For years, the promise of Artificial Intelligence has been tethered to the concept of the planner—the ability to formulate a sequence of actions to achieve a complex goal in an uncertain environment. We saw glimpses of this in the symbolic AI era, where systems like STRIPS (Stanford Research Institute Problem Solver) manipulated logical predicates to transform an initial state into a goal state. These systems were rigorous, mathematically elegant, and completely brittle. They lacked the robustness to handle ambiguity, perception, or the messy noise of the real world. Then came the Large Language Models (LLMs). They brought unprecedented fluency and pattern matching capabilities, yet they largely operate as stochastic oracles, generating one token at a time, often lacking a coherent plan to reach a final destination.
We are currently witnessing the emergence of a synthesis, a middle path that doesn’t require the rigid scaffolding of full symbolic systems but moves beyond the myopic token generation of standard LLMs. This bridge is being built by Recursive Language Models (RLMs)—systems that utilize recursion not just as a programming technique, but as a cognitive architecture for reasoning. By iterating on their own outputs and maintaining a hierarchical context, RLMs are beginning to exhibit the properties of planning, effectively turning the “what next” prediction of a transformer into a “how to get there” strategy.
The Limitations of Monolithic Generation
To understand why RLMs represent a significant shift, we must first diagnose the fundamental limitation of standard autoregressive models. When an LLM generates text, it is performing a sequential probability estimation. At each step, it looks at the context window and predicts the most likely next token. While this process can produce remarkably coherent essays or code snippets, it is inherently myopic. It does not possess an explicit goal state that it is trying to validate against; it is merely following the statistical gradient of its training data.
This leads to a phenomenon experienced by anyone who has spent significant time prompting models: the “drift.” A model might start writing a technical specification with clear intent, but three paragraphs in, it loses the thread, hallucinates details, or contradicts its earlier statements. The model lacks an internal executive function that monitors the generation process against a plan.
Symbolic systems, conversely, suffer from the opposite problem. They define the goal state explicitly ($P_{goal}$) and search the state space for a path from $P_{initial}$ to $P_{goal}$. This is excellent for domains with perfect information (like chess or theorem proving) but fails catastrophically when the environment is partially observable or the state space is too large to enumerate. The “Frame Problem”—the difficulty of specifying everything that does not change when an action is taken—plagues these systems.
RLMs attempt to resolve this tension. They acknowledge that pure statistical generation is too unguided, while pure symbolic search is too rigid. Instead, they introduce recursion: a process where the model uses its own output as input for a subsequent, more refined iteration, effectively creating a feedback loop that mimics the planning cycle.
Defining Recursive Language Models
When we talk about RLMs in the context of planning, we are not strictly referring to recursive programming functions within the model’s weights (though the architecture is likely a transformer). We are referring to the inference strategy and the reasoning architecture. An RLM treats the generation of a solution not as a single forward pass, but as a recursive decomposition problem.
Consider the difference between writing a novel in one sitting versus outlining it first. A standard LLM attempts to write the novel in one continuous stream. An RLM acts like an author who writes an outline (high-level abstraction), then recursively expands each section of the outline into paragraphs, and then recursively edits those paragraphs for style and coherence.
Mathematically, we can visualize this as a function $f$ applied iteratively to a context $C$:
$C_{t+1} = f(C_t, G_{goal})$
Where $C_t$ is the current context (including the prompt and previous generations) and $G_{goal}$ is the target objective. Unlike a standard LLM which minimizes the loss of the immediate next token, the RLM optimizes for the satisfaction of the objective $G_{goal}$ over multiple iterations $t$.
This recursive approach allows the model to build a “scratchpad” or a chain of thought (CoT) that evolves. In the first iteration, the model might generate a rough sketch of the solution. In the second iteration, it reads that sketch as context and fills in the gaps. In the third, it checks for logical consistency and refines the details. This is planning without the explicit state transition operators of symbolic AI; the “state” is simply the text in the context window, and the “transition” is the model’s prediction.
