Breaking the Illusion of Review Reliability under Static Evaluation: SCOPE Fuzzing for LLM-based Scientific Reviewers
cs.AI
Submitted: 2026-09-29
Updated: 2026-09-29
Sources
- MARG: Multi-Agent Review Generation for Scientific Papers
- Reviewer2: Optimizing Review Generation Through Prompt Generation
- Can You Trick the Grader? Adversarial Persuasion of LLM Judges
- BadScientist: Can a Research Agent Write Convincing but Unsound Papers that Fool LLM Reviewers?
- AgentReview: Exploring Peer Review Dynamics with LLM Agents
- Paraphrasing Adversarial Attack on LLM-as-a-Reviewer
- Aspect-Guided Multi-Level Perturbation Analysis of Large Language Models in Automated Peer Review
- LLM-REVal: Can We Trust LLM Reviewers Yet?
- ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing
- The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
- When Reject Turns into Accept: Quantifying the Vulnerability of LLM-Based Scientific Reviewers to Indirect Prompt Injection
- AI-Driven Review Systems: Evaluating LLMs in Scalable and Bias-Aware Academic Reviews
- AcademicGPT: Empowering Academic Research
- Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review
- DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process
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