21 citations · 56 across the 26 of their papers we have counts for
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cs.AI2026
Evaluating Agentic Capabilities Before Post-Training: An End-to-End Approach
Jiarui Qin, Junjie Huang, Yunjia Xi +5
Agentic capabilities are increasingly important for modern LLMs, yet remain difficult to evaluate during pretraining. Existing approaches typically rely on static trajectory-derive…
cs.AI2025
APTBench: Benchmarking Agentic Potential of Base LLMs During Pre-Training
Jiarui Qin, Yunjia Xi, Junjie Huang +6
With the rapid development of LLM-based agents, there is a growing trend to incorporate agent-specific data into the pre-training stage of LLMs, aiming to better align LLMs with re…
cs.AI2025
Superplatforms Have to Attack AI Agents
Jianghao Lin, Jiachen Zhu, Zheli Zhou +4
Over the past decades, superplatforms, digital companies that integrate a vast range of third-party services and applications into a single, unified ecosystem, have built their for…