arXiv Machine Learning

FLOWREADER: Min-Cost Flow Optimization for Multi-Modal Long Document Q&A

arXiv:2606. 07235v1 Announce Type: cross Abstract: Long, multimodal documents force retrieval-augmented systems to assemble answers from evidence fragmented across text, tables, and slides broken across cells in a long table, spread over multiple slides, or split between a figure and its discussion.

arXiv AI

Jul 29

Salient Knowledge Pathways: Sparse Cross-Modal Routing for Efficient Knowledge-Intensive Multimodal Question Answering

arXiv:2607. 25422v1 Announce Type: new Abstract: Knowledge-intensive multimodal question answering (KI-MMQA) sits at the intersection of three expensive primitives: long visual token sequences, dense retrieval over large external corpora, and full cross-modal fusion.

By Noor Islam S. Mohammad, Ulu\u{g} Bayaz{\i}t

nlp efficiency multimodal benchmarks

arXiv AI

Jul 8

Modality Relevance is not Modality Utility: Post-hoc Selective Modality Escalation for Cost-Aware Multimodal RAG

arXiv:2607. 05438v1 Announce Type: cross Abstract: Multimodal retrieval-augmented generation (RAG) grounds a generator in evidence drawn from heterogeneous modalities -- text, tables, and images.

By Xue Li, Yiming Gai

llms rag multimodal

arXiv AI

Jul 21

Data-Efficient Curation for Multimodal Reasoning under Fixed Training Protocols

arXiv:2601. 10922v2 Announce Type: replace Abstract: We study data curation for multimodal reasoning in a fixed-protocol fine-tuning regime, where the base model, optimizer, training schedule, and evaluation pipeline are held constant and the main degree of freedom is the training data.

By Yosub Shin, Michael Buriek, Boris Sobolev, Pavel Bushuyeu, Vikas Kumar, Haoyang Xu, Samuel Watson, Igor Molybog

fine-tuning multimodal benchmarks safety

arXiv AI

Jun 30

LLM-Guided Planning for Multi-hop Reasoning over Multimodal Nuclear Regulatory Documents

arXiv:2606. 29399v1 Announce Type: new Abstract: Reviewing nuclear regulatory documents requires multi-hop reasoning across tens of thousands of pages, where judgments depend on evidence assembled across multiple chapters.

By Mingyu Jeon, Bokyeong Kim, Suwan Cho, Jae Young Suh, Yonggyun Yu

llms agents multimodal benchmarks safety

arXiv AI

Aug 11

DocAtlas: Long-Document Understanding as Mutable-State Interaction

arXiv:2608. 07527v1 Announce Type: cross Abstract: Long-document understanding requires models to find and combine evidence across many pages, layouts, tables, figures, and charts.

By Hongchen Wei, Yuanzhe Wang, Bei Liu, Yifan Yang, Qi Dai, Kai Qiu, Yunsheng Li, Dongdong Chen, Chong Luo, Zhenzhong Chen, Baining Guo

llms rag agents reinforcement-learning multimodal

arXiv AI

Jun 4

Overview of the EReL@MIR 2025 Multimodal Document Retrieval Challenge (Track 1)

arXiv:2606. 04240v1 Announce Type: cross Abstract: Retrieval over visually-rich documents, pages that interleave text with figures, tables, and charts, is essential for multimodal retrieval-augmented generation, yet most retrievers still discard the visual channel.

By Jingbiao Mei

llms rag fine-tuning multimodal

arXiv AI

Jun 2

RASER: Recoverability-Aware Selective Escalation Router for Multi-Hop Question Answering

arXiv:2606. 02488v1 Announce Type: new Abstract: Multi-hop question-answering systems often use expensive retrieval on every question.

By Yuyang Li, Zihe Yan, Tobias K\"afer

llms rag nlp benchmarks

arXiv Machine Learning

Jul 16

Concurrent Image Understanding and Generation: Self-Correcting Coupled Markov Jump Processes

arXiv:2607. 13188v1 Announce Type: new Abstract: Human cognition does not separate understanding and generation.

By Minh-Quan Le, Armand Comas, Alexandros Lattas, Stylianos Moschoglou, Pedro V\'elez, Amit Raj, Aaron Germuth, Thabo Beeler, Dimitris Samaras, Di Qiu

diffusion multimodal benchmarks

arXiv AI

Jun 12

MiniMax Sparse Attention

arXiv:2606. 13392v1 Announce Type: new Abstract: Ultra-long-context capability is becoming indispensable for frontier LLMs: agentic workflows, repository-scale code reasoning, and persistent memory all require the model to jointly attend over hundreds of thousands to millions of tokens, yet the quadratic cost of softmax attention makes this untenable at deployment scale.

By Xunhao Lai, Weiqi Xu, Yufeng Yang, Qiaorui Chen, Yang Xu, Lunbin Zeng, Xiaolong Li, Haohai Sun, Haichao Zhu, Vito Zhang, Pengyu Zhao

llms agents efficiency multimodal

arXiv AI

Jun 9

Decision-Aware Memory Cards: Counterfactual-Inspired Context Selection and Compression for Tool-Using LLM Agents

arXiv:2606. 08151v1 Announce Type: new Abstract: Tool-using LLM agents often fail not because relevant text is absent, but because decisive evidence is not selected, compressed, or surfaced at action time.

By Xinyu Guan, Qianyang Zhao, Yuming Deng

llms rag agents fine-tuning benchmarks

arXiv AI

Jun 16

Lost at the End: Primacy Bias in Multimodal Retrieval-Augmented Question Answering

arXiv:2606. 16494v1 Announce Type: cross Abstract: Knowledge-based visual question answering (KB-VQA) lets vision-language systems answer questions that exceed their parametric knowledge by conditioning a reader on passages retrieved from a Wikipedia-scale knowledge base.

By Jieyuan Liu, Jianyang Gu, Shijie Chen, Jefferson Chen, Zhen Wang

llms rag nlp multimodal benchmarks safety

arXiv AI

Jun 12

Rethinking RAG in Long Videos: What to Retrieve and How to Use It?

arXiv:2606. 13141v1 Announce Type: new Abstract: Retrieval-augmented generation is moving beyond text into long, egocentric video, where systems must select query-relevant chunks across multiple modalities and temporal granularities.

By Yuho Lee, Jisu Shin, Nicole Hee-Yeon Kim, Jihwan Bang, Juntae Lee, Kyuwoong Hwang, Fatih Porikli, Hwanjun Song

rag benchmarks

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