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Researchers ask whether large language models can serve as event world models for systems with multiple actors. Models struggle when other actors strongly affect outcomes.
Source: arXiv
https://arxiv.org/abs/2610.30488
#MachineLearning
An empirical study of handshake overhead in AI agent communication protocols presents phase-decomposed benchmarks for MCP, A2A, and ACP. A legacy MCP handshake takes three round trips and 230 ms at 50 ms RTT.
Source: arXiv
https://arxiv.org/abs/2610.30151
#MachineLearning
Researchers addressed the high-degree regime in Lehmer's problem using a human-AI discovery loop. They derived a five-parameter construction called the Generalized Boyd algorithm.
Source: arXiv
https://arxiv.org/abs/2610.30277
#MachineLearning
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