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Treating automated feature transformations as permutation-invariant hierarchies rather than ordered sequences eliminates a fundamental representation bias, enabling policy-guided RL to efficiently navigate non-convex search spaces.
Scaling and post-hoc interpretability hit a hard wall in agentic AI because models cannot recover causal invariances that observational interaction data never contained in the first place.
Achieve state-of-the-art ranking performance across diverse metrics with a single model, sidestepping the need for metric-specific optimization.
