https://arxiv.org/api/E1t0tdNlbazvN58Bc1TKiajGdp4 2026-10-03T20:13:18Z 15 46 0 http://arxiv.org/abs/2609.18886v2 2026-09-22T12:46:57Z

Traditional blockchain-based document notarization follows a snapshot-oriented model in which each document revision is represented as an independent state anchored on-chain through a cryptographic reference. While effective for immutable artifacts, this approach becomes inadequate when documents evolve through collaborative editing. Concurrent modifications create divergent document versions that must be reconciled outside the notarization layer, while even minor changes require generating and distributing new document snapshots. Conversely, collaborative replication frameworks such as CRDTs provide deterministic reconciliation of concurrent updates, but do not inherently provide independently verifiable evidence of when contributions were published. This paper introduces Fluid Notarization, a notarization paradigm in which document evolution itself becomes the object of notarization. Rather than certifying isolated states, Fluid Notarization certifies a graph of causally related evolution artifacts generated by a JSON-native delta-CRDT. The proposed model builds upon Melda, which represents document changes as compact, content-addressed deltas linked through causal dependencies. Blockchain notarization is reduced to recording identifiers of these evolution artifacts, while synchronization, reconstruction, and conflict resolution remain entirely off-chain. The resulting architecture combines two complementary guarantees: deterministic convergence provided by the CRDT and independently auditable proof-of-existence, provenance, and publication evidence provided by the blockchain. A prototype implementation and validation scenario based on collaboratively edited electronic health records demonstrate the feasibility of the approach and highlight the advantages of notarizing document evolution rather than successive document snapshots.

2026-09-16T16:20:19Z Amos Brocco Giuliano Gremlich Roberto Guidi http://arxiv.org/abs/2609.08508v1 2026-09-08T09:55:10Z

Knowledge graphs have become a key resource for integrating heterogeneous data and powering downstream tasks such as question answering, entity linking, and semantic search. They are built and maintained incrementally, either (i) fully automated, e.g., YAGO, (ii) semiautomatically with community oversight, e.g., DBpedia, or (iii) manually through collaborative editing, e.g., Wikidata. Understanding the evolution of knowledge graphs is essential as changes may reflect real-world updates, error corrections, or noise introduced by vandalism, all of which affect the reliability of downstream applications. Among openly available knowledge graphs, Wikidata is the most challenging case to study evolution, with over 120 million entities edited by humans and bots and an edit history spanning more than a decade. Although Wikidata exposes change data in various formats (e.g., periodic dumps and real-time event streams), none support analytical queries over the complete edit history. Therefore, we present WiDiff, a tool that extracts changes from Wikidata's complete edit history and provides a unified interface for large-scale analytical queries over it.

2026-09-08T09:55:10Z Carolina Cortés Lisa Ehrlinger Lorena Etcheverry Felix Naumann http://arxiv.org/abs/2608.23740v1 2026-08-24T18:28:33Z

Concurrent multi-agent coding promises division of labor across modules, robustness through redundancy, and parallel exploration at the natural granularity of multi-file projects. Realtime collaborative editing protocols solve this coordination problem for human teams via Conflict-free Replicated Data Types (CRDTs), but the LLMs underneath generate one token at a time and existing multi-agent coding systems inherit this serial limit: they either sequence agents through phase handoffs or pool independent samples without coordination, and a single agent abandons up to half of hard tasks with a one-file stub-and-exit. AgentRoom is a realtime collaborative editing protocol for concurrent coding agents. Its runtime layer exposes file-level claim, status, and broadcast as MCP tools on a CRDT-merged shared filesystem. Five frontier coding-CLI models ran four backend coding tasks, with cross-language checks in Python DevBench and Rust+axum. For CLI-stable models, AgentRoom with 2 agents abandons fewer tasks than Solo and has less run-to-run variation. At matched-compute, one positive mean LLM-judge contrast puts AgentRoom over parallel-merge. The other contrast, a bundle probe, puts full AgentRoom above each partial case: an ordering rather than a percentage split. Coordination, not parallelism or CRDT-merge, bears the load.

