arXiv:2607. 15418v1 Announce Type: new Abstract: We introduce DrawingVQA, the first benchmark designed to evaluate multimodal large language models (MLLMs) on real-world construction drawings -- a core media in architecture, civil, and many other engineering practices.
arXiv:2605. 30794v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated significant achievements in general visual question answering (VQA) tasks.
MechReason is a new benchmark for multi-image, multi-hop reasoning in mechanical engineering, featuring 12,000 question-answer pairs with explicit reasoning chains and 21,000 visual items across nine evidence types. It covers eight task types—explanation, prediction, design, and diagnosis—within four reasoning dimensions, and is constructed through a four-stage pipeline that extracts engineering claims, generates verification-masked questions, and validates multimodal quality. Even state‑of‑the‑art models score only 62.89% on this dataset, highlighting its difficulty.
arXiv:2610.10066v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in both visual understanding and code generation. However, existing...
arXiv:2608. 15006v1 Announce Type: cross Abstract: Although visual reasoning is crucial for solving complex geometry tasks, existing vision-language models rely heavily on text-only reasoning.
arXiv:2511. 17731v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting has proven remarkably effective for eliciting complex reasoning in large language models (LLMs).
arXiv:2606. 10833v1 Announce Type: new Abstract: Vision-Language Models (VLMs) demonstrate strong performance on general multimodal reasoning benchmarks, yet their ability to perform engineering reasoning remains largely unexplored.
arXiv:2608. 09281v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) perform strongly on engineering imagery, yet existing benchmarks mostly test drawing recognition, information extraction, or compliance checking, leaving open whether models can combine distributed visual evidence with engineering principles to reach a conclusion.
arXiv:2602. 02465v2 Announce Type: replace Abstract: Frontier models are transitioning from multimodal large language models (MLLMs) that merely ingest visual information to unified multimodal models (UMMs) capable of native interleaved generation.
CoVA‑SFT is a new large‑scale dataset comprising 51.9K samples and over 222K multimodal reasoning steps that teach models to interleave text and visual abstractions across five layout families and 17 complex tasks. It includes explicit rationale formulations, agentic renderings, and verification loops to help models build and maintain internal visual workspaces for purely textual reasoning problems. A companion benchmark, CoVA‑Bench, contains 1,700 held‑out test samples for reproducible evaluation, and models fine‑tuned on CoVA‑SFT outperform all interleaved CoT baselines by more than 2× on average, though they still lag behind strong text‑only CoT baselines.
SenseNova-U1.5 is an 8B‑MoT native unified multimodal model that can understand, reason about, and generate visual content without using an encoder or VAE. It improves visual fidelity and text rendering through spatially coherent patch reconstruction, large‑scale training on curated generation and editing data, and native resolutions up to 4K. Post‑training, specialized experts for visual aesthetics, bilingual text rendering, infographic generation, and image editing are optimized and distilled into a multi‑expert framework, yielding advances in image fidelity, complex composition, multi‑reference editing, and instruction following.
GeoReform is a reflective formalization evolution framework designed to improve multimodal geometry problem solving by treating formalization as an optimizable policy rather than a fixed parser. It iteratively runs the reasoning pipeline, collects failures, diagnoses representation defects, and mutates the policy to better select, ground, group, and present geometric entities, relations, constraints, and targets. On the Geometry3K benchmark, GeoReform raises Qwen3VL-2B accuracy from 42.0% to 56.0%, demonstrating that effective formalization is crucial for enhancing multimodal geometry reasoning.