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A Practitioner’s Guide to Kolmogorov-Arnold Networks

Kolmogorov-Arnold Networks (KANs) have recently emerged as a promising alternative to traditional Multilayer Perceptrons (MLPs), inspired by the Kolmogorov-Arnold representation theorem. Unlike MLPs, which use fixed activation functions on nodes, KANs employ learnable univariate basis functions on edges, offering enhanced expressivity and interpretability. This review provides a systematic and comprehensive overview of the rapidly expanding KAN landscape, moving beyond simple performance comparisons to offer a structured synthesis of theoretical foundations, architectural variants, and practical implementation strategies. By collecting and categorizing a vast array of open-source implementations, we map the vibrant ecosystem supporting KAN development. We begin by bridging the conceptual gap between KANs and MLPs, establishing their formal equivalence and highlighting the superior parameter efficiency of the KAN formulation. A central theme of our review is the critical role of the basis function; we survey a wide array of choices, including B-splines, Chebyshev and Jacobi polynomials, ReLU compositions, Gaussian RBFs, and Fourier series, and analyze their respective trade-offs in terms of smoothness, locality, and computational cost. We then categorize recent advancements into a clear roadmap, covering techniques for improving accuracy, efficiency, and regularization. Key topics include physics-informed loss design, adaptive sampling, domain decomposition, hybrid architectures, and specialized methods for handling discontinuities. Finally, we provide a practical “Choose-Your-KAN” guide to help practitioners select appropriate architectures, and we conclude by identifying current research gaps. The associated GitHub repository https://github.com/AmirNoori68/kan-review complements this paper and serves as a structured reference for ongoing KAN research.

Another protease, pepsin, cuts in the same general region of the antibody molecule as papain but on the carboxy-terminal side of the disulfide bonds (see Fig

3.3). This produces a fragment, the F(ab′)2 fragment, in which the two -binding arms of the antibody molecule remain linked. In this case the remaining part of the is cut into several small fragments. The F(ab′)2 fragment has exactly the same antigen-binding characteristics as the original antibody but is unable to interact with any effector molecule. It is thus of potential value in therapeutic applications of antibodies as well as in research into the functional role of the Fc portion.

Genetic engineering techniques also now permit the construction of many different -related molecules. One important type is a truncated Fab comprising only the of a linked by a stretch of synthetic peptide to a V domain of a . This is called , named from Fragment v ariable. Fv molecules may become valuable therapeutic agents because of their small size, which allows them to penetrate tissues readily. They can be coupled to protein toxins to yield immunotoxins with potential application, for example, in tumor therapy in the case of a Fv specific for a tumor (see Chapter 14).

Experimental proof of long-suspected atomic decay pathway adds new detail to ‘nuclear periodic table’

For the first time, a research team from the University of Cologne has observed the electron capture decay of technetium-98, an isotope of the chemical element technetium (Tc). Electron capture decay is a process in which an atomic nucleus “captures” an electron from its inner shell. The electron merges with a proton in the nucleus to form a neutron, turning the element into a different one. The working group from the Nuclear Chemistry department has thus confirmed a decades-old theoretical assumption.

The findings contribute to a more comprehensive understanding of technetium processes and extend the chart of nuclides—the “nuclear periodic table.” The study was published under the title “Electron-capture decay of 98 Tc” in the journal Physical Review C.

As early as the 1990s, researchers suspected that technetium-98 could also decay by capturing an electron, but no proof could be found, as the isotope only is available in extremely small quantities. For the current study, the Cologne research team used around three grams of technetium-99, which contains tiny traces of the rare isotope technetium-98 (around 0.06 micrograms).

Advanced imaging reveals how electrocatalysts simultaneously generate hydrogen and organic compounds

Hybrid water electrolysers are recent devices, which produce hydrogen or other reduction products at the cathode, while valuable organic oxidation products are formed at the anode. This innovative approach significantly increases the profitability of hydrogen production.

Another advantage is that organic oxidation reactions (OOR) for producing the valuable compounds are quite environmentally friendly compared to the conventional synthesis processes which often require aggressive reagents. However, organic oxidation reactions are very complex, involving multiple catalyst states, , intermediate products, the formation and dissolution of bonds, and varying product selectivity. Research on OOR is still in its infancy.

Calorimetric experiment achieves tightest bound on electron neutrino mass

In a Physical Review Letters study, the HOLMES collaboration has achieved the most stringent upper bound on the effective electron neutrino mass ever obtained using a calorimetric approach, setting a limit of less than 27 eV/c² at 90% credibility.

This result validates a decades-old experimental vision and demonstrates the scalability needed for next-generation neutrino mass experiments.

While oscillation experiments have measured the differences between neutrino mass states, the actual individual mass values—the absolute neutrino mass scale—remain unknown. Pinning down these values would help complete our understanding of the Standard Model of particle physics.

A problem that takes quantum computers an unfathomable amount of time to solve

It’s a well-known fact that quantum calculations are difficult, but one would think that quantum computers would facilitate the process. In most cases, this is true.

Quantum bits, or qubits, use , like superposition and entanglement, to process many possibilities simultaneously. This allows for exponentially faster computing for complex problems. However, Thomas Schuster, of California Institute of Technology, and his research team have given quantum computers a problem that even they can’t solve in a reasonable amount of time—recognizing phases of matter of unknown quantum states.

The team’s research can be found in a paper published on the arXiv preprint server.

Researchers realize a driven-dissipative Ising spin glass using a cavity quantum electrodynamics setup

Spin glasses are physical systems in which the small magnetic moments of particles (i.e., spins) interact with each other in a random way. These random interactions between spins make it impossible for all spins to satisfy their preferred alignments; a condition known as ‘frustration.

Researchers at Stanford University recently realized a new type of spin , namely a driven-dissipative Ising spin glass in a (QED) . Their paper, published in Physical Review Letters, is the result of over a decade of studies focusing on creating spin glasses with cavity QED.

“Spin glasses are a general model for , and specifically for neural networks—spins serve as neurons connected by their mutually frustrating interactions,” Benjamin Lev, senior author of the paper, told Phys.org.

Benchmarking large language models for personalized, biomarker-based health intervention recommendations

The use of large language models (LLMs) in clinical diagnostics and intervention planning is expanding, yet their utility for personalized recommendations for longevity interventions remains opaque. We extended the BioChatter framework to benchmark LLMs’ ability to generate personalized longevity intervention recommendations based on biomarker profiles while adhering to key medical validation requirements. Using 25 individual profiles across three different age groups, we generated 1,000 diverse test cases covering interventions such as caloric restriction, fasting and supplements. Evaluating 56,000 model responses via an LLM-as-a-Judge system with clinician validated ground truths, we found that proprietary models outperformed open-source models especially in comprehensiveness. However, even with Retrieval-Augmented Generation (RAG), all models exhibited limitations in addressing key medical validation requirements, prompt stability, and handling age-related biases. Our findings highlight limited suitability of LLMs for unsupervised longevity intervention recommendations. Our open-source framework offers a foundation for advancing AI benchmarking in various medical contexts.

Silcox, C. et al. The potential for artificial intelligence to transform healthcare: perspectives from international health leaders. NPJ Digit. Med. 7, 88 (2024).

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TSMC Reportedly Constructing Four Plants For 1.4nm Wafers, Mass Production Happening In H2 2028, A Single Facility Can Bring In $16 Billion Revenue

A report claims that TSMC is pouring billions to construct four factories for 1.4nm production, but the payoff is expected to be massive

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