ALT We study bounded deviation of non-deterministic finite transducers under the Hamming distance: the bounded comparison problem asks, given two transducers and k ∈ ℕ, whether for every input the two transducers produce words at Hamming distance at most k. This problem is known to be decidable in polynomial time when k is fixed, and in co-NP otherwise. We show that the problem is NL-complete when k is fixed, co-NP-complete when k is given in binary, and it is DP-complete to decide if the distance is exactly k. We also prove that if the two transducers have bounded comparison, then the maximal distance is at most quadratic in the size of both transducers, and that this bound is asymptotically tight. We prove the results on deviations problem, which asks similar questions on the distance of the pairs of input and output of a single transducer, and show that these two families of problems are logspace many-one equivalent.

ALT It has been conjectured that the Parikh (commutative) image of every language over an infinite alphabet recognized by an automaton with registers is defined by a rational expression. This conjecture is known to hold for all languages recognized by one-register automata. We refine this result by proving that the star-height of the Parikh image of any language recognized by a one-register automaton is universally bounded by two. Furthermore, we show that one-register context-free languages have rational commutative images of arbitrarily high star height. We then disprove the conjecture for multiple registers, as well as disprove the equivalence of commutative expressive power between context-free grammars and automata over infinite alphabets. In other words, we show that Parikh's theorem fails for infinite alphabets.

ALT Prefix parsing asks whether an input prefix can be extended to a complete string generated by a given grammar. In the weighted setting, it also provides prefix probabilities, which are central to context-free language modeling, psycholinguistic analysis, and syntactically constrained generation from large language models. We introduce the prefix grammar transformation, an efficient reduction of prefix parsing to ordinary parsing. Given a grammar, our method constructs another grammar that generates exactly the prefixes of its original strings. Prefix parsing is then solved by applying any ordinary parsing algorithm on the transformed grammar without modification. The reduction is both elegant and practical: the transformed grammar is only a small factor larger than the input, and any optimized implementation can be used directly, eliminating the need for bespoke prefix-parsing algorithms. We also present a strategy-based on algorithmic differentiation-for computing the next-token weigh