arXiv (All)
open access
Fractional State Space Transition for Long Sequence Modeling
State Space Models (SSMs) compress sequence history into a bounded recurrent state, making the resulting memory law a central architectural choice for long-context performance. Most modern SSMs rely on ODE-based dynamics that lead to exponential forgetting, limiting their ability to retain informati
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Multistep forecasting of long memory series using fractional exponential models#941681
- CyFA: Linear Sequence Modeling with Relative-Time-Partitioned Memory#1131101
- Riccati State Space Models: Non-iterative Parallelization for Nonlinear Sequence Modeling#1130028
- Elastic Spectral State Space Models for Train-Once Budgeted Inference#1013504
- Separating Stream Stability from Long-Term Recall in Language Models#975333
Record · ID 1131156
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive