The paper introduces a novel framework that converts structural framing plans from PDF drawings into editable finite‑element model drafts without requiring task‑specific detector training. It combines a deterministic geometry extraction stage—estimating scale, recognizing five entity classes, and assembling a drafting grammar—with an agentic vision‑language refinement stage that proposes corrections, performs admission tests, and ensures fail‑closed transactions. Evaluation on a 100‑plan benchmark shows high accuracy, with scale within 0.1% and recall/precision values above 0.86 for all component types.
By Mohammad Talebi-Kalaleh, Qipei Mei
The paper investigates why reasoning‑augmented text‑to‑image models like GoT‑R1 sometimes fail on compositional prompts. By separating the explicit textual plan from the decoder, the authors show that the decoder faithfully executes the plan while the planner often writes incorrect spatial relations, especially for phrasing‑dependent cues. Editing or replacing the plan improves image quality without retraining, demonstrating the viability of modular planner‑decoder architectures.
By Ashritha Gonuguntla
We study what happens when a single general-purpose large language model acts as the sole researcher on a long-horizon neural architecture design problem. The agent receives a scientific question, an initial hypothesis and motivation, a compute budget, and research affordances (source and experiment management, experiment tracking, literature access, and persistent memory), then autonomously proposes, implements, evaluates, and records experiments over an extended period.
arXiv:2605.19410v2 Announce Type: replace
Abstract: Segmentation has become easy when the concept is known, requiring retrieval of a learned visual grounding from text. It remains hard for open ad-ho...
By Zilin Wang, Stella X. Yu
AgenticCADedit introduces a stateful, tool‑mediated approach to multimodal 3D CAD editing, transforming the process from generating a single complete program to executing a sequence of incremental, verifiable actions on a persistent CAD state. By committing each step, inspecting geometry, and selectively reverting faulty operations, the method preserves partial progress and builds upon earlier edits. Experiments across three large language models show substantial gains in validity and acceptance, with the weakest baseline model’s validity rising from 51.0% to 94.8% and a token‑cost reduction of 66.7% compared to neuralCAD‑Edit.
By Saptarshi Neil Sinha, Mika Silvan Goschke, Paul Julius K\"uhn, Arjan Kuijper, Michael Weinmann
arXiv:2607. 25487v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models translate natural-language commands into robot action sequences, but leading systems on the LIBERO-Plus robustness benchmark use three- to seven-billion-parameter backbones whose memory demands can exceed embedded robotic budgets.
By Minhyeok Lee, Chiyoung Kim, Chanhoe Gu, Seongrok Kim, Sanghyuk Roy Choi, Donghwan Hwang, Donghun Ryu, Seokhyun Kim
arXiv:2607. 07984v1 Announce Type: new Abstract: Neural architecture search (NAS) methods have grown increasingly efficient, yet they remain bounded by manually engineered search spaces that require substantial domain expertise and must be rebuilt for every new task.
By Seokhoon Jeong, Mijung Kim, Taehwan Kim
In 2026, the SHROOM-Visions shared task was launched at the UncertaiNLP Workshop co‑located with EMNLP to address hallucinations in large vision‑language models. The task builds on the SHEEP dataset and asks participants to detect and classify fine‑grained hallucination spans in image‑conditioned text generation across four languages (Chinese, English, French, Italian) using a five‑class taxonomy. The competition attracted 27 teams and over 600 system submissions, with top systems achieving character‑level, label‑conditioned, and IoU scores of 0.58, 0.46, and 0.51 respectively, surpassing baselines by 30‑40 points.
By Ra\'ul V\'azquez, Aman Sinha, Chuyuan Li, Claudio Savelli, Eduardo Cal\`o, Emilio Raimond, Stella Frank, Hengyu Luo, Flavio Giobergia, Vincent Segonne, Lorenzo Vaiani, J\"org Tiedemann, Timothee Mickus
BlueprintAgent (BPA) is a multimodal agent that converts scanned reinforced‑concrete building blueprints into simulation‑ready frame models by treating a large language model (MLLM) as the primary reader and decision maker, while OCR and computer vision provide localized evidence. BPA implements engineering constraints as callable validators that trigger targeted MLLM revisits over specific regions, enabling precise beam‑column support, span count, and 3D continuity checks. In evaluations on 300 real scanned sheets from 20 projects, BPA achieved a macro‑averaged Beam F1 of 0.994, vastly outperforming single‑MLLM zero‑shot (0.301) and fixed‑pipeline (0.820) baselines.
By Zhouyuan Xu, Chen Yang, Linhao Wang, Jiansheng Fan, Chen Wang
PlanSightRAG is a visual-first multimodal retrieval‑augmented generation system designed to automate question answering and compliance checking of civil standard plans. It processes plan imagery directly, using a ColNomic‑3B multi‑vector retrieval engine, an agentic Planner‑Retriever‑Auditor‑Synthesizer, and MaxSim heatmaps to provide an evidence trail. The system achieves high recall on a new 4,056‑pair benchmark from five state DOTs, and demonstrates near‑perfect verdict accuracy on synthetic compliance drawings when a rule threshold is supplied, outperforming OCR‑based baselines.
By Nabaraj Subedi, Shuvo Dip Datta, Ahmed Abdelaty, Shivanand Venkanna Sheshappanavar
The paper introduces the Necessary Tool‑Evidence Path (NTEP) annotation scheme and its associated reward mechanism (NTEP‑R) to better supervise vision‑language models that use external tools. By explicitly specifying which evidence is needed and penalizing redundant tool calls, the authors train an 8B‑parameter model that shows improved accuracy and tool‑use efficiency across seven image‑grounded benchmarks. The approach demonstrates that fine‑grained supervision of tool‑evidence paths is essential for robust agentic VLM performance.
By Xingming Long, Yu Liu, Zhiwei Yang, Hanqi Feng, Shaojie Zhang, Barnabas Poczos, Chao Jiang, Zhenbo Luo, Lei Jiang, Pei Fu
arXiv:2602. 03045v2 Announce Type: replace Abstract: Large language models have recently enabled text-to-CAD systems that synthesize parametric CAD programs (e.
By Bo Yuan, Zelin Zhao, Petr Molodyk, Bin Hu, Yongxin Chen