There’s a peculiar comfort in watching a large language model lay out its thoughts step-by-step. You ask it to solve a logic puzzle, and it responds not just with an answer, but with a narrative: “First, I will identify the constraints. Then, I will map the variables. Finally, I will test the hypothesis.” It feels like peering into a cognitive process, a digital mind working through a problem with the same deliberation we use ourselves. We call this “Chain-of-Thought” (CoT) prompting, and for many, it represents the frontier of AI reasoning. But there is a fundamental distinction between performing a reasoning simulation and actually reasoning. And mistaking the former for the latter is a trap that even seasoned engineers can fall into, leading to brittle systems that collapse under the slightest pressure.
The allure of CoT is understandable. Early in the era of large language models, we discovered that simply asking for an answer often yielded mediocre results, especially on multi-step problems. The models would leap to plausible but incorrect conclusions. The breakthrough, popularized by researchers at Google in 2022, was to instruct the model to “think step by step.” This simple phrase, it turned out, dramatically improved performance on arithmetic, commonsense, and symbolic reasoning tasks. The model would generate a sequence of intermediate reasoning steps before arriving at a final answer, and this process often mirrored the logical structure required for a correct solution.
But we must be precise about what is happening here. The model is not maintaining an internal state of a problem, nor is it building a mental model of the world. It is, at its core, a text-generation engine. When we ask it to “think step by step,” we are essentially prompting it to generate a sequence of text that statistically resembles the kind of reasoning traces found in its training data. The model is a brilliant mimic. It has read countless math textbooks, logic proofs, and instructional guides. It knows the form of a reasoned argument. When it generates a CoT response, it is completing a pattern: “Given a problem of type X, a typical solution looks like this sequence of steps.”
The Illusion of Deliberation
This distinction is not merely academic; it is the difference between a robust reasoning system and a fragile parlor trick. A true reasoning system maintains a persistent, evolving state of the problem. As it takes a step, it updates its internal model, checks constraints, and prunes invalid paths. The chain of thought in a human or a classical algorithm is a byproduct of this internal state manipulation. For an LLM, the chain of thought is the manipulation. The state is the context window itself.
This leads to the first major fragility: context drift and the tyranny of the token window. A long CoT prompt consumes a significant portion of the available context. As the model generates its reasoning steps, the earlier parts of the problem and the initial constraints begin to “scroll off” the left side of the context window. The model’s attention mechanism, while powerful, is not perfect. It can lose track of a subtle condition mentioned dozens of tokens ago. The reasoning process becomes a game of telephone, where the initial message degrades with each subsequent step. The model might solve the intermediate steps correctly but forget the ultimate goal, or violate a constraint it established at the beginning. It’s like trying to solve a complex maze while only being able to see the last few feet of the path you’ve walked.
Consider a simple planning task: “Schedule a meeting for next Tuesday at 2 PM, but only if the room is available, and make sure to invite Alice, who is in the London time zone, and Bob, who is in California. Also, ensure it doesn’t conflict with the project deadline, which is the following Monday.” A human planner would immediately break this into sub-goals: check room availability, convert times, check calendars, verify the deadline. An LLM using CoT might start listing steps, but by the time it gets to the fourth or fifth step, the “project deadline” constraint might be fainter in its attention weights, leading it to schedule a meeting that technically fits the time slots but ignores the looming deadline. The reasoning is linear and unidirectional; there is no mechanism to go back and re-evaluate an earlier decision based on new information gathered later in the chain.
Error Accumulation: The Cascade of Failure
The second, and perhaps more critical, fragility is error accumulation. In a classical algorithm or a symbolic planner, each step is a discrete, verifiable operation. If a sorting algorithm swaps two elements, that swap is a deterministic fact. If a symbolic planner places a block on another, the state of the world is updated. Errors can occur, but they are local and can be detected by subsequent checks.
In a CoT LLM, every step is a probabilistic generation. The model might make a small arithmetic error in step 3 of a 10-step calculation. This error is not flagged or corrected; it becomes part of the context for step 4. The model, now operating on flawed premises, will generate step 4 based on the incorrect result of step 3. This error propagates and often amplifies. The final answer is almost certainly wrong, and the entire chain of reasoning, while looking plausible, is built on a faulty foundation. There is no backtracking, no iterative refinement. It’s a single, forward-only pass through a series of probabilistic token predictions.
