Building complex applications with AI requires more than good prompts. It requires a structured framework — and that framework has a name. What Is Spec Driven Development? Spec Driven Development (SDD) is a structured AI coding workflow where AI agents generate code based on formal specifications rather than freeform prompts. It’s how engineering teams manage AI when building large, complex systems — the same way you’d onboard a human developer by giving them proper documentation, architectural guidelines, and […]

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Contextual Reinforcement Learning (CRL) tackles the problem of solving a set of related Contextual Markov Decision Processes (CMDPs) that vary across different context variables. Traditional approaches–independent training and multi-task learning–struggle with either excessive computational costs or negative transfer. A recently proposed multi-policy approach, Model-Based Transfer Learning (MBTL), has demonstrated effectiveness by strategically selecting a few tasks to train and zero-shot transfer. However, CMDPs encompass a wide range of problems, exhibiting structural properties that vary from problem to problem. […]

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arXiv:2601.08834v1 Announce Type: new Abstract: Reading text from images or scanned documents via OCR models has been a longstanding focus of researchers. Intuitively, text reading is perceived as a straightforward perceptual task, and existing work primarily focuses on constructing enriched data engineering to enhance SFT capabilities. In this work, we observe that even advanced OCR models exhibit significantly higher entropy in formatted text (emph{e.g.}, formula, table, etc.) compared to plain text, often by an order of magnitude. These […]

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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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A crucial consideration when developing and deploying Large Language Models (LLMs) is the human values to which these models are aligned. In the constitutional framework of alignment models are aligned to a set of principles (the constitution) specified in natural language. However, it is unclear how to fairly determine this constitution with widespread stakeholder input. In this work we propose Grounded Constitutional AI (GCAI), a unified framework for generating constitutions of principles that are representative of both users’ […]

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Task sequencing (TS) is one of the core open problems in Deep Learning, arising in a plethora of real-world domains, from robotic assembly lines to autonomous driving. Unfortunately, prior work has not convincingly demonstrated the generalization ability of meta-learned TS methods to solve new TS problems, given few initial demonstrations. In this paper, we demonstrate that deep neural networks can meta-learn over an infinite prior of synthetically generated TS problems and achieve a few-shot generalization. We meta-learn a […]

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arXiv:2601.05379v1 Announce Type: new Abstract: Deploying deep neural networks on edge devices is often limited by the memory traffic and compute cost of dense linear operators. While quaternion neural networks improve parameter efficiency by coupling multiple channels through Hamilton products, they typically retain unstructured dense weights; conversely, structured matrices enable fast computation but are usually applied in the real domain. This paper introduces EdgeLDR, a practical framework for quaternion block-circulant linear and convolutional layers that combines quaternion channel […]

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arXiv:2601.05065v1 Announce Type: cross Abstract: A key challenge in network science is the detection of communities, which are sets of nodes in a network that are densely connected internally but sparsely connected to the rest of the network. A fundamental result in community detection is the existence of a nontrivial threshold for community detectability on sparse graphs that are generated by the planted partition model (PPM). Below this so-called “detectability limit”, no community-detection method can perform better than […]

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