arXiv:2601.20756v1 Announce Type: cross Abstract: Score-based diffusion models have recently been extended to infinite-dimensional function spaces, with uses such as inverse problems arising from partial differential equations. In the Bayesian formulation of inverse problems, the aim is to sample from a posterior distribution over functions obtained by conditioning a prior on noisy observations. While diffusion models provide expressive priors in function space, the theory of conditioning them to sample from the posterior remains open. We address this, assuming […]

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I’m learning the foundations of RL in alignment with the Alberta Plan for AI research and have been running through sets of experiments to both learn and experiment. To that end I spent the last month validating different methods for streaming deep RL on a non-stationary, adversarial dataset of real SSH honeypot observations. This work focuses on prediction and is in line with steps 1 & 2 of the Alberta Plan (Sutton, Bowling, & Pilarski 2022). After implementing […]

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Lightweight LLM for converting text to structured data Novel training procedure and decoding mechanism enable model to outperform much larger foundation model prompted to perform the same task. Conversational AI Karim Bouyarmane February 06, 10:14 AM February 06, 10:27 AM One of the most important features of todays generative models is their ability to take unstructured, partially unstructured, or poorly structured inputs and convert them into structured objects that conform to specific schemas relational-database fixed schemas, document store […]

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In our Haskell in Production series, we interview developers and technical leaders from companies that use Haskell for real-world tasks. We cover benefits, downsides, common pitfalls, and tips for building useful Haskell products. Our today’s guest is Leo Zhang, who works in the engineering team at FOSSA – a tool for open-source risk management. In the interview, we talk about his experience using Haskell in FOSSA’s backend analysis services and CLI. Read further to learn about the benefits […]

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arXiv:2603.11991v1 Announce Type: cross Abstract: Zero-shot text classification (ZSC) offers the promise of eliminating costly task-specific annotation by matching texts directly to human-readable label descriptions. While early approaches have predominantly relied on cross-encoder models fine-tuned for natural language inference (NLI), recent advances in text-embedding models, rerankers, and instruction-tuned large language models (LLMs) have challenged the dominance of NLI-based architectures. Yet, systematically comparing these diverse approaches remains difficult. Existing evaluations, such as MTEB, often incorporate labeled examples through supervised […]

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The compliance industrial complex is selling a new product. Having peddled GDPR checklists and SOC2 reports for years, the same vendors are offering AI governance frameworks with the promises of making your neural networks ethical, safe, and compliant. While the policies sound reasonable, with impressive documentation and an expensive consultant, none of it is stopping breaches. As a cybersecurity ghostwriter who has collaborated with several Silicon Valley Startups and tech founders in the APAC region, I’ve watched hundreds […]

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Distilling reasoning capabilities from Large Reasoning Models (LRMs) into smaller models is typically constrained by the limitation of rejection sampling. Standard methods treat the teacher as a static filter, discarding complex "corner-case" problems where the teacher fails to explore valid solutions independently, thereby creating an artificial "Teacher Ceiling" for the student. In this work, we propose Hindsight Entropy-Assisted Learning (HEAL), an RL-free framework designed to bridge this reasoning gap. Drawing on the educational theory of the Zone of […]

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arXiv:2410.07427v4 Announce Type: replace-cross Abstract: Implicit networks are a class of neural networks whose outputs are defined by the fixed point of a parameterized operator. They have enjoyed success in many applications including natural language processing, image processing, and numerous other applications. While they have found abundant empirical success, theoretical work on its generalization is still under-explored. In this work, we consider a large family of implicit networks defined parameterized contractive fixed point operators. We show a generalization […]

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