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Zero-shot in-context inference trained strictly on synthetic causal graphs can outperform fully tuned AutoML pipelines across diverse real-world tabular benchmarks.
Frozen tabular foundation models can dominate competitive ML benchmarks without parameter updates if an LLM agent is tasked strictly with evolving the semantic data pipeline around them.
