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cs.AI2026
Anytime Safe PAC Efficient Reasoning
Chengyao Yu, Hao Zeng, Youxin Zhu +3
Large Reasoning Models (LRMs) have demonstrated remarkable performance on complex tasks but suffer from high computational costs and latency. While selective thinking strategies im…
cs.AI2026
GIFT: LLM-Guided State-Reward Interface for Financial Reinforcement Learning
Yanyan Wu, Boyi Zhang, Yanlin Liu +10
Financial portfolio trading is naturally formulated as a reinforcement learning problem, where an agent sequentially rebalances assets under changing market conditions to balance r…
cs.AI2026
Conditional Performance Guarantee for Large Reasoning Models
Jianguo Huang, Hao Zeng, Bingyi Jing +2
Large reasoning models have shown strong performance through extended chain-of-thought reasoning, yet their computational cost remains significant. Probably approximately correct (…