IBM Research - Thomas J. Watson Research Center

Advanced CMOS technology nodes face limitations in linearity due to their reduced breakdown voltages, whereas III–V semiconductors offer superior device performance but have limited BEOL and integration capabilities, making complex circuit design challenging. The precise and efficient integration of III–V MMICs with CMOS ICs enables leveraging the most suitable device for each function, thereby achieving enhanced dynamic range. However, this approach demands precise integration of the ICs with carefully controlled interconnects. This article presents the demonstration of a heterogeneously integrated GaN LPTV switched-transmission-line circulator combined with a CMOS SOI clock generation IC, harnessing the high integration density of CMOS alongside the superior switching characteristics of GaN. Measurement results demonstrate a dc transmission loss of 2.0dB and port-to-port isolation >17dB across a 3-dB bandwidth from dc to 3.35GHz. The circulator achieved IP1dB of 12.1dBm and IIP3 of 25.6dBm, attributed to voltage-boosted swings and precise, robust phase alignment from the CMOS SOI IC. Furthermore, the circulator and clock chip operate using only one single-phase external clock at 2.6GHz, simplifying system-level integration significantly compared to other solutions that require complex multiphase clock sources.

CURE2026 workshop provides a venue for research on how interactive and intelligent interfaces can be leveraged to ensure effective uncertainty communication. With the increased capabilities of black-box AI models, particularly large language models (LLMs), there is a growing urgency to understand exactly what these models can and cannot do. Effective uncertainty communication can help users calibrate their trust and reliance on the AI system. However, effectively conveying uncertainty, be it through conversation or by visualizing their confidence levels, remains a major challenge, as users often get confused or misinterpret uncertainty information due to poorly designed interfaces. CURE2026 workshop brings together researchers from HCI, AI, design, and psychology to investigate how we can design, develop, and evaluate intelligent and interactive interfaces for uncertainty communication. The workshop includes a mini-conference section aimed at inspiring discussion and investigating the role of intelligent and interactive interfaces in uncertainty communication. The workshop also features keynotes by Professors Jessica Hullman and Katrien Verbert. Finally, a part of the workshop is dedicated to an interactive activity to discuss and develop guidelines for communicating uncertainty across different domains and tasks.

We establish that a broad class of effective learning rules-those that improve a scalar performance measure over a given time window-can be expressed as natural gradient descent with respect to an appropriately defined metric. Specifically, parameter updates in this class can always be written as the product of a symmetric positive-definite matrix and the negative gradient of a loss function encoding the task. Given the high level of generality, our findings formally support the idea that the gradient is a fundamental object underlying all learning processes. Our results are valid across a wide range of common settings, including continuous- time, discrete-time, stochastic, and higher-order learning rules, as well as loss functions with explicit time dependence. Beyond providing a unified framework for learning, our results also have practical implications for control as well as experimental neuroscience.

We present a novel approach to quantizing Markov chains. The approach is based on the Markov chain coupling method, which is frequently used to prove fast mixing. Given a particular coupling, e.g., a grand coupling, we construct a completely positive and trace preserving map. This quantum map has a unique fixed point, which corresponds to the quantum sample (qsample) of the classical Markov chain's stationary distribution. We show that the convergence time of the quantum map is directly related to the coupling time of the Markov chain coupling.

Metformin is the only antihyperglycemic biguanide targeting type 2 diabetes mellitus with proven safety. Although a mechanism of action involving tight inhibition of the respiratory complex I has been proposed for hydrophobic biguanides, it remains elusive for the hydrophilic metformin, whose excellent pharmacological tolerance depends on weak complex I inhibition without competitive nature. Here we solved cryo-electron microscopy structures of the metformin-bound porcine respirasome. Our structural and kinetic data are consistent with a model in which metformin enters complex I only in its open state and becomes trapped at the ubiquinone redox site by ubiquinone-induced conformational closing of the enzyme. By contrast, the hydrophobic proguanil alone occupies both the entrance and the redox site of the ubiquinone channel in open and closed complex I and is kinetically consistent with competitive inhibition with conformation-dependent affinities. Our data provide the molecular basis for metformin's well-known superior properties, such as a wide therapeutic window and positive ubiquinone cooperativity, leading to its clinical success and facilitating future therapeutic developments.

