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arXiv AI By Wentao Sun, Jo\~ao Paulo Nogueira, Dominique Verchere, Mathieu Acher, Alonso Silva

Externalized CPDAG Summaries Improve LLM Causal Deduction

Read the original on arXiv AI →

The paper introduces Structured Thinking, a two‑turn pipeline that first generates a typed, schema‑constrained CPDAG summary and then uses that graph to answer causal queries. On the Corr2Cause benchmark, this approach boosts the Qwen3.5‑27B model’s F1 score from 73.0 to 86.4, outperforming a PC‑instruction baseline and demonstrating that externalizing the latent causal structure improves large‑language‑model reasoning. The study also shows that scrambling the CPDAG or using a prose intermediate degrades performance, supporting the principle that explicit, constrained representations aid downstream inference.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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