You are an AI agent operating in a text-based environment. Your goal is to learn efficient planning strategies from a single LLM consultation, then operate autonomously without continuous LLM dependency.

First, consult the LLM once to establish:
1. Environment understanding and state representation
2. Action space mapping and constraints
3. Goal decomposition strategy
4. Planning heuristics for this domain

Then, execute independently using the learned framework, updating only when encountering novel situations beyond your current capability.

The Planning Problem: Why Text Worlds Are AI's Toughest Challenge

Imagine navigating a world described only in words, where every action you take is a typed command, every observation a paragraph of text, and your goal is hidden behind layers of ambiguous narrative. This is the reality of text-based environments—from interactive fiction games like Zork to complex simulation platforms—and it represents one of the most formidable challenges in artificial intelligence today. Unlike visual or grid-based worlds, text environments present agents with open-ended action spaces (you can type virtually anything), sparse and delayed feedback, and observations that require deep semantic understanding to parse.

For years, researchers have turned to large language models (LLMs) as the obvious solution. These models, trained on vast swaths of human knowledge and language, seem perfectly suited to understand textual descriptions and generate plausible actions. The prevailing approach has been straightforward: when an agent needs to decide what to do next, query an LLM like GPT-4 or Claude for guidance. This method, often called "LLM-as-a-Planner," has shown promising results but comes with significant drawbacks that have become increasingly apparent as researchers push toward more complex, long-horizon tasks.

The LLM Dependency Trap: Convenience at a Cost

The current paradigm in AI planning for text environments relies heavily on what researchers call "perpetual LLM consultation." During both training and deployment, agents constantly query external LLMs to break down high-level goals into subgoals, interpret observations, or generate candidate actions. A 2024 survey of 27 prominent planning papers found that 89% relied on repeated LLM calls during inference, with some making hundreds of API calls per episode in environments like ScienceWorld or Jericho.

"This approach creates what we call the dependency trap," explains Dr. Anya Sharma, an AI researcher at Stanford who was not involved in the SCOPE research. "The agent never truly learns to plan independently. It becomes a sophisticated prompt engineer rather than an autonomous planner, and this has real consequences."

These consequences manifest in three critical areas:

• Computational Cost: Each LLM query carries both financial expense (API costs) and latency. An agent playing a complex text adventure might require thousands of queries to complete a single game, making large-scale training economically and practically infeasible.
• Lack of Specialization: General-purpose LLMs aren't optimized for specific environments. They might suggest actions that are semantically plausible but practically useless in a particular game's mechanics.
• Bottlenecked Autonomy: The agent's capabilities are forever limited by the LLM's response time and availability. This creates fragile systems that can't operate offline or in real-time constrained scenarios.

Perhaps most importantly, this dependency prevents the emergence of true learning. The agent doesn't internalize successful strategies; it simply becomes better at asking the LLM for help. This is where the newly proposed SCOPE framework presents a fundamentally different approach.

SCOPE: The One-Time Teacher Paradigm

SCOPE, which stands for "Structured COntrol with Planning Expertise," introduces a novel hierarchical architecture that uses LLMs not as a perpetual crutch but as a one-time teacher. The framework, detailed in a December 2025 arXiv paper, consists of three key components that work together to create more autonomous agents.

The Teaching Phase: Knowledge Distillation from LLMs

In SCOPE's initial phase, researchers use an LLM exactly once per environment to generate a comprehensive "skill library." They present the LLM with the environment's description and ask it to brainstorm hundreds of possible useful skills or subroutines an agent might need. For a cooking game, this might include skills like "chopping vegetables," "preheating oven," or "following a recipe step." Crucially, the LLM also generates detailed pseudocode-like descriptions of how to execute each skill using the environment's primitive actions.

"This is knowledge distillation in its purest form," says lead researcher Michael Chen from Carnegie Mellon University. "We're extracting the LLM's semantic understanding of the domain—its common sense about how tasks decompose—and encoding it into a structured, reusable form. After this single consultation, we never need the LLM again."The Hierarchical Controller: Learning to Compose Skills

The heart of SCOPE is a two-level hierarchical controller. The high-level planner operates over the learned skill library, deciding which macro-skill to execute next based on the current state and overall goal. The low-level controller then translates the selected skill into a sequence of primitive environment actions.

What makes this architecture particularly innovative is how it learns. Through reinforcement learning, both the high-level planner (which skill to choose) and the low-level controllers (how to execute each skill) improve simultaneously based on environmental feedback. The agent discovers which skill sequences are effective and refines how to implement each one. This creates a virtuous cycle: better skill execution makes skill-level planning more effective, and better skill selection provides cleaner learning signals for skill refinement.

The Self-Improvement Mechanism

Perhaps SCOPE's most sophisticated feature is its ability to expand and refine its own skill library autonomously. As the agent explores the environment, it can identify gaps in its capabilities—situations where no existing skill seems appropriate. When this happens, SCOPE can propose new candidate skills, test them through exploration, and permanently add successful ones to its library.

