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This paper introduces a Bangla event detection benchmark with noisy text (ASR, orthographic corruption) and evaluates encoder-only and decoder-only LLMs, finding decoder models more robust to noise.
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.