activity
20222026
most citedGold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise Detection

2 citations · 5 across the 18 of their papers we have counts for

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17 papers · 1 filter

cs.CL2026

Co-Evolution in Agentic Systems: Toward Self-Directed Evolution Beyond Human Design

Qing Zong, Jiayu Liu, Junhao Shen +9

Agentic systems are increasingly expected to improve after deployment, yet single-entity self-evolution is often bounded by a static learning context, such as fixed tasks and feedb…

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.CL2025

CritiCal: Can Critique Help LLM Uncertainty or Confidence Calibration?

Qing Zong, Jiayu Liu, Tianshi Zheng +7

Accurate confidence calibration in Large Language Models (LLMs) is critical for safe use in high-stakes domains, where clear verbalized confidence enhances user trust. Traditional…

cs.CL2024

ComparisonQA: Evaluating Factuality Robustness of LLMs Through Knowledge Frequency Control and Uncertainty

Qing Zong, Zhaowei Wang, Tianshi Zheng +2

The rapid development of LLMs has sparked extensive research into their factual knowledge. Current works find that LLMs fall short on questions around low-frequency entities. Howev…

cs.CL20241 cited

What Really is Commonsense Knowledge?

Quyet V. Do, Junze Li, Tung-Duong Vuong +3

Commonsense datasets have been well developed in Natural Language Processing, mainly through crowdsource human annotation. However, there are debates on the genuineness of commonse…

cs.CL2024

Concept-Reversed Winograd Schema Challenge: Evaluating and Improving Robust Reasoning in Large Language Models via Abstraction

Kaiqiao Han, Tianqing Fang, Zhaowei Wang +2

While Large Language Models (LLMs) have showcased remarkable proficiency in reasoning, there is still a concern about hallucinations and unreliable reasoning issues due to semantic…