pooling

Introduces PoolBench, a benchmark that isolates pooling strategies in decoder-only LLM concept representation, evaluating 19 strategies across 17 concepts and 3 models to provide a standardized protocol for comparing pooling choices.

The paper introduces Riemannian Mean Pooling (RMP), a method that aggregates token embeddings from pre-trained language models using Riemannian geometry via pullback metrics, showing improved performance over Euclidean pooling on sentence classification tasks.

The paper identifies 'temporal credit dilution' in learned dynamics models where global readouts focus on spurious correlates rather than brief physical events. It proposes CREST, a training-free method that re-anchors pooled representations using event core estimates, improving out-of-distribution robustness.