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20242026
most citedSimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains

1 citations · 3 across the 18 of their papers we have counts for

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cs.AI2026

MiniCorp: The Last Mile to an AI Agent Corporation

Jingying Zeng, Zhenwei Dai, Jinning Li +6

The last mile toward enterprise AGI is a company that runs itself. Training and adapting such agents require longitudinal enterprise data, which remain scarce, costly to acquire, a…

cs.SE2026

Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs

Yuxuan Lu, Ziyi Wang, Yingzhou Lu +12

Training tool-calling agents requires large-scale trajectory data with verifiable labels, yet existing approaches either synthesize environments that diverge from real API behavior…

cs.AI2026

Position: Agentic Evolution is the Path to Evolving LLMs

Minhua Lin, Hanqing Lu, Zhan Shi +11

As Large Language Models (LLMs) move from curated training sets into open-ended real-world environments, a fundamental limitation emerges: static training cannot keep pace with con…

cs.AI2026

How Do Latent Reasoning Methods Perform Under Weak and Strong Supervision?

Yingqian Cui, Zhenwei Dai, Bing He +7

Latent reasoning has been recently proposed as a reasoning paradigm and performs multi-step reasoning through generating steps in the latent space instead of the textual space. Thi…

cs.AI2026

How Far Are LLMs from Professional Poker Players? Revisiting Game-Theoretic Reasoning with Agentic Tool Use

Minhua Lin, Enyan Dai, Hui Liu +11

As Large Language Models (LLMs) are increasingly applied in high-stakes domains, their ability to reason strategically under uncertainty becomes critical. Poker provides a rigorous…