Can large language models actually create executable plans?

Solving planning tasks requires two distinct capabilities: (a) having planning domain knowledge — actions, preconditions, effects, hierarchical recipes, past cases — and (b) assembling that knowledge into an executable plan that handles subgoal and resource interactions. LLMs are strong at (a) and fail at (b). Only about 12% of plans that GPT-4 generates are actually executable without errors and goal-reaching.

The confusion between these two capabilities explains much of the conflicting literature on LLM planning. "Many papers claiming planning abilities of LLMs, on closer examination, wind up confusing general planning knowledge extracted from the LLMs for executable plans. When all we are looking for are abstract plans, such as 'wedding plans,' with no intention of actually executing said plans directly, it is easy to confuse them for complete executable plans."

Self-critiquing makes things worse, not better. LLMs "hallucinate both false positives and false negatives while verifying the solutions they generate." With self-verification, performance actually diminishes compared to systems with external sound verifiers. The nature of feedback — whether binary or detailed — shows minimal impact on generation, suggesting "the core issue lies in the LLM's binary verification capabilities rather than the granularity of feedback." Since Does self-revision actually improve reasoning in language models?, the self-critiquing failure in planning is the same mechanism operating on a different task type.

The proposed architecture is the LLM-Modulo framework: a generate-test-critique loop where LLMs generate candidate plans and a bank of external critics evaluates them. LLMs play multiple roles — guessing candidates, translating formats, helping users flesh out specifications, helping experts acquire domain models — but are never ascribed planning or verification abilities. Plans produced by this compound system have formal soundness guarantees because of the external critics.

Kambhampati's framing is precise: "LLMs are amazing giant external non-veridical memories that can serve as powerful cognitive orthotics for human or machine agents, if rightly used." The "non-veridical" is key — LLMs reconstruct completions probabilistically rather than indexing and retrieving exactly. "The boon ('creativity') and bane ('hallucination') of LLMs is that n-gram models will naturally mix and match."

Since Can language models understand without actually executing correctly?, the planning finding is a specific instance: LLMs comprehend planning domains (extract valid action descriptions, preconditions, effects) without being competent to execute plans (sequence actions that handle interactions and constraints). Since Why do language models fail to act on their own reasoning?, planning adds a third data point to the knowing-doing gap: 87% correct rationales in sequential decisions, 64% correct actions, and now 12% executable plans — the gap widens as task complexity increases.

Papers: LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks, Can Large Language Models Really Improve by Self-critiquing Their Own Plans?, Can Large Language Models Reason and Plan?