mlsanityData to 7 Oct 2026

Agents That Certify Their Own Exploits: Confidence-Scheduled Restricted Responses for Safe Opponent Exploitation

arXiv 2607.28520 ↗First posted 30 Jul 2026cs.GT cs.AI cs.MAcited by 4

Not on the map. Why?

Abstract (arXiv)

An agent playing a Nash-equilibrium strategy in a two-player zero-sum imperfect-information game secures the game value but forfeits the additional value offered by a flawed opponent. Diffuse deviations pose a particular challenge: binary release rules may gather too little evidence to act, while a full best response to an incomplete opponent model can be highly exploitable. We introduce budget-constrained confidence-scheduled restricted responses (CS-RNR), the first opponent-exploitation method whose safety guarantee is a certificate the agent computes on the strategy it actually deploys, so that every exploit it commits to is one it has audited itself. The method tracks pooled action frequencies with anytime-valid confidence sequences and treats a frequency as exploitable only once its interval separates from an equilibrium reference. The confirmed deviations define a conservative opponent model, which a restricted-response solve turns into candidate counter-strategies over a grid of pin levels. Before deployment, each complete candidate is evaluated by a full-tree best response. The resulting certificate is compared with a user-specified budget and committed atomically with the strategy. Because this check is performed on the played strategy, model quality determines the exploitation achieved while the certificate controls reference-relative expected loss. In Leduc hold'em, CS-RNR obtains 6.2× the steady-state gain of a money-verified binary gate while keeping every deployed strategy within budget. A trajectory mixture using the same estimator reaches 13.6× the budget. Across Leduc, Liar's Dice, and 5-rank Leduc, all 36,000 audited hands satisfy the reported certificate tolerance.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 53 references that are in the map. First posted on arXiv 30 Jul 2026.

Builds on

23 of 53 references.

References · 23

  1. 2017DeepStack: Expert-Level Artificial Intelligence in No-Limit Pokercited by 228
  2. 2018Time-uniform, nonparametric, nonasymptotic confidence sequencescited by 220
  3. 2020Combining Deep Reinforcement Learning and Search for Imperfect-Information Gamescited by 110
  4. 2018Deep Counterfactual Regret Minimizationcited by 102
  5. 2014Solving Large Imperfect Information Games Using CFR+cited by 78
  6. 2018Solving Imperfect-Information Games via Discounted Regret Minimizationcited by 68
  7. 2020Faster Game Solving via Predictive Blackwell Approachability: Connecting Regret Matching and Mirror Descentcited by 56
  8. 2017Safe and Nested Subgame Solving for Imperfect-Information Gamescited by 39
  9. 2018Depth-Limited Solving for Imperfect-Information Gamescited by 24
  10. 2023Regret Matching+: (In)Stability and Fast Convergence in Gamescited by 13
  11. 2024RL-CFR: Improving Action Abstraction for Imperfect Information Extensive-Form Games with Reinforcement Learningcited by 10
  12. 2026Effective, Efficient, and General Information Abstraction for Imperfect-Information Extensive-Form Gamescited by 7
11 more
  1. 2026Real-Time Parallel Counterfactual Regret Minimizationcited by 6
  2. 2026Correlated Chance Sampling for Monte Carlo Counterfactual Regret Minimizationcited by 6
  3. 2026PokerSkill: LLMs Can Play Expert-Level Poker without Training or Solverscited by 5
  4. 2023Learning not to Regretcited by 2
  5. 2025Adapting Beyond the Depth Limit: Counter Strategies in Large Imperfect Information Gamescited by 2
  6. 2026Test-then-Punish: A Statistical Approach to Repeated Gamescited by 2
  7. 2026StratFormer: Adaptive Opponent Modeling and Exploitation in Imperfect-Information Gamescited by 2
  8. 2023Nonparametric Strategy Testcited by 1
  9. 2025Faster Game Solving via Asymmetry of Step Sizescited by 1
  10. 2025Deep (Predictive) Discounted Counterfactual Regret Minimizationcited by 1
  11. 2026AlphaExploitem: Going Beyond the Nash Equilibrium in Poker by Learning to Exploit Suboptimal Playcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.