NLP systems increasingly aggregate claims from retrieved passages, documents, and generated reasoning steps. Yet current factuality methods mostly verify claims individually or compare pairs of claims, leaving open the question of whether the entire aggregated claim set can be retained coherently. This misses a fundamental failure mode: claims may be locally plausible and pairwise compatible while still being jointly inconsistent. We formalize global consistency repair: given an inconsistent natural-language claim set, preserve the largest coherent subset while localizing the compact sources of inconsistency. We introduce QXR, an adaptive diagnosis-and-repair framework that treats an LLM as a noisy set-level consistency judge, localizes minimal conflicting subsets, and repairs the claim set by deleting a small hitting set of claims. Across LLM judges and benchmarks derived from VitaminC, FEVER, and conflicting-evidence RAG, QXR preserves substantially more valid claims than direct all-at-once judging while maintaining high precision. These results suggest that factuality in claim aggregation systems should be treated as a set-level repair problem, not only as local verification.
PEARL: Auditable Repair for Scientific Reasoning Graph Extraction
School of Computer Science, Wuhan University（武汉大学计算机科学学院）；Artificial Intelligence Innovation and Incubation, Fudan University（复旦大学人工智能创新与孵化）；Department of Computer Science, Faculty of Science, University of Bath（巴斯大学理学院计算机科学系）；College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）
FactReview: Evidence-Grounded Peer Review with Execution-Based Claim Verification
Rensselaer Polytechnic Institute（罗切斯特理工学院）；Sun Yat-sen University（中山大学）；Southeast University（东南大学）；The Hong Kong University of Science and Technology（香港科技大学）
ProvenanceGuard: Source-Aware Factuality Verification for MCP-Based LLM Agents
Multiverse Computing；Parque Cientifico y Tecnológico de Gipuzkoa（吉普斯夸科技园）；Centre for Social Innovation（社会创新中心）；Donostia International Physics Center（多诺斯蒂亚国际物理中心）；Ikerbasque Foundation for Science（伊克尔巴斯克科学基金会）
In the era of intelligent biohybrid neurotechnologies for brain repair, new fanciful terms are appearing in the scientific dictionary to define what has so far been unimaginable. As the emerging neurotechnologies are becoming increasingly polyhedral and sophisticated, should we talk about evolution and rank the intelligence of these devices?
University of Montpellier（蒙彼利埃大学）；University of Edinburgh（爱丁堡大学）
In the presence of inconsistencies, repair techniques thrive to restore consistency by reasoning with several repairs. However, since the number of repairs can be large, standard inconsistent tolerant semantics usually yield few answers. In this paper, we use the notion of syntactic distance between repairs following the intuition that it can allow us to cluster some repairs "close" to each other. In this way, we propose a generic framework to answer queries in a more personalise fashion.
Carleton University（卡尔顿大学）；Universidad del Desarrollo（发展大学）
We propose and investigate a semantics for "peer data exchange systems" where different peers are related by data exchange constraints and trust relationships. These two elements plus the data at the peers' sites and their local integrity constraints are made compatible via a semantics that characterizes sets of "solution instances" for the peers. They are the intended -possibly virtual- instances for a peer that are obtained through a data repair semantics that we introduce and investigate. The semantically correct answers from a peer to a query, the so-called "peer consistent answers", are d…
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Microsoft Research（微软研究院）
Controversial claims are abundant in online media and discussion forums. A better understanding of such claims requires analyzing them from different perspectives. Stance classification is a necessary step for inferring these perspectives in terms of supporting or opposing the claim. In this work, we present a neural network model for stance classification leveraging BERT representations and augmenting them with a novel consistency constraint. Experiments on the Perspectrum dataset, consisting of claims and users' perspectives from various debate websites, demonstrate the effectiveness of our …
Computational Complexity of Preferred Subset Repairs on Data-Graphs
University of Sheffield（谢菲尔德大学）；Instituto de Ciencias de la Computación（计算机科学研究所）；Universidad de Buenos Aires（布宜诺斯艾利斯大学）；Artificial Intelligence Research Institute（人工智能研究所）
Université de Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800（波尔多大学、法国国家科学研究中心、波尔多INP、LaBRI、UMR 5800）；DI ENS, ENS, CNRS, PSL University & Inria（ENS DI、ENS、法国国家科学研究中心、巴黎 sciences et lettres 大学及Inria）；University of Paderborn（帕德博恩大学）
