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20202026
most citedExplainability for Large Language Models: A Survey

20 citations · 68 across the 17 of their papers we have counts for

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5 papers · 1 filter

cs.CL2026

Faithful Activation Verbalization: Reducing Hallucinations in LLM Representation Interpretation

Haiyan Zhao, Zirui Hei, Wei Shi +3

Activation verbalization methods such as Activation Oracle and Natural Language Autoencoders decode hidden representations of large language models into human-readable natural lang…

cs.AI2026

Do LLMs Know Their Vulnerable Scenarios?

Ziheng Peng, Huiqi Deng, Haoran Jing +5

Safety-aligned large language models are trained to refuse harmful requests, yet embedding the same requests in particular scenarios can bypass their safeguards. Existing red-teami…

cs.LG2026

Your LLM, Your Style: Behavioral Mode Axes for LLM Behavioral Control

Haoze Liu, Run Liu, Haiying Xu +6

Large language models (LLMs) increasingly act in interactive settings where their behavioral styles affect user experience, safety, and downstream decision making. Existing LLM per…

cs.AI2026

SkillEval: Decomposing Agent Skill Quality into Interpretable Signals

Jiahui Han, Qinuo Li, Ziheng Peng +6

Agent skills provide reusable knowledge that helps agents solve specialized tasks. As their use expands, evaluating skill quality becomes increasingly important. Existing evaluatio…

cs.AI2026

AgentDoG: A Diagnostic Guardrail Framework for AI Agent Safety and Security

Dongrui Liu, Qihan Ren, Chen Qian +40

The rise of AI agents introduces complex safety and security challenges arising from autonomous tool use and environmental interactions. Current guardrail models lack agentic risk…