The paper introduces DETECT-REMASK-REPAIR, a diffusion-based method for updating outdated spans in existing summaries while keeping supported content intact. It identifies, masks, and repairs only the changed regions using masked diffusion language models. Experiments on DialogSum and a new StreamSum benchmark show that this localized repair improves faithfulness, reduces repair time to under half a second, and offers trade‑offs between faithfulness, speed, and preservation of the original summary.
By Hao Zou, Zachary Horvitz, Chandhru Karthick, Zhou Yu, Kathleen McKeown
The paper introduces ChronoFake, a benchmark of 30,706 news articles that isolates temporally disordered fake news, where factual events are rearranged into false chronologies. It identifies the "evidence coherence trap," where verification relies on locally coherent but temporally mismatched evidence. To counter this, the authors propose DelSA, a document-selection agent that iteratively refines evidence during training and uses a frozen rulebook for inference, leveraging an LLM for evidence characterization and a verifier-trained reranker for timeline consistency, achieving superior performance on ChronoFake and competitive results on other datasets.
By Chi Wang, Min Gao, Yifei Zhang, Bincheng Gu, Zongwei Wang, Wentao Li
arXiv:2609.00470v1 Announce Type: new
Abstract: Retrieval-Augmented Generation (RAG) grounds large language models in external corpora, but implicit trust in retrieved documents creates a critical at...
By Muhaimin Bin Munir, Akib Jawad Ononto, Nazia Shehnaz Joynab, Bhavani Thuraisingham, Latifur Khan
arXiv:2607. 04281v1 Announce Type: cross Abstract: Semantic caching reduces the latency and cost of retrieval-augmented generation (RAG) by serving cached answers to semantically similar queries, but most existing methods do not model the time-varying freshness of open-web evidence.
By Muhammad Mansoor, Tahir Ahmad, Yeo-Chan Yoon
MORA is a drift‑robust time‑series anomaly detection framework that addresses the ambiguity between distribution drift and true anomalies by defining a temporal change disambiguation problem. It reconstructs a local target from paired short‑ and long‑term views, using the reconstruction gap as a measure of contextual support to adjust the primary anomaly score. The method requires no drift annotations or online adaptation and demonstrates strong robustness to non‑stationarity while maintaining sensitivity to genuine anomalies on four TSAD benchmarks.
By Xudong Mou, Tiejun Wang, Rui Wang, Hui Wang, Pin Liu, Tianyu Wo, Xudong Liu, Renyu Yang
arXiv:2606. 12828v1 Announce Type: new Abstract: Do research topics in artificial intelligence grow gradually, or do they advance through abrupt, detectable jumps?
By Rasul Khanbayov, Hasan Kurban
arXiv:2607. 18289v1 Announce Type: cross Abstract: Continual anomaly detection (CAD) studies how models can adapt to evolving data distributions while retaining performance on previously observed regimes.
By Kamil Faber, Mateusz Smendowski, Roberto Corizzo
arXiv:2606. 26511v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) gives agents access to accumulated knowledge, but has no model of time.
By Neeraj Yadav
arXiv:2606. 07789v1 Announce Type: new Abstract: Data stream mining is fundamentally challenged by concept drift, where distributional changes can degrade model performance.
By Vitor Cerqueira, Heitor Murilo Gomes, Marco Heyden, Bernhard Pfahringer, Albert Bifet
MAADBench is a refreshable benchmark for anomaly detection in multi‑agent systems powered by large language models. It addresses the challenge of keeping benchmarks current by sampling and coupling generative tasks, generating trace data under configurable LLM backbones, and automatically providing deterministic step‑level labels. The authors evaluated 25 anomaly‑detection methods on 5,200 labeled traces, finding that existing approaches depend heavily on supervision, struggle with subtle MAS‑specific anomalies, and lack robustness across different LLM backbones.
By Lei Ma, Dennis Hofmann, Haowen Xu, Joshua DeOliveira, Peter VanNostrand, Lei Cao, Elke Rundensteiner
arXiv:2608. 08512v1 Announce Type: new Abstract: Evolving documents, such as laws, tax codes, and software documentation, are amended, replaced, and sometimes reverted over time, so a question has different correct answers at different dates.
By Mahbub E Sobhani, Md. Faiyaz Abdullah Sayeedi, Fahmid Hasan Chowdhury, Md Adnan Arefeen, Farig Sadeque, Md. Faizul Bari, Swakkhar Shatabda
arXiv:2606. 20959v2 Announce Type: replace Abstract: Language models may encode both outdated facts and their newer replacements.
By Elias Hossain, Sourav Saha, Tasfia Nuzhat Ornee, Sanjeda Sara Jennifer, Umesh Chandra Biswas, Shubhashis Roy Dipta, Rajib Rana, Niloofar Yousefi