Automatic Combination of Sample Selection Strategies for Few-Shot Learning
ACSESS automatically combines 23 sample selection strategies to outperform individual strategies in few-shot learning on text and image datasets.