AI RESEARCH
FRACTAL: SSM with Fractional Recurrent Architecture for Computational Temporal Analysis of Long Sequences
arXiv CS.AI
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ArXi:2605.08833v1 Announce Type: new Effective sequence modeling fundamentally requires balancing the retention of unbounded history with the high-resolution detection of abrupt short-term variations common in real-world phenomena. However, existing state space models (SSMs) relying on high-order polynomial projection operators (HiPPO) face a critical trade-off where uniform measures dilute recent information to maintain timescale invariance, while exponential measures sacrifice global context to capture local dynamics.