most citedA Survey of Vibe Coding with Large Language Models

4 citations · 8 across the 13 of their papers we have counts for

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cs.CL2026

Dynamic Rollout Editing for Reducing Overthinking in RL-Trained Reasoning Models

Zihao Wei, Wenjie Shi, Liang Pang +8

Long-form chain-of-thought reasoning can improve LLM performance on complex tasks, but models often continue generating unnecessary reasoning after a correct answer has emerged. We…

cs.CL2025

LLM Latent Reasoning as Chain of Superposition

Jingcheng Deng, Liang Pang, Zihao Wei +6

Latent reasoning offers a computation-efficient alternative to Chain-of-Thought but often suffers from performance degradation due to distributional misalignment and ambiguous chai…

cs.CL2025★ 1 cited

Large Language Model Sourcing: A Survey

Liang Pang, Jia Gu, Sunhao Dai +7

Due to the black-box nature of large language models (LLMs) and the realism of their generated content, issues such as hallucinations, bias, unfairness, and copyright infringement…

cs.CL2025

The Evolution of Thought: Tracking LLM Overthinking via Reasoning Dynamics Analysis

Zihao Wei, Liang Pang, Jiahao Liu +7

Test-time scaling via explicit reasoning trajectories significantly boosts large language model (LLM) performance but often triggers overthinking. To explore this, we analyze reaso…

cs.CL2025

GloSS over Toxicity: Understanding and Mitigating Toxicity in LLMs via Global Toxic Subspace

Zenghao Duan, Zhiyi Yin, Zhichao Shi +6

This paper investigates the underlying mechanisms of toxicity generation in Large Language Models (LLMs) and proposes an effective detoxification approach. Prior work typically con…

cs.CL2025★ 3 cited

from Benign import Toxic: Jailbreaking the Language Model via Adversarial Metaphors

Yu Yan, Sheng Sun, Zenghao Duan +5

Current studies have exposed the risk of Large Language Models (LLMs) generating harmful content by jailbreak attacks. However, they overlook that the direct generation of harmful…

cs.CL2025

Related Knowledge Perturbation Matters: Rethinking Multiple Pieces of Knowledge Editing in Same-Subject

Zenghao Duan, Wenbin Duan, Zhiyi Yin +5

Knowledge editing has become a promising approach for efficiently and precisely updating knowledge embedded in large language models (LLMs). In this work, we focus on Same-Subject…