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In this work, we extend a latent-variable framework for CoT prompting and study its behavior on two prototypical out-of-distribution (OOD) scenarios: (i) the latent variables for CoT steps are permuted into novel combinations, and (ii) the latent variables uniformly scaled by a factor. Our experiments demonstrate that CoT inference generalizes effectively to OOD samples whose latent variables closely resemble those seen during training, but its performance degrades as this similarity decreases. These findings provide foundational insights into the strengths and limitations of CoT prompting under OOD conditions and suggest directions for developing more resilient reasoning strategies in future LLMs.","url_abs":"https://arxiv.org/abs/2504.12991v1","url_pdf":"https://arxiv.org/pdf/2504.12991v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"chain-of-thought-prompting-for-out-of","repo_url":"https://github.com/d09942015ntu/cot_ood_latent","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"in-context-learning","task_name":"In-Context Learning"}],"methods":[{"method_slug":"cot-prompting","method_name":"CoT Prompting"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2504.12991","atlas_url":"https://app.syntology.ai/?focus=2504.12991","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.12991"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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