While reasoning-augmented large language models (RLLMs) significantly enhance complex task performance through extended reasoning chains, they inevitably introduce substantial unnecessary token consumption—particularly for simpler problems where Short Chain-of-Thought (Short CoT) suffices. This ”overthinking” phenomenon leads to inefficient resource usage without proportional accuracy gains. To address this issue, we propose Self-Route, a dynamic reasoning framework that automatically selects between general and reasoning modes based on model capability estimation. Our approach introduces a lightweight pre-inference stage to extract capability-aware embeddings from hidden layer representations, enabling real-time evaluation of the model’s ability to solve problems. We further construct Gradient-10K, a model difficulty estimation-based dataset with dense complexity sampling, to train the router for precise capability boundary detection. Extensive experiments demonstrate that Self-Route achieves comparable accuracy to reasoning models while reducing token consumption by 30-55% across diverse benchmarks. The proposed framework demonstrates consistent effectiveness across models with different parameter scales and reasoning paradigms, highlighting its general applicability and practical value.

Multi-agent debate systems are used to competitively discuss and resolve complex tasks. However, existing approaches often exhibit pseudo-debate behavior: agents either ignore opponents’ arguments or compromise too readily without critical evaluation. To address this, we propose a three-stage debate framework grounded in abstract argumentation theory. First, Debate Structuring uses principled prompts to elicit well-formed debate interactions. Second, Debate Imitation filters high-quality debates to fine-tune LLMs on proper argumentative conduct. Third, Debate Internalization employs a debate-aware reinforcement learning objective to instill adversarial reasoning patterns. To better quantify debate capability, we introduce two novel metrics: Rebuttal Engagement Score (RES), assessing how thoroughly models engage with opposing arguments, and Belief Governance Score (BGS), evaluating whether models appropriately retain or revise beliefs based on argument strength. Extensive evaluations across six benchmarks show that our framework not only improves task performance but also cultivates more authentic and robust debate behavior in LLMs. Our work contributes a theory-inspired training framework and evaluation suite for advancing multi-agent debate systems.

Large language models (LLMs) have mastered abundant simple and explicit commonsense knowledge through pre-training, enabling them to achieve human-like performance in simple commonsense reasoning. Nevertheless, LLMs struggle to reason with complex and implicit commonsense knowledge that is derived from simple ones (such as understanding the long-term effects of certain events), an aspect humans tend to focus on more. Existing works focus on complex tasks like math and code, while complex commonsense reasoning remains underexplored due to its uncertainty and lack of structure. To fill this gap and align with real-world concerns, we propose a benchmark Com² focusing on complex commonsense reasoning. We first incorporate causal event graphs to serve as structured complex commonsense. Then we adopt causal theory (e.g., intervention) to modify the causal event graphs and obtain different scenarios that meet human concerns. Finally, an LLM is employed to synthesize examples with slow thinking, which is guided by the logical relationships in the modified causal graphs. Furthermore, we use detective stories to construct a more challenging subset. Experiments show that LLMs struggle in reasoning depth and breadth, while post-training and slow thinking can alleviate this. The code and data are available at https://github.com/Waste-Wood/Com2.