In this study, we utilized the multimodal capabilities of OpenAI GPT-4o to automatically generate jaw cyst findings on dental panoramic radiographs. To improve accuracy, we constructed a Self-correction Loop with Structured Output (SLSO) framework and verified its effectiveness. A 10-step process was implemented for 22 cases of jaw cysts, including image input and analysis, structured data generation, tooth number extraction and consistency checking, iterative regeneration when inconsistencies were detected, and finding generation with subsequent restructuring and consistency verification. A comparative experiment was conducted using the conventional Chain-of-Thought (CoT) method across seven evaluation items: transparency, internal structure, borders, root resorption, tooth movement, relationships with other structures, and tooth number. The results showed that the proposed SLSO framework improved output accuracy for many items, with 66.9%, 33.3%, and 28.6% improvement rates for tooth number, tooth movement, and root resorption, respectively. In the successful cases, a consistently structured output was achieved after up to five regenerations. Although statistical significance was not reached because of the small size of the dataset, the overall SLSO framework enforced negative finding descriptions, suppressed hallucinations, and improved tooth number identification accuracy. However, the accurate identification of extensive lesions spanning multiple teeth is limited. Nevertheless, further refinement is required to enhance overall performance and move toward a practical finding generation system.

Though humans seem to be remarkable learners, arguments in cognitive science and philosophy of mind have long maintained that learning something fundamentally new is impossible. Specifically, Jerry Fodor's arguments for radical concept nativism hold that most, if not all, concepts are innate and that what many call concept learning never actually leads to the acquisition of new concepts. These arguments have deeply affected cognitive science, and many believe that the counterarguments to radical concept nativism have been either unsuccessful or only apply to a narrow class of concepts. This paper first reviews the features and limitations of prior arguments. We then identify three critical points - related to issues of expressive power, conceptual structure, and concept possession - at which the arguments in favor of radical concept nativism diverge from describing actual human cognition. We use ideas from computer science and information theory to formalize the relevant ideas in ways that are arguably more scientifically productive. We conclude that, as a result, there is an important sense in which people do indeed learn new concepts.

Historically, these systems included purely statistical models, but modern LLMs are deep artificial neural network s trained via machine learning . Once trained, an LLM may be implemented for various purposes, such as in chatbot s and personal assistants, or for translation, sentiment analysis and document review. 2 T he indisputably impressive performance of LLMs on a wide variety of task raises pressing questions about their capacities, and the mechanisms underlying those capacities . For instance, authors have grapple d with the questions of whether LLMs understand language (Bender & Koller, 2020; Mitchell & Krakauer, 2022) whether they possess concepts (Butlin, 2023) or to what extent they possess a theory of mind (Kosinski, 2024; Ullman 2023) . This paper focuses on the representational capacities of LLMs . D o LLMs rely on representations? If so, what do those representations represent? Much r esearch in AI -- for instance, studies using p robing classifiers (Belinkov, 2022), and methods for " e diting " models' representations (Hernandez et al., 202 4; Meng et al., 2022) -- assume s that a representational lens is appropriate . But a key question is whether LLMs can represent real world entities, or only "shallow" linguistic contents that don't reach into extra - linguistic reality (Butlin, 2021; Coelho Mollo & Millière, 2023; Yildirim & Paul, 2024) .

Soft Fin-Ray grippers can perform delicate and careful manipulation, which has caused notable attention in different fields. These grippers can handle objects of various forms and sizes safely. The internal structure of the Fin-Ray finger plays a significant role in its adaptability and grasping performance. However, modeling the non-linear grasp force and deformation behaviors for design purposes is challenging. Moreover, when the Fin-Ray finger becomes more rigid and capable of exerting higher forces, it becomes less delicate in handling objects. The contrast between these two objectives gives rise to a multi-objective optimization problem. In this study, we employ finite element method (FEM) to estimate the deflections and contact forces of the Fin-Ray, grasping cylindrical objects. This dataset is then used to construct a multilayer perception (MLP) for prediction of the contact force and the tip displacement. The FEM dataset consists of three input and four target features. The three input features of the MLP and optimization design variables are the thickness of the front and supporting beams, the thickness of the cross beams, and the equal spacing between the cross beams. In addition, the target features are the maximum contact forces and maximum tip displacements in x- and y-directions. The magnitude of maximum contact force and magnitude of maximum tip displacement are the two objectives, showing the trade-off between force and delicate manipulation in soft Fin-Ray grippers. Furthermore, the optimized set of solutions are found using multi-objective optimal techniques. We use non-dominated sorting genetic algorithm (NSGA-II) method for this purpose. Our findings demonstrate that our methodologies can be used to improve the design and gripping performance of soft robotic grippers, helping us to choose a design not only for delicate grasping but also for high-force applications.

