Thinking with Anchors: Grounded and Efficient Document Reasoning
- URL: http://arxiv.org/abs/2608.04424v1
- Date: Wed, 05 Aug 2026 04:09:05 GMT
- Title: Thinking with Anchors: Grounded and Efficient Document Reasoning
- Abstract summary: We present ADOPD 2026, a reasoning-oriented extension of ADOPD.<n>ADOPD 2026 turns page decomposition into spatially grounded document understanding.<n>By connecting page decomposition to verifiable visual-anchor reasoning, ADOPD 2026 provides a task framework.
- Score: 87.07530736788898
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Existing document understanding benchmarks have largely focused on locating page elements, yet real-world document intelligence requires models to reason jointly about region semantics, spatial relations, and visual structure. We present ADOPD 2026, a reasoning-oriented extension of ADOPD that turns page decomposition into spatially grounded document understanding. ADOPD 2026 enriches page anchors inherited from ADOPD 2024 dataset with human-cleaned captions, semantic tags, and generated chain-of-thought (CoT) traces grounded to document regions. Instead of treating boxes, masks, and tags as independent supervision signals, we cast text blocks, visual entities, semantic labels, bounding boxes, and polygon masks as a shared vocabulary of visual anchors. This representation supports three connected capabilities. First, region-level semantic tagging asks models to identify document element types from both page context and local appearance, revealing long-tail semantic failures that standard layout benchmarks often hide. Second, unified vision-language grounding generates text regions and visual entities together with coordinates or polygonal outlines, transforming detection and segmentation outputs into structured anchors that can be reused by downstream reasoning systems. Third, current state-of-the-art models still struggle with dense counting tasks evaluated on DocCount, a benchmark derived from ADOPD 2026, highlighting the need for the Thinking-with-Anchors pipeline in document semantic understanding. By connecting page decomposition to verifiable visual-anchor reasoning, ADOPD 2026 provides a task framework that moves document understanding beyond localization toward anchor-grounded document intelligence.
Related papers
- ConceptFormer: Learning Adaptive Latent Concepts for Query-Document Alignment in Visual Document Retrieval [62.80735412748694]
ConceptFormer is a latent concept representation learning framework for visual document retrieval.<n>It query-relevant evidence as continuous, query-conditioned latent concepts that explicitly bridge localized visual evidence and semantic relevance.<n>It achieves 16.7% and 22.1% relative improvements in average NDCG@10 over the strongest visual retrieval baseline and the strongest OCR-based text retrieval baseline, respectively.
arXiv Detail & Related papers (2026-08-16T12:07:12Z) - MPDocBench-Parse: Benchmarking Practical Multi-page Document Parsing [74.84107522458798]
MPDocBench-Parse is a benchmark for multi-page document parsing in real-world applications.<n>It contains 433 manually annotated documents with 3,246 pages, covering 15 document types in English and Chinese, with diverse layout styles.
arXiv Detail & Related papers (2026-05-21T07:36:41Z) - Chain of Evidence: Pixel-Level Visual Attribution for Iterative Retrieval-Augmented Generation [19.889854990300595]
Iterative Retrieval-Augmented Generation (iRAG) has emerged as a powerful paradigm for answering complex multi-hop questions.<n>We present textbfChain of Evidence (CoE), a retriever-agnostic visual attribution framework.
arXiv Detail & Related papers (2026-05-02T06:40:42Z) - ReAlign: Optimizing the Visual Document Retriever with Reasoning-Guided Fine-Grained Alignment [28.897559367200376]
Visual document retrieval aims to retrieve a set of document pages relevant to a query from visually rich collections.<n>Existing methods often employ Vision-Language Models (VLMs) to encode queries and visual pages into a shared embedding space.<n>We propose Reasoning-Guided Alignment (ReAlign) to provide fine-grained visual document descriptions as supervision signals for training.
arXiv Detail & Related papers (2026-04-08T14:47:27Z) - Multimodal OCR: Parse Anything from Documents [72.69545534962234]
dots.mocr treats visual elements such as charts, diagrams, tables, and icons as first-class parsing targets.<n>It reconstructs both text and graphics as structured outputs, enabling more faithful document reconstruction.<n>It supports end-to-end training over heterogeneous document elements.
arXiv Detail & Related papers (2026-03-13T14:42:21Z) - MoDora: Tree-Based Semi-Structured Document Analysis System [62.01015188258797]
Semi-structured documents integrate diverse interleaved data elements arranged in various and often irregular layouts.<n>MoDora is an LLM-powered system for semi-structured document analysis.<n> Experiments show MoDora outperforms baselines by 5.97%-61.07% in accuracy.
arXiv Detail & Related papers (2026-02-26T14:48:49Z) - Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding [42.506971197471195]
We introduce two fine-grained structured datasets: DocMark-Pile, comprising approximately 3.8M pretraining data pairs for document parsing, and DocMark-Instruct, featuring 624k fine-tuning data annotations for grounded instruction following.<n>Our proposed model significantly outperforms existing state-of-theart MLLMs across a range of visual document understanding benchmarks.
arXiv Detail & Related papers (2025-05-08T17:37:36Z) - Hypergraph based Understanding for Document Semantic Entity Recognition [65.84258776834524]
We build a novel hypergraph attention document semantic entity recognition framework, HGA, which uses hypergraph attention to focus on entity boundaries and entity categories at the same time.
Our results on FUNSD, CORD, XFUNDIE show that our method can effectively improve the performance of semantic entity recognition tasks.
arXiv Detail & Related papers (2024-07-09T14:35:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.