GraphEcho shows that LLM agents mistake repeated encounters as additional corroboration; tests reveal model-dependent judgment shifts with redundant supporting paths
Read the original at arxiv.org→arXiv:2609.17695v1 Announce Type: new Abstract: A large language model (LLM) agent can follow more graph paths without acquiring more independent evidence. GraphEcho tests whether agents mistake these repeated...
Original headline: "GraphEcho: Structural Redundancy and Evidence Provenance in LLM Graph Agents"
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- Sep 17, 04:00 UTC arXiv cs.AI lead source GraphEcho: Structural Redundancy and Evidence Provenance in LLM Graph Agents