arXiv:2608. 09374v1 Announce Type: new Abstract: Electrical circuit analysis requires more than recognizing components in an image.
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:2609.15668v1 Announce Type: cross Abstract: Through pre-training on extensive text and image datasets, current multi-modal large language models (MLLMs) achieve strong performance on general ta...
The paper introduces an enhanced end‑to‑end circuit analysis framework built on Gemini 2.5 Pro, targeting engineering education. It addresses two key failure modes—circuit‑recognition hallucinations and reasoning‑process hallucinations—by adding a YOLO detector for source polarity re‑identification and an ngspice verification loop for iterative refinement. The resulting pipeline achieves 97.59 % accuracy on 83 undergraduate problems, markedly outperforming the baseline Gemini model and demonstrating significant gains across varied diagram styles and textbooks.
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.
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.
The paper introduces VLM-CAD, a workflow that uses Vision Language Models (VLMs) for analog circuit sizing while mitigating spatial blindness and logical hallucinations. It incorporates a neuro‑symbolic parsing module, Image2Net, to convert schematics into topological graphs and JSON, and an Explainable Trust Region Bayesian Optimization method, ExTuRBO, to guide design decisions with sensitivity evidence. Experiments on 12 sizing tasks across six circuits and four technology platforms show a Strict Pass@1 of 23.3% and a Relaxed Pass@1 of 91.7%.
arXiv:2607. 12982v1 Announce Type: new Abstract: Math reasoning has achieved significant progress with the rapid advancement of Multimodal Large Language Models (MLLMs), however analytic geometry remains largely underexplored, primarily due to the scarcity of annotated samples.
ChemVTS-Bench is a domain-authentic benchmark that evaluates Visual‑Textual‑Symbolic reasoning in multimodal large language models for chemistry. It presents diverse chemical problems—organic molecules, inorganic materials, and 3D crystal structures—in three input modes: visual-only, visual‑text hybrid, and SMILES-based symbolic. The benchmark includes an automated agent workflow for inference, answer verification, and failure diagnosis, and shows that visual-only inputs and structural chemistry remain challenging for current models.
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:2609.08254v1 Announce Type: new Abstract: Learning direct current circuit concepts requires learners to connect invisible physical quantities, such as current, voltage, resistance, and power, w...
arXiv:2603. 23867v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have been applied to a wide range of reasoning tasks, yet it remains unclear whether they can reason robustly under distribution shifts.
arXiv:2607. 11338v1 Announce Type: new Abstract: Symbolic expressions can effectively characterize and predict circuit behavior, but deriving them directly from circuit schematics is challenging.