Beyond Tracking or Shortcut: Composition-Bounded Predictive States in Poker Autoregressive Models

• URL: http://arxiv.org/abs/2607.19369v1
• Date: Sat, 13 Jun 2026 15:56:26 GMT
• Title: Beyond Tracking or Shortcut: Composition-Bounded Predictive States in Poker Autoregressive Models
• Abstract summary: Hidden-state probes often recover latent labels in imperfect-information sequence models.<n>This paper studies this ambiguity in a no-range Limit Hold'em autoregressive model trained only on action and value targets.
• Score: 4.810191717262418
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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. 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. Action/value+composition baselines reach 16.5-16.7% top-10 accuracy while composition-residual hidden probes fall to 11.4-12.2%, and matched-composition comparisons are negative in every seed. We call this evidence pattern composition-bounded predictive support: hidden states remain behavior-predictive and opponent-range correlated, but most recoverable range information is explained by visible betting composition rather than residual hidden-state structure. This is a case-study claim about opponent-range representational evidence, not exact Bayesian posterior tracking or a causal belief mechanism. Synthetic control and oracle validations show that the same diagnostics accept posterior-sensitive states and reject raw composition states under matched controls. Thus positive belief probes should be interpreted through targeted alternatives before being treated as evidence of belief tracking.

Related papers

• MNIST-PRO: MNIST is Back as a Partially Observable World for AI Agents [74.83535434786145]
  MNIST-PRO is a benchmark that isolates agentic perception by converting MNIST digit recognition into a sequential, glimpse-based search task with lookback constraints.<n>We evaluate ten multimodal models across four memory representations, including raw visual history, textual states, structured metric grid maps, and a consolidated visual canvas.
  arXiv  Detail & Related papers  (2026-08-31T16:05:39Z)
• Evidence of Absence: Cross-Modal Abductive Risk Perception to Sustain World Models When Vision Fails [11.206758778146282]
  A structured world-state is designed to preserve prediction-critical content when perception degrades, but it presumes observations to populate it.<n>We address how to sustain the world model from a complementary modality by treating the absence of expected co-evidence as evidence of a hidden cause.<n>A microphone-array front-end estimates the bearing of engine and tire sources and extracts approach-rate evidence.
  arXiv  Detail & Related papers  (2026-08-15T00:38:17Z)
• Residual Dominance as a Structural Account of Last-Item Reliance in Causal Self-Attention Recommenders [47.8060666024071]
  We show that SASRec-style models exhibit highly localized last-item reliance.<n>We then find that residual addition sharply shifts the full-block representation toward same-position contributions.<n>Changing the residual strength induces a monotonic trade-off between structural mixing and last-item reliance.
  arXiv  Detail & Related papers  (2026-08-14T07:09:58Z)
• Evidence-Type Competition: When Can Interventional Data Teach Language Models Causal Direction? [0.0]
  interventional data is widely regarded as the gold standard for teaching models causal reasoning.<n>We test this assumption in a fully controlled synthetic environment pitting observational correlation against causal effect.<n>What governs whether interventional evidence is used is not the training mixture but the evidence type present in the context at inference time.
  arXiv  Detail & Related papers  (2026-07-31T14:49:18Z)
• The Knowing-Saying Gap: When Probes See Errors that Confidence Misses [20.46211358172975]
  Linear probes detect corrupted context in language models with near-perfect accuracy, yet this does not translate into reliable failure prediction.<n>Across multi-hop arithmetic chains, probes that detect corruption turn out to be uninformative about final answer correctness.<n>As a real-time monitor, probe-based interventions are sharply model and error-type dependent.
  arXiv  Detail & Related papers  (2026-07-21T12:13:10Z)
• Unsupervised Keypoints for Real-Time Fall Detection: Comparative Analysis Under Real-world Conditions with Predictive Bandwidth Reduction [0.5959007032504439]
