Large language models (LLMs) have demonstrated limitations in handling combinatorial optimization problems involving long-range reasoning, partially due to causal hallucinations and huge search space. As for causal hallucinations, i.e., the inconsistency between reasoning and corresponding state transition, this paper introduces the Causal Relationship Enhancement (CRE) mechanism combining cause-effect interventions and the Individual Treatment Effect (ITE) to guarantee the solid causal rightness between each step of reasoning and state transition. As for the long causal range and huge search space limiting the performances of existing models featuring single-direction search, a Dual-End Searching (DES) approach is proposed to seek solutions by simultaneously starting from both the initial and goal states on the causal probability tree. By integrating CRE and DES (CreDes), our model has realized simultaneous multi-step reasoning, circumventing the inefficiencies from cascading multiple one-step reasoning like the Chain-of-Thought (CoT). Experiments demonstrate that CreDes significantly outperforms existing State-Of-The-Art (SOTA) solutions in long-range reasoning tasks in terms of both accuracy and time efficiency.

翻译：大语言模型在处理涉及长程推理的组合优化问题时表现出局限性，部分归因于因果幻觉和巨大的搜索空间。针对因果幻觉——即推理步骤与相应状态转移之间的不一致性，本文提出因果关系增强机制，结合因果干预与个体处理效应，确保推理步骤与状态转移间具备坚实的因果正确性。针对长因果链与巨大搜索空间对现有单向搜索模型的性能限制，提出双端搜索方法，通过在因果概率树上同时从初始状态和目标状态出发寻求解路径。通过集成因果增强与双端搜索，本模型实现了同步多步推理，避免了类似思维链等串联式单步推理的低效性问题。实验表明，在长程推理任务中，CreDes在准确性与时间效率方面均显著优于现有最优解决方案。