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20242026
most citedKimi K2.5: Visual Agentic Intelligence

2 citations · 2 across the 9 of their papers we have counts for

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cs.CL2026

Kimi K3: Open Frontier Intelligence

Kimi Team, Tongtong Bai, Yifan Bai +398

We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token context window. Kimi K3 is…

cs.CL2026

When Calibration Rankings Reverse: Accuracy-Controlled Evaluation for Fair Comparison of LLMs

Zhichao Yang, Caiqi Zhang, Ruihan Yang +3

Calibration evaluates whether a model confidence aligns with its empirical accuracy. Existing studies often compare the calibration of different large language models using global…

cs.CL2026

Confidence Estimation for LLMs in Multi-turn Interactions

Caiqi Zhang, Ruihan Yang, Xiaochen Zhu +5

While confidence estimation is a promising direction for mitigating hallucinations in Large Language Models (LLMs), current research overwhelmingly focuses on single-turn settings.…

cs.CL20262 cited

Kimi K2.5: Visual Agentic Intelligence

Kimi Team, Tongtong Bai, Yifan Bai +339

We introduce Kimi K2.5, an open-source multimodal agentic model designed to advance general agentic intelligence. K2.5 emphasizes the joint optimization of text and vision so that…

cs.CL2025

Atomic Calibration of LLMs in Long-Form Generations

Caiqi Zhang, Ruihan Yang, Zhisong Zhang +4

Large language models (LLMs) often suffer from hallucinations, posing significant challenges for real-world applications. Confidence calibration, as an effective indicator of hallu…

cs.CL2025

The Lighthouse of Language: Enhancing LLM Agents via Critique-Guided Improvement

Ruihan Yang, Fanghua Ye, Jian Li +5

Large language models (LLMs) have recently transformed from text-based assistants to autonomous agents capable of planning, reasoning, and iteratively improving their actions. Whil…