Model Detail
Perceptron: Perceptron Mk1.5
—Perceptron: Perceptron Mk1.5 is a multimodal model released by Perceptron. And supports text+image+audio+video->text inputs.
Perceptron: Perceptron Mk1.5 is priced at $0.15/M input tokens and $1.5/M output tokens. Operationally the model offers a 37K-token context window, which matters when sizing it for prompt-heavy or latency-sensitive workloads. At this input rate the model sits in the commodity tier and is suitable for high-volume workloads where per-call cost dominates the decision.
Perceptron: Perceptron Mk1.5 is published on Hugging Face but our pipeline has not yet captured architecture, license, or parameter-count metadata for this entry. The data is refreshed daily, so these fields typically populate within 24–48 hours of release.
Perceptron: Perceptron Mk1.5 is best fit for mixed text-and-image reasoning tasks such as document understanding, and high-volume batch jobs where per-call cost dominates the budget. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
LearnedCache: eBPF-Integrated Perceptron-Based Eviction Policies for the Linux Page Cache
arXiv:2605.26168v2 Announce Type: replace-cross Abstract: Any device that runs Linux uses the Linux page cache, a central pillar in OS and application performance, serving to reduce extraneous disk access. Many page cache eviction policies have been developed but remain bound by the rigidity of heur
From Conceptual Hydrologic Models to Conceptually Interpretable Neural Networks: A Snow-Water Mass-Conserving-Perceptron Framework for Discovering Catchment-Scale Precipitation-Storage-Runoff Representations
arXiv:2607.26492v1 Announce Type: new Abstract: The Mass-Conserving Perceptron (MCP) establishes a modeling paradigm in which conceptual hydrologic models can be reformulated as physically constrained, conceptually interpretable neural networks. Here, we develop a snow-water MCP network framework an
Statistical physics of deep learning: Optimal learning of a multi-layer perceptron near interpolation
arXiv:2510.24616v5 Announce Type: replace-cross Abstract: For four decades statistical physics has been providing a framework to analyse neural networks. A long-standing question remained on its capacity to tackle deep learning models capturing rich feature learning effects, thus going beyond the na
LearnedCache: An eBPF-Integrated Perceptron-Based Eviction Policy for the Linux Page Cache
arXiv:2605.26168v1 Announce Type: cross Abstract: Linux is the foundation of the digital age, accounting for the majority of the cloud and mobile OS markets. Any device that runs Linux uses the Linux page cache, a central pillar in OS and application performance, serving to reduce extraneous disk ac
Gradient Descent as a Perceptron Algorithm: Understanding Dynamics and Implicit Acceleration
arXiv:2512.11587v2 Announce Type: replace Abstract: Even for the gradient descent (GD) method applied to neural network training, understanding its optimization dynamics, including convergence rate, iterate trajectories, function value oscillations, and especially its implicit acceleration, remains
