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

activity
20242026
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6 papers

cs.CV2026

Boogu-Image-0.1: Boosting Open Agentic Multimodal Generation via Understanding under a Minimal Budget

Guoxuan Chen, Chufeng Xiao, Haoran Yang +30

Boogu-Image-0.1 is an open-source multimodal model family that supports high-quality text-to-image generation, fast inference, instruction-based image editing, and bilingual (Chine…

cs.CV2026

Detached Skip-Links and -Probe: Decoupling Feature Aggregation from Gradient Propagation for MLLM OCR

Ziye Yuan, Ruchang Yao, Chengxin Zheng +3

Multimodal large language models (MLLMs) excel at high-level reasoning yet fail on OCR tasks where fine-grained visual details are compromised or misaligned. We identify an overloo…

cs.LG2026

On Surprising Effectiveness of Masking Updates in Adaptive Optimizers

Taejong Joo, Wenhan Xia, Cheolmin Kim +2

Training large language models (LLMs) relies almost exclusively on dense adaptive optimizers with increasingly sophisticated preconditioners. We challenge this by showing that rand…

cs.LG2025

Deeper with Riemannian Geometry: Overcoming Oversmoothing and Oversquashing for Graph Foundation Models

Li Sun, Zhenhao Huang, Ming Zhang +1

Message Passing Neural Networks (MPNNs) is the building block of graph foundation models, but fundamentally suffer from oversmoothing and oversquashing. There has recently been a s…

cs.LG2025

Type-Compliant Adaptation Cascades: Adapting Programmatic LM Workflows to Data

Chu-Cheng Lin, Daiyi Peng, Yifeng Lu +2

Reliably composing Large Language Models (LLMs) for complex, multi-step workflows remains a significant challenge. The dominant paradigm -- optimizing discrete prompts in a pipelin…

cs.CL2024

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Petko Georgiev, Ving Ian Lei +1132

In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over…