When a graduate found a baby Florida scrub millipede, she put it in a kiddie pool. Then it got busy reproducing. Breakthroughs, discoveries, and DIY tips sent six days a week. While Florida is perhaps best known for its beaches and wetlands, its landscape hosts other notable features: ridges . Millions of years ago, sea levels were higher than they are today, and these elevated areas of land became like islands.

Large language models (LLMs) have shown strong performance in data-rich domains such as programming, but their reliability in engineering tasks remains limited. Circuit analysis -- requiring multimodal understanding and precise mathematical reasoning -- highlights these challenges. Although Gemini 2.5 Pro improves diagram interpretation and analog-circuit reasoning, it still struggles to consistently produce correct solutions when given both text and circuit diagrams. At the same time, engineering education needs scalable AI tools capable of generating accurate solutions for tasks such as automated homework feedback and question-answering. This paper presents an enhanced, end-to-end circuit problem solver built on Gemini 2.5 Pro. We first benchmark Gemini on a representative set of undergraduate circuit problems and identify two major failure modes: 1) circuit-recognition hallucinations, particularly incorrect source polarity detection, and 2) reasoning-process hallucinations, such as incorrect current directions. To address recognition errors, we integrate a fine-tuned YOLO detector and OpenCV processing to isolate voltage and current sources, enabling Gemini to re-identify source polarities from cropped images with near-perfect accuracy. To reduce reasoning errors, we introduce an ngspice-based verification loop in which Gemini generates a .cir file, ngspice simulates the circuit, and discrepancies trigger iterative regeneration with optional human-in-the-loop review. Across 83 problems, the proposed pipeline achieves a 97.59% success rate (81 correct solutions), substantially outperforming Gemini 2.5 Pro's original 79.52% accuracy. This system extends LLM capabilities for multimodal engineering problem-solving and supports the creation of high-quality educational datasets and AI-powered instructional tools.

Explainability of decision-making AI systems (XAI), and specifically neural networks (NNs), is a key requirement for deploying AI in sensitive areas [18]. A recent trend in explaining NNs is based on formal methods and logic, providing explanations for the decisions of machine learning systems [24, 31, 32, 41, 42, 44] accompanied by provable guarantees regarding their correctness. Yet, rigorous exploration of the continuous feature space requires to estimate decision boundaries with complex shapes. This, however, remains a challenge because existing explanations [24, 31, 32, 41, 42, 44] constrain only individual features and hence fail capturing relationships among the features that are essential to understand the reasons behind the multi-parametrized classification process. We address the need to provide interpretations of NN systems that are as meaningful as possible using a novel concept of Space Explanations, delivered by a flexible symbolic reasoning framework where Craig interpolation [12] is at the heart of the machinery.

Dataset Documentation: We have documented our dataset for intended researchers as required. The link to download the models after fine-tuning is https://mega.nz/file/M9FEWCjD# To fill the lack of benchmarks for general loop invariant generation, we propose LIG-MM, a loop invariant generation benchmark of memory manipulation programs. Table 1 below shows the basics of the code in LIG-MM. Multiple examples are shown in Sec. 3, and the Table 1: Statistics of our proposed LIG-MM benchmark.

Constructing abstract interpreters that provide global soundness guarantees remains a major obstacle in abstract interpretation. We investigate whether modern LLMs can reduce this burden by leveraging them to synthesize sound, non-trivial abstract interpreters across multiple abstract domains in the setting of neural network verification. We formulate synthesis as a constrained optimization problem and introduce a novel mathematically grounded cost function for measuring unsoundness under strict syntactic and semantic constraints. Based on this formulation, we develop a unified framework that unifies LLM-based generation with syntactic and semantic validation and a quantitative cost-guided feedback mechanism. Empirical results demonstrate that our framework not only matches the quality of handcrafted transformers, but more importantly, discovers sound, high-precision transformers for complex nonlinear operators that are absent from existing literature.

Neural Information Processing Systems http://nips.cc/

Recently, various strategies for distributed training of large language models (LLMs) have been proposed.

'People thought I was a communist doing this as a non-profit': is Wikipedia's Jimmy Wales the last decent tech baron? In an online landscape characterised by doom and division, the people's encyclopedia stands out - a huge collective endeavour giving everyone free access to the sum of human knowledge. But with Elon Musk branding it'Wokipedia' and AI looming large, can it survive? W ikipedia will be 25 years old in January. Jimmy Wales's daughter will be 25 and three weeks. It's not a coincidence: on Boxing Day 2000 Wales's then wife, Christine, gave birth to a baby girl, but it quickly became clear that something wasn't right. She had breathed in contaminated amniotic fluid, resulting in a life-threatening condition called meconium aspiration syndrome. An experimental treatment was available at the hospital near where they lived in San Diego. Did they want to try it?

People Who Say They're Experiencing AI Psychosis Beg the FTC for Help The Federal Trade Commission received 200 complaints mentioning ChatGPT between November 2022 and August 2025. Several attributed delusions, paranoia, and spiritual crises to the chatbot. On March 13, a woman from Salt Lake City, Utah called the Federal Trade Commission to file a complaint against OpenAI's ChatGPT. She claimed to be acting "on behalf of her son, who was experiencing a delusional breakdown." "The consumer's son has been interacting with an AI chatbot called ChatGPT, which is advising him not to take his prescribed medication and telling him that his parents are dangerous," reads the FTC's summary of the call.