arXiv (All)
open access
QuantMLA: Function-Aligned Dual-Path Quantization for Low-Bit MLA KV Caching
Multi-Head Latent Attention (MLA) enables expressive multi-head attention with compact caches for its content and decoupled RoPE paths, yet cache memory still scales linearly with context length and batch size. In this work, we establish a systematic model of MLA's dual-path quantization errors, cha
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
- Membrane: A Self-Evolving Contrastive Safety Memory for LLM Agent Defense#973202
- OSCAR: Offline Spectral Covariance-Aware Rotation for 2-bit KV Cache Quantization#244554
- OSCAR: Offline Spectral Covariance-Aware Rotation for 2-bit KV Cache Quantization#200462
- Channel-Wise and Token-Aware Post-Training Quantization for Visual State Space Duality#1122670
- Mamba with Hierarchical Memory: Solving Representation Bottleneck in Long Sequence Modeling#424023
Record · ID 1131961
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