arXiv AI August 11, 2026 By Chenchen Lin, Sanbao Su, Rachel Luo, Yuxiao Chen, Yan Wang, Marco Pavone, Fei Miao

TGIF: Text-Guided Layer Fusion Mitigates Hallucination in Multimodal LLMs

arXiv:2601. 03100v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) typically rely on a single late-layer feature from a frozen vision encoder, leaving the encoder's rich hierarchy of visual cues under-utilized.

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arXiv Computer Vision

Sep 1

Enhancing Self-Supervised Visual Representation Learning via Low-Rank Adapted LLMs

arXiv:2507.00754v3 Announce Type: replace Abstract: The integration of Large Language Model (LLMs) blocks with Vision Transformers (ViTs) holds immense promise for vision-only tasks by leveraging the...

By Selim Kuzucu, Muhammad Ferjad Naeem, Anna Kukleva, Federico Tombari, Bernt Schiele

llms fine-tuning

arXiv AI

Jun 3

Vision Inference Former: Sustaining Visual Consistency in Multimodal Large Language Models

arXiv:2605. 18160v2 Announce Type: replace-cross Abstract: In recent years, multimodal large language models (MLLMs) have achieved remarkable progress, primarily attributed to effective paradigms for integrating visual and textual information.

By Xinpeng Dong, Min Zhang, Kairong Han, Xu Tan, Fei Wu, Kun Kuang

llms computer-vision multimodal benchmarks safety

arXiv AI

Jun 9

Late-Layer Fusion is Enough: Dual-Path Vision Token Routing for Multimodal Large Language Models under Visual Saturation

arXiv:2606. 09131v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) commonly inherit the deep, symmetric Transformer backbone designed for unimodal text modeling, and apply the same computation uniformly to image and language tokens.

By Siyuan Liu, Jinyang Wu

llms diffusion multimodal benchmarks

arXiv AI

Jun 26

Mitigating Hallucinations via Inter-Layer Consistency Aggregation in Large Vision-Language Models

arXiv:2505. 12343v2 Announce Type: replace-cross Abstract: Despite the impressive capabilities of Large Vision-Language Models (LVLMs), they remain susceptible to hallucinations, where generated content is inconsistent with the input image.

By Kai Tang, Jinhao You, Yichen Guo, Yiding Sun, Dongxu Zhang, Wenya Wang, Hanze Li, Tao Luo, Renyuan Li, Xiande Huang

llms fine-tuning multimodal benchmarks safety

arXiv Machine Learning

4d ago

Constructive Distortion: Improving MLLMs with Attention-Guided Image Warping

The paper introduces AttWarp, a lightweight technique that uses a multimodal large language model’s cross‑modal attention to perform rectilinear warping of input images at test time. By reallocating spatial resolution toward query‑relevant regions without altering model weights or architecture, AttWarp preserves global context while making small objects and subtle relationships easier for the model to read. Experiments on five benchmarks and four MLLMs show consistent accuracy gains, improved compositional reasoning, and reduced hallucinations compared to baseline image‑manipulation methods.

By Dwip Dalal, Gautam Vashishtha, Utkarsh Mishra, Jeonghwan Kim, Madhav Kanda, Hyeonjeong Ha, Svetlana Lazebnik, Heng Ji, Unnat Jain

llms multimodal benchmarks safety

arXiv AI

Sep 10

STAR-Pro: Stage-Wise Token Adaptive Reduction with Progressive Refinement for Efficient Large Vision-Language Models

arXiv:2609.05916v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) achieve strong multimodal understanding, but the hundreds to thousands of visual tokens they process impose subs...

By Yichen Guo, Tinghao Wang, Qizhe Zhang, Lingbei Meng, Yuan Zhang, Jiajun Cao, Hao Jiang, Chenwei Wu, Jixian Wu, Sixiang Chen, Tao Luo, Hongyang Cheng, Kai Tang, Chenxi Li, Renyuan Li, Xiande Huang, Wenya Wang, Shanghang Zhang

llms efficiency multimodal benchmarks

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Enhancing Self-Supervised Visual Representation Learning via Low-Rank Adapted LLMs

Vision Inference Former: Sustaining Visual Consistency in Multimodal Large Language Models

Late-Layer Fusion is Enough: Dual-Path Vision Token Routing for Multimodal Large Language Models under Visual Saturation

Mitigating Hallucinations via Inter-Layer Consistency Aggregation in Large Vision-Language Models

Constructive Distortion: Improving MLLMs with Attention-Guided Image Warping

STAR-Pro: Stage-Wise Token Adaptive Reduction with Progressive Refinement for Efficient Large Vision-Language Models