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New framework synthesizes and verifies register automata for data sequences

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework for synthesizing and verifying Deterministic Register Automata (DRAs) over data sequences. These DRAs are capable of expressing properties that depend on value comparisons across different positions in a sequence, which traditional finite automata cannot handle. The framework includes a local-search synthesis procedure and an exact robustness checker, implemented in a tool called RAExc. This approach has been applied to create interpretable DRA surrogates for neural sequence classifiers, demonstrating high accuracy and verified robustness on benchmark languages and the sequentialized MNIST dataset. AI

IMPACT Introduces a novel method for creating interpretable models for sequence classification, potentially improving understanding of complex data patterns.

RANK_REASON Technical report detailing a new method for synthesizing and verifying automata over data sequences. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework synthesizes and verifies register automata for data sequences

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Technical report detailing a new method for synthesizing and verifying automata over data sequences. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Chih-Duo Hong, Hongjian Jiang, Anthony W. Lin, Oliver Markgraf, Julian Parsert, Tony Tan ·

    Synthesis and Verification of Register Automata over Data Sequences (Technical Report)

    arXiv:2511.19100v2 Announce Type: replace-cross Abstract: Data sequences, such as time series, traces, and sensor streams, are words over large or infinite data domains. Their properties often depend on comparisons between values at different positions, which finite automata cann…

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