SOURCE-LINKED INTELLIGENCE
GraphEcho: Structural Redundancy and Evidence Provenance in LLM Graph Agents
A large language model (LLM) agent can follow more graph paths without acquiring more independent evidence. GraphEcho tests whether agents mistake these repeated encounters for additional corroboration. The benchmark varies path counts and evidential origins while holding evidence content fixed, and evaluates both judgments and active exploration. Controlled synthetic experiments reveal model-dependent judgment shifts, but redundant supporting paths increase the share of repeated walks across all evaluated frozen agents. Provenance-aware post-training (PAPT) reduces revisits and improves synth
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-15T18:07:09.000Z
First collected: 2026-09-20T08:20:57.646Z. This is not the publication date.