most citedInternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

8 citations · 14 across the 16 of their papers we have counts for

collaborators

32 papers

cs.AI2026

MiroFlow: Towards High-Performance and Robust Open-Source Agent Framework for General Deep Research Tasks

Shiqian Su, Sen Xing, Xuan Dong +13

Despite the remarkable progress of large language models (LLMs), the capabilities of standalone LLMs have begun to plateau when tackling real-world, complex tasks that require inte…

cs.CL2025

MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling

MiroMind Team, Song Bai, Lidong Bing +52

We present MiroThinker v1.0, an open-source research agent designed to advance tool-augmented reasoning and information-seeking capabilities. Unlike previous agents that only scale…

cs.CV2025

NaViL: Rethinking Scaling Properties of Native Multimodal Large Language Models under Data Constraints

Changyao Tian, Hao Li, Gen Luo +11

Compositional training has been the de-facto paradigm in existing Multimodal Large Language Models (MLLMs), where pre-trained vision encoders are connected with pre-trained LLMs th…

cs.CV2025

Vlaser: Vision-Language-Action Model with Synergistic Embodied Reasoning

Ganlin Yang, Tianyi Zhang, Haoran Hao +15

While significant research has focused on developing embodied reasoning capabilities using Vision-Language Models (VLMs) or integrating advanced VLMs into Vision-Language-Action (V…

cs.CL2025

Sequential Diffusion Language Models

Yangzhou Liu, Yue Cao, Hao Li +13

Diffusion language models (DLMs) have strong theoretical efficiency but are limited by fixed-length decoding and incompatibility with key-value (KV) caches. Block diffusion mitigat…

cs.CV20254 cited

InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency

Weiyun Wang, Zhangwei Gao, Lixin Gu +72

We introduce InternVL 3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL…