continual learning
continual learning on Beyond Market Intelligence: a running collection of 4 stories we have gathered and hand-picked because they are worth your time. Every post here touches on continual learning in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around continual learning, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Deepity: A C++ library showing Predictive Coding Networks can match Backprop (97.73% on MNIST in 60s) [P]
Deepity, a newly developed C++ library, demonstrates the potential of Predictive Coding Networks (PCNs) to rival established backpropagation methods in machine learning. Through innovative algorithmic caching and incorporating recent research on Direct Kolen-Pollack Feedback Alignment, Deepity achieves 97.73% test accuracy on MNIST within 59.5 seconds – remarkably close to PyTorch’s 98.27% in 70 seconds. This significant performance leap addresses a historical challenge with PCN implementations.
Latent Reasoning Landscape in 2026: Mapping BDH-CQ, HRM/TRM, Coconut [D]
The trajectory toward Artificial General Intelligence (AGI) may be shifting away from complex, verbose chains of thought and toward latent reasoning—architectures that operate beyond the token stream. Our analysis, "Latent Reasoning Landscape in 2026," maps five distinct families of this emerging approach, from continuous autoregressive models like Coconut to task-trained recursive solvers. This exploration highlights BDH-CQ's impressive performance and scaling potential. As we move toward more efficient models, the question arises: what becomes of the readable traces crucial for interpretability?
![Continual Learning of Frontier Models for SovereignAI. Tech Report + Open Weights Model [R]](https://preview.redd.it/3fzb6dga0ilh1.png?width=640&crop=smart&auto=webp&s=28aa5b3250dc5aab05341f6874be2181cbd67ce4)
Continual Learning of Frontier Models for SovereignAI. Tech Report + Open Weights Model [R]
Frontier AI model development is often perceived as the domain of large, well-funded organizations, creating an imbalance in access and power. This report challenges that notion, arguing that continual learning on readily available open-weight models empowers a wider range of institutions to achieve frontier performance and build SovereignAI capabilities. Introducing Thomson, a new model demonstrating competitive results across diverse domains—including agentic tasks and multilingualism—with significantly reduced compute costs. As highlighted in our recent article, "Prompt injection ranks No.
Exploring continual learning without replay buffers: Our findings using dynamic task-similarity routing [P]
Continual learning often relies on memory-intensive replay buffers, but our research introduces Coincidex, an open-source framework that bypasses this limitation. By employing a dynamic task-similarity routing layer, Coincidex intelligently directs data streams without caching historical samples. Initial benchmarks demonstrate graceful transfer in clean task boundaries, achieving strong results without manual tuning. While performance can degrade with chaotic, long-tail task sequences, we share this lightweight alternative to empower exploration and welcome community feedback on addressing these challenges. Explore the code and architecture here: [https://github.com/rakib-nyc/coincidex](https://github.com/rakib-nyc