  Video captures fall-related posture and motion, yet deployment is limited by privacy, computation, and bandwidth.<n>We propose a privacy-preserving framework that replaces RGB transmission with compact motion representations based on unsupervised keypoints and predictive temporal modeling.<n>We evaluate the framework on the UR Fall Detection and Human Fall datasets using random, subject-disjoint, and occlusion-based splits.
  arXiv  Detail & Related papers  (2026-07-16T18:58:43Z)
• A Held-Out Transition-Pair Falsifier for Long-Horizon Non-Abelian State Tracking [0.5076419064097734]
  We introduce a held-out transition-pair falsifier for finite non-Abelian group tracking.<n>In a controlled $S_3 times S_3$ benchmark, a projected recurrent state model trained only on length-8 sequences produces error-free final-state predictions.
  arXiv  Detail & Related papers  (2026-06-05T13:26:41Z)
• Ghost: Plausible Yet Unlearnable Trajectories via On-Manifold Substitution for Next-POI Privacy [34.07056031726073]
  A publisher who releases check-in trajectories inadvertently publishes a strong predictor of every user's future locations.<n>We address this risk by generating unlearnable trajectories, perturbed sequences that yield victim models with degraded next-Point-of-Interest (next-POI) accuracy on clean test inputs.<n>We propose Ghost, a manifold-aligned framework whose perturbations look like plausible human check-in sequences yet leave no learnable signal behind.
  arXiv  Detail & Related papers  (2026-06-02T14:31:14Z)
• Counterfactual Likelihood Tests for Indirect Influence in Private Reasoning Channels [51.56484100374058]
  This paper presents a counterfactual likelihood test for measuring influence between private reasoning channels.<n>The method replaces an upstream private block with a length-matched donor block, holds the public token sequence and downstream target fixed, and measures the downstream target's negative-log-likelihood shift.
  arXiv  Detail & Related papers  (2026-05-18T20:27:43Z)
• On the Structural Non-Preservation of Epistemic Behaviour under Policy Transformation [51.56484100374058]
  We formalise such information-conditioned interaction patterns as behavioural dependency.<n>This induces a probe-relative notion of $$-behavioural equivalence and a within-policy behavioural distance.<n>Results identify structural conditions under which probe-conditioned behavioural separation is not preserved under common policy transformations.
  arXiv  Detail & Related papers  (2026-02-24T22:55:21Z)
• CLUE: Non-parametric Verification from Experience via Hidden-State Clustering [64.50919789875233]
  We show that correctness of a solution is encoded as a geometrically separable signature within the trajectory of hidden activations.<n>ClUE consistently outperforms LLM-as-a-judge baselines and matches or exceeds modern confidence-based methods in reranking candidates.
  arXiv  Detail & Related papers  (2025-10-02T02:14:33Z)
• BURN: Backdoor Unlearning via Adversarial Boundary Analysis [73.14147934175604]
  Backdoor unlearning aims to remove backdoor-related information while preserving the model's original functionality.<n>We propose Backdoor Unlearning via adversaRial bouNdary analysis (BURN), a novel defense framework that integrates false correlation decoupling, progressive data refinement, and model purification.
  arXiv  Detail & Related papers  (2025-07-14T17:13:06Z)
• Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding [49.1865229301561]
  We present a simple approach to partial identification using existing causal sensitivity models and show empirically that Caus-Modens gives tighter outcome intervals. The last of our three diverse benchmarks is a novel usage of GPT-4 for observational experiments with unknown but probeable ground truth.
  arXiv  Detail & Related papers  (2023-06-15T21:42:40Z)
• A New Bandit Setting Balancing Information from State Evolution and Corrupted Context [52.67844649650687]
  We propose a new sequential decision-making setting combining key aspects of two established online learning problems with bandit feedback. The optimal action to play at any given moment is contingent on an underlying changing state which is not directly observable by the agent. We present an algorithm that uses a referee to dynamically combine the policies of a contextual bandit and a multi-armed bandit.
  arXiv  Detail & Related papers  (2020-11-16T14:35:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.