@inproceedings{thakur-etal-2026-think,
    title = "Think Like You Execute: Verifiable Chain of Thought from Program Traces",
    author = "Thakur, Shailja  and
      Saxena, Vaibhav  and
      Kulkarni, Rohan  and
      Singh, Shivdeep  and
      Selvam, Parameswaran  and
      Kanayama, Hiroshi  and
      Patel, Hima",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.53/",
    pages = "775--795",
    ISBN = "979-8-89176-394-4",
    abstract = "Teaching language models to reason about code execution is still an open problem. Current synthetic Chain-of-Thought (CoT) training data often consists of plausible-sounding explanations generated by teacher models, not verifiable accounts of actual program behavior. This causes models to learn logically flawed reasoning patterns despite syntactic correctness.We address this by grounding CoT generation directly in program execution traces. Our pipeline instruments code to capture dynamic behavior, narrates execution traces into natural language, and actively verifies each rationale against the trace. We systematically create 54,000 execution-verified, bi-directional rationales that teach models to reason both forward (input$\rightarrow$output) and backward (output$\rightarrow$input). Models fine-tuned on our verified data achieve substantial improvements, with a performance boost of +24.2 on LiveCodeBench-Exec, +22.3 on CruxEval-Output, and +21.1 on CruxEval-Input, demonstrating that verification quality directly determines both reasoning and code generation capabilities."
}