InSituMeasure is a new benchmark that tests multimodal large language models on situated measurement tasks in industrial scenes. It includes 2,922 real monitoring images of eight types of engineering instruments, with detailed gauge-attribute labels and noise tags for failure diagnosis. The benchmark defines metrics for numerical accuracy, unit consistency, rejection of unanswerable queries, and alignment of model failures with annotated error factors, revealing that even the best models achieve only 25.7% joint value‑unit accuracy and 51.8% confidence‑diagnosis F1.
arXiv:2607. 16243v1 Announce Type: cross Abstract: Multimodal industrial anomaly inspection assistants are a critical component of next-generation smart factories, enabling interactive vision-language-based querying.
The paper introduces VertiCue-Bench, a diagnostic benchmark designed to test whether multimodal large language models (MLLMs) can perceive, ground, and utilize vertical structure information in remote-sensing natural scenes. It presents a three-stage framework—Perception, Grounding, Utilization—and a Representation Intervention Spectrum across various modalities to evaluate ten state-of-the-art models. The study finds a significant Vertical Structure Utilization Gap: while models show some geometric perception, they struggle to accurately link vertical evidence to spatial entities and incorporate it into semantic decisions.
arXiv:2606. 17904v1 Announce Type: new Abstract: Language models increasingly serve as advisory systems in maintenance operations.
arXiv:2607. 24539v1 Announce Type: new Abstract: Multimodal large language models (LLMs) can combine topology, measurements, and incident text for grid diagnosis, yet answer accuracy does not establish that task-appropriate evidence was used.
arXiv:2607. 09544v1 Announce Type: cross Abstract: Despite strong performance on many multimodal tasks, vision-language models (VLMs) still struggle with basic object counting.
Existing video benchmarks evaluate action recognition on consumer videos, egocentric recordings, or simulated industrial environments. They do not test vision-language models under the visual and procedural conditions of real industrial CCTV, where workers appear as distant figures amid dust, steam, low light, glare, occlusion, and overlapping activities.
arXiv:2601.18493v2 Announce Type: replace Abstract: Vision--language models (VLMs) show promise for disaster-response remote sensing, but existing benchmarks mainly emphasize scene-level or damage-ce...
arXiv:2607. 06420v1 Announce Type: cross Abstract: Visual counting is a fundamental pillar of multimodal intelligence, requiring a seamless integration of fine-grained grounding and spatial reasoning.
DisasterInsight is a building‑centric benchmark designed to evaluate vision‑language models (VLMs) for disaster response. Built on the xBD satellite dataset, it adds OpenStreetMap‑derived functional labels to 134,108 building instances and offers 15 task types, including instance assessment, scene counting, multi‑instance reasoning, and structured report generation. Experiments show that VLMs excel at visible damage detection but struggle with building function, multi‑instance reasoning, counting, and grounded reporting, and instruction tuning only partially mitigates these gaps.
arXiv:2608.29193v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) can assign similar confidence to answers that fail for different reasons. We propose HalluPrism, a behavioral...
arXiv:2608.29286v1 Announce Type: new Abstract: As Multimodal Large Language Models (MLLMs) evolve into sophisticated interactive assistants, their reliability depends not only on following instructi...
arXiv:2608.22232v1 Announce Type: new Abstract: Real-world situation appearances can deviate from their underlying physical states, challenging the reliability of multimodal large language models (ML...