An Inspectable LLM Council for Multi-Model Research Answer Aggregation
Long-form research questions often require answers that combine complementary details, reconcile conflicting estimates, and retain qualifications across several large language model (LLM) outputs. We present an inspectable LLM Council with two synthesis stages: an analyst organizes three independent candidate answers into a structured textual state, and a separately invoked writer composes the final response from the question and that state. We evaluate the workflow on 100 closed-book tasks from the Deep Research Accuracy, Completeness, and Objectivity (DRACO) benchmark, retaining 600 final answers, 100 analyst states, and 720 whole-answer judgments. Under the primary rubric judge, Counci...
| Authors | Shuai Wu, Xue Li, Yanna Feng et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 April 2026 |
| Last updated at source | 5 October 2026 |
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