The paper introduces Distance Marching, a generative modeling technique that learns the distance from data manifold surfaces. By training a neural network to predict signed distance, the method enables high-quality generation through a marching procedure, offering an alternative to diffusion and flow-based models with competitive results.
The paper introduces Distance Marching, a generative modeling technique that learns the distance from data manifold surfaces. By training a neural network to predict signed distance, the method enables high-quality generation through a marching procedure, offering an alternative to diffusion and flow-based models with competitive results.
HelixDB is a specialized data layer designed for protein structure prediction models like AlphaFold. It provides efficient storage, retrieval, and management of protein data to support AI-driven structural biology research.
The article discusses how local reasoning—focusing on individual components or small parts of a system—can be used to verify or ensure global properties like correctness, security, or consistency. It explores techniques and formal methods that allow developers to deduce system-wide guarantees from localized checks, aiming to make complex systems more tractable to reason about.
A landscape grid categorizes major theories of consciousness, including Integrated Information Theory, Global Workspace Theory, Higher-Order Thought Theory, and Predictive Processing, mapping them across dimensions such as phenomenal vs. access consciousness and cognitive vs. neural foundations.
A series of articles on designing systems, covering topics such as system architecture, design patterns, and practical implementation approaches for building scalable and maintainable software systems.
A new text-to-speech model aims to bridge the uncanny valley in synthetic speech, seeking more natural and human-like vocal output to improve user experience in voice applications.
TiRex-2 is a large-scale time series foundation model developed by NX-AI, designed to generalize across diverse time series tasks without task-specific training. It supports zero-shot forecasting, classification, and anomaly detection using a transformer-based architecture trained on a broad corpus of time series data.
Lotus is an open-source framework for high-performance bulk AI tasks, offering up to 30x speedups for LLM-based jobs and optimized agentic workflows compared to standard approaches.
modusregel is a minimal, visually clean Emacs modeline package designed to complement the Modus themes, offering a simple and beautiful alternative to default modeline configurations.
ProteinTensor is a new Parquet-like tensor format designed for protein-structure machine learning, enabling efficient storage and retrieval of protein data tensors for ML pipelines.
The GitHub repository introduces the Coordination Repository Pattern, a software architecture pattern for managing distributed coordination, and Pi-Env, a related tool or implementation environment for that pattern.
Zk.golf is a collaborative platform where developers compete to optimize ZK circuit constraints, using peer-reviewed techniques to reduce circuit sizes. It fosters a fearless, gamified environment for learning and sharing optimization strategies across different proving systems.
SurrealDB has launched Scale, a new cloud tier designed for high availability and large-scale deployments. The offering includes multi-region support, horizontal scaling, and enhanced reliability features for enterprise workloads running on SurrealDB Cloud.
OctoSense is a self-supervised learning framework designed for multimodal robot perception, enabling robots to learn from raw sensor data without manual labeling. The system integrates multiple sensory inputs to improve understanding and interaction with complex environments, advancing autonomous robotic capabilities.
This page provides lecture notes and resources on optimal transport theory and its applications in machine learning, covering topics like the Wasserstein distance, computational methods (Sinkhorn algorithm), and use cases in domain adaptation, generative models, and gradient flows.
This introduction covers the basics of genomics, including cells, genomes, DNA, and chromosomes, aimed at engineers. It explains how DNA is structured and organized within cells to encode genetic information.
These notes summarize "Principles of Neural Design" by Peter Sterling and Simon Laughlin, covering how biological brains achieve efficiency through principles like minimizing wiring, energy use, and signal noise while maximizing computational power per unit resource.
Cotal is an agentic coordination layer designed to enable autonomous agents to work together efficiently, facilitating task delegation, communication, and collaboration between AI agents.
The article discusses strategies for avoiding fallback mechanisms in distributed systems, emphasizing the importance of designing systems that handle failures gracefully without relying on degraded fallback modes that can mask underlying issues and complicate debugging.
Contour agroforestry combines trees, shrubs, and crops along land contours to restore dryland ecosystems. This approach reduces water runoff, improves soil moisture, and enhances climate resilience, offering a sustainable solution for degraded lands facing increasing drought risks.
DocETL is a declarative and agentic map-reduce system designed for processing and transforming unstructured documents. It allows users to define complex document processing pipelines using a high-level specification, leveraging AI agents to perform tasks like extraction, summarization, and transformation in a scalable manner.
An interview explores Domain Storytelling, a collaborative modeling technique where domain experts and developers use pictographic language to visualize business processes. The approach helps create shared understanding and software models aligned with real business needs.
Ella is presented as a deterministic compute engine designed for ultra-low-latency systems, aiming to provide predictable performance for time-sensitive applications.
The article introduces Pulpie, a suite of Pareto-optimal models designed for web-scale data cleaning. It presents models that balance performance and computational cost to filter low-quality text from web datasets, improving the efficiency of training large language models.
Modelith offers lightweight tooling for domain modeling, helping developers design and work with domain models more efficiently by providing focused utilities and abstractions.