The Mechanics of Recursive Planning
The power of RLMs lies in their ability to manipulate the granularity of information. Planning is essentially a multi-scale process. When a human plans a trip from New York to London, they don’t think about the firing of individual neurons in their brain to move their leg. They think in high-level actions: “Book flight,” “Pack bag,” “Go to airport.” Only when they arrive at the airport do they decompose the “Go to airport” action into lower-level steps: “Check traffic,” “Call Uber,” “Navigate to terminal.”
RLMs achieve this through recursive decomposition. A common implementation pattern involves a “master” model generating a high-level plan, and a “sub-model” (often the same model with a different prompt) executing specific steps. However, a more elegant approach—and the one that truly defines an RLM—is when the model manages this hierarchy internally via context management.
Let’s look at a practical example: generating a complex software application. A standard LLM prompt: “Write a Python script for a web scraper” might yield a basic script, but it will likely lack error handling, proxy rotation, and data storage logic. It is a flat solution to a multi-dimensional problem.
An RLM approach would look different. The initial generation might be a high-level architecture description:
Iteration 1 (Architecture): The system needs a fetcher module, a parser module, a storage module, and a scheduler.
The model then recursively processes this architecture. It takes the “Fetcher module” line and generates the code for it. Then it takes that code and generates the unit tests for it. Then it takes the unit tests and the code and checks for coverage. This recursive loop creates a depth of solution that a single-pass generation cannot achieve.
The “planning” occurs because the model must maintain consistency across these recursive layers. If the parser module expects a specific data structure, the fetcher module must provide it. In a standard LLM, the fetcher and parser might be generated with slightly different assumptions, leading to integration errors. In an RLM, the recursive context forces the model to reconcile these assumptions before finalizing the code.
Recursion vs. Symbolic Reasoning: The Trade-off
Why not just use a symbolic planner? If the goal is to plan, shouldn’t we use a system that guarantees logical validity?
The answer lies in the “brittleness cost.” Symbolic planners require a complete world model. If you are planning a route for a robot, a symbolic planner needs to know the exact coordinates of every obstacle. If an obstacle moves, the plan fails. Symbolic systems struggle with partial observability. They cannot easily handle the “fuzzy” logic required to interpret natural language or visual data.
RLMs, powered by the statistical priors of their training data, possess a robust “common sense” world model embedded in their weights. They don’t need to be told explicitly that “ice melts when heated” or “traffic jams occur at 5 PM”; they infer these constraints from the patterns in their training data.
However, LLMs are notoriously unreliable at strict logical deduction. They might confidently state that $2 + 2 = 5$ if the context is manipulated correctly. This is where the recursive approach acts as a corrective.
By using recursion, we impose a structure on the LLM’s generation that mimics symbolic verification. Consider the process of theorem proving. A symbolic system uses rigid rules of inference. An RLM can simulate this by generating a “proof tree” recursively. It proposes a lemma, then recursively attempts to prove that lemma using the axioms available in its context.
While it doesn’t guarantee mathematical correctness in the way a symbolic prover does (because the model might hallucinate a step), it drastically increases the reliability of the reasoning process compared to a single-shot generation. It forces the model to “show its work,” and in doing so, it exposes logical gaps that can be detected and corrected in subsequent iterations.
The Role of Self-Correction and Reflection
A critical component of planning is the ability to recognize a dead end and backtrack. This is a recursive function in itself: $Plan(State)$ checks if $State$ is valid; if not, it returns $Plan(AlternativeState)$.
Standard LLMs are notoriously bad at admitting error. Once a model generates a token, it is generally committed to that trajectory due to the attention mechanism’s focus on previous tokens. RLMs, however, can implement a “reflection” step. This is a specific type of recursion where the model generates a plan, then switches roles to become a critic.
For example, in a coding task, an RLM might generate a function. Then, it appends a prompt to the context: “Review the following code for potential bugs and logical errors.” The model then generates a critique. Finally, it uses that critique to modify the original code.
This is distinct from simple “chain of thought” prompting. Chain of thought is linear: $A \rightarrow B \rightarrow C$. Recursive planning is hierarchical and iterative: $A \rightarrow (A_{critique} \rightarrow A_{refined}) \rightarrow B$.