We develop a linear response framework for interpretability that treats a neural network as a Bayesian statistical mechanical system. A small perturbation of the data distribution, for example shifting the Pile toward GitHub or legal text, induces a first-order change in the posterior expectation of an observable localized on a chosen component of the network. The resulting susceptibility can be estimated efficiently with local SGLD samples and factorizes into signed, per-token contributions that serve as attribution scores. We combine these susceptibilities into a response matrix whose low-rank structure separates functional modules such as multigram and induction heads in a 3M-parameter transformer.

Scientists have released the first X-ray images of a mysterious, sphere-shaped object recovered in Colombia, which locals claim is of alien origin. The so-called'UFO' was spotted in March over the town of Buga, zig-zagging through the sky in a way that defies the movement of conventional aircraft. The object was recovered shortly after it landed and has since been analyzed by scientists, who discovered it features three layers of metal-like material and 18 microspheres surrounding a central nucleus they are calling'a chip.' Dr Jose Luis Velazquez, a radiologist who examined the sphere, reported finding'no welds or joints,' which would typically indicate human fabrication. He and his team concluded: 'It is of artificial origin, in that it shows no evidence of welding, and its internal structure is composed of high-density elements. More testing is needed to establish its origin.'

We develop a correspondence between the structure of Turing machines and the structure of singularities of real analytic functions, based on connecting the Ehrhard-Regnier derivative from linear logic with the role of geometry in Watanabe's singular learning theory. The correspondence works by embedding ordinary (discrete) Turing machine codes into a family of noisy codes which form a smooth parameter space. On this parameter space we consider a potential function which has Turing machines as critical points. By relating the Taylor series expansion of this potential at such a critical point to combinatorics of error syndromes, we relate the local geometry to internal structure of the Turing machine. The potential in question is the negative log-likelihood for a statistical model, so that the structure of the Turing machine and its associated singularity is further related to Bayesian inference. Two algorithms that produce the same predictive function can nonetheless correspond to singularities with different geometries, which implies that the Bayesian posterior can discriminate between distinct algorithmic implementations, contrary to a purely functional view of inference. In the context of singular learning theory our results point to a more nuanced understanding of Occam's razor and the meaning of simplicity in inductive inference.

Accurately determining the shape and location of internal structures within deformable objects is crucial for medical tasks that require precise targeting, such as robotic biopsies. We introduce LUDO, a method for accurate low-latency understanding of deformable objects. LUDO reconstructs objects in their deformed state, including their internal structures, from a single-view point cloud observation in under 30 ms using occupancy networks. We demonstrate LUDO's abilities for autonomous targeting of internal regions of interest (ROIs) in highly deformable objects. Additionally, LUDO provides uncertainty estimates and explainability for its predictions, both of which are important in safety-critical applications such as surgical interventions. We evaluate LUDO in real-world robotic experiments, achieving a success rate of 98.9% for puncturing various ROIs inside highly deformable objects. LUDO demonstrates the potential to interact with deformable objects without the need for deformable registration methods.

Our main contributions can be summarized as Named entity recognition (NER) is a fundamental follows: problem in information retrieval that aims to identify We present the semi-Markov and CYK-like mentions of entities and their associated types models for non-nested and nested NER, respectively in natural language documents. As such, the problem -- although we do not claim that can be reduced to the identification and classification these approaches for NER are new, our presentation of segments of texts. In particular, we of the CYK-like algorithm differs focus on mentions that have the following properties: from previous work as it is tailored to the NER problem and guarantees uniqueness of 1. continuous, i.e. a mention corresponds to a derivations; contiguous sequence of words; We introduce a novel search space for nested 2. potentially nested, i.e. one mention can be inside NER that has no significant loss in coverage another, but they can never partially overlap.

Learning from nature has been a quest of humanity for millennia. While this has taken the form of humans assessing natural designs such as bones, butterfly wings, or spider webs, we can now achieve generating designs using advanced computational algorithms. In this paper we report novel biologically inspired designs of diatom structures, enabled using transformer neural networks, using natural language models to learn, process and transfer insights across manifestations. We illustrate a series of novel diatom-based designs and also report a manufactured specimen, created using additive manufacturing. The method applied here could be expanded to focus on other biological design cues, implement a systematic optimization to meet certain design targets, and include a hybrid set of material design sets.