Data-Centric AI

Customer networks are messy. Product graphs are messy. Fraud rings are messy. Supply-chain graphs are messy. The usual engineering reflex is also messy: when the graph model disappoints, add another architecture, another positional encoding, another “graph-aware” module, another clever acronym to the pile. The paper Semantic Refinement with LLMs for Graph Representations suggests a quieter alternative: before changing the model, change what the model is asked to read.1 ...

TL;DR for operators Forecasting teams usually ask the same question first: which model should we use? DCATS suggests a more operationally useful question: which related histories should this model learn from? The paper introduces DCATS, a Data-Centric Agent for Time Series, an LLM-agent framework that improves forecasting by selecting auxiliary time series for fine-tuning rather than by designing a new forecasting architecture.1 In the authors’ traffic forecasting study, GPT-4 Turbo reads metadata about nearby or similar California traffic sensors, proposes candidate neighbour sets, lets lightweight forecasting models test those proposals, and then refines the next round using validation error. ...