The Evolution of Large Model Chain of Thought (CoT)
In the rapid development of artificial intelligence, people have always expected large models to "emerge" with high-level intelligence akin to humans once their parameter count breaches a certain threshold. However, simply stacking parameters and burning money for computing power does not necessarily guarantee that AI will truly master complex logical reasoning. The critical key that allowed large models to achieve a qualitative leap in logical, mathematical, and decision-making capabilities is a prompt engineering technique called "Chain of Thought" (CoT).
What is CoT?
If traditional large models are "buzzers" that rely on intuition for Input $\rightarrow$ Output mapping, then CoT forces the model to clearly write down the intermediate "solution steps" before giving the final answer.
Just like in a math exam, directly writing down the answer, even if correct, won't get you full marks; you must list out the "known conditions, derivation formulas, and calculation processes" step by step. CoT cleverly utilizes this point by requiring the model to display intermediate reasoning chains (Input $\rightarrow$ Reasoning Chain $\rightarrow$ Output), which greatly reduces the probability of errors in complex tasks and grants the model a certain degree of interpretability.
Innovation Driven by Demand: The Evolutionary Path of CoT Technology
Since its introduction in 2022, CoT has evolved from a simple prompting trick into a complex reasoning architecture. Its development is essentially a leap from single-thread passive deduction to structured active search.
I - The Enlightenment Phase: Few-shot-CoT and Zero-shot-CoT
- Few-shot-CoT: This is the starting point of CoT. Researchers discovered that manually providing a few examples with detailed reasoning steps in the prompt leads the large model to "follow suit" and generate corresponding deduction processes when answering new questions.
- Zero-shot-CoT: To eliminate reliance on high-quality manual examples, researchers found a "magic spell"—simply adding "Let's think step by step" at the end of the prompt can automatically activate the large model's reasoning mechanism, prompting it to generate steps autonomously.
II - Improving Robustness: CoT-Self-Consistency (CoT-SC)
The flaw of a single chain of thought is its extremely low error tolerance: making a wrong turn at a crossroad in a maze leads to global failure. CoT-SC introduces the concept of "multi-thread parallel solving." It allows the model to generate multiple different reasoning paths for the same problem and finally selects the most frequent answer through a Majority Vote mechanism. This method significantly improves the stability and accuracy of reasoning.
III - Structured Search: Tree of Thoughts (ToT)
If CoT-SC is like having several people solve a problem independently and then comparing answers, then ToT is like letting AI deduce moves like a human playing chess. ToT models the reasoning process as a search tree:
- Nodes and Expansion: Each reasoning step is a node, and multiple branches can be expanded when facing a divergence.
- Evaluation and Backtracking: The model can evaluate the success rate of the current path. If it hits a dead end, it can use algorithms like Depth-First Search (DFS) or Breadth-First Search (BFS) to backtrack and try new branches from a previous step.
IV - Advanced Network Expansion: GoT, AoT, and PoT
- Graph of Thoughts (GoT): Elevates the tree structure into a "directed acyclic graph." It allows complex thoughts to intersect: several different ideas can be merged (aggregation), or iteratively refined at a single point, greatly accommodating non-linear thinking scenarios.
- Algorithm of Thoughts (AoT): To reduce the massive computing consumption caused by frequent LLM calls in ToT and GoT, AoT internalizes the logic of classic algorithms like depth-first search into a single model generation, achieving efficient self-correction through a single continuous context.
- Program of Thoughts (PoT): Addressing the shortcoming of frequent errors in numerical calculations, PoT makes the model solely responsible for writing out the calculation logic code using programming languages like Python, which is then executed by an external interpreter to obtain the result, achieving a perfect decoupling of "reasoning" and "computation."
V - Efficiency Breakthrough: Skeleton-of-Thought (SoT)
To solve the latency issue caused by step-by-step reasoning in large models, SoT mimics the human habit of writing articles by "outlining first, then filling in the details." It asks the model to first generate the skeleton of the answer and then expand the details for each skeleton node in parallel, thereby drastically reducing response latency without losing much coherence.
2025-2026 Frontier Tech: Self-Optimizing Chain-of-Action-Thought (COAT)
Although methods like ToT have achieved structured reasoning, they often rely on external heuristic rules or human intervention to decide when to stop or backtrack. In the latest AI evolution, CoT has officially entered a phase of complete self-optimization.
Chain-of-Action-Thought (COAT) formulates LLM reasoning as a sequential decision-making problem. A prime example is the Satori Model introduced at ICML 2025. The model generates not only text but also specific meta-action tokens:
<|continue|>: Encourages the model to build upon its current reasoning trajectory.<|reflect|>: Prompts the model to pause and verify if there are logical flaws in prior steps.<|explore|>: When a dead-end is identified, it signals the model to abandon the current logic and explore alternative solutions.
By combining the Restart and Explore (RAE) mechanism in reinforcement learning (RL), small models with billions of parameters (like the 7B scale) using the COAT architecture have demonstrated performance surpassing traditional giant models in complex mathematical and cross-domain reasoning tasks, achieving an overtake via "small models + refined autonomous search."
Limitations and Future Outlook
Undeniably, chain of thought technology still faces severe challenges today:
- Computing Power and Cost Bottlenecks: Generating massive intermediate steps means huge token consumption and extremely high inference latency, making it difficult to popularize in real-time scenarios requiring rapid responses.
- Model Scale Sensitivity: Traditional CoT is highly dependent on the innate parameter scale of the base model (usually requiring tens of billions of parameters). Forcing small models to use it often leads to logical collapse (although COAT is attempting to break this barrier).
- Hallucinations of Pseudo-Logic: The "because... therefore..." output by the model is sometimes just statistical fitting at the probability level rather than true causal deduction. Users can easily be deceived by seemingly flawless pseudo-logic.
The chain of thought is not only the engine for improving AI reasoning capabilities but also a spotlight for humans to peek into the inner workings of the model's "black box." From initially accepting prompts passively to now being able to spontaneously reflect, correct errors, and explore, the logical closed loop of large language models is becoming increasingly complete.