AI RESEARCH
EMA Is Not All You Need: Mapping the Boundary Between Structure and Content in Recurrent Context
arXiv CS.AI
•
ArXi:2604.08556v1 Announce Type: cross What exactly do efficient sequence models gain over simple temporal averaging? We use exponential moving average (EMA) traces, the simplest recurrent context (no gating, no content-based retrieval), as a controlled probe to map the boundary between what fixed-coefficient accumulation can and cannot represent. EMA traces encode temporal structure: a Hebbian architecture with multi-timescale traces achieves 96% of a supervised BiGRU on grammatical role assignment with zero labels, surpassing the supervised model on structure-dependent roles.