Presents three new training procedures for regression trees that enforce convex output constraints at training time and validates them on synthetic and hierarchical time-series data.
arXiv preprint arXiv:2402.03559 , year=
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A malicious agent in multi-agent LLM consensus systems can be trained via a surrogate world model and RL to reduce consensus rates and prolong disagreement more effectively than direct prompt attacks.
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Output-Constrained Decision Trees
Presents three new training procedures for regression trees that enforce convex output constraints at training time and validates them on synthetic and hierarchical time-series data.
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Insider Attacks in Multi-Agent LLM Consensus Systems
A malicious agent in multi-agent LLM consensus systems can be trained via a surrogate world model and RL to reduce consensus rates and prolong disagreement more effectively than direct prompt attacks.