This paper presents a novel approach to sentiment classification using the application of Combinatorial Fusion Analysis (CFA) to integrate an ensemble of diverse machine learning models, achieving state-of-the-art accuracy on the IMDB sentiment analysis dataset of 97.072%. CFA leverages the concept of cognitive diversity, which utilizes rank-score characteristic functions to quantify the dissimilarity between models and strategically combine their predictions. This is in contrast to the common process of scaling the size of individual models, and thus is comparatively efficient in computing resource use. Experimental results also indicate that CFA outperforms traditional ensemble methods by effectively computing and employing model diversity. The approach in this paper implements the combination of a transformer-based model of the RoBERTa architecture with traditional machine learning models, including Random Forest, SVM, and XGBoost.

As organisations rapidly adopt artificial intelligence (AI), they encounter a host of complex ethical challenges. These challenges are made even more difficult by the nature of AI itself: it is not a static or uniform technology but rather an evolving ecosystem of tools and systems that interact dynamically with their environments. Managers are under increasing pressure to address these challenges and justify governance investments, while established frameworks for responsible AI governance are still emerging. The central question of thew paper, then, is how managers make sense of these evolving ethical challenges. One productive way to explore this - that we also adopt in this paper - is drawing parallels with the field of Corporate Social Responsibility (CSR). Like AI governance, CSR also involves responding to ambiguous expectations and CSR research provides valuable insights into how organizations engage in "sensemaking" (a process of interpreting and giving meaning to complex situations in order to guide decisions and justify actions). In CSR literature, two dominant forms of sensemaking are identified: "value-driven" and "instrumental". The value-driven approach is rooted in ethical principles and moral commitments; the instrumental approach treats governance as a strategic tool, aligning ethical practices with business goals. Drawing from this conceptual framework, the paper presents a study of managers from diverse sectors to explore how they make sense of AI ethics and governance. The analysis reveals that AI governance sensemaking similarly falls into these two categories, value-driven and instrumental, but importantly, neither approach is sufficient on its own. Instead, our qualitative study finds that managers should blend these two approaches into what we describe as a "holistic sensemaking strategy". By adopting a holistic framework that integrates both perspectives, managers are better equipped to navigate the evolving and ambiguous terrain of AI governance, thus making investments in governance that are both principled and practical.

GEOtiled enables efficient computation of high-resolution terrain parameters from digital elevation models (DEMs) by decomposing large regions into smaller, parallelizable tiles. Originally developed as GEOtiled-G with support for three parameters (Slope, Aspect, Hillshade) via GDAL, we present GEOtiled-SG, an enhanced version that integrates the SAGA GIS library to compute over 15 parameters, expanding its utility in Earth science. To offset SAGA’s computational overhead, GEOtiled-SG introduces three optimizations: concurrent DEM cropping, buffer-aware mosaicking, and unified concurrency across workflow stages. Evaluations show that GEOtiled-SG maintains GEOtiled-G’s performance on the original parameters and offers consistent speedups across the expanded set. The framework is open source on GitHub, with data hosted on Dataverse, supporting reproducible, scalable terrain analysis.

Machine learning traditionally emphasizes developing models for given datasets, but real-world data is often messy, making model improvement insufficient for enhancing performance. AI for data editing (AI4DE) is an emerging field that systematically improves datasets, leading to significant practical ML advancements. While experienced data scientists have manually refined datasets through trial-and-error and intuition, AI4DE approaches data enhancement as a systematic engineering discipline. AI4DE represents a shift from focusing on models to the underlying data used for training and evaluation. Despite the dominance of common model architectures and predictable scaling rules, building and using datasets remain labor-intensive and costly, lacking infrastructure and best practices. The AI4DE movement aims to develop efficient, high-productivity open data engineering tools for modern ML systems. This workshop seeks to foster an interdisciplinary AI4DE community to address practical data challenges, including data collection, generation, labeling, preprocessing, augmentation, quality evaluation, debt, and governance. By defining and shaping the AI4DE movement, this workshop aims to influence the future of AI and ML, inviting interested parties to contribute through paper submissions