arXiv:2601.08919v1 Announce Type: new Abstract: A good deal of recent research has focused on how Large Language Models (LLMs) may be used as `judges’ in place of humans to evaluate the quality of the output produced by various text / image processing systems. Within this broader context, a number of studies have investigated the specific question of how effectively LLMs can be used as relevance assessors for the standard ad hoc task in Information Retrieval (IR). We extend […]

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Large Language Models (LLMs) often fail to generate correct code on the first attempt, which requires using generated unit tests as verifiers to validate the solutions. Despite the success of recent verification methods, they remain constrained by a "scaling-by-quantity" paradigm. This brute-force approach suffers from a critical limitation: it yields diminishing returns in fault detection while causing severe test redundancy. To address this, we propose MIST-RL (Mutation-based Incremental Suite Testing via Reinforcement Learning), a framework that shifts the […]

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Release: asgi-gzip 0.3 I ran into trouble deploying a new feature using SSE to a production Datasette instance, and it turned out that instance was using datasette-gzip which uses asgi-gzip which was incorrectly compressing event/text-stream responses. asgi-gzip was extracted from Starlette, and has a GitHub Actions scheduled workflow to check Starlette for updates that need to be ported to the library… but that action had stopped running and hence had missed Starlette’s own fix for this issue. I […]

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there’s a phenomenon part of compression-aware intelligence that is known as contradiction under compression. it’s where conflicting info is forced into a coherent narrative so contradiction under compression implies that AI systems often prioritize efficient narrative over the un-compressed truth which leads to “fractures” in the logic submitted by /u/Ok-Worth8297 [link] [comments]

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arXiv:2601.02492v1 Announce Type: new Abstract: We introduce variational spectral learning (VSL), a machine learning framework for solving partial differential equations (PDEs) that operates directly in the coefficient space of spectral expansions. VSL offers a principled bridge between variational PDE theory, spectral discretization, and contemporary machine learning practice. The core idea is to recast a given PDE [ mathcal{L}u = f quad text{in} quad Q=Omegatimes(0,T), ] together with boundary and initial conditions, into differentiable space–time energies built from strong-form […]

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arXiv:2601.21470v1 Announce Type: cross Abstract: We study semi-supervised stochastic optimization when labeled data is scarce but predictions from pre-trained models are available. PPI and SVRG both reduce variance through control variates — PPI uses predictions, SVRG uses reference gradients. We show they are mathematically equivalent and develop PPI-SVRG, which combines both. Our convergence bound decomposes into the standard SVRG rate plus an error floor from prediction uncertainty. The rate depends only on loss geometry; predictions affect only the […]

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In this quiz, you’ll test your understanding of the concepts in the video course SQLite and SQLAlchemy in Python: Move Your Data Beyond Flat Files. By working through this quiz, you’ll revisit how Python, SQLite, and SQLAlchemy work together to give your programs reliable data storage. You’ll also check your grasp of primary and foreign keys, SQLAlchemy’s Core and ORM layers, and the many-to-many relationships that tie your data together. [ Improve Your Python With 🐍 Python Tricks […]

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