We prove that given any α-approximation LOCAL algorithm for Minimum Dominating Set (MDS) on planar graphs, we can construct an f(g)-round (3α + 1)-approximation LOCAL algorithm for MDS on graphs embeddable in a given Euler genus-g surface. Heydt et al. [European Journal of Combinatorics (2025)] gave an algorithm with α = 11 + ϵ, from which we derive a (34 + ϵ)-approximation algorithm for graphs of genus g, therefore improving upon the current state of the art of 24g + O(1) due to Amiri et al. [ACM Transactions on Algorithms (2019)]. It also improves the approximation ratio of 91 + ϵ due to Czygrinow et al. [Theoretical Computer Science (2019)] in the particular case of orientable surfaces.

Common definitions of the “standard” LOCAL model tend to be sloppy and even self-contradictory on one point: do the nodes update their state using an arbitrary function or a computable function? So far, this distinction has been safe to neglect, since problems where it matters seem contrived and quite different from e.g. typical local graph problems studied in this context.

Locally checkable labeling problems (LCLs), introduced by Naor and Stockmeyer, are the standard formalism for studying local distributed graph problems. They capture many natural problems, such as coloring, maximal independent set, and sinkless orientation, while still being restrictive enough to enable general complexity-theoretic results. However, recent work has also revealed artificial LCLs with counterintuitive behavior, including quantum and shared-randomness advantages, exotic round complexities, dependence on computability assumptions, and undecidability phenomena. This raises a natural question: are these phenomena artifacts of the particular Naor-Stockmeyer definition, or are they inherent to local checkability?

The online-LOCAL and SLOCAL models are extensions of the LOCAL model where nodes are processed in a sequential but potentially adversarial order. So far, the only problem we know of where the global memory of the online-LOCAL model has an advantage over SLOCAL is 3-coloring bipartite graphs. Recently, Chang et al. [PODC 2024] showed that even in grids, 3-coloring requires Ω(log n) locality in deterministic online-LOCAL. This result was subsequently extended by Akbari et al. [STOC 2025] to also hold in randomized online-LOCAL. However, both proofs heavily rely on the assumption that the algorithm does not have access to the orientation of the underlying grid. In this paper, we show how to lift this requirement and obtain the same lower bound (against either model) even when the algorithm is explicitly given a globally consistent orientation of the grid.

For k ≥ 0, k-partial (k+1)-coloring asks to color the nodes of an n-node graph using a palette of k+1 colors such that every node v has at least min{k,deg(v)} neighbors colored with colors different from its own color. Hence, proper (Δ+1)-coloring is the special case of k-partial (k+1)-coloring when k = Δ. Ghaffari and Kuhn [FOCS 2021] recently proved that there exists a deterministic distributed algorithm that solves proper (Δ+1)-coloring of n-node graphs with maximum degree Δ in O(log n ⋅ log²Δ) rounds under the LOCAL model of distributed computing. This breakthrough result is achieved via an original iterated rounding approach. Using the same technique, Ghaffari and Kuhn also showed that there exists a deterministic algorithm that solves proper O(a)-coloring of n-node graphs with arboricity a in O(log n ⋅ log³a) rounds. It directly follows from this latter result that k-partial O(k)-coloring can be solved deterministically in O(log n ⋅ log³k) rounds. We develop an extension of the Ghaffari and Kuhn algorithm for proper (Δ+1)-coloring, and show that it solves k-partial (k+1)-coloring, thus generalizing their main result. Our algorithm runs in O(log n ⋅ log³k) rounds, like the algorithm that follows from Ghaffari and Kuhn’s algorithm for graphs with bounded arboricity, but uses only k+1 color, i.e., the smallest number c of colors such that every graph has a k-partial c-coloring. Like all the previously mentioned algorithms, our algorithm actually solves the general list-coloring version of the problem. Specifically, every node v receives as input an integer demand d(v) ≤ deg(v), and a list of at least d(v)+1 colors. Every node must then output a color from its list such that the resulting coloring satisfies that every node v has at least d(v) neighbors with colors different from its own. Our algorithm solves this problem in O(log n ⋅ log³k) rounds where k = max_v d(v). Moreover, in the specific case where all lists of colors given to the nodes as input share a common colors c^* known to all nodes, one can save one log k factor. In particular, for standard k-partial (k+1)-coloring, which corresponds to the case where all nodes are given the same list {1,… ,k+1}, one can modify our algorithm so that it runs in O(log n ⋅ log²k) rounds, and thus matches the complexity of Ghaffari and Kuhn’s algorithm for (Δ+1)-coloring for k = Δ.