This iterative refinement is essentially a gradient descent on the “error surface” of the solution. In symbolic systems, this is explicit backtracking. In RLMs, it is a probabilistic exploration of the solution space. The model isn’t just predicting the next line of code; it is predicting the next line of code that satisfies the constraints established by the previous critique.
Managing Context Windows in Recursive Architectures
One of the practical challenges in implementing RLMs is the context window limitation. As the recursion deepens, the “history” of the planning process grows. If you recursively expand every step of a complex plan, you will inevitably hit the token limit of the model.
This constraint forces a specific architectural choice: summarization and abstraction. In a recursive planning system, not all generated tokens are treated equally. The system must decide what to keep in the active context and what to summarize.
Imagine a tree of reasoning. The root is the initial problem. The first level of children are the high-level steps. The second level are the details. If we keep the entire tree in the context, we run out of space. An effective RLM implementation uses a “sliding window” of attention, focusing on the current branch of the recursion while maintaining a summarized “header” of the other branches.
This mimics human working memory. We can hold a complex plan in our heads, but we only focus on the immediate step. The details of the previous steps fade into long-term memory (summarized context), while the current step remains in high-resolution focus.
Programmatically, this looks like a state machine where the “state” is the current context window. As the model recurses deeper, it pushes a summary of the current state onto a stack and loads the new sub-problem into the context. When the sub-problem is solved, it pops the stack and resumes the higher level.
RLMs in the Wild: Tool Use and Execution
The true test of a planning system is not just generating a plan, but executing it. RLMs are proving to be superior to standard LLMs in tool-use scenarios (agents) precisely because of their recursive nature.
When an LLM is asked to use a calculator or a search engine, a standard approach involves a single function call. But complex tasks require multiple tool uses in a sequence where the output of one informs the input of the next.
Consider a scenario where an AI needs to analyze a dataset, write a report, and publish it. A standard LLM might hallucinate the data analysis. An RLM, however, can structure the process recursively:
- Plan: Generate the steps (Analyze Data $\rightarrow$ Write Report $\rightarrow$ Publish).
- Execute (Analyze Data): The model recognizes it needs real data. It recursively pauses the “Write Report” step to call a tool (e.g., a Python interpreter) to generate or fetch data.
- Integrate: It takes the output of the tool (the data) and injects it back into the context.
- Resume: It resumes the “Write Report” step, now grounded in the actual data.
This recursive interleaving of planning and acting is known as “ReAct” (Reasoning and Acting) in the literature, and it is a prime example of RLM principles at work. The model doesn’t just generate a static plan; it generates a plan, acts on a part of it, observes the result, and then recursively replans based on the new observations.
This is a massive leap toward true agency. A symbolic agent requires a pre-defined world model with all possible actions and their consequences enumerated. An RLM agent can define its own actions (via prompting) and adapt its plan based on the unpredictable outputs of those actions.
The “Thought” Token: Internal Recursion
There is a fascinating micro-level manifestation of recursion happening inside modern LLMs: the generation of “thinking” tokens. In models trained specifically for reasoning (like OpenAI’s o1 or DeepSeek’s R1), the model outputs a long string of internal reasoning before producing the final answer.
These “thought” tokens are essentially a recursive simulation running inside the context window. The model generates a step of reasoning, appends it to the context, and then uses that new token as the basis for the next step of reasoning.
It is a literal recursion loop:
$Output = Model(Input + PreviousOutput)$
Where $PreviousOutput$ is the chain of thought.
This technique allows the model to perform “Tree of Thoughts” (ToT) implicitly. Instead of exploring a tree of possibilities externally, the model explores them internally by writing them out. It might write “Option A seems good because X, but Option B is better because Y.” This is the model using its own output to refine its internal state.
For the engineer, this is significant because it decouples the complexity of the reasoning from the complexity of the model architecture. We don’t necessarily need a larger model; we need a model that is allowed to “think” longer. By increasing the number of recursive steps (the length of the thought chain), we can solve problems that require planning depth rather than just breadth of knowledge.
