IMO-CoT is introduced, a novel, selective, information-rich benchmark derived from International Mathematics Olympiad (IMO) problems, designed to evaluate CoT reasoning capabilities in LLMs.
Large language models (LLMs) have recently improved their problem-solving abilities and can solve complex mathematical problems with an increasing accuracy, necessitating the development of more challenging benchmarks. Over the years, the performance of LLMs on several benchmark datasets has also improved, motivating t...
Anurag Dutta, S. Priya, A. Ramamoorthy et al.· AppliedMath· 0 citations
This work presents a theoretical framework that reveals how reasoning steps can amplify error through three failure modes: incorrect sub-task decomposition, incorrect sub-task solving, and incorrect final answer summarization, and introduces structured interventions that adapt CoT generation according to the identified...
Haibo Jin, Peiyan Zhang, Man Luo et al.· Neural Information Processin...· 1 citation
Introducing Relay-Bench, an unsaturated, holistic, text-only benchmark that measures LLMs'ability to complete an assortment of tasks from distinct domains in a single prompt. The leading model, GPT-5.5 (xHigh), scores 43.3%. The test set entirely consists of composite problems: groups of single-domain subproblems that...
Reason Popper-ly, a neurosymbolic framework that uses inductive logic programming (ILP) to learn relation composition rules from reasoning traces and deploys them as an online verifier for step-level correction, consistently improves terminal accuracy over standard CoT.
A novel RAIE taxonomy along four scaling dimensions is proposed, which optimizes the entire thought process through search algorithms and self-verification, and introduces a task-oriented guideline for choosing the best TTS strategy.
Jia-Yu An, Zheng Chen, Yong-Cheng Jing et al.· Proceedings of the Thirty-Fi...· 0 citations
This work proposes ChainPrune, a novel reasoning path semantic structural optimization method to efficiently and controllably synthesize self-generated high-quality training data and incorporates a DPO-based preference learning method combined with supervised loss, effectively mitigating false reward suppression.
Weihang Pan, Zhengxu Yu, Yuxiang Zhang et al.· 1 citation
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