AutStr: Symbolic Infinite Structures in Python

AutStr is a Python library for symbolic representation and manipulation of infinite relational structures. It represents infinite mathematical objects — the integers ℤ, the localizations ℤ[1/p], whole classes of finite graphs and groups — as finite automata, and lets you query them with first-order and monadic second-order logic. Because the representation is exact and the logic is decidable, a single small framework acts as several tools at once:

• 🧮 a computer algebra system for infinite domains — exact algebra over infinite sets and relations, not floating point;
• ⊢ a decision procedure / theorem prover — decide first-order and MSO statements over infinite structures (Presburger and Büchi arithmetic, MSO over graphs), returning a proof-carrying yes/no;
• 🔬 a finite algebra & model-theory system — decide a property across an entire family of finite structures with one compiled automaton;
• ⚙️ an algorithm synthesizer — turn a logical specification into a provably linear-time algorithm.

All four are the same underlying object — an automatic presentation — viewed from different angles.

Version 2.0 (July 2026) adds uniformly automatic classes for whole families of finite structures, and a rewritten batched-NumPy automata core that is 10²–10³× faster than v1, with JAX as an optional accelerator. See the release announcement.

Installation

pip install autstr              # NumPy-only core — installs anywhere
pip install autstr[jax]         # + JAX-accelerated batch word processing
pip install autstr[graphs]      # + networkx conversion for the graph classes

Requires Python 3.10–3.14.

Synthesizing linear-time algorithms

Write what you want as a logical formula; AutStr compiles it — once — into a finite automaton that decides it. On structurally restricted inputs (bounded tree-depth, bounded pathwidth) that automaton is a linear-time algorithm, even for properties that are NP-hard in general — a constructive, streaming form of Courcelle’s theorem.

import networkx as nx
from autstr.graphs import TreeDepthClass, TreeDepthGraph

cls = TreeDepthClass(3)

# Bipartiteness as a monadic second-order formula, compiled ONCE for the whole
# class into a 6-state automaton:
bipartite, _ = cls.evaluate(
    'exists c.(all x.(all y.((not E(x,y)) or '
    '((Subset(x,c) and (not Subset(y,c))) or '
    '((not Subset(x,c)) and Subset(y,c))))))')

triangle = TreeDepthGraph.from_networkx(nx.cycle_graph(3))
bipartite.accepts([(s,) for s in cls.advice(triangle)])   # False — in microseconds

Deciding the property on a graph is a single linear pass, and the work batches beautifully (optionally on a GPU via the JAX backend):

• Perfectly linear decision time (through-the-origin fit, R² = 1.0000); the JAX backend decides a million-vertex graph in ~20 ms per query.
• Batched evaluation classifies tens of thousands of graphs at once at ~90 million vertices / second — about 190× a naive per-graph loop.

Programming with infinite sets

Because relations are first-class infinite objects, you can also write algorithms that manipulate them directly. Here is the Sieve of Eratosthenes running over the actual infinite set of integers — no bound, no array:

from autstr.arithmetic import VariableETerm as Var

x = Var('x')

def infinite_sieve(steps):
    candidates = x.gt(1)                          # the infinite set {2, 3, 4, ...}
    primes = []
    for _ in range(steps):
        for (p,) in candidates:                   # enumerates smallest-first
            primes.append(p); break               # ... so this is the next prime
        multiples = (x.eq(primes[-1] * Var('y'))).drop(['y'])
        candidates = candidates & ~multiples      # remove its multiples, symbolically
    return primes, candidates

primes, remaining = infinite_sieve(4)
# primes    == [2, 3, 5, 7]
# remaining  is the infinite set enumerating 11, 13, 17, 19, 23, 29, ...

Uniformly automatic classes

New in version 2, a uniformly automatic class presents an entire family of finite structures by giving every automaton an extra tape that reads an advice string. A query is compiled once for the class and then decides any member. Built-in classes cover:

References

• Abu Zaid, F. Algorithmic Solutions via Model Theoretic Interpretations. Dissertation, RWTH Aachen University, 2016. DOI
• Abu Zaid, F. Uniformly Automatic Classes of Finite Structures. FSTTCS 2018. DOI
• Abu Zaid, F., Grädel, E., & Reinhardt, F. Advice Automatic Structures and Uniformly Automatic Classes. CSL 2017. DOI
• Blumensath, A., & Grädel, E. Automatic Structures. LICS 2000. URL
• Khoussainov, B., & Nerode, A. Automatic presentations of structures. LCC 1994. DOI
• Khoussainov, B., Rubin, S., & Stephan, F. Automatic Structures: Richness and Limitations. LMCS 2007. arXiv DOI