arXiv AI
SuPER-Mamba: Surprisal Prioritization for Extended-Context Routing in Mamba
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arXiv AI
Aug 28
MambaCSP: Hybrid-Attention State Space Models for Hardware-Efficient Channel State Prediction
MambaCSP is a hybrid-attention state space model that replaces transformer-based backbones with a linear-time Mamba architecture for channel state prediction. By adding lightweight patch‑mixer attention layers, it captures long‑range dependencies while maintaining hardware efficiency. Experiments on MISO‑OFDM show 9‑12% higher accuracy, 3× faster throughput, 2.6× lower VRAM usage, and 2.9× faster inference compared to LLM‑based methods.
arXiv AI
Sep 30
Pretrain Once, Route Anywhere: Towards a Foundation Model for LLM Routing
arXiv:2609.37362v1 Announce Type: new Abstract: Large language model (LLM) routing aims to assign each query to the most suitable model from a heterogeneous candidate pool, improving the quality--eff...
arXiv AI
Sep 10
Do Dynamic Routers Need Memory? HeRo: History-Aware Routing for Efficient LLM Inference
arXiv:2609.08189v1 Announce Type: new Abstract: Dynamic layer routing reduces the inference cost of Large Language Models (LLMs) by learning to skip layers for individual tokens. Existing methods, ho...
arXiv Machine Learning
Jun 9
BUDDY: BUdget-Driven DYnamic Depth Routing for Adaptive Large Language Model Inference
arXiv:2606. 09514v1 Announce Type: new Abstract: Large language models (LLMs) incur high inference cost due to their depth and parameter scale.
arXiv Machine Learning
Oct 16
TurboMamba: Faster Inference, Faster Training, and Better Prediction with Lexical Memory
TurboMamba is a new language‑model architecture that combines a global Mamba‑3 model with a small Transformer to achieve faster inference, faster training, and better token prediction. It introduces three lightweight enhancements—direct access to the previous block’s last token, a small correction to next‑token scores, and a learned mixture with recent‑token frequencies—to improve local predictions. In experiments on FineWeb‑Edu, TurboMamba outperforms Transformer, Mamba‑3, Jamba, and Zamba, achieving the lowest mean test prediction loss and significantly higher training throughput and batch latency speedups.
arXiv Machine Learning
Jul 14
An Exact Instrument for State Usage in Selective State-Space Models, and the Input-Driven Migration It Reveals
arXiv:2607. 11796v1 Announce Type: new Abstract: Selective state-space models such as Mamba route information through a bank of first-order modes whose input coupling is set by a learned selection mechanism.