Challenges and Failure Modes
While RLMs represent a powerful paradigm, they are not a silver bullet. The recursive nature introduces new failure modes that engineers must be aware of.
Error Propagation: In a linear generation, an error might lead to a hallucination. In a recursive system, an error in the initial planning phase can cascade through all subsequent recursive steps. If the high-level architecture is flawed, the detailed code will be flawed, no matter how well-written the individual functions are. The system might confidently implement the wrong plan with perfect syntax.
Computational Cost: Recursion is expensive. Generating a plan, critiquing it, and refining it requires multiple forward passes of the model. This increases latency and token usage. For real-time applications, the trade-off between planning depth and response time is critical.
Repetition Loops: Without proper termination conditions, a recursive model can get stuck in a loop, generating the same critique and the same “improved” solution repeatedly. The model needs a mechanism to recognize when the marginal utility of further refinement drops below a threshold.
Context Drift: Even with summarization, maintaining coherence over a long recursive session is difficult. The model might forget the original constraints of the problem after hundreds of tokens of intermediate reasoning. This is an active area of research, with solutions like “memory streams” and “vector-based context retrieval” being integrated into RLM architectures.
Implementation Strategies for Developers
For developers looking to build systems that leverage these principles, the key is to move away from monolithic prompts and toward multi-turn, stateful interactions.
Instead of asking the model to “Solve problem X,” structure your application as a state machine. Define states such as “Planning,” “Drafting,” “Critiquing,” and “Finalizing.” Use the LLM to transition between these states.
Here is a conceptual Python-like pseudocode for an RLM agent handling a complex task:
def recursive_planner(goal, context):
plan = llm_generate("Create a plan to achieve: " + goal)
for step in plan:
# Execute the step
result = execute_step(step)
# Recursive check: Is the result satisfactory?
critique = llm_generate(f"Critique the result: {result} against goal: {goal}")
if "unsatisfactory" in critique:
# Recursive refinement
refined_result = refine_step(step, result, critique)
context.append(refined_result)
else:
context.append(result)
return context
This structure ensures that every step is validated against the overarching goal, creating a feedback loop that is characteristic of recursive planning.
Furthermore, developers should experiment with “prompt chaining” where the output of one LLM call is strictly formatted to serve as the input for the next. By enforcing a schema (e.g., JSON) on the intermediate recursive steps, we add a layer of symbolic structure to the probabilistic generation. This hybrid approach—using rigid schemas for intermediate steps while allowing free-form generation for the final output—leverages the strengths of both symbolic and neural paradigms.
The Future: Emergent Planning Capabilities
We are currently in a transition period where the distinction between “language modeling” and “planning” is dissolving. As models become larger and more capable, and as inference-time compute (the amount of computation done during generation) increases, the recursive behaviors we are engineering manually are beginning to emerge naturally.
The ultimate goal is a system that can plan as fluidly as it speaks. RLMs are the stepping stone to that future. They demonstrate that we do not need to wait for a new “Artificial General Intelligence” architecture to appear. We can repurpose the transformer, a device designed for next-token prediction, into a recursive engine capable of complex, multi-step reasoning.
For the engineer, this is an invitation to experiment. The tools are available not just to query a model, but to orchestrate it. By wrapping LLMs in recursive loops, by giving them the ability to reflect on their own output, and by structuring their generation into hierarchical plans, we are effectively teaching these systems to think. We are building the bridge between the stochastic parrot and the strategic planner, one recursive call at a time.
The implications extend beyond simple coding tasks or text generation. Imagine recursive models planning logistics for supply chains, designing drug discovery protocols, or coordinating swarms of autonomous drones. In these domains, the ability to decompose a high-level goal into executable steps, monitor the execution, and adapt to unforeseen circumstances is paramount. RLMs provide the framework to do this without the heavy burden of hand-crafting symbolic world models for every new domain.
As we continue to push the boundaries of what these models can do, the focus will shift from “what can the model generate” to “how does the model reason.” The recursive architecture is the key to unlocking that reasoning, providing a glimpse into a future where AI doesn’t just predict the next word, but understands the path to the destination.
