Get Started
Home
Topics
Search
Library
Research questionHow does downstream learning error scale with depth when partially informed agents pass only predictions along M-covered paths?Each agent observes only a subset of raw features and receives its parents’ predictions rather than their underlying data. As information is repeatedly compressed into predictions, later agents may diverge from a learner with access to all features.
AI
Machine Learning
Multi-agent Systems
Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.Optimal Rates for Agentic Networked Information AggregationThis applies to theoretical DAG paths in which every block of M consecutive agents collectively observes all raw features. The supplied results analyze linear regression with mean squared error and logit-passing logistic classification, establishing depth-dependent upper and lower bounds and distinguishing behavior below and above depth M².research paper · Sep 4, 2026
Related questions
How can downstream analyses propagate upstream uncertainty without feedback using only posterior samples?How do initialization correlation decay and feature-function Hermite rank determine residual networks’ scaling and continuous-depth limit?How can Broad Learning Systems resist extreme errors and unreliable samples during noisy training?When do explicitly organized recurrent interactions improve dynamical learning over generic reservoirs under matched state dimensions and controlled tuning?