HRBench: Benchmarking and Understanding Thinking-Mode Switch Strategies in Hybrid-Reasoning LLMs
arXiv:2605.28398v1 Announce Type: new Abstract: Hybrid-reasoning large language models (LLMs) expose explicit controls over reasoning effort, allowing users or systems to trade off answer quality against inference cost. However, existing methods for adaptive thinking-mode selection are typically evaluated under different models, datasets, and implementation assumptions, making it difficult to compare their practical behavior. We introduce HRBench, a unified evaluation framework for studying thinking-mode switching in hybrid-reasoning LLMs. HRBench organizes the design space along two axes: thr
The proliferation of advanced LLMs necessitates efficient resource management and performance optimization, making benchmarking hybrid reasoning crucial for practical deployment and economic viability.
A unified framework for evaluating hybrid-reasoning LLMs allows for standardized comparison and accelerates the development of more efficient and capable AI systems, impacting their practical application across industries.
The introduction of HRBench provides a common ground for assessing 'thinking-mode switch strategies' in LLMs, enabling more transparent and effective development of AI agents.
- · AI developers
- · Cloud providers
- · Enterprises adopting AI
- · AI research institutions
- · Inefficient LLM architectures
- · Proprietary, non-standardized evaluation methods
Improved resource efficiency and performance of LLMs in inference tasks.
Faster and more reliable deployment of advanced AI agents in complex environments.
Enhanced competition among LLM providers based on transparent performance and cost metrics, driving further innovation.
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Read at arXiv cs.AI