The Archive

MLP-LDRU architecture improves length generalization in neural networks via log-depth recurrent units with associativity-biased operators.

Neural-symbolic framework for complex query answering over knowledge graphs with multiple free variables.

>Long-context inference in large language models is bottlenecked by the quadratic cost of full attention. Existing efficient alternatives often rely either on native sparse training or on heuristic token eviction, creating an undesirable trade-off among efficiency, training…

Found a really clean Claude Code use case that's not coding-coding. Remotion (React for video) means motion graphics are JSX components. So I describe what I want in plain English, Claude Code writes the component, I render. Lower thirds, intros, overlays, all reusable…

OpenAI research: 4M Americans use ChatGPT for small business operations, reducing entrepreneurship costs.

Comprehensive study of model-generated agent skills lifecycle: extraction, consumption, adaptation across experience generation and domain-level reuse.

There is a harsh truth about Elon Musk's "truth-seeking" AI chatbot Grok: It's not very good, and not many people are using it. That's the takeaway of a new Reuters report, which found that Grok barely appears in federal records of how the US government used AI last year. It's…

Graph neural networks predict stability landscapes in synchronization networks, deriving per-node stability indices from topology.

RaNNDy uses fixed random weights in hidden layers for transfer operator approximation, reducing training cost via closed-form output layer solution.

GNN-LRP explainability method reduces computational complexity for walk-level relevance propagation in graph neural networks.

Dirichlet-based Monte Carlo Dropout framework improves uncertainty quantification in neural networks without full Bayesian complexity.

Scaling laws for sparse-activation neural networks show double-descent and asymmetric loss dynamics.

Study of training sample requirements for neural network inverse kinematics in robotic manipulators.

NHODE combines Hamiltonian neural networks with neural ODEs to learn partially observed dynamical systems without full state access.

Vague Reddit post hinting at Anthropic product news without substantive details.

Hivemind, an open-source Claude Code plugin that auto-generates reusable skills from repeated user prompts as slash commands.

We propose the Integrable Context-Dependent Demand Network (ICDN), a demand-first neural model for multiproduct retail demand. The model learns log-demand as a smooth, context-conditioned function of log-prices, allowing elasticities to be derived exactly from the learned…

Modern smart grids rely on dense measurement infrastructures, communication links, and intelligent field devices. Although this improves supervision and control, it also increases vulnerability to cyber-physical disruptions. Operators must distinguish physical incidents, such…

Real-world sensor-based learning systems require uncertainty estimation that is both reliable and computationally efficient. Evidential Deep Learning (EDL) provides single-pass uncertainty estimation by modeling the class probabilities via Dirichlet distributions, where the…

Establishes near-optimal approximation and generalization bounds for Multiple Neural Operators in multi-task learning with shared representations.

Hierarchical offline GCRL framework using relativised options and representational abstraction to reuse experience across state-goal symmetries.

Holographic functions framework connects sampling, structural, and computational complexity bounds for Boolean functions and neural networks.

ArXiv paper shows small open-source models drop honesty from 35% to 0% when prompt tone shifts from neutral to pressuring language.

The SpaceX IPO is here, and it's more than just an historic public offering that could make Elon Musk the world's first trillionaire. It also reveals more ways in which Elon Musk's companies interact and overlap with each other, shuffling money around in ways that are often…

Survey of approximation theory for neural networks covering universal approximation, quantitative rates, depth/width efficiency over four decades.

Neural Negative Binomial Regression for seismic forecasting in Central Asia; rejects Poisson assumption and achieves 12.5% lower CRPS than baseline.

Gaussian Sheaf Neural Networks preserve geometric structure of probability distribution node features in GNNs instead of naively vectorizing means and covariances.

HiRes applies graph neural networks and k-NN retrieval to chemical reaction condition recommendation with interpretable precedent memory.

Physics-informed active learning reduces training data requirements for neural operators solving PDEs.

SymbolicLight V1 combines spiking neural dynamics with dual-path attention for efficient language modeling with high activation sparsity.