arXiv:2509. 19376v2 Announce Type: replace-cross Abstract: We present a lightweight, model-agnostic temporal layer for RAG and use cybersecurity data to separate two problems that are usually conflated.
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.
arXiv:2606. 12828v1 Announce Type: new Abstract: Do research topics in artificial intelligence grow gradually, or do they advance through abrupt, detectable jumps?
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.
arXiv:2606. 26511v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) gives agents access to accumulated knowledge, but has no model of time.
arXiv:2606. 07789v1 Announce Type: new Abstract: Data stream mining is fundamentally challenged by concept drift, where distributional changes can degrade model performance.
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.
arXiv:2606. 20959v2 Announce Type: replace Abstract: Language models may encode both outdated facts and their newer replacements.
arXiv:2606. 20216v1 Announce Type: cross Abstract: Machine learning algorithms deployed for evolving streaming environments must handle the non-stationary data distributions, commonly referred to as concept drift.
arXiv:2606. 05252v1 Announce Type: cross Abstract: Security teams routinely simulate attacks against their own systems to check whether their monitoring would catch a real intruder.
arXiv:2603. 22934v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) improves large language model applications by grounding generation in retrieved evidence, but also introduces corpus poisoning as a new attack surface.
The paper evaluates a deterministic supersession memory, MemStrata, for retrieval‑augmented generation (RAG) systems on real software history. Using 707 GitHub issues, the authors extracted 130 clean atomic state transitions where a single value changes from pre‑fix to post‑fix. MemStrata achieved 0.91 answer accuracy versus 0.57–0.59 for standard RAG, eliminating stale‑fact errors that RAG returned 36–38% of the time, while maintaining comparable retrieval latency.
SIDScope is a diagnostic tool that evaluates Semantic‑ID interfaces used in generative recommendation systems. It normalizes item‑to‑code artifacts, verifies provenance, profiles mapping structure, and compares revisions while tracking path‑to‑item outcomes in generated traces. Using nine tokenizer exports from Amazon and Yelp data, SIDScope shows that interface health depends on multiple signals and reveals gaps in prefix alignment, trace accounting, and mapping refresh effects.