arXiv AI June 16, 2026 By Viktor Kewenig, Andrew Lampinen, Samuel A. Nastase, Christopher Edwards, Quitterie Lacome D'Elascombe, Akilles Rechardt, Jeremy I Skipper, Gabriella Vigliocco

Attention, not scale, drives human-AI alignment in multimodal language prediction

Read the original on arXiv AI →

arXiv:2308. 06035v4 Announce Type: replace Abstract: Humans routinely draw on visual context to predict upcoming words.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 1

ADAPT: Attention Dynamics Alignment with Preference Tuning for Faithful MLLMs

arXiv:2606. 31054v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are critically hampered by hallucination, generating content inconsistent with the provided image.

By Zhiyuan Yao, Zheren Fu, Zhixiao Zheng, Jiajun Li, Yi Tu, Zhendong Mao

llms diffusion reinforcement-learning multimodal benchmarks safety

arXiv AI

Jul 21

What Makes Linguistic Representations Good Models of High-Level Visual Perception in the Human Brain?

arXiv:2607. 16214v1 Announce Type: cross Abstract: Image descriptions represented with language models (LMs) predict human brain responses to naturalistic images in high-level visual regions, but the factors driving this predictivity remain unclear.

By Anna Bavaresco, Ina Klari\'c, Raquel Fern\'andez, Marie-Francine Moens

llms rag fine-tuning safety

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 17

Plug-and-Adapt: Multimodal Coreference Resolution at First Sight with a Pretrained Alignment Model

arXiv:2606. 17950v1 Announce Type: cross Abstract: Visual information helps resolve ambiguity in coreference resolution, leading to notable performance gains.

By Jinghan Wu, Jing Li, Ivor W. Tsang, Xuetao Zhang

multimodal benchmarks safety

arXiv AI

Aug 10

Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin

arXiv:2608. 06411v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) achieve strong performance across diverse vision-language tasks, but their efficiency is limited by the cost of processing numerous visual tokens.

By Yuyao Sun, Tao Deng, Shuang Li, Deqing Wang, Hao Geng, Minjun Yu

llms efficiency multimodal benchmarks

arXiv AI

Jun 3

Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale

arXiv:2604. 18572v2 Announce Type: replace-cross Abstract: The Platonic Representation Hypothesis suggests that neural networks trained on different modalities (e.

By A. Sophia Koepke, Daniil Zverev, Shiry Ginosar, Alexei A. Efros

llms multimodal safety

Related stories

ADAPT: Attention Dynamics Alignment with Preference Tuning for Faithful MLLMs

What Makes Linguistic Representations Good Models of High-Level Visual Perception in the Human Brain?

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

Plug-and-Adapt: Multimodal Coreference Resolution at First Sight with a Pretrained Alignment Model

Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin

Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale