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