{"ID":3084565,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-06T15:44:26.945507316Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.05268","arxiv_id":"2606.05268","title":"Aggregating LLM-Based Weak Verifiers for Spatial Layout Generation","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.","short_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...","url_abs":"https://arxiv.org/abs/2606.05268","url_pdf":"https://arxiv.org/pdf/2606.05268v1","authors":"[\"Sharon Zhang\",\"R. Kenny Jones\",\"Jiajun Wu\",\"Maneesh Agrawala\"]","published":"2026-06-03T16:50:49Z","proceeding":"cs.GR","tasks":"[\"cs.GR\",\"cs.LG\"]","methods":"[\"Large Language Model\"]","has_code":false}