2026-08-24T18:28:33Z Seonglae Cho Donghyun Lee http://arxiv.org/abs/2607.28101v1 2026-07-30T12:10:45Z

Modern collaborative editing systems require efficient mechanisms for managing concurrent updates across distributed replicas. Sequence Conflict-free Replicated Data Types (CRDTs) have become the de facto standard for supporting decentralized collaboration; they enable decentralized replicas to apply operations in arbitrary order while converging to a common state. Although existing sequence CRDTs guarantee Strong Eventual Consistency (SEC), they often suffer from uncontrolled identifier growth and increasing memory consumption during long-running, highly concurrent editing sessions, which severely limits their scalability and performance. This paper presents the Extended Stern-Brocot Tree (ESBT), a mathematically grounded identifier allocation scheme for distributed collaborative text editing, providing a dense, deterministic, and compact identifier space. The proposed allocation strategy bounds identifier growth while preserving deterministic ordering and Strong Eventual Consistency. Experimental evaluation using workloads of up to 100,000 concurrent operations generated across 50 collaborating sites shows that ESBT improves responsiveness by 28 to 88% under pure insertions and 59 to 74% under mixed insertion/deletion workloads, while reducing identifier memory consumption by 50 to 75% in beginning and random insertion patterns compared with the best-performing baseline sequence CRDTs (Logoot and LSEQ). Under the adversarial middle-insertion workload (10,000 operations), ESBT further improves responsiveness by 86.53% and reduces identifier size by 92.81%. These results demonstrate that ESBT effectively addresses the principal scalability limitations of existing sequence CRDTs and provides an efficient and scalable foundation for large-scale collaborative editing systems.

2026-07-30T12:10:45Z Moulay Driss Mechaoui Abdessamad Imine http://arxiv.org/abs/2607.24512v1 2026-07-27T14:53:40Z

Mathematical models are central to formalizing research problems, yet their documentation often falls short of FAIR principles. Knowledge bases such as the Mathematical Model Database (MathModDB) address this gap by providing curated, semantically rich representations of mathematical models. Built on Wikibase, the same open-source infrastructure underlying Wikidata, MathModDB utilizes Semantic Web technologies to support Linked Open Data, collaborative editing, and the storage of semantically enriched metadata, making it a domain-specific knowledge graph within the broader Wikidata ecosystem. However, access to MathModDB currently requires either navigating a complex web interface or proficiency in SPARQL and Wikibase APIs, posing significant barriers for potential users. In addition, the combination of such curated knowledge bases with actual research data stored, e.g., in Dataverse repository instances, remains a challenge. To overcome these limitations, we propose integrating Large Language Models (LLMs) with MathModDB via a Model Context Protocol (MCP) server that exposes a vector-indexed schema retrieval and Steiner-tree-based join planner, combining dialogue-based natural language interaction with curated, epistemically grounded knowledge. Although instantiated on MathModDB, the architecture can be applied to other Wikibase-based systems. We demonstrate that this approach enables epistemically grounded LLM usage, improves model explainability and accessibility beyond what the standard Wikibase interface offers, and simplifies interoperability with external databases and tools, such as Dataverse data repositories. We illustrate the benefits of combining the accessibility of an LLM with the epistemic safety of a curated knowledge base through the adaptability of the MCP protocol by two use cases involving mathematical models in the fields of continuum mechanics and enzyme kinetics.

