CVAug 5

Thinking with Anchors: Grounded and Efficient Document Reasoning

arXiv:2608.0442412.3
Predicted impact top 21% in CV · last 90 daysOriginality Synthesis-oriented
AI Analysis

For researchers in document understanding, this benchmark provides a new evaluation framework to test reasoning beyond simple localization, but it is an incremental extension of existing datasets.

The paper introduces ADOPD 2026, a document understanding benchmark that extends ADOPD with region-level semantic tagging, unified vision-language grounding, and dense counting tasks, aiming to move beyond localization to grounded reasoning. It reports that current state-of-the-art models still struggle with dense counting tasks on the derived DocCount benchmark.

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.

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