arXiv subjects

Jaehyun Koo

Publications and source records attributed to Jaehyun Koo.

6 recordsLinked to original sources

arXiv2026

What Gets Lost When Memory Becomes Media? Evaluating AI-Generated Oral History Visualization

What gets lost when memory becomes media? Diaspora oral-history interviews require a double transformation; first-person recollection to third-person scene, present interview room to past time and place. When generative AI performs this transformation, no agreed criteria for success exist. We derive success conditions from oral-history theory, design 15 metrics around three failure modes, and compare a Multi-Agent Scene-decomposition pipeline (MAS) with a Single Summarization Pipeline (SSP) across 82 interviews from diaspora communities, spanning from oral interviews to 6-image sequences. Scene-planning and narrative preservation conflict in the majority of cases, and the narrative-structure strength of the source testimony is the primary predictor of this conflict. We propose a failure-mode-based evaluation framework, an empirical analysis of conflict conditions, and a routing protocol for system selection based on narrative-structure strength.

cs.HC↗

arXiv2025

Dynamic Graph Coloring: Sequential, Parallel, and Distributed

We present a simple randomized algorithm that can efficiently maintain a $(\Delta+1)$ coloring as the graph undergoes edge insertion and deletion updates, where $\Delta$ denotes an upper bound on the maximum degree. A key advantage is the algorithm's ability to process many updates simultaneously, which makes it naturally adaptable to parallel and distributed computations. Concretely, it gives a unified framework across the models, leading to the following results: - In the sequential setting, the algorithm processes each update in $O(1)$ expected time, worst-case. This matches and strengthens the results of Henzinger and Peng [TALG 2022] and Bhattacharya et al. [TALG 2022], who achieved an $O(1)$ bound but amortized over the sequence of updates (in expectation and with high probability, respectively), whose work improved the $O(\log \Delta)$ expected amortized bound of Bhattacharya et al. [SODA'18]. - In the parallel setting, the algorithm processes each batch of updates using $O(1)$ work per update in the batch in expectation, and in $\text{poly}(\log n)$ depth with high probability. This is, in a sense, an ideal parallelization of the above results. - In the distributed setting, the algorithm can maintain a coloring of the network graph as (potentially many) edges are added or deleted. The maintained coloring is always proper; it may become partial upon updates, i.e., some nodes may temporarily lose their colors, but quickly converges to a full, proper coloring. Concretely, each insertion and deletion causes at most $O(1)$ nodes to become uncolored, but this is resolved within $\tilde{O}(\log n)$ rounds with high probability (e.g., in the absence of further updates nearby--the precise guarantee is stronger, but technical). Importantly, the algorithm incurs only $O(1)$ expected message complexity and computation per update.

cs.DS↗

arXiv2025

Parallel Batch-Dynamic Algorithms for Spanners, and Extensions

This paper presents the first parallel batch-dynamic algorithms for computing spanners and sparsifiers. Our algorithms process any batch of edge insertions and deletions in an $n$-node undirected graph, in $\text{poly}(\log n)$ depth and using amortized work near-linear in the batch size. Our concrete results are as follows: - Our base algorithm maintains a spanner with $(2k-1)$ stretch and $\tilde{O}(n^{1+1/k})$ edges, for any $k\geq 1$. - Our first extension maintains a sparse spanner with only $O(n)$ edges, and $\tilde{O}(\log n)$ stretch. - Our second extension maintains a $t$-bundle of spanners -- i.e., $t$ spanners, each of which is the spanner of the graph remaining after removing the previous ones -- and allows us to maintain cut/spectral sparsifiers with $\tilde{O}(n)$ edges.

cs.DS↗

arXiv2025

Parallel Batch-Dynamic Coreness Decomposition with Worst-Case Guarantees

We present the first parallel batch-dynamic algorithm for approximating coreness decomposition with worst-case update times. Given any batch of edge insertions and deletions, our algorithm processes all these updates in $ \text{poly}(\log n)$ depth, using a worst-case work bound of $b\cdot \text{poly}(\log n)$ where $b$ denotes the batch size. This means the batch gets processed in $\tilde{O}(b/p)$ time, given $p$ processors, which is optimal up to logarithmic factors. Previously, an algorithm with similar guarantees was known by the celebrated work of Liu, Shi, Yu, Dhulipala, and Shun [SPAA'22], but with the caveat of the work bound, and thus the runtime, being only amortized.

cs.DS↗

arXiv2024

An Optimal MPC Algorithm for Subunit-Monge Matrix Multiplication, with Applications to LIS

We present an $O(1)$-round fully-scalable deterministic massively parallel algorithm for computing the min-plus matrix multiplication of unit-Monge matrices. We use this to derive a $O(\log n)$-round fully-scalable massively parallel algorithm for solving the exact longest increasing subsequence (LIS) problem. For a fully-scalable MPC regime, this result substantially improves the previously known algorithm of $O(\log^4 n)$-round complexity, and matches the best algorithm for computing the $(1+ε)$-approximation of LIS.

cs.DS↗

arXiv2024

Anarchy in the APSP: Algorithm and Hardness for Incorrect Implementation of Floyd-Warshall

The celebrated Floyd-Warshall algorithm efficiently computes the all-pairs shortest path, and its simplicity made it a staple in computer science classes. Frequently, students discover a variant of this Floyd-Warshall algorithm by mixing up the loop order, ending up with the incorrect APSP matrix. This paper considers a computational problem of computing this incorrect APSP matrix. We will propose efficient algorithms for this problem and prove that this incorrect variant is APSP-complete.

cs.DS↗