Phantom Merge: When Your Large Language Model Agents Pick One but Tell You About Another
LLM agents increasingly operate over multiple entities, where reliable response generation requires not only retrieving relevant evidence but also preserving its association with the correct entity. We identify Phantom Merge (PM), an entity-evidence binding failure in which an agent attributes unsupported claims to its committed entity, potentially even when the task is completed correctly. We formalize PM at the claim level and characterize its prevalence and error subtypes across four agent domains and four backbone families. To address this problem, we propose a two-stage framework for claim-level risk estimation and correction. First, Anchor-Grounded Risk (AGR) combines a representati...
| Authors | Wangshu Zhu, Qianwen Guo, Yushun Dong |
|---|---|
| Primary category | cs.AI |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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