SOURCE-LINKED INTELLIGENCE
MIRAGE: How Conversation State Shapes Historical Evidence Use in Multimodal Personal Agents
Multimodal large language model (MLLM) agents are increasingly used as personal assistants for long-running tasks. Their utility depends on continuity: agents must retrieve and use earlier evidence across dialogue, files, and workspace state. However, agents can generate plausible answers even when access to that history has degraded, causing outcome-only evaluation to overestimate true evidence use. We present MIRAGE (Multimodal Interaction Retrieval, Attribution, and Grounding Evaluation), a controlled study of historical evidence use under conversation-state variation in multimodal personal
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-25T10:59:10.000Z
First collected: 2026-09-21T10:02:02.728Z. This is not the publication date.