This guide explains what threat models are and why they matter in security. It covers key concepts like assets, adversaries, attack vectors, and risk assessment, using informal examples to help readers think systematically about potential harms and defenses.
Software teams should adopt sustainability metrics to maintain long-term health, not just speed. Without them, teams risk burnout and decline, as illustrated by the cautionary tale of developer Kennan Frost who burned out from unsustainable pace.
The article explores how local reasoning—examining small, isolated parts of a system—can be used to verify global properties like correctness, security, and performance. It discusses formal methods and programming language techniques that enable developers to prove system-wide behaviors from local code analysis, emphasizing the importance of modularity and abstraction in software verification.
The article discusses how applying matrix orthogonalization techniques to recurrent neural network models improves their long-term memory retention and training stability.
This paper introduces a method for discretizing reward models, aiming to improve the efficiency and interpretability of reinforcement learning from human feedback (RLHF) by converting continuous reward signals into discrete categories.
The Green Metrics Tool provides a dashboard for measuring and visualizing the energy consumption and environmental impact of software applications. It enables developers to analyze the carbon footprint of their code through detailed metrics and reports.
The paper introduces Scalable GANs with Transformers (SGT), a new family of generative models that combines GAN training with transformer architectures. By addressing training stability issues, SGT achieves state-of-the-art image generation quality on ImageNet with fewer parameters than previous models.
Google Research introduced TabFM, a zero-shot foundation model for tabular data that works without task-specific fine-tuning. It handles diverse data types (numbers, categories, dates, text) and supports classification, regression, and imputation tasks.
A history of wearable foundation models tracing their evolution from early sensor data processing to modern transformer-based architectures that analyze heart rate and activity for personalized health insights.
The article discusses how local reasoning (examining individual components) can be used to verify global properties in complex systems, drawing on examples from programming languages and formal verification to illustrate the principles.
SlateDB is a new open-source embedded database built as an "object-native" LSM-tree that stores its base SST files directly in cloud object storage (like S3 or GCS) rather than on local disk, aiming to simplify stateful workloads in serverless and cloud environments.
GenomeNarrator is a service that generates clinical-grade reports from raw genetic data obtained from consumer DNA testing companies like 23andMe and AncestryDNA, aiming to provide more medically actionable insights from existing genetic information.
OM Core is a tool for building multidimensional models without relying on traditional spreadsheet cell formulas. It offers a new approach to modeling complex data structures.
The article demonstrates how to model the Covid-19 outbreak using the J programming language, applying SIR and SEIR epidemiological models to publicly available case data for China and South Korea, and exploring curve fitting and parameter estimation.
A guide explaining threat models as a method to identify security risks by defining assets, adversaries, and their capabilities, helping readers make informed security choices rather than following generic advice.
Kilo AI introduces "Auto Efficient," a system that automatically selects the most cost-effective AI model for each request based on complexity, aiming to reduce costs and latency without sacrificing quality.
TheoremGraph is a searchable graph of over 18 million mathematical statements from the Formal Abstracts dataset, showing dependencies between theorems across arXiv, Wikipedia, ProofWiki, and Stack Exchange. It enables users to explore citation links and the logical structure of mathematical knowledge.
The article discusses how programming language type systems and static analysis can enforce global properties (like memory safety or data race freedom) through local reasoning techniques, allowing developers to verify complex invariants without needing to understand the entire codebase at once.
Manifest-Driven Development (MDD) is a software methodology that uses a central manifest file to define project structure, dependencies, and build processes. This approach aims to improve project clarity and automation by making the manifest the single source of truth for development workflows.
The article introduces Prism, a research language designed as an alternative to Haskell and Rust for large-scale systems. It features an impure functional design with a monadic effect system, built-in parallel and distributed computation support, and a focus on ergonomic tooling including language servers and debuggers for practical use.
Proxylity offers free or discounted access to its proxy service platform for academic researchers, students, and non-profit organizations. The program aims to support educational and humanitarian work by providing tools for web data collection and online research.
The article introduces the Zero Weights Language Model (MSE-GLM), a novel approach in natural language processing that incorporates zero-weight techniques to enhance model efficiency and performance, potentially reducing computational costs while maintaining accuracy in language tasks.
Flint is a fast, open-source C library for number theory, offering arbitrary-precision arithmetic, polynomial and matrix operations, and support for various number types. It is widely used in mathematics and cryptography for its performance and efficient algorithms.
The article introduces Prism, an impure functional programming language that integrates typed effects using a system based on algebraic effects and row polymorphism. It demonstrates how Prism's type system tracks side effects like state, exceptions, and I/O, allowing pure and impure code to coexist safely within a unified framework.
A technical deep dive into the push_back implementation of LLVM's SmallVector, exploring its design trade-offs, memory management strategies, and performance characteristics compared to std::vector.
The website "Counterexamples in Type Systems" collects examples of programming language type systems that are unsound, meaning they accept programs that crash or violate type safety at runtime.