technocracy

Causal Cellular Context Transfer Learning (C3TL): An Efficient Architecture for Prediction of Unseen Perturbation Effects

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Predicting the effects of chemical and genetic perturbations on quantitative cell states is a central challenge in computational biology, molecular medicine and drug discovery. Recent work has leveraged large-scale single-cell data and massive foundation models to address this task. However, such computational resources and extensive datasets are not always accessible in academic or clinical settings, hence limiting utility. Here we propose a lightweight framework for perturbation effect prediction that exploits the structured nature of biological interventions and specific […]

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An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking

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arXiv:2602.12354v1 Announce Type: new Abstract: LinkedIn Feed enables professionals worldwide to discover relevant content, build connections, and share knowledge at scale. We present Feed Sequential Recommender (Feed-SR), a transformer-based sequential ranking model for LinkedIn Feed that replaces a DCNv2-based ranker and meets strict production constraints. We detail the modeling choices, training techniques, and serving optimizations that enable deployment at LinkedIn scale. Feed-SR is currently the primary member experience on LinkedIn’s Feed and shows significant improvements in member engagement […]

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Detoxification of large language models via regularized fine-tuning

Detoxification of large language models via regularized fine-tuning

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Detoxification of large language models via regularized fine-tuning Attribute-controlled fine-tuning can produce LLMs that adhere to policy while achieving competitive performance on general benchmarks. Conversational AI Charith Peris November 21, 03:40 PM November 21, 03:40 PM Large language models (LLMs) have demonstrated impressive performance across a variety of tasks, but, as has been clear in multiple instances, they carry the risk of producing inappropriate, unsafe, or biased outputs. When generating responses, a successfully trained LLM should comply with […]

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Google ordered to put clearer links in AI search and let UK publishers opt out

Google ordered to put clearer links in AI search and let UK publishers opt out

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UK regulators today ordered Google to put clearer attributions and links to publishers’ content in its AI-generated search features. The UK’s Competition and Markets Authority (CMA) also said Google must give publishers a way to opt out of AI features in search. “In a world first, publishers will now have effective tools to prevent their content being used to power AI features in search, such as AI Overviews,” the CMA said today. “This will put publishers, like news […]

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Qworld: Question-Specific Evaluation Criteria for LLMs

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arXiv:2603.23522v1 Announce Type: new Abstract: Evaluating large language models (LLMs) on open-ended questions is difficult because response quality depends on the question’s context. Binary scores and static rubrics fail to capture these context-dependent requirements. Existing methods define criteria at the dataset level or generate them in a single pass, which limits their ability to explore the evaluation space implied by each question. We introduce One-Question-One-World (Qworld), a method that generates question-specific evaluation criteria using a recursive expansion tree. […]

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NLP Research in the Era of LLMs

NLP Research in the Era of LLMs

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NLP research has undergone a paradigm shift over the last year. A range of large language models (LLMs) has validated the unreasonable effectiveness of scale. Currently, the state of the art on most benchmarks is held by LLMs that are expensive to fine-tune and prohibitive to pre-train outside of a few industry labs. In the past, a barrier to doing impactful research has often been a lack of awareness of fruitful research areas and compelling hypotheses to explore. […]

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Graph-Based Fraud Detection with Dual-Path Graph Filtering

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arXiv:2604.14235v1 Announce Type: new Abstract: Fraud detection on graph data can be viewed as a demanding task that requires distinguishing between different types of nodes. Because graph neural networks (GNNs) are naturally suited for processing information encoded in graph form through their message-passing operations, methods based on GNN models have increasingly attracted attention in the fraud detection domain. However, fraud graphs inherently exhibit relation camouflage, high heterophily, and class imbalance, causing most GNNs to underperform in fraud detection […]

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Quoting Richard Fontana

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FWIW, IANDBL, TINLA, etc., I don’t currently see any basis for concluding that chardet 7.0.0 is required to be released under the LGPL. AFAIK no one including Mark Pilgrim has identified persistence of copyrightable expressive material from earlier versions in 7.0.0 nor has anyone articulated some viable alternate theory of license violation. […] — Richard Fontana, LGPLv3 co-author, weighing in on the chardet relicensing situation Tags: open-source, ai-ethics, llms, ai, generative-ai, ai-assisted-programming

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Robotic Assembly Using Deep Reinforcement Learning

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Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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"Super ZSNES" is a stab at a modern SNES emulator from the original developers

“Super ZSNES” is a stab at a modern SNES emulator from the original developers

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Aficionados of game console emulator history will almost certainly be familiar with ZSNES, an MS-DOS-based (and, later, Windows-based) emulator for the Super Nintendo Entertainment System that originally launched back in 1997. Originally written in x86 assembly code, it was known best for its performance on low-end PCs and was capable of running some games at full speed on chips as slow as a 233 MHz Pentium II, though it usually did so at the expense of emulation accuracy. […]

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