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arxiv: 2606.17811 · v1 · pith:EMAUO7XOnew · submitted 2026-06-16 · 💻 cs.LO

UMB: A Unified Markov Binary Format for Probabilistic Model Checking (extended version)

Roman Andriushchenko , Arnd Hartmanns , Joshua Jeppson , Sebastian Junges , Tobias Meggendorfer , David Parker , Tim Quatmann , Maximilian Weininger This is my paper

Pith reviewed 2026-06-26 22:30 UTC · model grok-4.3

classification 💻 cs.LO
keywords probabilistic model checkingbinary file formatexplicit-state modelsmodel interoperabilityMarkov modelsfile exchangePython library
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The pith

A binary format built on bit-level primitives unifies representation and exchange of probabilistic systems for model checking.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces the UMB format to solve the absence of a shared low-level representation for probabilistic models. Existing tools rely on custom textual formats that create interoperability barriers and reading-writing overhead. UMB encodes a general mathematical model of probabilistic systems using a small set of bit-level primitive data structures, yielding an efficient and extensible explicit-state file format. The format ships with a Python library for model handling and validation plus cross-tool infrastructure, and has been adopted by multiple tools to support new workflows.

Core claim

UMB provides a clean, unified, and efficient explicit-state file format for representing a wide range of probabilistic systems, based on a general underlying mathematical model and encoded using a small set of bit-level primitive data structures.

What carries the argument

The UMB format, which encodes models via bit-level primitive data structures on top of a general mathematical model of probabilistic systems.

If this is right

  • Interoperability between probabilistic model checking tools increases because models can be exchanged without custom converters.
  • Overhead from writing and reading model files decreases due to the compact binary encoding.
  • New practical use cases become feasible through the supplied Python library and continuous validation infrastructure.
  • Adoption by prominent tools allows shared model repositories and cross-tool experiments.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Standard benchmarks could be distributed once in UMB rather than duplicated across tool-specific formats.
  • Future tools could be written to read UMB directly, reducing the need for high-level language parsers in some workflows.
  • The format's extensibility via bit-level primitives may support emerging model types without breaking existing readers.

Load-bearing premise

A single binary format built on bit-level primitives can represent the full range of probabilistic systems handled by existing tools without significant loss of performance or expressiveness.

What would settle it

A concrete probabilistic system from an existing tool that cannot be losslessly encoded in UMB or that produces measurably slower model-checking times after conversion to and from the format.

Figures

Figures reproduced from arXiv: 2606.17811 by Arnd Hartmanns, David Parker, Joshua Jeppson, Maximilian Weininger, Roman Andriushchenko, Sebastian Junges, Tim Quatmann, Tobias Meggendorfer.

Figure 1
Figure 1. Figure 1: An annotated transition system (left) and an MDP resulting from annotat [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Summary of our format efficiency experiments. From left to right, we [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Summary of our format efficiency experiments for [PITH_FULL_IMAGE:figures/full_fig_p019_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of model checking time of Prism (left), mcsta (middle), and Storm (right) when using UMB vs. a high-level language. Timings in seconds. is consistently faster compared to high-level inputs across all tools, sometimes by several orders of magnitude [PITH_FULL_IMAGE:figures/full_fig_p020_4.png] view at source ↗
read the original abstract

