FRACTAL: Fractional Recurrent Architecture Improves Long Sequence Modeling
A recent study published on arXiv (2605.08833) introduces FRACTAL, which stands for Fractional Recurrent Architecture for Computational Temporal Analysis of Long sequences. This innovative architecture incorporates fractional measure theory into its recursive memory updates, addressing the shortcomings of current state space models (SSMs) that utilize HiPPO projection operators. These existing models encounter a dilemma: uniform measures can dilute recent information to achieve timescale invariance, whereas exponential measures compromise global context for local dynamics. FRACTAL generates projection operators with analytically defined spectral characteristics and an adjustable singularity index, enhancing responsiveness to recent signal changes while maintaining spectral integrity. This approach effectively balances the retention of extensive historical data with the precise detection of sudden short-term fluctuations typical in real-world scenarios.
Key facts
- Paper arXiv:2605.08833 proposes FRACTAL architecture
- FRACTAL integrates fractional measure theory into recursive memory updates
- Addresses trade-off in SSMs using HiPPO projection operators
- Derives projection operators with tunable singularity index
- Amplifies sensitivity to recent signal perturbations
- Preserves spectral structure for long-term context
- Balances unbounded history retention with short-term variation detection
- Published on arXiv as new announcement type
Entities
Institutions
- arXiv