arXiv AI June 17, 2026 By Shiqi He, Insu Jang, Mosharaf Chowdhury

Mordal: Automated Pretrained Model Selection for Vision Language Models

arXiv:2502. 00241v2 Announce Type: replace-cross Abstract: Incorporating multiple modalities into large language models (LLMs) is a powerful way to enhance their understanding of non-textual data, enabling them to perform multimodal tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 12

M*: A Modular, Extensible, Serving System for Multimodal Models

arXiv:2606. 12688v1 Announce Type: cross Abstract: We are entering a new era of composite model architectures that integrate diverse components such as vision encoders, language backbones, diffusion and flow heads, audio codecs, action generators, and world-model predictors.

By Atindra Jha, Naomi Sagan, Keisuke Kamahori, Irmak Sivgin, Rohan Sanda, Steven Gao, Mark Horowitz, Luke Zettlemoyer, Olivia Hsu, Jure Leskovec, Baris Kasikci, Stephanie Wang

llms diffusion robotics efficiency multimodal

arXiv AI

Jun 9

An Effective Router for Vision-Language Model Selection

arXiv:2606. 08970v1 Announce Type: new Abstract: Vision-language models (VLMs) with varying performance and resource requirements are widely deployed, making it difficult for users to select the most appropriate one among numerous VLM candidates.

By Can Wang, Shengwei Wang, Bolin Zhang, Zhiying Tu, Dianhui Chu

llms multimodal

arXiv AI

2d ago

AiSearch: Interactive Multi-Modal Search with VLMs

AiSearch is a flexible multimodal retrieval framework that uses Vision Language Models (VLMs) to enable natural language search over images and videos. It supports interactive search refinement through user feedback, allowing results to be tailored to the user's intent in real time. The system also provides visual benchmarking across multiple VLMs, enabling users to choose the most suitable model for their specific task.

By Ali Koksal, Mei Chee Leong, Vicky Sintunata, Ching Ling Chin, Wee Teck Fong

llms multimodal benchmarks

arXiv Computer Vision

Sep 1

VisLens: Single-Pass Interpretable Visual Search for Multimodal LLMs

arXiv:2608.30705v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) struggle with fine-grained Visual Search, the task of locating small or rare objects in high-resolution images...

By Jingyi He, Sanghwan Kim, Zeynep Akata

llms reinforcement-learning multimodal

arXiv Machine Learning

Aug 21

Projector Is All You Train

arXiv:2608. 19726v1 Announce Type: cross Abstract: The typical training process of a multimodal large language model (MLLM) involves adapting both the language model backbone and the projector between the backbone and a modality-specific encoder.

By Nyx Iskandar, Saathvik Selvan, Slater Victoroff

llms fine-tuning efficiency multimodal benchmarks

arXiv AI

Jun 16

Visual-Seeker: Towards Visual-Native Multimodal Agentic Search via Active Visual Reasoning

arXiv:2606. 15231v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have demonstrated impressive capabilities in many visual tasks, but they often struggle with factual grounding when confronted with complex, open-world scenarios.

By Zhengbo Zhang, Changtao Miao, Jinbo Su, Zhaowen Zhou, Chunxia Zhang, Xukai Wang, Ruiqi Liu, Kaiyuan Zheng, Jiansheng Cai, Bo Zhang, Zhe Li, Shiming Xiang, Ying Yan

llms agents multimodal benchmarks data-engineering

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Related stories

M*: A Modular, Extensible, Serving System for Multimodal Models

An Effective Router for Vision-Language Model Selection

AiSearch: Interactive Multi-Modal Search with VLMs

VisLens: Single-Pass Interpretable Visual Search for Multimodal LLMs

Projector Is All You Train

Visual-Seeker: Towards Visual-Native Multimodal Agentic Search via Active Visual Reasoning