The scarcity of high-quality, logically sound data is a critical bottleneck for advancing the mathematical reasoning of Large Language Models (LLMs). Our work confronts this challenge by turning decades of automated theorem proving research into a scalable data engine. Rather than relying on error-prone LLMs or complex proof-assistant syntax like Lean and Isabelle, our framework leverages E-prover's saturation capabilities on the vast TPTP axiom library to derive a massive, guaranteed-valid corpus of theorems. Our pipeline is principled and simple: saturate axioms, filter for "interesting" theorems, and generate tasks. With no LLMs in the loop, we eliminate factual errors by construction. This purely symbolic data is then transformed into three difficulty-controlled challenges: entailment verification, premise selection, and proof reconstruction. Our zero-shot experiments on frontier models reveal a clear weakness: performance collapses on tasks requiring deep, structural reasoning. Our framework provides both the diagnostic tool to measure this gap and a scalable source of symbolic training data to address it. We make the code and data publicly available. https://github.com/sileod/reasoning_core https://hf.co/datasets/reasoning-core/rc1

Welcome to our weekly FiftyOne tips and tricks blog where we recap interesting questions and answers that have recently popped up on Slack, GitHub, Stack Overflow, and Reddit. FiftyOne is an open source machine learning toolset that enables data science teams to improve the performance of their computer vision models by helping them curate high quality datasets, evaluate models, find mistakes, visualize embeddings, and get to production faster. Ok, let's dive into this week's tips and tricks! "Is there a way to just get bounding boxes around the possibly missing and possibly spurious objects in my dataset?" Here, George is asking about how to isolate potential mistakes in ground truth labels on a dataset.

Metaverse Blockchain company Marvion, a fully owned subsidiary of Bonanza Goldfields Corp., is pleased to share that a memorandum of understanding has been signed with ComicAsia to launch "DRACULA: Rising Sun NFT" collection on Marvion's Metastudio. A total of 200 NFT listings of the collection will be live on Metastudio, allowing fans and collectors to buy and collect these via cryptocurrency and fiat payment methods. Recommended AI: How is Artificial Intelligence (AI) Changing the Future of Architecture? Commenting on the collaboration, Raymond Chua, CEO of Marvion said, "We are very excited to work with ComicAsia as we believe we can help them to tap into a wider fan base in the crypto community. The DRACULA: Rising Sun NFTs will be embedded with on-chain legal documentation to prove its provenance, and they will be compatible with multi-chains and come with royalty functionality. At Marvion, we focus on media and entertainment content, including comics. Even though content properties can be digital in nature today, they exist in the real world as intangible assets, such as intellectual property, licenses and contractual rights, with intrinsic value to be unlocked. We certainly look forward to the official launch of ComicAsia's NFTs on Metastudio."

What model or models can we use in Machine learning for Tissue regeneration. In the movies Elysium and Prometheus they presented a medBay also Disney have the same theory on how it could work, the only question is how we would cause tissue regeneration and how it would be implicated. The Homo sapiens sapiens have a strand of DNA that can rapidly regenerate cells. Gecko's in the animal realm is one particular case of high regeneration without presenting any deformation or Keloids when regenerating. All we need to do is learn how to activate and control this strand onto wounds and disease.

On the one hand, machine learning is a fantastic way to simplify many tedious processes, such as data entry.

This book is a collection of many of the seminal papers from the first decade of research in artificial intelligence in medicine (AIM). The editors state that the need for such a collection became evident when a two-day AIM tutorial was held at Stanford in 1980, following the annual national AIM research workshop. The 19 papers included in the book are each introduced by a short section written by the editors. Typically one page in length, these introductory sections are designed to place the paper into context in the field. In addition, the editors have included introductory and concluding chapters of their own.

Machine Learning has bcrn a constant, theme t,hroughout AI's two decades of existence In this ovcrview t,hc authors analyze various aspects including the major met,hodological approaches advocated in Machine Learning research, Machine learning has always been an integral part of artificial intelligcncc, and it.s This paper is a modified and extended version of the first chapt.er of Machine Learnznq, An Artijicrul Intelligence Approach, with per mission of the publisher: Tioga Press (Palo Alto, Ch) The research described here was sponsored in palt, by the Office of Naval & scar& More recently, new symbolic met,hods and knowledge-intcnsivc techniques have yielded promising results and these in t.urn have led to the current, revival in machine lcwrning research This article examines some basic methodological issues, proposes a classification of machine learning techniques, and provides a historical review of t,he major research directions The Objectives of Machine Learning The field of machine learning can bc organized around three primary research foci: At, present, itisi ructing a cotnJnit,er or a computer-controlled robot, to perform a t,ask requires one t,o define a comple1.e and correct, algoril,hm for that. Prcsrnt-day computer systeitis cannot truly learn to J)erform a La& through exa1nJ)lcs or by analogy Lo a similar, J)rcviously-solved t,ask. Nor can they improve significantly on t,lle basis of)asl, tnistakes, or acquire new abilities l)y observing and itnit,ating exJ)erts Macllinc learning research strives to open IShe possibility of instructing computers in such new ways, and t.liereby promises Lo ease lhe burden of hand-progratnmirlg growing volutttes of increasingly coniplcx informat ioti into lhe computers of t.omorrow. The t,raditiotlal argumenl that an cnginecring approacll need not reflect human or biological J)erformanc:c is not, truly applicable t,o tuachine learning.

This special issue of AI Magazine on dialogue with robots brings together a collection of articles on situated dialogue. The contributing authors have been working in interrelated fields of human-robot interaction, dialogue systems, virtual agents, and other related areas and address core concepts in spoken dialogue with embodied robots or agents. Several of the contributors participated in the AAAI Fall Symposium on Dialog with Robots, held in November 2010, and several articles in this issue are extensions of work presented there. The articles in this collection address diverse aspects of dialogue with robots, but are unified in addressing opportunities with spoken language interaction, physical embodiment, and enriched representations of context. Research on computational models and mechanisms for supporting spoken dialogue dates back to the earliest days of AI research, including Alan Turing's reflection about how machine intelligence could be evaluated.

Collaborative query routing is a new paradigm for web search that treats both established search engines and other publicly available indexes as intelligent peer agents in a search network. The approach makes it transparent for anyone to build his or her own (micro) search engine by integrating established web search services, desktop search, and topical crawling techniques. The challenge in this model is that each of these agents must learn about its environment--the existence, knowledge, diversity, reliability, and trustworthiness of other agents--by analyzing the queries received from and results exchanged with these other agents. We present the 6S peer network, which uses machine-learning techniques to learn about the changing query environment. We show that simple reinforcement learning algorithms are sufficient to detect and exploit semantic locality in the network, resulting in efficient routing and highquality search results.

The majority of work in knowledge representation has dealt with the technicalities of relating predicate calculus to other formalisms and with the details of various schemes for default reasoning. There has almost been an aversion to addressing the problems that arise in actually representing large bodies of knowledge with content. However, deep, important issues must be addressed if we are to ever have a large intelligent knowledge-based program: What ontological categories would make up an adequate set for carving up the universe? What are the important facts and heuristics most humans today know about solid objects? In short, we must bite the bullet.