2026-07-27T14:53:40Z Preprint submitted to 6th Wikidata Workshop@ISWC Jan Range Björn Schembera Dominik Göddeke http://arxiv.org/abs/2606.28815v2 2026-07-06T18:07:59Z

Mathswitch is an open-source project that imports mathematical concept records from sources such as Wikidata, Wikipedia, MathWorld, Encyclopedia of Mathematics, nLab, ProofWiki, and Agda-Unimath, and links records that refer to the same concept. It does not reorganize or redefine the imported content; each source retains its own structure. The current focus is on importing concept data from Wikidata and the resources it links to, with plans to expand to further sources and better concept linking. Because the concept set is approximated through queries over Wikidata's collaboratively edited graph, the imported data is noisy: some items are non-mathematical, while others are ambiguous. In this paper, we test whether a voting ensemble of LLM judges can filter this noise. We evaluate it on Wikidata items with known MathWorld identifiers as a positive control, and examine how classification changes when database identifiers are removed from context. We then inspect the cases where the judges disagree with MathWorld and group these disagreements into three categories (degenerate descriptions, narrow scope bias, and editorial-scope mismatches) that suggest different remediation strategies.

2026-06-27T08:55:44Z Submitted (pre-peer-review) version. Accepted at CICM 2026; the Version of Record will appear in Springer LNAI. We'll add the DOI once the proceedings are published Katja Berčič Slobodan Stanojevikj http://arxiv.org/abs/2507.21329v1 2025-07-28T20:51:55Z

High-quality answers in technical Q&A platforms like Stack Overflow (SO) are crucial as they directly influence software development practices. Poor-quality answers can introduce inefficiencies, bugs, and security vulnerabilities, and thus increase maintenance costs and technical debt in production software. To improve content quality, SO allows collaborative editing, where users revise answers to enhance clarity, correctness, and formatting. Several studies have examined rejected edits and identified the causes of rejection. However, prior research has not systematically assessed whether accepted edits enhance key quality dimensions. While one study investigated the impact of edits on C/C++ vulnerabilities, broader quality aspects remain unexplored. In this study, we analyze 94,994 Python-related answers that have at least one accepted edit to determine whether edits improve (1) semantic relevance, (2) code usability, (3) code complexity, (4) security vulnerabilities, (5) code optimization, and (6) readability. Our findings show both positive and negative effects of edits. While 53.3% of edits improve how well answers match questions, 38.1% make them less relevant. Some previously broken code (9%) becomes executable, yet working code (14.7%) turns non-parsable after edits. Many edits increase complexity (32.3%), making code harder to maintain. Instead of fixing security issues, 20.5% of edits introduce additional issues. Even though 51.0% of edits optimize performance, execution time still increases overall. Readability also suffers, as 49.7% of edits make code harder to read. This study highlights the inconsistencies in editing outcomes and provides insights into how edits impact software maintainability, security, and efficiency that might caution users and moderators and help future improvements in collaborative editing systems.

2025-07-28T20:51:55Z Accepted in the 41st International Conference on Software Maintenance and Evolution (ICSME 2025 - Research Track) Saikat Mondal Chanchal K. Roy http://arxiv.org/abs/2506.14146v1 2025-06-17T03:20:41Z

Vertical-domain large language models (LLMs) play a crucial role in specialized scenarios such as finance, healthcare, and law; however, their training often relies on large-scale annotated data and substantial computational resources, impeding rapid development and continuous iteration. To address these challenges, we introduce the Collaborative Editable Model (CoEM), which constructs a candidate knowledge pool from user-contributed domain snippets, leverages interactive user-model dialogues combined with user ratings and attribution analysis to pinpoint high-value knowledge fragments, and injects these fragments via in-context prompts for lightweight domain adaptation. With high-value knowledge, the LLM can generate more accurate and domain-specific content. In a financial information scenario, we collect 15k feedback from about 120 users and validate CoEM with user ratings to assess the quality of generated insights, demonstrating significant improvements in domain-specific generation while avoiding the time and compute overhead of traditional fine-tuning workflows.

