Role of latent spatial representations in LLM planning retrieval

Determine whether latent spatial representations within large language models additionally contribute to retrieval efficiency when solving planning tasks framed as Blocksworld and their isomorphic generalized P* (Path-Star) graph formulations, beyond the evidence for an active algorithmic simulation strategy.

Background

The paper maps Blocksworld planning to a generalized P* (Path-Star) topology, where the table is the root and each stack of blocks is a branch. Because the PDDL encoding scrambles predicates, identifying the correct branch requires retrieving scattered dependencies across the full context, a process that could be computationally demanding without global structure.

The authors analyze reasoning traces from Gemini 3.0 Pro and find evidence of an active, serial algorithmic simulation mechanism. However, they hypothesize that latent geometric or spatial representations might also help the model efficiently retrieve the correct branch without exhaustive search. Whether such latent spatial representations contribute to retrieval efficiency remains explicitly unresolved.