arXiv (All)
open access
At Equal Inference Cost, Multi-Agent Structure Does Not Beat a Single Frozen Agent
Multi-agent LLM pipelines, such as Planner-Executor-Critic teams, often report gains over single agents, but these gains usually come with higher inference cost because the team makes multiple model calls per environment step. Existing automated methods search over roles, topologies, and prompts, bu
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
- FORGE: Form-Optimal Routing of Grounded Evidence for Frozen LLM Agents#1119431
- Harness Engineering in LLM Tool Use via Agent-Native Reusable Tool Primitives#822166
- Mo' Models, Mo' Problems: How to best select model pools when designing Multi-Agent Systems#1123678
- Mo' Models, Mo' Problems: How to best select model pools when designing Multi-Agent Systems#919440
- Scaling External Knowledge Input Beyond Context Windows of LLMs via Multi-Agent Collaboration#153072
Record · ID 927520
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