mlsanityData to 7 Oct 2026

The Myhill-Nerode Theorem for Bounded Interaction: Canonical Abstractions via Agent-Bounded Indistinguishability

arXiv 2603.21399 ↗First posted 22 Mar 2026cs.AIcited by 2

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Abstract (arXiv)

Any capacity-limited observer induces a canonical quotient on its environment: two situations that no bounded agent can distinguish are, for that agent, the same. We formalise this for finite POMDPs. A fixed probe family of finite-state controllers induces a closed-loop Wasserstein pseudometric on observation histories and a probe-exact quotient merging histories that no controller in the family can distinguish. The quotient is canonical, minimal, and unique-a bounded-interaction analogue of the Myhill-Nerode theorem. For clock-aware probes, it is exactly decision-sufficient for objectives that depend only on the agent's observations and actions; for latent-state rewards, we use an observation-Lipschitz approximation bound. The main theorem object is the clock-aware quotient; scalable deterministic-stationary experiments study a tractable coarsening with gap measured on small exact cases and explored empirically at larger scale. We validate theorem-level claims on Tiger and GridWorld. We also report operational case studies on Tiger, GridWorld, and RockSample as exploratory diagnostics of approximation behavior and runtime, not as theorem-facing evidence when no exact cross-family certificate is available; heavier stress tests are archived in the appendix and artifact package.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 5 of its 58 references that are in the map. First posted on arXiv 22 Mar 2026.

Builds on

5 of 58 references.

References · 5

  1. 2017Near Optimal Behavior via Approximate State Abstractioncited by 63
  2. 2021Towards Robust Bisimulation Metric Learningcited by 49
  3. 2022A Theory of Abstraction in Reinforcement Learningcited by 26
  4. 2013Solving POMDPs by Searching in Policy Spacecited by 13
  5. 2024Bisimulation Metrics are Optimal Transport Distances, and Can be Computed Efficientlycited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.