Dynamic Epistemic Decay: A Multi-Dimensional Framework for Knowledge Currency in Retrieval-Augmented Generation

Large language models and retrieval-augmented generation systems treat all knowledge as uniformly persistent, ignoring a well-established property of information: that different types of knowledge expire at fundamentally different rates. This paper introduces the Dynamic Epistemic Decay Framework, a formal multi-dimensional theory that characterizes knowledge validity as a function of five independent decay dimensions: temporal decay (𝜆𝜆𝑡𝑡), paradigm decay (𝜆𝜆𝑝𝑝), uncertainty decay (𝜆𝜆𝑢𝑢), dependency decay (𝜆𝜆𝑑𝑑), and zero decay (𝜆𝜆0). We implement this framework as a four-phase retrieval pipeline and evaluate it on the TempQuestions benchmark (n=1,740) against three baselines: standard cosine similarity, BM25 lexical retrieval, and naive recency ranking. Decay-weighted retrieval achieves 92.1% accuracy versus 13.5% for standard semantic retrieval—a 78.6 percentage point improvement—with zero regressions on stable factual queries. On semantically complex temporal benchmarks where lexical heuristics fail, the framework dominates a more resourced BM25 baseline (90.2% vs 1.6% on date-bounded role queries). Epistemic modulation (Phase 4) and dependency graph reasoning (Phase 3) further demonstrate correct mechanism behavior on specialized benchmarks, validated via proof-of-concept implementation. Unlike temporal KG completion approaches that require structured annotation, and unlike contrastive training approaches to time-sensitive RAG, the decay framework is training-free and operates directly over unstructured text corpora. We argue that the decay framework completes the separation of concerns that RAG began: decoupling not just factual storage from model parameters, but factual currency from both.