arXiv:2503. 06573v3 Announce Type: replace-cross Abstract: Recent LLMs have shown remarkable success in following user instructions, yet handling instructions with multiple constraints remains a significant challenge.
By Gili Lior, Asaf Yehudai, Ariel Gera, Liat Ein-Dor
Instruction-following ability is critical for deploying large language models in real-world applications, where downstream components depend on the output satisfying specific constraints. Modern deployments increasingly handle the full task in a single LLM call, with one prompt specifying a layered output whose overall artifact, structural sections, and nested fields must each satisfy concrete constraints.
arXiv:2603. 18558v2 Announce Type: replace-cross Abstract: Long-form video question answering requires reasoning over extended temporal contexts, making frame selection a critical bottleneck for multi-modal large language models (MLLMs) bound by finite context windows.
By Dan Ben-Ami, Gabriele Serussi, Kobi Cohen, Chaim Baskin
arXiv:2608. 05249v1 Announce Type: cross Abstract: Real-world multimodal instructions often bundle multiple requirements with unequal importance, yet most multimodal training data still reduce instruction following to answering one self-contained question.
By Xiaomin He, Dongling Xiao, Jiahao Xie, Ruiqi Lu, Qianle Wang, Zhongbin Guo, Wanxuan Sun
arXiv:2606. 03871v1 Announce Type: cross Abstract: Visual instruction tuning effectively adapts a pre-trained Large Language Model (LLM) to process image information alongside text.
By Luis Palacios, Lorenzo Basile, Diego Doimo, Alberto Cazzaniga
arXiv:2606. 29445v1 Announce Type: cross Abstract: Video understanding is a fundamental capability for multimodal intelligence, and recent Multimodal Large Language Models (MLLMs) have achieved remarkable performance on Video Question Answering (VideoQA) benchmarks.
By Sunqi Fan, Qingle Liu, Runqi Yin, Meng-Hao Guo, Shuojin Yang