arXiv (All)
open access
KITE: KV-Invariant Transformer Expansion for Efficient Agentic LLM Scaling
Scaling a language model is not only a question of final quality: the architectural choice determines how much computation is spent during training, prompt processing, and autoregressive decoding to achieve certain model quality. An ideal model architecture should lower all above computation costs t
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
- Learning Rate Transfer for Hybrid Transformer-SSM Architectures#1156107
- TRISDUCTION ENGINE: Omega System Role#81396
- Choosing a large language model interfacing mechanism based on sample question … — Box, Inc. (US20240362497A1)#607537
- Analysis of Quantized and Efficiently Adapted Protein Language Models#1155600
- vla.cpp: A Unified Inference Runtime for Vision-Language-Action Models#1049867
Record · ID 1063505
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