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cs.AI2026
Finding the Minimal Parameter Budget for Implicit Reasoning: A Data Complexity Driven Scaling Law for Language Models
Xinyi Wang, Shawn Tan, Shenbo Xu +4
Reasoning is a core capability of language models (LMs), yet it remains unclear how much model capacity is necessary to support reasoning during pretraining. In this work, we study…
cs.AI2025
Dynamic Speculative Agent Planning
Yilin Guan, Qingfeng Lan, Sun Fei +5
Despite their remarkable success in complex tasks propelling widespread adoption, large language-model-based agents still face critical deployment challenges due to prohibitive lat…
cs.AI2024
Game-theoretic LLM: Agent Workflow for Negotiation Games
Wenyue Hua, Ollie Liu, Lingyao Li +9
This paper investigates the rationality of large language models (LLMs) in strategic decision-making contexts, specifically within the framework of game theory. We evaluate several…