The paper introduces TEMPS, a modular temporal branch that enhances semantic retrievers by adding a temporal scoring component. TEMPS resolves anchored temporal expressions into intervals, matches them to Gaussian distributions, and trains an anchor-date-conditioned encoder using grounding supervision without hand‑labeled data. On three temporal benchmarks, TEMPS improves MRR across all tested backbones and raises R@1 from 19.92 to 25.39 on the TS‑Retriever, surpassing prior temporal state‑of‑the‑art methods.
By Mourad Hassani, Julien Romero, Amel Bouzeghoub, Christian Jacquelinet
arXiv:2606. 27881v1 Announce Type: cross Abstract: Temporal variation poses a unique challenge for named entity recognition (NER) in historical texts, where entities drift in surface form and salience across time.
By Emanuela Boros
arXiv:2609.23880v1 Announce Type: new
Abstract: Temporal Information Retrieval (TIR) has been increasingly critical given the rise of Retrieval-Augmented Generation (RAG). Since temporally mismatched...
By Soyeon Kim, Hyunjin Kim, JinYeong Bak, Steven Euijong Whang
arXiv:2606. 02991v1 Announce Type: cross Abstract: We introduce TypewriterLM, a 7.
By Xiaoxi Luo, Zachary Shinnick, Niclas Griesshaber, Yixuan Wang, Junchi Yu, Freda Shi, Philip Torr, Yao Lu
arXiv:2606. 17664v1 Announce Type: cross Abstract: Unsupervised dense retrievers offer scalability by learning semantic similarity from unlabeled documents via contrastive learning, but they struggle to capture the temporal relevance, retrieving semantically related but temporally misaligned documents-an important aspect when a document collection spans multiple time periods (e.
By HyunJin Kim, Jaejun Shim, Young Jin Kim, JinYeong Bak
arXiv:2607. 17486v1 Announce Type: cross Abstract: As large language models (LLMs) process increasingly longer prompts, computation and KV-cache memory costs have emerged as major bottlenecks in inference systems.
By Oteo Mamo, Hyunjin Yi, Joydhriti Choudhury, Shangqian Gao, Weikuan Yu
Event analysis is an essential and fundamental direction of information extraction, involving various event-centric tasks at different granularity of documents. While large language models (LLMs) have preliminarily achieved promising performance in part of these tasks individually, their capability in event analysis still lacks comprehensive understanding due to restricted document granularity, task designs, and data source of existing benchmarks.
While deep learning models, particularly transformer-based architectures, have shown impressive performance in time series forecasting, the application of retrieval-augmented generation (RAG) in this domain remains limited. Since RAG has proven effective in enhancing the capabilities of large language models by incorporating relevant external information, retrieving similar time series sequences as references might also improve accuracy in time series forecasting tasks.
arXiv:2607. 26070v1 Announce Type: cross Abstract: Large language model (LLM)-based agentic search systems are often evaluated as if the underlying LLM were the only component that matters, yet their measured performance also depends on the surrounding search environment: the Wikipedia snapshot, preprocessing pipeline, chunking policy, retrieval backend, tool schema, observation format, and answer submission rule.
By Guanming Xiong, Penghui Zhang
arXiv:2607. 04088v1 Announce Type: cross Abstract: LongEval-Sci evaluates scientific retrieval under collection change, where a system should be effective on the current corpus and remain usable as documents accumulate over time.
By Yingdong Yang, Haijian Wu
arXiv:2601.14121v2 Announce Type: replace
Abstract: Identifying when and where a news image was taken is crucial for journalists and forensic experts to produce credible stories and debunk misinforma...
By Jonathan Tonglet, Iryna Gurevych, Tinne Tuytelaars, Marie-Francine Moens
arXiv:2606. 14941v1 Announce Type: new Abstract: Time series forecasting models often benefit from historical patterns.
By Shiqiao Zhou, Zipeng Wu, Holger Sch\"oner, Edouard Fouch\'e, IAG Wilson, Shuo Wang