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      "statement": "# piKL and Diplodocus human-regularized planning family\n\npiKL anchors search to imitation-learned policies through KL-regularized utilities; DiL-piKL distributes regularization strengths and RL-DiL-piKL trains Diplodocus for human-compatible no-press Diplomacy.\n\n## Scientific scope\n\nRelated planning and learning extensions are one contribution family, with source-specific bylines. Regularized regret bounds are not universal unregularized Nash guarantees: two-player zero-sum equilibrium statements require their stated assumptions, positive-support anchor/log constants and appropriate regularization. Seven-player tournament strength is empirical, and no-press omits natural-language negotiation.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nAthul Paul Jacob; David J. Wu; Gabriele Farina; Adam Lerer; Anton Bakhtin; Jacob Andreas; Noam Brown; David J Wu; Jonathan Gray; Alexander H Miller\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Modeling Strong and Human-Like Gameplay with KL-Regularized Search](https://arxiv.org/pdf/2112.07544v1)\n- [Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning](https://arxiv.org/pdf/2210.05492v1)\n",
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