Industry News
April 22, 2026
1 min read
2 views

On Solving the Multiple Variable Gapped Longest Common Subsequence Problem

Original Source

ArXiv AI (cs.AI)

by Marko Djukanovi\'c, Nikola Balaban, Christian Blum, Aleksandar Kartelj, Sa\v{s}o D\v{z}eroski, \v{Z}iga Zebec
arXiv:2604.18645v1 Announce Type: new Abstract: This paper addresses the Variable Gapped Longest Common Subsequence (VGLCS) problem, a generalization of the classical LCS problem involving flexible gap constraints between consecutive solutions' characters. The problem arises in m

arXiv:2604.18645v1 Announce Type: new Abstract: This paper addresses the Variable Gapped Longest Common Subsequence (VGLCS) problem, a generalization of the classical LCS problem involving flexible gap constraints between consecutive solutions' characters. The problem arises in molecular sequence comparison, where structural distance constraints between residues must be respected, and in time-series analysis where events are required to occur within specified temporal delays. We propose a search framework based on the root-based state graph representation, in which the state space comprises a generally large number of rooted state subgraphs. To cope with the resulting combinatorial explosion, an iterative beam search strategy is employed, dynamically maintaining a global pool of promising candidate root nodes, enabling effective control of diversification across iterations. To exploit the search for high-quality solutions, several known heuristics from the LCS literature are utilized into the standalone beam search procedure. To the best of our knowledge, this is the first comprehensive computational study on the VGLCS problem comprising 320 synthetic instances with up to 10 input sequences and up to 500 characters. Experimental results show robustness of the designed approach over the baseline beam search in comparable runtimes.

Tags:AI

Original Content Credit

This summary is sourced from ArXiv AI (cs.AI). For the complete article with full details, research data, and author insights, please visit the original source.

Visit ArXiv AI (cs.AI)

Related Articles

Explainable AML Triage with LLMs: Evidence Retrieval and Counterfactual Checks
ArXiv AI (cs.AI)
Industry News1m

Explainable AML Triage with LLMs: Evidence Retrieval and Counterfactual Checks

arXiv:2604.19755v1 Announce Type: new Abstract: Anti-money laundering (AML) transaction monitoring generates large volumes of alerts that must be rapidly triaged by investigators under strict audit and governance constraints. While large language models (LLMs) can summarize heter

Apr 23, 2026