LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning

We present Team asdfo123's submission to the LLMSR@XLLM25 shared task, which evaluates large language models on producing finegrained, controllable, and interpretable reasoning processes.Systems must extract all problem conditions, decompose a chain of thought into statement-evidence pairs, and verify the logical validity of each pair.Leveraging only the off-the-shelf Meta-Llama-3-8B-Instruct, we craft a concise few-shots, multi-turn prompt that first enumerates all conditions and then guides the model to label, cite, and adjudicate every reasoning step.A lightweight post-processor based on regular expressions normalises spans and enforces the official JSON schema.Without fine-tuning, external retrieval, or ensembling, our method ranks 5th overall, achieving macro-F 1 scores on par with substantially more complex and resource-consuming pipelines.We conclude by analysing the strengths and limitations of our approach and outlining directions for future research in structural reasoning with LLMs.Our code is available at https:// github.com/asdfo123/LLMSR-asdfo123.