2025-06-17T03:20:41Z Kaiwen Tang Aitong Wu Yao Lu Guangda Sun http://arxiv.org/abs/2503.15120v1 2025-03-19T11:29:00Z

Communication access real-time translation (CART) is an essential accessibility service for d/Deaf and hard of hearing (DHH) individuals, but the cost and scarcity of trained personnel limit its availability. While Automatic Speech Recognition (ASR) offers a cheap and scalable alternative, transcription errors can lead to serious accessibility issues. Real-time correction of ASR by non-professionals presents an under-explored CART workflow that addresses these limitations. We conducted a user study with 75 participants to evaluate the feasibility and efficiency of this workflow. Complementary, we held focus groups with 25 DHH individuals to identify acceptable accuracy levels and factors affecting the accessibility of real-time captioning. Results suggest that collaborative editing can improve transcription accuracy to the extent that DHH users rate it positively regarding understandability. Focus groups also showed that human effort to improve captioning is highly valued, supporting a semi-automated approach as an alternative to stand-alone ASR and traditional CART services.

2025-03-19T11:29:00Z 8 pages, 2 figures, to be published in Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA '25) Korbinian Kuhn Verena Kersken Gottfried Zimmermann 10.1145/3706599.3719984 http://arxiv.org/abs/2409.09934v3 2024-12-15T23:57:19Z

We introduce Coordination-free Collaborative Replication (CCR), a new method for maintaining consistency across replicas in distributed systems without requiring explicit coordination messages. CCR automates conflict resolution, contrasting with traditional Data-sharing systems that typically involve centralized update management or predefined consistency rules. Operational Transformation (OT), commonly used in collaborative editing, ensures consistency by transforming operations while maintaining document integrity across replicas. However, OT assumes server-based coordination, which is unsuitable for modern, decentralized Peer-to-Peer (P2P) systems. Conflict-free Replicated Data Type (CRDT), like Two-Phase Sets (2P-Sets), guarantees eventual consistency by allowing commutative and associative operations but often result in counterintuitive behaviors, such as failing to re-add an item to a shopping cart once removed. In contrast, CCR employs a more intuitive approach to replication. It allows for straightforward updates and conflict resolution based on the current data state, enhancing clarity and usability compared to CRDTs. Furthermore, CCR addresses inefficiencies in messaging by developing a versatile protocol based on data stream confluence, thus providing a more efficient and practical solution for collaborative data sharing in distributed systems.

2024-09-16T02:17:58Z Masato Takeichi http://arxiv.org/abs/2404.04695v1 2024-04-06T17:42:03Z

Real-time collaborative editing in computational notebooks can improve the efficiency of teamwork for data scientists. However, working together through synchronous editing of notebooks introduces new challenges. Data scientists may inadvertently interfere with each others' work by altering the shared codebase and runtime state if they do not set up a social protocol for working together and monitoring their collaborators' progress. In this paper, we propose a real-time collaborative editing model for resolving conflict edits in computational notebooks that introduces three levels of edit protection to help collaborators avoid introducing errors to both the program source code and changes to the runtime state.

2024-04-06T17:42:03Z April Yi Wang Zihan Wu Christopher Brooks Steve Oney 10.1145/3643796.3648453 http://arxiv.org/abs/2311.12649v1 2023-11-21T14:49:00Z

MathGloss is a project to create a knowledge graph (KG) for undergraduate mathematics from text, automatically, using modern natural language processing (NLP) tools and resources already available on the web. MathGloss is a linked database of undergraduate concepts in mathematics. So far, it combines five resources: (i) Wikidata, a collaboratively edited, multilingual knowledge graph hosted by the Wikimedia Foundation, (ii) terms covered in mathematics courses at the University of Chicago, (iii) the syllabus of the French undergraduate mathematics curriculum which includes hyperlinks to the automated theorem prover Lean 4, (iv) MuLiMa, a multilingual dictionary of mathematics curated by mathematicians, and (v) the nLab, a wiki for category theory also curated by mathematicians. MathGloss's goal is to bring together resources for learning mathematics and to allow every mathematician to tailor their learning to their own preferences. Moreover, by organizing different resources for learning undergraduate mathematics alongside those for learning formal mathematics, we hope to make it easier for mathematicians and formal tools (theorem provers, computer algebra systems, etc) experts to "understand" each other and break down some of the barriers to formal math.

