这篇论文提出了PoTRE，用四个不同角色的智能体协作推理，在HLE上拿了新SOTA，而且比单纯堆参数的模型更省计算量。
PoTRE将推理过程解耦为四个代理：对抗细化代理、分层策略规划代理、频谱搜索代理和直接链代理，并通过任务自适应聚合层融合。在ARC-AGI-2、HLE和PRBench Finance三个基准上评估，PoTRE在HLE上达到49.92%的准确率，超越此前官方最佳成绩。该框架使用相似或更少的推理token实现了优于同质基线的推理性能。
PoTRE: Test-Time Reasoning inspired by Cognitive Heterogeneity
While Large Language Models (LLMs) excel at many tasks, they frequently struggle with complex reasoning that requires long-horizon planning and iterative error correction. Furthermore, standard single-stream prompting proves brittle when models encounter novel abstractions or rigorous domain constraints. We introduce PoTRE (Poly-Topological Reasoning Ensembles), a heterogeneous framework that decouples inference into four agents: (1) Adversarial Refinement Agent, (2) Hierarchical strategic Planning Agent, (3) Spectrum Search Agent, and (4) Direct Chain Agent. A final Task-Adaptive Aggregation Layer dynamically reconciles these perspectives -- via final candidate selection, semantic synthesis, or neuro-symbolic verification -- to produce a robust global solution. We evaluate PoTRE on three frontier benchmarks: ARC-AGI-2, Humanity's Last Exam (HLE), and PRBench Finance. PoTRE achieves state-of-the-art accuracy of 49.92% on HLE, surpassing the previous best official score. We demonstrate that this architectural heterogeneity achieves improved reasoning performance using similar or fewer inference tokens compared to heavily scaled homogeneous baselines.