A tabular foundation model with LLM-as-Observer features predicts AI agent decisions in controlled games, outperforming baselines by 4 AUC points and 14% lower error at K=16 interactions.
citation dossier
TabICL: A tabular foundation model for in-context learning on large data
1Pith papers citing it
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cs.LGtop field · 1 papers
UNVERDICTEDtop verdict bucket · 1 papers
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Pith has found this work in 1 reviewed paper. Its strongest current cluster is cs.LG (1 papers). The largest review-status bucket among citing papers is UNVERDICTED (1 papers). For highly cited works, this page shows a dossier first and a bounded explorer second; it never tries to render every citing paper at once.
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cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling
A tabular foundation model with LLM-as-Observer features predicts AI agent decisions in controlled games, outperforming baselines by 4 AUC points and 14% lower error at K=16 interactions.