Ares: Adaptive Reasoning Effort Selection for Efficient LLM Agents
Modern agents powered by thinking LLMs achieve high accuracy through long chain-of-thought reasoning but incur substantial inference costs. While many LLMs now support configurable reasoning levels (e.g., high/medium/low), static strategies are often ineffective: using low-effort modes at every step leads to significant performance degradation, while random selection fails to preserve accuracy or provide meaningful cost reduction. However, agents should reserve high reasoning effort for difficult steps like navigating complex website structures, while using lower-effort modes for simpler steps like opening a target URL. In this paper, we propose Ares, a framework for per-step dynamic reas...
| Authors | Jingbo Yang, Bairu Hou, Wei Wei et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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