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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.
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.
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...
arXiv:2606. 02991v1 Announce Type: cross Abstract: We introduce TypewriterLM, a 7.
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.
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.