HAL (France)
open access
Doing More with Less: A Survey on Routing Strategies for Resource Optimisation in Large Language Model-Based Systems
Background: Large Language Model (LLM)-based systems, such as conversational agents, are usually designed with monolithic, static architectures that rely on a single, general-purpose LLM to handle all user queries. However, these systems may be inefficient as different queries may require different
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
- Uncertainty Quantification for LLM Agents: A Taxonomy, an Evaluation Protocol, and an Empirical Study#984321
- Evaluating LLMs on Conversational Text-to-SQL under Chain Ambiguity and Intent Drift#798188
- Evaluating LLMs on Conversational Text-to-SQL under Chain Ambiguity and Intent Drift#796775
- Quantifying Logical Consistency in Transformers via Query-Key Alignment#987415
- DEALS: Decentralized Expertise-Aware Load Serving for Multi-Agent LLM Systems#1118841
Record · ID 1039037
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