This is where the comparison with explicit planning becomes stark. A symbolic planner, like those used in classical AI (e.g., STRIPS, PDDL), operates on a formal representation of the world state and a set of possible actions. The planner searches through a state space, not a token space. It can explore multiple branches, discard paths that lead to dead ends, and verify that a sequence of actions achieves the desired goal state. It reasons by manipulating symbols and checking logical constraints, not by generating text that looks like reasoning. The output of a symbolic planner is a verifiable plan, a sequence of actions guaranteed to achieve the goal from the initial state, assuming the model of the world is correct.
LLMs lack this formal verification step. Their “planning” is an emergent property of predicting the next most likely word in a sequence that resembles a plan. This is why they are notoriously bad at tasks that require strict adherence to rules, like generating valid JSON or SQL queries without syntax errors, unless they are specifically constrained or use tools to verify the output. The chain of thought might describe a perfect JSON structure, but the final generated text might still have a missing comma or a mismatched bracket, because the generative process is not bound by the formal rules of JSON syntax. It’s only bound by the statistical likelihood of characters appearing in sequence.
Hidden Reasoning vs. Explicit Planning
There’s a fascinating nuance in the architecture of modern LLMs called “hidden reasoning” or “implicit reasoning.” This is the model’s ability to solve problems without generating an explicit chain of thought. Sometimes, you can ask a complex question and, after a short pause, the model returns a correct answer directly. This suggests that the model is performing a significant amount of computation within its internal neural activations—the “hidden layers”—before the output generation even begins. The information is processed, transformed, and integrated across the network in a high-dimensional space that we cannot easily interpret.
This is fundamentally different from both CoT and explicit planning. In hidden reasoning, the model is leveraging its vast parameter space to find a solution path. It’s less like following a recipe and more like an intuitive leap, a pattern recognition on a colossal scale. The problem is that this process is a black box. We can’t inspect the “thoughts” that led to the answer. We can’t debug them or verify their correctness. The answer simply appears, a product of incomprehensible matrix multiplications.
Explicit planning, in contrast, is transparent and auditable. A classical planner’s output is a sequence of actions we can inspect, critique, and modify. We can ask why it chose a particular action and trace the logic back through the state transitions. This transparency is crucial for building reliable systems. When a CoT prompt fails, debugging is a frustrating process of tweaking the prompt and hoping for a different emergent behavior. When a symbolic planner fails, we can analyze the state space, the goal conditions, or the action definitions to find the source of the problem. The failure is deterministic and understandable.
The fragility of CoT becomes most apparent when we move from closed-domain puzzles to open-ended, real-world problems. In a math problem, the rules are fixed. In a real-world scenario, the context is dynamic and the goalposts can shift. A CoT-based agent tasked with “researching the best new laptop” might generate a plan: 1. Identify key criteria (CPU, RAM, storage). 2. Search for recent reviews. 3. Compare top 3 models. 4. Make a recommendation. This seems reasonable. But what if the search reveals that a new processor is about to be released, making current models obsolete? A human researcher would pivot, adjusting their plan. The CoT agent, locked into its initial reasoning chain, is likely to continue with its original steps, producing an outdated recommendation. It lacks the meta-cognitive ability to recognize that its underlying assumptions have changed.
Recursive Execution and Tool Use: A Hybrid Approach
This is not to say that generating step-by-step text is useless. It’s a powerful technique for guiding a model, especially when combined with other architectures. The key is to stop thinking of the LLM as a reasoning engine and start thinking of it as a component in a larger, more robust system. This is where concepts like recursive execution and tool use come into play.
Instead of asking a single model to produce a long, monolithic chain of thought, we can break the problem down and use the model for what it’s good at: generating sub-goals or acting as a natural language interface to other systems. This is the core idea behind frameworks like ReAct (Reasoning and Acting), where the model is prompted to generate both a reasoning step and an action to take (e.g., a search query or an API call). The result of that action is then fed back into the context, and the model generates the next step. This creates a loop that grounds the model’s reasoning in real-time information and external tools, preventing it from drifting into fantasy.
Let’s revisit the laptop research example. A ReAct-style agent would proceed differently:
Thought: I need to find the best laptop. First, I should identify the most important criteria for a laptop in 2024.
