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20212026
most citedA Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection

10 citations · 23 across the 42 of their papers we have counts for

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6 papers · 1 filter

cs.AI2026

From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms

Jinghao Luo, Yuchen Tian, Chuxue Cao +6

Large Language Model (LLM)-based agents have fundamentally reshaped artificial intelligence by integrating external tools and planning capabilities. While memory mechanisms have em…

cs.SE2026

Fine-grained Approaches for Confidence Calibration of LLMs in Automated Code Revision

Hong Yi Lin, Chunhua Liu, Haoyu Gao +2

In today's AI-assisted software engineering landscape, developers increasingly depend on LLMs that are highly capable, yet inherently imperfect. The tendency of these models to pro…

cs.CL2026

A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM

Bo Wang, Jing Ma, Hongzhan Lin +4

Explainable fake news detection aims to assess the veracity of news claims while providing human-friendly explanations. Existing methods incorporating investigative journalism are…

cs.CV2026

Probabilistic Concept Graph Reasoning for Multimodal Misinformation Detection

Ruichao Yang, Wei Gao, Xiaobin Zhu +5

Multimodal misinformation poses an escalating challenge that often evades traditional detectors, which are opaque black boxes and fragile against new manipulation tactics. We prese…

cs.CL2026

Towards Comprehensive Stage-wise Benchmarking of Large Language Models in Fact-Checking

Hongzhan Lin, Zixin Chen, Zhiqi Shen +5

Large Language Models (LLMs) are increasingly deployed in real-world fact-checking systems, yet existing evaluations focus predominantly on claim verification and overlook the broa…

cs.CL2026

DiffCoT: Diffusion-styled Chain-of-Thought Reasoning in LLMs

Shidong Cao, Hongzhan Lin, Yuxuan Gu +2

Chain-of-Thought (CoT) reasoning improves multi-step mathematical problem solving in large language models but remains vulnerable to exposure bias and error accumulation, as early…