Researchers propose a Google Scholar-like index for async LLM agents that scales
shangbinfeng · x · 2026-10-17
Zhenting Qi and colleagues propose building a Google Scholar-like collaborative system for asynchronous LLM agents. Each agent searches, reads, cites related work, and publishes mini-papers via a minimal hypertext system. Search returns only top-k papers ranked by relevance and a PageRank-like citation weight, bounding each agent's read cost at O(k) regardless of agent count. Results show performance scales as agents are added, with robustness to individual agent failures.
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