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Research questionHow can LLMs generate reliable, adaptive tests that expose one another’s model-specific weaknesses?Static benchmarks become stale and may miss failures specific to individual models. Model-generated tests can also be inconsistent or lack trustworthy reference answers, making genuine weaknesses difficult to distinguish from flawed probes.
AI
Evaluation & Benchmarks
Multi-agent Systems
Natural Language Processing
Latest papersRecent research connected to this question, newest first.LivingArena: Do LLMs Know What Other LLMs Don't? Peer-Probing as Scalable EvaluationApplies to a turn-taking peer-probing setup in which LLMs use interaction histories to target suspected weaknesses and produce verifiable questions. The evidence covers a 3,600-round tournament involving ten models and reports recurring model-specific weaknesses alongside asymmetry between answer quality and question-construction reliability.research paper · Sep 2, 2026
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