Selective Context Retention for Tabular Foundation Models on Data Streams

Tabular stream learning requires predictions on sequentially arriving examples under bounded memory and potential distribution shift. Standard stream learning methods operate by updating model state, such as split statistics or ensemble members. Recently, tabular foundation models (TFMs) that make predictions conditioned on a labeled context in an in-context manner have emerged as a natural alternative for stream learning. This turns stream adaptation via model updates into bounded context management, where the system must decide which examples to keep in the context. In this paper, we propose a future-information view that measures the usefulness of a context by the information it provides about near-future queries. This motivates preserving recent examples, retaining uncertain examples, and removing redundant examples. We instantiate these signals as RECAP (REtained Context for Adaptive Prediction), a simple context policy with entropy-gated admission and redundancy-aware eviction. RECAP achieves the highest prequential accuracy against six classical stream learners on seven streams. Under the same TFM backbone and context budget, it improves on DualFIFO, a prior context management approach. This advantage persists under noisy feedback, with gains of 0.27 to 1.95 percentage points over DualFIFO across three streams when 30% of feedback labels are corrupted. Code and datasets are available at https://anonymous.4open.science/r/RECAP-8D1B/.