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From the 1 of 6 linked papers with an AI index.

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6 papers

cs.AI2026

S1-Omni: A Unified Multimodal Reasoning Model for Scientific Understanding, Prediction, and Generation

Jiahao Zhao, Junyi Liu, Lifeng Xu +19

We present S1-Omni, a unified multimodal reasoning model for scientific understanding, prediction, and generation. AI for Science (AI4S) has advanced significantly through domain-s…

cs.CV2026

Fre-Res: Frequency-Residual Video Token Compression for Efficient Video MLLMs

Yigui Feng, Qinglin Wang, Yang Liu +1

The paper introduces Fre-Res, a dual‑track video token compression method for video multimodal large language models that keeps a few high‑fidelity spatial anchor tokens while enco…

cs.CV2026

S1-Omni-Image: A Unified Model for Scientific Image Understanding, Generation, and Editing

Qingxiao Li, Zikai Wang, Qingli Wang +1

We present S1-Omni-Image, an open-weight unified multimodal model for scientific image understanding, generation, and editing. Unlike general-purpose image generation models, scien…

cs.CV2026

S1-VL: Scientific Multimodal Reasoning Model with Thinking-with-Images

Qingxiao Li, Lifeng Xu, QingLi Wang +4

We present S1-VL, a multimodal reasoning model for scientific domains that natively supports two complementary reasoning paradigms: Scientific Reasoning, which relies on structured…

cs.SE2025

SemGuard: Real-Time Semantic Evaluator for Correcting LLM-Generated Code

Qinglin Wang, Zhihong Sun, Ruyun Wang +4

Large Language Models (LLMs) can translate natural language requirements into code, yet empirical analyses of representative models reveal that semantic errors-programs that compil…

cs.CL2025

ChartReasoner: Code-Driven Modality Bridging for Long-Chain Reasoning in Chart Question Answering

Caijun Jia, Nan Xu, Jingxuan Wei +4

Recently, large language models have shown remarkable reasoning capabilities through long-chain reasoning before responding. However, how to extend this capability to visual reason…