arXiv Machine Learning August 14, 2026 By Zirui Cheng, Xun Xu, Tiankai Chen, Fady Rezk, Bowen Zheng, Xiaodong Shi, Shijie Li, Kangkang Lu, Bharadwaj Veeravalli, Nancy F. Chen

MAG: MAnifold Guided Semi-Supervised Multi-modal In-Context Learning

Read the original on arXiv Machine Learning →

arXiv:2608. 12724v1 Announce Type: new Abstract: Few-shot in-context learning (ICL) with multi-modal large language models (MLLMs) enables task adaptation without parameter updates, but its performance is highly sensitive to the quality and coverage of the selected demonstrations.

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

arXiv Machine Learning

Jun 4

Hyper-ICL: Attention Calibration with Hyperbolic Anchor Distillation for Multimodal In-Context Learning

arXiv:2606. 04434v1 Announce Type: cross Abstract: Multimodal In-Context Learning (ICL) has emerged as a practical inference paradigm for Multimodal Large Language Models, where a small set of interleaved image-text In-Context Demonstrations (ICDs) conditions the model to solve new tasks.

By Niloufar Alipour Talemi, Hossein Kashiani, Fatemeh Afghah

llms efficiency multimodal benchmarks

arXiv AI

Jun 30

LC-ICL: Label-Guided Contrastive In-Context Learning for Robust Information Extraction

arXiv:2606. 29407v1 Announce Type: cross Abstract: There has been increasing interest in exploring the capabilities of advanced large language models (LLMs) in the field of information extraction (IE), specifically focusing on tasks related to named entity recognition (NER) and relation extraction (RE).

By Xiao You, Tianwei Yan, Shan Zhao

llms nlp

arXiv AI

Jun 11

Task-Aware Structured Memory for Dynamic Multi-modal In-Context Learning

arXiv:2606. 11853v1 Announce Type: cross Abstract: Multi-modal large language models (MLLMs) depend on in-context learning (ICL) for rapid task adaptation, but their scalability is severely limited by finite context windows and the growing cost of key-value (KV) caches in long multi-modal sequences.

By Zhirui Chen, Ziwei Chen, Ling Shao

llms efficiency multimodal safety

arXiv Machine Learning

Jul 13

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing

arXiv:2607. 08839v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are typically designed under the assumption that all modalities available during training will also be accessible at inference.

By Dominick Reilly, Qiyu Wu, Hiromi Wakaki, Srijan Das, Yuki Mistufuji

llms multimodal safety

arXiv AI

Jul 7

Context Tuning for In-Context Optimization

arXiv:2507. 04221v3 Announce Type: replace-cross Abstract: We introduce Context Tuning, a simple and effective method to significantly enhance few-shot adaptation of large language models (LLMs) without weight updates.

By Jack Lu, Ryan Teehan, Zhenbang Yang, Mengye Ren

llms benchmarks

arXiv Machine Learning

Jun 15

Context-aware Modality-Topology Co-Alignment for Multimodal Attributed Graphs

arXiv:2606. 14172v1 Announce Type: new Abstract: Multimodal Attributed Graphs (MAGs) model real-world entities by coupling graph topology with heterogeneous attributes such as text and images.

By Sirui Zhang, Xu Wang, Zhengyu Wu, Xunkai Li, Hongchao Qin

multimodal safety

Related stories

Hyper-ICL: Attention Calibration with Hyperbolic Anchor Distillation for Multimodal In-Context Learning

LC-ICL: Label-Guided Contrastive In-Context Learning for Robust Information Extraction

Task-Aware Structured Memory for Dynamic Multi-modal In-Context Learning

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing

Context Tuning for In-Context Optimization

Context-aware Modality-Topology Co-Alignment for Multimodal Attributed Graphs