2023-11-21T14:49:00Z Lucy Horowitz Valeria de Paiva http://arxiv.org/abs/2311.10975v1 2023-11-18T05:05:21Z

Discussing research-sensemaking questions on Community Question and Answering (CQA) platforms has been an increasingly common practice for the public to participate in science communication. Nonetheless, how users strategically craft research-sensemaking questions to engage public participation and facilitate knowledge construction is a significant yet less understood problem. To fill this gap, we collected 837 science-related questions and 157,684 answers from Zhihu, and conducted a mixed-methods study to explore user-developed strategies in proposing research-sensemaking questions, and their potential effects on public engagement and knowledge construction. Through open coding, we captured a comprehensive taxonomy of question-crafting strategies, such as eyecatching narratives with counter-intuitive claims and rigorous descriptions with data use. Regression analysis indicated that these strategies correlated with user engagement and answer construction in different ways (e.g., emotional questions attracted more views and answers), yet there existed a general divergence between wide participation and quality knowledge establishment, when most questioning strategies could not ensure both. Based on log analysis, we further found that collaborative editing afforded unique values in refining research-sensemaking questions regarding accuracy, rigor, comprehensiveness and attractiveness. We propose design implications to facilitate accessible, accurate and engaging science communication on CQA platforms.

2023-11-18T05:05:21Z 31 pages, 5 figures. Accepted for publication in Proceedings of the ACM on Human-Computer Interaction (CSCW 2024) Changyang He Yue Deng Lu He Qingyu Guo Yu Zhang Zhicong Lu Bo Li http://arxiv.org/abs/2305.16799v1 2023-05-26T10:19:54Z

Optimizing the phrasing of argumentative text is crucial in higher education and professional development. However, assessing whether and how the different claims in a text should be revised is a hard task, especially for novice writers. In this work, we explore the main challenges to identifying argumentative claims in need of specific revisions. By learning from collaborative editing behaviors in online debates, we seek to capture implicit revision patterns in order to develop approaches aimed at guiding writers in how to further improve their arguments. We systematically compare the ability of common word embedding models to capture the differences between different versions of the same text, and we analyze their impact on various types of writing issues. To deal with the noisy nature of revision-based corpora, we propose a new sampling strategy based on revision distance. Opposed to approaches from prior work, such sampling can be done without employing additional annotations and judgments. Moreover, we provide evidence that using contextual information and domain knowledge can further improve prediction results. How useful a certain type of context is, depends on the issue the claim is suffering from, though.

2023-05-26T10:19:54Z Accepted as a long paper at ACL 2023 Gabriella Skitalinskaya Henning Wachsmuth http://arxiv.org/abs/2303.05995v1 2023-03-10T15:55:57Z

Context. Software Engineering (SE) has low female representation due to gender bias that men are better at programming. Pair programming (PP) is common in industry and can increase student interest in SE, especially women; but if gender bias affects PP, it may discourage women from joining the field. Objective. We explore gender bias in PP. In a remote setting where students cannot see their peers' gender, we study how perceived productivity, technical competency and collaboration/interaction behaviors of SE students vary by perceived gender of their remote partner. Method. We developed an online PP platform (twincode) with a collaborative editing window and a chat pane. Control group had no gender information about their partner, while treatment group saw a gendered avatar as a man or woman. Avatar gender was swapped between tasks to analyze 45 variables on collaborative coding behavior, chat utterances and questionnaire responses of 46 pairs in original study at the University of Seville and 23 pairs in the replication at the University of California, Berkeley. Results. No significant effect of gender bias treatment or interaction between perceived partner's gender and subject's gender in any variable in original study. In replication, significant effects with moderate to large sizes in four variables within experimental group comparing subjects' actions when partner was male vs female.

2023-03-10T15:55:57Z Amador Durán Pablo Fernández Beatriz Bernárdez Nathaniel Weinman Aslıhan Akalın Armando Fox