arXiv CS
open access
Decentralized LLM Workflows via Sequential Auctions
We present a scalable framework for solving multi-step tasks through a decentralized market of self-interested agents. Prior theoretical work demonstrated that agents organized through sequential auctions can converge to a globally optimal policy in a Markov decision process (MDP), but relied on the
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
- Decentralized LLM Workflows via Sequential Auctions#1357856
- From Preference to Reciprocity: Decentralized Matching with Empirically Grounded LLM-agent Based Modeling#1128938
- Truthful and Expected-Payment-Optimal Model Routing in Graph-Based Agentic Workflows#1345139
- MANAGING DECENTRALIZED AI AGENTS AND LEGAL UNCERTAINTY#401627
- Official Review · NEURIPS 2024#787157
Record · ID 1348898
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