Beyond Tracking or Shortcut: Composition-Bounded Predictive States in Poker Autoregressive Models
This paper studies this ambiguity in a no-range Limit Hold'em autoregressive model trained only on action and value targets, not on an opponent's hand or range. Opponent-range probes are positive after action/value controls in two of three seeds, and the behavior head predicts held-out actions about five percentage points above a baseline using only observable public history. However, visible public betting composition explains more opponent-range signal than residual hidden states, suggesting that most recoverable information comes from betting summaries.
- ▪This paper studies this ambiguity in a no-range Limit Hold'em autoregressive model trained only on action and value targets, not on an opponent's hand or range.
- ▪Opponent-range probes are positive after action/value controls in two of three seeds, and the behavior head predicts held-out actions about five percentage points above a baseline using only observable public history.
- ▪However, visible public betting composition explains more opponent-range signal than residual hidden states, suggesting that most recoverable information comes from betting summaries.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2607.19369 |
| Publication time | Thu, 23 Jul 2026 00:00:00 -0400 |
| Retrieval time | 2026-07-23T04:57:27.114Z |
| Last seen | 2026-07-23T04:57:27.114Z |
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| Summary source text | contentText |
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| Publisher visit | Yes — open original |
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| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2607.19369 (cs) [Submitted on 13 Jun 2026] Title:Beyond Tracking or Shortcut: Composition-Bounded Predictive States in Poker Autoregressive Models Authors:Quanhao Li, Qianyu Chen View a PDF of the paper titled Beyond Tracking or Shortcut: Composition-Bounded Predictive States in Poker Autoregressive Models, by Quanhao Li and 1 other authors View PDF HTML (experimental) Abstract:Hidden-state probes often recover latent labels in imperfect-information sequence models, but this alone does not establish that a model maintains a posterior belief distribution over hidden states. This paper studies this ambiguity in a no-range Limit Hold'em autoregressive model trained only on action and value targets, not on an opponent's hand or range.
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