#60: DeepSeek, CAG, and the Future of AI Reasoning

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

Good morning, AI enthusiasts! The last two weeks in AI have been all about Deepseek-R1. So this week’s issue includes resources and discussions on that, along with emerging techniques such as CAG, AI agent frameworks like AutoGen, AG2, and Semantic Kernel, and more. Enjoy the read!

What’s AI Weekly

This week in What’s AI, I explore Cache-Augmented Generation (CAG), which has emerged as a true alternative to RAG. RAG is great for accuracy but needs time to search through and compare documents. And it gets worse the more data you have. This is where CAG comes in and says, “What if we just preloaded all that knowledge directly into the model’s memory?” So, let’s understand what CAG is, how it differs from RAG, and when to use both. Read the article here or watch the video on YouTube.

— Louis-François Bouchard, Towards AI Co-founder & Head of Community

Learn AI Together Community section!

Featured Community post from the Discord

Fuwafuwari. has built a resource website that can serve as a roadmap for anyone starting in the field of AI. It includes curated roadmaps, videos, articles, and other learning materials. You can start learning through the open-sourced GitHub repository. You can also contribute material and support a community member. If you have any questions or suggestions, reach out in the thread!

AI poll of the week!

Only a handful of people knew about DeepSeek a few days ago. Yet, thanks to the release of DeepSeek-R1, it’s been arguably the most discussed company, and the polls also show a clear inclination towards using DeepSeek. Is price guiding your decision? Tell us in the thread!

Meme of the week!

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TAI Curated section

Article of the week

Advancing Time Series Forecasting: A Comparative Study of Mamba, GRU, KAN, GNN, and ARMA Models By Shenggang Li

This article evaluates five models for multivariate time series forecasting: Mamba, GRU, KAN, GNN, and ARMA. It highlights the strengths and limitations of each, with KAN excelling due to its dynamic weighting for nonlinear relationships, achieving the lowest error rates. GRU performed well in capturing sequential dependencies, while Mamba balanced accuracy and interpretability. GNN showed moderate results, requiring further tuning, and ARMA struggled with multivariate complexity. It also proposes integrating Mamba and KAN for a unified framework, combining temporal modeling with nonlinear adaptability. This comparative study provides insights into selecting and enhancing models for complex forecasting tasks.

Our must-read articles

1. AutoGen, AG2, and Semantic Kernel: Complete Guide By Naveen Krishnan

This article provides a comprehensive guide to three AI agent frameworks: AutoGen, AG2, and Semantic Kernel. It explores their architectures, features, and use cases, offering practical examples for implementation. AutoGen introduces asynchronous messaging, modularity, debugging tools, and applications like AutoGen Studio for rapid prototyping. AG2, a community-driven evolution of AutoGen, focuses on agent orchestration and collaboration. Semantic Kernel, a lightweight framework, supports enterprise-grade AI integration. It concludes by highlighting the strengths of each framework, helping developers select the most suitable option for building intelligent, autonomous systems tailored to their needs.

2. DeepSeek-R1: The Open-Source AI That Thinks Like OpenAI’s Best By Yash Thube

This article introduces DeepSeek-R1, an open-source language model designed to rival OpenAI’s advanced models in reasoning tasks at a fraction of the cost. Using innovative techniques like Group Relative Policy Optimization (GRPO) and reinforcement learning, the model self-learns reasoning strategies without extensive human feedback. DeepSeek-R1 excels in benchmarks like AIME 2024 and coding tasks, offering clear, structured outputs. By releasing its weights and distillation recipes, DeepSeek democratizes AI, enabling developers to create specialized, cost-effective models.

3. Why Phi-4 14B Is So Much Better Than GPT-4o And o1 — Here The Results By Gao Dalie (高達烈)

This article compares Microsoft’s Phi-4 model with GPT-4o and O1, highlighting Phi-4’s strengths in mathematical reasoning and efficiency. Phi-4 features 14 billion parameters and excels in tasks requiring logical thinking, such as solving equations and financial modeling. It achieves high benchmark scores, surpassing larger models like Google’s Gemini Pro. It also demonstrates Phi-4’s usability on standard hardware through quantization techniques, making it accessible for local deployment. While O1 is faster, Phi-4’s open-source nature and performance make it a practical choice for developers with limited resources.

4. Bayesian State-Space Neural Networks (BSSNN): A Novel Framework for Interpretable and Probabilistic Neural Models By Shenggang Li

This article introduces the Bayesian State-Space Neural Network (BSSNN), a framework combining Bayesian principles, state-space modeling, and neural networks to enhance interpretability and probabilistic forecasting. BSSNN explicitly models joint and marginal probabilities, enabling it to predict outcomes (Y|X) and reverse inferences (X|Y). The article details its architecture, training process, and performance evaluation, comparing it to logistic regression. While BSSNN demonstrates improved accuracy and flexibility, challenges like computational demands and potential underfitting are noted.

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