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Just two incoming connections per agent completely break the finite-depth impossibility of single-parent aggregation, recovering the globally optimal predictor across arbitrary distributions in $O(d \log d)$ depth.
Watermarking can effectively safeguard LLMs from model stealing attacks without sacrificing performance.
