RishiAhuja/ahuja2026retrieval

The project serves as the official code for an ICLR 2026 workshop paper. Its primary value is academic and conceptual: proving that retrieval mechanisms can outperform context-window scaling for stochastic time series. However, from a competitive intelligence standpoint, the project has zero stars and forks, indicating it has not yet transitioned from a research artifact to a tool with community gravity. The 'inverse scaling law' insight is highly valuable for builders of Time Series Foundation Models (TSFMs), but the implementation itself offers no moat. Frontier labs like Google (TimesFM) and Amazon (Chronos) are already dominant in this niche; if the paper's thesis is proven correct, these labs will likely integrate RAG-style retrieval into their existing model architectures, effectively Sherlocking this project. The displacement horizon is very short because the technical barrier to adding FAISS or similar vector search to an existing forecasting pipeline is low for established players like Nixtla or the major cloud providers.