SEAL revives saturated benchmarks via adaptive LLM meta-judging in elimination matches, matching full pairwise accuracy with roughly half the calls across code, math, QA, and agent tasks.
arXiv preprint arXiv:2203.04592 , year=
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RQGM enables co-evolution of agents and evaluators across epochs with non-stationary utilities, reporting gains in coding pass rates, paper acceptance, and proof grading over prior self-improving agents.
Headache specialists preferred their own literature summaries over those from Sonnet, GPT-4o, and Llama 3.1 in a blinded evaluation, though AI summaries were sometimes indistinguishable.
citing papers explorer
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SEAL: Can Saturated Benchmarks Be Revived by LLM-as-a-Meta-Judge?
SEAL revives saturated benchmarks via adaptive LLM meta-judging in elimination matches, matching full pairwise accuracy with roughly half the calls across code, math, QA, and agent tasks.
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The Red Queen G\"odel Machine: Co-Evolving Agents and Their Evaluators
RQGM enables co-evolution of agents and evaluators across epochs with non-stationary utilities, reporting gains in coding pass rates, paper acceptance, and proof grading over prior self-improving agents.
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Ten Headache Specialists versus Artificial Intelligence for Clinical Literature Summarization: A Critical Evaluation and Comparison
Headache specialists preferred their own literature summaries over those from Sonnet, GPT-4o, and Llama 3.1 in a blinded evaluation, though AI summaries were sometimes indistinguishable.