Action: search_web(“key criteria for laptops 2024”)
Observation: [Search results show articles discussing the importance of CPU performance, battery life, and display quality, with a note that a new generation of chips is imminent.]
Thought: The search results indicate a new chip generation is coming. I should adjust my plan to focus on current models that offer good value, as the new ones will be expensive. I will search for reviews of top current models.
Action: search_web(“best laptops 2024 reviews”)
…and so on.
Notice the crucial difference. The “chain of thought” is still present, but it’s interleaved with “observations” from the real world. The reasoning is not a closed loop; it’s an open dialogue with external data. This structure dramatically reduces error accumulation because each step is validated by an external source. It also mitigates context drift, as the most recent observation is always at the forefront of the context.
For even more complex tasks, we can introduce a recursive execution layer. Instead of a single agent trying to do everything, we can have a “manager” model that decomposes a high-level goal into sub-tasks. Each sub-task is then assigned to a specialized “worker” model or a traditional algorithm. For example, a task like “write a report on the quarterly sales of our top three competitors” could be broken down as follows:
- Manager: Decompose goal -> (a) Identify top three competitors. (b) Find their quarterly sales data. (c) Synthesize into a report.
- Worker A (Symbolic/Database): Query internal database for competitor list.
- Worker B (LLM + Tool Use): Use web search and financial APIs to gather sales data for the identified competitors.
- Worker C (LLM): Take the structured data from Worker B and write a coherent narrative report.
This architecture is far more resilient. The manager’s “reasoning” is a high-level plan, a symbolic decomposition of the problem. The workers execute discrete, verifiable steps. If Worker B fails to find data for one competitor, the manager can be alerted and can try a different strategy (e.g., use a different API, ask a human for input). The system doesn’t just march forward along a single chain of thought; it operates in a stateful, goal-oriented loop where failures are handled gracefully. This is the essence of planning: maintaining a goal, a set of sub-goals, and a state of the world, and reacting dynamically to achieve the objective.
Why Planners Outperform Long Prompts
The fundamental reason planners and recursive systems outperform long, monolithic CoT prompts is that they separate the problem representation from the solution search. In a CoT prompt, the problem representation is embedded in natural language, mixed in with the solution steps. This is an incredibly noisy and inefficient way to represent a problem. A single ambiguity in the prompt can derail the entire process.
In a symbolic planner, the problem is represented formally. The initial state, goal state, and possible actions are defined with mathematical precision. There is no ambiguity. The planner’s job is to search for a sequence of actions that transforms the initial state into the goal state. This search can be performed with powerful algorithms (like A* search) that are guaranteed to find a solution if one exists, and can be optimized for efficiency (e.g., finding the shortest path).
When we use an LLM as part of a planning system, we are essentially using it to translate a natural language request into this formal representation. The LLM generates the PDDL-like description of the problem, and a classical planner solves it. Or, the LLM generates the sequence of tool-using actions in a ReAct loop, which is itself a form of iterative planning. The LLM is the interface, not the engine. This leverages the model’s strengths—its ability to understand messy human language—while offloading the rigorous reasoning to a system that is actually designed for it.
Think of it like this: a CoT prompt is like asking a brilliant but forgetful mathematician to solve a complex problem on a whiteboard that is constantly being erased from the left side. They might get it right, especially for simple problems, but the risk of error is high. A planner is like giving the mathematician a notebook where they can write down each step, refer back to previous calculations, and cross-check their work. The recursive, tool-using system is like giving the mathematician a team of assistants and access to a library. The latter two are undeniably more powerful and reliable paradigms for problem-solving.
The evolution from simple prompting to CoT was a significant step. It showed us that we could guide LLMs to produce more structured and logical outputs. But the next, more profound leap is recognizing the limitations of that paradigm. True reasoning isn’t about generating a linear monologue of steps; it’s about maintaining state, verifying information, exploring alternatives, and adapting to new data. It’s about having a map of the problem space and a compass to navigate it, not just a trail of breadcrumbs left behind. The future of powerful AI systems lies not in making our models better mimics of reasoning, but in architecting systems where they, and other tools, can perform genuine, verifiable, and robust reasoning together. The chain of thought is a useful crutch, but we’re now learning to walk on our own, building systems that can think not just in words, but in states, actions, and goals.
