arXiv:2509.19376v2 Announce Type: replace Abstract: We present a lightweight, model-agnostic temporal layer for RAG and use cybersecurity data to separate two problems that are usually conflated. For freshness, a half-life recency prior surfaces the newest relevant item where a cosine-only baseline scores 0.00; on a hard NVD CVE test, where the freshest item is not the most similar, it reaches Latest@10 of 0.60 versus 0.20 for a semantic-then-newest baseline, but stays partial and parameter-sensitive. For topic evolution, a heuristic tracker's low 0.08 macro-F1 is driven by the labeling rule,
The paper leverages recent advancements in RAG systems to address the critical challenge of temporal relevance, specifically in the context of rapidly evolving information like cybersecurity threats.
This research directly addresses a significant limitation in current RAG systems: the ability to accurately prioritize fresh, relevant information over merely similar but outdated content, especially in fast-moving domains.
New temporal layers for RAG systems will become more sophisticated, offering better real-time information retrieval and potentially influencing how AI agents interact with dynamic datasets.
- · Cybersecurity analysts
- · AI agents developers
- · RAG system providers
- · Systems relying solely on semantic similarity for temporal data
- · Legacy knowledge management tools
Improved accuracy and timeliness of information retrieved by RAG systems in dynamic environments such as cybersecurity.
Reduced risk of AI agents acting on outdated information, leading to more reliable autonomous decision-making.
Acceleration of research into more advanced temporal reasoning models for AI, beyond simple recency priors, impacting real-time intelligence systems across industries.
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Read at arXiv cs.LG