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Backtracking line search is foundational in numerical optimization. The basic idea is to adjust the step-size of an algorithm by a constant factor until some chosen criterion (e.g. Armijo, Descent Lemma) is satisfied. We propose a novel way…

Optimization and Control · Mathematics 2025-05-28 Joao V. Cavalcanti , Laurent Lessard , Ashia C. Wilson

Searching for all occurrences of a pattern in a text is a fundamental problem in computer science with applications in many other fields, like natural language processing, information retrieval and computational biology. In the last two…

Information Retrieval · Computer Science 2012-10-01 Simone Faro , M. Oguzhan Külekci

Effective decision-making in the real world depends on memory that is both stable and adaptive: environments change over time, and agents must retain relevant information over long horizons while also updating or overwriting outdated…

Machine Learning · Computer Science 2026-01-22 Oleg Shchendrigin , Egor Cherepanov , Alexey K. Kovalev , Aleksandr I. Panov

We present the first mechanized, succinct, practical, complete, and proven-faithful semantics for a modern regular expression language with backtracking semantics. We ensure its faithfulness by proving it equivalent to a preexisting…

Programming Languages · Computer Science 2026-04-08 Aurèle Barrière , Victor Deng , Clément Pit-Claudel

Goal-level Independent and-parallelism (IAP) is exploited by scheduling for simultaneous execution two or more goals which will not interfere with each other at run time. This can be done safely even if such goals can produce multiple…

Programming Languages · Computer Science 2011-07-26 Pablo Chico de Guzmán , Amadeo Casas , Manuel Carro , Manuel V. Hermenegildo

Anchors is a popular local model-agnostic explanation technique whose applicability is limited by its computational inefficiency. To address this limitation, we propose a memorization-based framework that accelerates Anchors while…

Machine Learning · Computer Science 2026-01-29 Haonan Yu , Junhao Liu , Xin Zhang

Dense retrieval systems increasingly need to handle complex queries. In many realistic settings, users express intent through long instructions or task-specific descriptions, while target documents remain relatively simple and static. This…

Information Retrieval · Computer Science 2026-04-07 Seiji Maekawa , Moin Aminnaseri , Pouya Pezeshkpour , Estevam Hruschka

Detecting sensitive data such as Personally Identifiable Information (PII) and Protected Health Information (PHI) is critical for data security platforms. This study evaluates regex-based pattern matching algorithms and exact-match search…

Cryptography and Security · Computer Science 2025-02-13 Lokesh Koli , Shubham Kalra , Karanpreet Singh

This paper illustrates how a Prolog program, using chronological backtracking to find a solution in some search space, can be enhanced to perform intelligent backtracking. The enhancement crucially relies on the impurity of Prolog that…

Artificial Intelligence · Computer Science 2007-05-23 Maurice Bruynooghe

Ranking consistently emerges as a primary focus in information retrieval research. Retrieval and ranking models serve as the foundation for numerous applications, including web search, open domain QA, enterprise domain QA, and text-based…

Information Retrieval · Computer Science 2024-12-16 Hansa Meghwani

Backtracking (i.e., reverse execution) helps the user of a debugger to naturally think backwards along the execution path of a program, and thinking backwards makes it easy to locate the origin of a bug. So far backtracking has been…

Programming Languages · Computer Science 2013-09-23 Jooyong Yi

Services and applications based on the Memento Aggregator can suffer from slow response times due to the federated search across web archives performed by the Memento infrastructure. In an effort to decrease the response times, we…

Information Retrieval · Computer Science 2019-06-04 Martin Klein , Lyudmila Balakireva , Harihar Shankar

Synthetic data augmentation has emerged as a promising solution when pre-training is constrained by data rather than compute. We study how to design synthetic data algorithms that achieve better loss scaling: not only lowering loss at…

Machine Learning · Computer Science 2026-03-20 Konwoo Kim , Suhas Kotha , Yejin Choi , Tatsunori Hashimoto , Nick Haber , Percy Liang

Subgraph matching is a compute-intensive problem that asks to enumerate all the isomorphic embeddings of a query graph within a data graph. This problem is generally solved with backtracking, which recursively evolves every possible partial…

Databases · Computer Science 2020-12-29 Junya Arai , Makoto Onizuka , Yasuhiro Fujiwara , Sotetsu Iwamura

Regular expressions are a concise yet expressive language for expressing patterns. For instance, in networked software, they are used for input validation and intrusion detection. Yet some widely deployed regular expression matchers based…

Programming Languages · Computer Science 2013-01-08 James Kirrage , Asiri Rathnayake , Hayo Thielecke

Record linkage is an essential part of nearly all real-world systems that consume structured and unstructured data coming from different sources. Typically no common key is available for connecting records. Massive data cleaning and data…

Databases · Computer Science 2019-09-30 Thomas Gschwind , Christoph Miksovic , Julian Minder , Katsiaryna Mirylenka , Paolo Scotton

This report investigates enhancing semantic caching effectiveness by employing specialized, fine-tuned embedding models. Semantic caching relies on embedding similarity rather than exact key matching, presenting unique challenges in…

Given a regular expression $R$ and a string $Q$, the regular expression parsing problem is to determine if $Q$ matches $R$ and if so, determine how it matches, e.g., by a mapping of the characters of $Q$ to the characters in $R$. Regular…

Data Structures and Algorithms · Computer Science 2019-01-30 Philip Bille , Inge Li Gørtz

To tackle long-context reasoning tasks without the quadratic complexity of standard attention mechanisms, approaches based on agent memory have emerged, which typically maintain a dynamically updated memory when linearly processing document…

Computation and Language · Computer Science 2026-05-12 Baibei Ji , Xiaoyang Weng , Juntao Li , Zecheng Tang , Yihang Lou , Min Zhang

Previous research on code intelligence usually trains a deep learning model on a fixed dataset in an offline manner. However, in real-world scenarios, new code repositories emerge incessantly, and the carried new knowledge is beneficial for…

Software Engineering · Computer Science 2023-02-08 Shuzheng Gao , Hongyu Zhang , Cuiyun Gao , Chaozheng Wang