Structure and Dynamics of Agentic Innovation

We study scientific innovation as a dynamical system: the state is a citation network of research ideas distilled from published papers, and the dynamics are LLM agents that navigate the network and write new ideas (and reference links) back into it. Agents propose ideas freely; each new idea is then scored by whether a similar paper appears in a later conference, published after both the papers in the network and the agent model's training cutoff. We create multiple agents to navigate a 16,208-node network built from NeurIPS, ICLR, ICML, and AAAI papers (2020–2024) and change the number of corpus papers an agent is allowed to read, from 100 up to the whole corpus. We find three things. First, more reading is not better: the share of ideas that later papers go on to realize rises and then falls, peaking at 800 papers, while both extremes sink to the same floor, agents given no papers doing no better than agents free to read all 16,208. The number of ideas produced follows the same curve. Second, team size buys only throughput: the hit rate is flat from 10 to 150 agents while the total scales linearly, because the agents hardly interact—in 10,300 actions, one agent cited another's idea exactly once. Third, the human citation graph is not what helps them: deleting all 80,962 edges makes generalists better, while removing semantic search makes them worse. Agents move through the network by meaning rather than by links, and the links they write back ignore a paper's popularity and favor recent frontier work. Reading scope—not team size, not citation structure—is the lever on the quality of machine-generated research ideas.