mlsanityData to 7 Oct 2026

Handoff-H1: An Orchestrated Vision-Agent System for Material Quantity Takeoff from Construction Blueprints

arXiv 2608.15032 ↗First posted 15 Aug 2026cs.CL cs.AI cs.CVcited by 0

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Abstract (arXiv)

Converting a set of architectural blueprints into a complete material quantity takeoff requires visual perception across drawing sheets, dimensional and multi-hop reasoning, and grounding in construction conventions that the drawings never state. We present Handoff-H1, a takeoff system built from three layers: purpose-built computer-vision models that extract primitives; tool-using agents equipped with image operations and in-house visual-task tools, including CV-model-backed counting, detection and plan decomposition; and a persistent, hierarchically structured project foundation, grounded in a curated construction knowledge base. We evaluate on the Construction Blueprint Takeoff Benchmark: 10 real residential blueprint sets paired with consensus-validated expert takeoffs - 2,009 verified line items, restricted for scoring to the 1,348 primary-tier materials that drive an estimate - scored per trade by an LLM judge on material coverage and quantity Precision@25% ([email protected]) and combined into a weighted composite. Under identical scoring from the raw PDF, seven frontier and open-weight models span composites of 35-61, and independent professional estimators - scored against the same reconciled gold standard - post 77.6% (65.5% coverage, 87.9% [email protected]). Handoff-H1, working end-to-end from the raw PDF, reaches 81.6% (86.1% coverage, 78.8% [email protected]): roughly 20 points above the strongest frontier agent, and above the independent estimators by pairing near-human quantity precision with coverage they do not reach. The evaluation harness is public for the open harbor framework; the blueprint sets and ground truth are available upon request for research use.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 25 references that are in the map. First posted on arXiv 15 Aug 2026.

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16 of 25 references.

References · 16

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  4. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  5. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrievalcited by 519
  6. 2023NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmarkcited by 339
  7. 2024RAFT: Adapting Language Model to Domain Specific RAGcited by 258
  8. 2019CubiCasa5K: A Dataset and an Improved Multi-Task Model for Floorplan Image Analysiscited by 49
  9. 2025Retrieval-Augmented Generation with Hierarchical Knowledgecited by 46
  10. 2021FloorPlanCAD: A Large-Scale CAD Drawing Dataset for Panoptic Symbol Spottingcited by 28
  11. 2020Symbol Spotting on Digital Architectural Floor Plans Using a Deep Learning-based Frameworkcited by 17
  12. 2025CEQuest: Benchmarking Large Language Models for Construction Estimationcited by 6
4 more
  1. 2025Do Vision-Language Models Measure Up? Benchmarking Visual Measurement Reading with MeasureBenchcited by 6
  2. 2025Blueprint-Bench: Comparing spatial intelligence of LLMs, agents and image modelscited by 5
  3. 2019Automation of Construction Quantity Take-Off: Using Building Information Modeling (BIM)cited by 1
  4. 2026Multimodal Language Models Cannot Spot Spatial Inconsistenciescited by 1

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  2. 2026BlueprintAgent: Constraint-Triggered Targeted Revisits for Simulation-Ready Generation from Scanned Structural Blueprints
  3. 2026DrawingVQA: A Real-World Benchmark for Multi-Depth Visual-Textual Reasoning on Construction Drawings
  4. 2025Sketch2BIM: A Multi-Agent Human-AI Collaborative Pipeline to Convert Hand-Drawn Floor Plans to 3D BIM
  5. 2026PlanCraft: Sketch, Refine, and Furnish for Architect-Inspired Progressive 3D Residential Scene Generation
  6. 2026Brick-Composer: Using MLLMs for Assembly with Diverse Bricks
  7. 2026VLM-based automatic multi-granularity graph representation of building layouts for design informatics
  8. 2025BIMgent: Towards Autonomous Building Modeling via Computer-use Agents