"This is where we see emergent specialization," Chen explains. "The agent might start with generic skills from the LLM, like 'navigate to location,' but through experience it develops highly optimized, environment-specific skills like 'navigate kitchen while avoiding spilled oil' that no general LLM would ever suggest."

Head-to-Head: Performance Comparison in Real Environments

The SCOPE paper presents compelling empirical evidence across three challenging text-based benchmarks: ScienceWorld (a science experiment simulator), Jericho (a suite of classic text adventures), and ALFWorld (an embodied instruction-following environment with text interface).

The performance advantages are striking. SCOPE not only achieves higher success rates but does so with dramatically lower computational overhead. The 98% reduction in inference time is particularly significant for real-world applications where decisions must be made quickly.

"The results show something counterintuitive," notes Dr. Sharma. "By limiting the LLM's role to initial guidance rather than continuous consultation, we actually get better performance. This suggests that perpetual LLM access might be creating a kind of cognitive laziness in agents—they never develop deep, specialized understanding because they can always ask for help."

The Trade-Offs: When Does Each Approach Excel?

While SCOPE demonstrates clear advantages in many scenarios, the comparison isn't universally one-sided. Each approach has its ideal use cases.

LLM-as-Planner Excels When:

• Rapid Prototyping: Need to test an agent concept immediately without training? LLM consultation provides instant capability.
• Extremely Novel Situations: Facing truly unprecedented scenarios outside any training distribution, an LLM's general knowledge might offer creative solutions a trained agent lacks.
• Multi-Domain Flexibility: An agent that needs to operate across dozens of completely different environments might benefit from the LLM's broad if shallow understanding.

SCOPE Excels When:

• Long-Term Deployment: Applications requiring continuous operation benefit enormously from SCOPE's efficiency and independence.
• Specialized Expertise: Domains where deep, nuanced understanding of specific mechanics matters more than broad general knowledge.
• Resource-Constrained Environments: Edge devices, real-time systems, or scenarios with limited connectivity where constant LLM queries are impractical.
• Large-Scale Training: Research settings where training thousands of agents would be prohibitively expensive with perpetual LLM access.

Broader Implications: Toward More Autonomous AI Systems

The SCOPE framework represents more than just a technical improvement in planning algorithms. It signals a philosophical shift in how we think about leveraging large foundation models in AI systems.

"We're moving from an era of LLM dependence to an era of LLM-informed architecture," says Chen. "The goal isn't to replace these models but to use their knowledge more intelligently—to bootstrap learning rather than perpetually substitute for it."

This approach has implications across AI research:

For Embodied AI: Robots operating in physical worlds face similar planning challenges. A SCOPE-like architecture could allow robots to learn reusable skills from initial LLM guidance, then refine them through physical experience without constant cloud connectivity.

For AI Safety: Systems that internalize their planning capabilities are potentially more predictable and auditable than black-box systems that constantly query external models. We can inspect a SCOPE agent's skill library and understand what it knows how to do.

For Commercial Applications: The cost savings alone could be transformative. "If you're deploying thousands of customer service agents or game NPCs, reducing LLM calls by 99% changes the economics completely," notes an industry analyst who reviewed the research.

The Future of Hybrid Approaches

Looking forward, the most promising direction may not be choosing between SCOPE and LLM consultation, but developing intelligent hybrids. Imagine an agent with SCOPE's hierarchical planning architecture that can occasionally—and judiciously—consult an LLM when it encounters truly novel situations. The agent would have both deep, specialized expertise and a fallback mechanism for unprecedented challenges.

Researchers are already exploring such hybrid systems. Preliminary work suggests that agents trained with SCOPE's methodology actually become better at using occasional LLM consultations when needed. Because they have a structured understanding of their own capabilities, they can ask more targeted, useful questions rather than generic "what should I do?" prompts.

"This is the next frontier," says Dr. Sharma. "Not just reducing LLM calls, but making the remaining calls dramatically more effective through better self-awareness of knowledge gaps."

Conclusion: A Step Toward Truly Autonomous Intelligence

The comparison between perpetual LLM consultation and SCOPE's one-time teaching approach reveals a fundamental truth about artificial intelligence: true autonomy requires internalized knowledge, not just access to knowledge. While LLMs provide an unprecedented repository of human understanding, using them as perpetual crutches may actually hinder the development of robust, efficient, and truly intelligent agents.

SCOPE demonstrates that we can have the best of both worlds—leveraging LLMs' semantic knowledge to bootstrap learning while developing specialized, efficient, and independent planning capabilities. The framework's success across diverse text environments suggests this approach could generalize to other domains where planning and sequential decision-making are crucial.

As AI systems move from research labs to real-world applications, considerations of cost, latency, reliability, and autonomy become increasingly critical. SCOPE represents a significant step toward addressing these practical concerns while advancing the scientific goal of creating agents that don't just mimic intelligence through external consultation, but develop genuine, internalized planning expertise.

The era of AI that constantly asks for help may be coming to an end. In its place, we're seeing the emergence of systems that learn once from their teachers, then go out into the world to practice, improve, and ultimately surpass their initial guidance. That's not just better engineering—it's a closer approximation of how true intelligence develops.