CHAL is a multi-agent dialectic system that performs structured belief optimization over defeasible domains using Bayesian-inspired graph representations and configurable meta-cognitive value system hyperparameters.
arXiv preprint arXiv:2207.11719 , year=
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Analysis of MTS reveals poor GSNR and uninformative features; larger batches and distribution-based features yield 5.49% and 2.89% gains on benchmarks.
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CHAL: Council of Hierarchical Agentic Language
CHAL is a multi-agent dialectic system that performs structured belief optimization over defeasible domains using Bayesian-inspired graph representations and configurable meta-cognitive value system hyperparameters.
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On the Difficulty of Learning a Meta-network for Training Data Selection
Analysis of MTS reveals poor GSNR and uninformative features; larger batches and distribution-based features yield 5.49% and 2.89% gains on benchmarks.