CoT Evolved 3 New Chains for the Reasoning AI Era (Ep. 460)

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What started as a simple “let’s think step by step” trick has grown into a rich landscape of reasoning models that simulate logic, branch and revise in real time, and now even collaborate with the user. The episode explores three specific advancements: speculative chain of thought, collaborative chain of thought, and retrieval-augmented chain of thought (CoT-RAG).

Key Points Discussed

Chain of thought prompting began in 2022 as a method for improving reasoning by asking models to slow down and show their steps.

By 2023, tree-of-thought prompting and more branching logic began emerging.

In 2024, tools like DeepSeek and O3 showed dynamic reasoning with visible steps, sparking renewed interest in more transparent models.

Andy explains that while chain of thought looks like sequential reasoning, it’s really token-by-token prediction with each output influencing the next.

The illusion of “thinking” is shaped by the model’s training on step-by-step human logic and clever UI elements like “thinking…” animations.

Speculative chain of thought uses a smaller model to generate multiple candidate reasoning paths, which a larger model then evaluates and improves.

Collaborative chain of thought lets the user review and guide reasoning steps as they unfold, encouraging transparency and human oversight.

Chain of Thought RAG combines structured reasoning with retrieval, using pseudocode-like planning and knowledge graphs to boost accuracy.

Jyunmi highlighted how collaborative CoT mirrors his ideal creative workflow by giving humans checkpoints to guide AI thinking.

Beth noted that these patterns often mirror familiar software roles, like sous chef and head chef, or project management tools like Gantt charts.

The team discussed limits to context windows, attention, and how reasoning starts to break down with large inputs or long tasks.

Several ideas were pitched for improving memory, including token overlays, modular context management, and step weighting.

The conversation wrapped with a reflection on how each CoT model addresses different needs: speed, accuracy, or collaboration.

Timestamps & Topics

00:00:00 🧠 What is Chain of Thought evolved?

00:02:49 📜 Timeline of CoT progress (2022 to 2025)

00:04:57 🔄 How models simulate reasoning

00:09:36 🤖 Agents vs LLMs in CoT

00:14:28 📚 Research behind the three CoT variants

00:23:18 ✍️ Overview of Speculative, Collaborative, and RAG CoT

00:25:02 🧑‍🤝‍🧑 Why collaborative CoT fits real-world workflows

00:29:23 📌 Brian highlights human-in-the-loop value

00:32:20 ⚙️ CoT-RAG and pseudo-code style logic

00:34:35 📋 Pretraining and structured self-ask methods

00:41:11 🧵 Importance of short-term memory and chat history

00:46:32 🗃️ Ideas for modular memory and reg-based workflows

00:50:17 🧩 Visualizing reasoning: Gantt charts and context overlays

00:52:32 ⏱️ Tradeoffs: speed vs accuracy vs transparency

00:54:22 📬 Wrap-up and show announcements

Hashtags

#ChainOfThought #ReasoningAI #AIprompting #DailyAIShow #SpeculativeAI #CollaborativeAI #RetrievalAugmentedGeneration #LLMs #AIthinking #FutureOfAI

The Daily AI Show Co-Hosts: Andy Halliday, Beth Lyons, Brian Maucere, Eran Malloch, Jyunmi Hatcher, and Karl Yeh