DocXEval is the first comprehensive framework that adapts existing explainable AI methods and evaluation metrics to multimodal document understanding, covering text, layout, and image modalities. The authors systematically evaluate 13 XAI methods across 3 tasks, 8 datasets, 4 models, and 17 metrics, finding that Occlusion consistently performs best in faithfulness and interpretability. The study also provides meta‑evaluation insights, diagnostic analyses of modality contributions, spatial attribution patterns, and potential dataset biases, offering a benchmark and practical guide for future research.
arXiv:2508. 04427v2 Announce Type: replace-cross Abstract: Multimodal learning has witnessed remarkable advancements in recent years, particularly with the integration of attention-based models, leading to significant performance gains across a variety of tasks.
The ICDAR2026 Competition on Multimodal Reasoning over Documents in Multiple Domains introduced a new Visual Question Answering benchmark that tests reasoning over documents from eight distinct domains such as business reports, scientific papers, and engineering drawings. Twenty valid submissions from eight teams were evaluated, featuring approaches ranging from zero‑shot vision‑language models to multi‑agent ensembles and fine‑tuned multimodal systems. Results indicate that the most effective systems employ structured evidence extraction, retrieval, verification, and orchestration across multiple components rather than single‑pass prompting.
arXiv:2608. 11907v2 Announce Type: replace-cross Abstract: As Large Vision-Language Models increasingly aim to integrate visual generation and understanding within a single parameter space, evaluating such structural unification in a cohesive manner remains a critical challenge.
arXiv:2606. 16092v1 Announce Type: cross Abstract: Real-world documents combine text with tables, charts, photographs, and diagrams arranged in diverse layouts, yet existing research on multimodal large language models (MLLMs) for document QA predominantly produces text-only responses, underutilizing these visual elements.
arXiv:2607. 07179v1 Announce Type: cross Abstract: Document Visual Question Answering (DocVQA) presents a complex multimodal challenge, requiring models to exploit visual, textual, and layout information from documents.
arXiv:2608. 11907v1 Announce Type: cross Abstract: As Large Vision-Language Models increasingly aim to integrate visual generation and understanding within a single parameter space, evaluating such structural unification in a cohesive manner remains a critical challenge.
Retrieval-Augmented Generation (RAG) streamlines long-document understanding by leveraging retrieval mechanisms to restrict input images to a highly curated subset. However, existing multimodal RAG pipelines primarily face two critical challenges: first, standard semantic similarity retrievers frequently fetch topically overlapping yet answer-void distractor pages that mislead downstream generation; second, rigid single-pass pipelines heavily depend on initial retrieval success, where any omission of core evidence inevitably causes cascading errors.
The paper introduces TIC‑Bench, a new benchmark for evaluating multimodal large language models on deeply interleaved text‑image contexts. It covers logical, temporal, and spatial association tasks, totaling 2,280 questions across eight specific types. The authors benchmarked ten state‑of‑the‑art MLLMs, finding a significant performance gap versus human experts and highlighting persistent challenges in integrating evidence across interleaved visual and textual inputs.
arXiv:2609.37225v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have shown strong potential for universal multimodal representation learning. However, existing methods eith...
arXiv:2605. 29861v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have advanced autonomous agents from deep search, which retrieves concise factual answers, to deep research, which synthesizes scattered evidence into long-form reports.
arXiv:2607. 04625v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) streamlines long-document understanding by leveraging retrieval mechanisms to restrict input images to a highly curated subset.
The paper introduces Lens, a training‑free framework that aligns multimodal representations with the semantic perspective required by downstream tasks. Lens uses a task‑specific readout phrase to anchor the perspective and then aggregates token states after the full input, ensuring the extracted representation reflects task‑conditioned evidence integration rather than generic salient content. The method achieves a Precision@1 of 63.9 across 36 MMEB datasets, outperforming the nearest training‑free baseline by 10.2 points.