Aggregating LLM-Based Weak Verifiers for Spatial Layout Generation

Sharon Zhang; R. Kenny Jones; Jiajun Wu; Maneesh Agrawala

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arXiv · 2606.05268

Aggregating LLM-Based Weak Verifiers for Spatial Layout Generation

Sharon Zhang·R. Kenny Jones·Jiajun Wu·Maneesh Agrawala

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Abstract

We present a pipeline for building and aggregating task-specific, LLM-generated weak (imperfect) verifiers into a strong verifier for spatial layout domains. Given a task description, our pipeline asks an LLM to synthesize a collection of verifier programs using a layout verification DSL. Each individual LLM-generated verifier usually provides an imperfect check for a match between the layout and the corresponding task description. We show that by aggregating the responses of many such verifiers we can produce a stronger verifier. Moreover, by applying techniques from weak learning, our pipeline can learn how to aggregate the weak verifiers from a very sparse set of human labeled example layouts (about 10). We find that the strong verifiers produced by our pipeline outperform the status-quo approach of using a set of LLM judges to directly check whether a layout matches a task description, raising F1-scores by up to 7X across a variety of 3D room layout and 2D poster design tasks. We also demonstrate that verifier-guided layout generation using natural language feedback from our strong verifiers improves layout quality of a base layout generator by up to 66.2% according to a human evaluator.

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Sharon Zhang, R. Kenny Jones, Jiajun Wu, Maneesh Agrawala. 2026-06-03. Aggregating LLM-Based Weak Verifiers for Spatial Layout Generation. https://arxiv.org/abs/2606.05268

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