This paper presents the unified Markov binary (UMB) format, an efficient, extensible, and well-supported explicit-state file format for representing a wide range of probabilistic systems. UMB addresses the problem that, while probabilistic model checking tools often support common high-level modelling languages, there is no effective mechanism for exchanging low-level model representations. In practice, textual, tool-specific formats are used, hampering interoperability and resulting in large overheads in writing and reading model files. UMB provides a clean, unified, and efficient solution, based on a general underlying mathematical model, and encoded using a small set of bit-level primitive data structures. The format has already been adopted by prominent tools and comes with a convenient Python library for reading, manipulating, creating, and validating models, plus infrastructure for cross-tool installation and continuous validation. We report on both the efficiency of the file format and the new practical use cases that it facilitates.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper presents the Unified Markov Binary (UMB) format, a binary explicit-state file format for a wide range of probabilistic models (DTMCs, CTMCs, MDPs, parametric). It claims to solve interoperability problems caused by tool-specific textual formats by providing an efficient, extensible representation based on a general underlying mathematical model encoded with a small set of bit-level primitive data structures. The work reports adoption by prominent tools, a supporting Python library for reading/manipulating/validating models, and infrastructure for cross-tool use, along with efficiency results and new practical use cases.

Significance. If the format achieves its claimed generality and efficiency without loss of expressiveness or performance for the full range of models, it would reduce I/O overhead in probabilistic model checking workflows and enable standardized model exchange across tools.

major comments (2)
  1. [Abstract] Abstract and evaluation sections: the central claims of efficiency, extensibility, and successful representation of the full range of probabilistic systems (including parametric and continuous-time models) are asserted without quantitative benchmarks, error measurements, file-size comparisons, or I/O timing data against existing tool-specific formats; this evidence is load-bearing for the efficiency and adoption claims.
  2. [Encoding and model mapping sections] Sections describing the bit-level encoding and mathematical model mapping: the paper does not detail how transition probabilities/rates are stored (e.g., IEEE floats vs. fixed-width rationals) or how parametric/symbolic parameters and unbounded rates are handled within the claimed small set of primitives; if additional structures are required, this undermines the 'small set' and 'no significant loss of expressiveness' assertions.
minor comments (1)
  1. The Python library and validation infrastructure are described at a high level; concrete usage examples or API signatures would improve clarity for readers intending to adopt the format.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below, indicating revisions where appropriate to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract and evaluation sections: the central claims of efficiency, extensibility, and successful representation of the full range of probabilistic systems (including parametric and continuous-time models) are asserted without quantitative benchmarks, error measurements, file-size comparisons, or I/O timing data against existing tool-specific formats; this evidence is load-bearing for the efficiency and adoption claims.

    Authors: The evaluation section does report efficiency results and new use cases enabled by UMB, consistent with the abstract summary. We agree, however, that the presentation would be strengthened by additional explicit quantitative data such as direct file-size and I/O timing comparisons against tool-specific formats. We will revise the evaluation section to include these benchmarks. revision: yes

  2. Referee: [Encoding and model mapping sections] Sections describing the bit-level encoding and mathematical model mapping: the paper does not detail how transition probabilities/rates are stored (e.g., IEEE floats vs. fixed-width rationals) or how parametric/symbolic parameters and unbounded rates are handled within the claimed small set of primitives; if additional structures are required, this undermines the 'small set' and 'no significant loss of expressiveness' assertions.

    Authors: The format is built on a small set of bit-level primitives that encode the required data types for probabilities, rates, and parametric models without loss of expressiveness. We will add explicit clarification in the revised encoding and model-mapping sections describing the storage of transition probabilities/rates and the handling of parametric and continuous-time elements. revision: yes

Circularity Check

0 steps flagged

No circularity: format specification with no derivation chain

full rationale

This paper defines a binary file format (UMB) for exchanging explicit-state probabilistic models. It contains no mathematical derivations, equations, predictions, fitted parameters, or uniqueness theorems. The central claims concern design choices for bit-level primitives, extensibility, and tool adoption; these are presented as engineering decisions rather than results derived from prior self-citations or self-definitional steps. No load-bearing argument reduces to its own inputs by construction. The paper is self-contained as a specification document.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper introduces a data format rather than a mathematical theory; no free parameters, axioms, or invented physical entities are described in the abstract.

pith-pipeline@v0.9.1-grok · 5715 in / 1044 out tokens · 32600 ms · 2026-06-26T22:30:40.743575+00:00 · methodology

discussion (0)

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