arXiv:2603.15672v1 Announce Type: new Abstract: Hardware design errors discovered after fabrication require costly physical respins that can delay products by months. Existing electronic design automation (EDA) tools enforce structural connectivity rules. However, they cannot verify that connections are emph{semantically} correct with respect to component datasheets. For example, that a symbol’s pinout matches the manufacturer’s specification, or that a voltage regulator’s feedback resistors produce the intended output. We present DRCY, the first production-ready multi-agent LLM system that automates first-pass […]

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arXiv:2601.16278v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable multilingual capabilities, making them promising tools in both high- and low-resource languages. One particularly valuable use case is generating synthetic samples that can be used to train smaller models in low-resource scenarios where human-labelled data is scarce. In this work, we investigate whether these synthetic data generation capabilities can serve as a form of distillation, producing smaller models that perform on par with or even better […]

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Tropical cyclones (TCs) rank among the most destructive natural hazards, yet their forecasting faces fundamental trade-offs: numerical weather prediction (NWP) models are computationally prohibitive and struggle to leverage historical data, while existing deep learning (DL)-based intelligent models are variable-specific and deterministic, which fail to generalize across different forecasting variables. Here we present CycloneMAE, a scalable multi-task forecasting model that learns transferable TC representations from multi-modal data using a TC structure-aware masked autoencoder. By coupling a discrete probabilistic gridding […]

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Hello Everyone, Since the last few months, I have been studying about world models and along side built a library for learning, training and building new world model algorithms, pytorch-world. Added a bunch of world model algorithms, components and environments. Still working on adding more. If you find it interesting, I would love to know your thoughts on how I can improve this further or open for collaboration and contributions to make this a better project and useful […]

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arXiv:2601.19055v1 Announce Type: cross Abstract: We study how to fine-tune LLMs using user-edit deployment data consisting of a set of context, an agent’s response, and user edits. This deployment data is naturally generated by users in applications such as LLMs-based writing assistants and coding agents. The _natural_ origin of user edits makes it a desired source for adapting and personalizing LLMs. In this setup, there emerges a unification of various feedback types namely preferences, supervised labels, and cost […]

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AI tools and agents are reshaping how researchers work, from proving theorems to training neural networks. Yet for many, it remains unclear how these tools fit into everyday research practice. This paper is a practical guide to AI-assisted research in mathematics and machine learning: We discuss how researchers can use modern AI systems productively, where these systems help most, and what kinds of guardrails are needed to use them responsibly. It is organized into three parts: (I) a […]

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If you are like me and spend most of your time thinking about what happens inside the model,and not much on the hardware side of things this video will definitely fascinate you. Dwarkesh and Reiner Pope spent two hours at a blackboard going through the actual hardware economics of training and running LLMs and i got to learn a lot things i previously didn’tknow obviously. One of my biggest takeaways for me was the 6ND formula for calculating […]

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Interpreting the decisions of complex computer vision models is crucial to establish trust and accountability, especially in safety-critical domains. An established approach to interpretability is generating visual attribution maps that highlight regions of the input most relevant to the model’s prediction. However, existing methods face a three-way trade-off. Propagation-based approaches are efficient, but they can be biased and architecture-specific. Meanwhile, perturbation-based methods are causally grounded, yet they are expensive and for vision transformers often yield coarse, patch-level explanations. […]

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