Define “correct” before you index anything RAG systems are judged by answers, but you cannot improve what you cannot measure. Before you touch embeddings, pick the real tasks your assistant must handle, then define success criteria in plain language. Keep the task list small. Common high-value tasks are Q&A over policy or product docs, troubleshooting workflows, summarisation with citations, and “what changed” across document versions. For each task type, define what a correct answer must do. Examples: it […]

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arXiv:2603.12506v1 Announce Type: new Abstract: Text-to-Image (T2I) generation is primarily driven by Diffusion Models (DM) which rely on random Gaussian noise. Thus, like playing the slots at a casino, a DM will produce different results given the same user-defined inputs. This imposes a gambler’s burden: To perform multiple generation cycles to obtain a satisfactory result. However, even though DMs use stochastic sampling to seed generation, the distribution of generated content quality highly depends on the prompt and the […]

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arXiv:2601.10993v2 Announce Type: replace Abstract: Outlier detection (OD) aims to identify abnormal instances, known as outliers or anomalies, by learning typical patterns of normal data, or inliers. Performing OD under an unsupervised regime-without any information about anomalous instances in the training data-is challenging. A recently observed phenomenon, known as the inlier-memorization (IM) effect, where deep generative models (DGMs) tend to memorize inlier patterns during early training, provides a promising signal for distinguishing outliers. However, existing unsupervised approaches that […]

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I sent the December edition of my sponsors-only monthly newsletter. If you are a sponsor (or if you start a sponsorship now) you can access a copy here. In the newsletter this month: An in-depth review of LLMs in 2025 My coding agent projects in December New models for December 2025 Skills are an open standard now Claude’s “Soul Document” Tools I’m using at the moment Here’s a copy of the November newsletter as a preview of what […]

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arXiv:2603.26713v1 Announce Type: cross Abstract: Electroencephalography (EEG)-based emotion recognition suffers from severe performance degradation when models are transferred across heterogeneous datasets due to physiological variability, experimental paradigm differences, and device inconsistencies. Existing domain adversarial methods primarily enforce global marginal alignment and often overlook class-conditional mismatch and decision boundary distortion, limiting cross-corpus generalization. In this work, we propose a unified Prototype-driven Adversarial Alignment (PAA) framework for cross-corpus EEG emotion recognition. The framework is progressively instantiated in three configurations: PAA-L, […]

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arXiv:2603.05711v1 Announce Type: new Abstract: Accurate, dense depth estimation is crucial for robotic perception, but commodity sensors often yield sparse or incomplete measurements due to hardware limitations. Existing RGBD-fused depth completion methods learn priors jointly conditioned on training RGB distribution and specific depth patterns, limiting domain generalization and robustness to various depth patterns. Recent efforts leverage monocular depth estimation (MDE) models to introduce domain-general geometric priors, but current two-stage integration strategies relying on explicit relative-to-metric alignment incur additional […]

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Published on December 18, 2025 5:53 PM GMT They say you’re supposed to choose your prior in advance. That’s why it’s called a “prior”. First, you’re supposed to say say how plausible different things are, and then you update your beliefs based on what you see in the world. For example, currently you are—I assume—trying to decide if you should stop reading this post and do something else with your life. If you’ve read this blog before, then […]

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arXiv:2601.00208v1 Announce Type: new Abstract: A significant impediment to high performance in key-value stores is the high cost of thread switching or stalls. While there are many sources for this, a major one is the contention for resources. And this cost increases with load as conflicting operations more frequently try to access data concurrently. Traditional latch-based approaches usually handle these situations by blocking one or more contending threads. Latch-free techniques can avoid this behavior. But the payoff may […]

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Table of Links Abstract and 1. Introduction Related Work Preliminaries and Notations Differentiable Structural Information 4.1. A New Formulation 4.2. Properties 4.3. Differentiability & Deep Graph Clustering LSEnet 5.1. Embedding Leaf Nodes 5.2. Learning Parent Nodes 5.3. Hyperbolic Partitioning Tree Experiments 6.1. Graph Clustering 6.2. Discussion on Structural Entropy Conclusion, Broader Impact, and References Appendix A. Proofs B. Hyperbolic Space C. Technical Details D. Additional Results 3. Preliminaries and Notations Herein, different from the typical setting of existing […]

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