March 2026

Thyroid nodule classification using ultrasound imaging is essential for early diagnosis and clinical decision-making; however, despite promising performance on in-distribution data, existing deep learning methods often exhibit limited robustness and generalisation when deployed across different ultrasound devices or clinical environments. This limitation is mainly attributed to the pronounced heterogeneity of thyroid ultrasound images, which can lead models to capture spurious correlations rather than reliable diagnostic cues. To address this challenge, we propose PEMV-thyroid, a Prototype-Enhanced Multi-View learning framework […]

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Learning from quantum data using classical machine learning models has emerged as a promising paradigm toward realizing quantum advantages. Despite extensive analyses on their performance, clean and fully labeled quantum data from the target domain are often unavailable in practical scenarios, forcing models to be trained on data collected under conditions that differ from those encountered at deployment. This mismatch highlights the need for new approaches beyond the common assumptions of prior work. In this work, we address […]

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Distributed learning is widely used for training large models on large datasets by distributing parts of the model or dataset across multiple devices and aggregating the computed results for subsequent computations or parameter updates. Existing communication algorithms for distributed learning such as ring all-reduce result in heavy communication overhead between servers. Since communication in large-scale systems uses optical fibers, we propose an Optical In-Network-Computing (OptINC) architecture to offload the computation in servers onto the optical interconnects. To execute […]

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We consider robustness against data corruption in offline multi-agent reinforcement learning from human feedback (MARLHF) under a strong-contamination model: given a dataset $D$ of trajectory-preference tuples (each preference being an $n$-dimensional binary label vector representing each of the $n$ agents’ preferences), an $ε$-fraction of the samples may be arbitrarily corrupted. We model the problem using the framework of linear Markov games. First, under a uniform coverage assumption – where every policy of interest is sufficiently represented in the […]

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Banking house JPMorgan Chase is asking its roughly 65,000 engineers and technologists to use AI tools as part of their regular workflow. Business Insider reported that managers are tracking how often staff use these tools. That use may also influence performance reviews. The report states employees are encouraged to use tools like ChatGPT and Claude Code when writing code, reviewing documents, or handling routine tasks. Internal systems then classify workers based on their level of use. Some are […]

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:::info Authors: Yuanzhi Li, Microsoft Research S´ebastien Bubeck, Microsoft Research Ronen Eldan, Microsoft Research Allie Del Giorno, Microsoft Research Suriya Gunasekar, Microsoft Research Yin Tat Lee, Microsoft Research ::: Abstract We continue the investigation into the power of smaller Transformer-based language models as initiated by TinyStories – a 10 million parameter model that can produce coherent English – and the follow-up work on phi-1, a 1.3 billion parameter model with Python coding performance close to the state-of-the-art. The […]

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Guiding collective motion in biological groups is a fundamental challenge in understanding social interaction rules and developing automated systems for animal management. In this study, we propose a deep reinforcement learning (RL) framework for the closed-loop guidance of fish schools using virtual agents. These agents are controlled by policies trained via Proximal Policy Optimization (PPO) in simulation and deployed in physical experiments with rummy-nose tetras (Petitella bleheri), enabling real-time interaction between artificial agents and live individuals. To cope […]

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AI-powered code review tools have become a staple in modern engineering. They automate repetitive checks, accelerate delivery, and maintain consistency across pull requests. For smaller teams or contained projects, this alone can drive meaningful gains in efficiency and output. But as organizations scale, the dynamic changes. Software is no longer a single, self-contained codebase; it’s an ecosystem of interconnected services, shared APIs, and constantly evolving dependencies. In these environments, reliability isn’t determined by how clean a pull request […]

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Every SaaS IT director knows the drill. Engineering is rushing a release to hit a revenue goal, a dozen new sales reps are demanding CRM access yesterday, and then a critical vulnerability notification lands in your inbox. Speed usually wins when the pressure is on, so you grant admin rights to unblock a developer or pause a fleet-wide update to keep a sales demo running smooth. These all feel like tactical necessities in the moment, but they create […]

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