MMA: Multimodal Memory Agent

• URL: http://arxiv.org/abs/2602.16493v1
• Date: Wed, 18 Feb 2026 14:30:35 GMT
• Title: MMA: Multimodal Memory Agent
• Abstract summary: Long-term agents depend on external memory; however, similarity surfaces stale, low-credibility, or conflicting items, which can trigger overconfident errors.<n>We propose Multimodal Memory Agent (MMA), which assigns each retrieved memory item a source credibility score.<n>Using this framework, we uncover the "Visual Placebo Effect", revealing how agents inherit latent visual biases from foundation models.
• Score: 12.124790246837485
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Long-horizon multimodal agents depend on external memory; however, similarity-based retrieval often surfaces stale, low-credibility, or conflicting items, which can trigger overconfident errors. We propose Multimodal Memory Agent (MMA), which assigns each retrieved memory item a dynamic reliability score by combining source credibility, temporal decay, and conflict-aware network consensus, and uses this signal to reweight evidence and abstain when support is insufficient. We also introduce MMA-Bench, a programmatically generated benchmark for belief dynamics with controlled speaker reliability and structured text-vision contradictions. Using this framework, we uncover the "Visual Placebo Effect", revealing how RAG-based agents inherit latent visual biases from foundation models. On FEVER, MMA matches baseline accuracy while reducing variance by 35.2% and improving selective utility; on LoCoMo, a safety-oriented configuration improves actionable accuracy and reduces wrong answers; on MMA-Bench, MMA reaches 41.18% Type-B accuracy in Vision mode, while the baseline collapses to 0.0% under the same protocol. Code: https://github.com/AIGeeksGroup/MMA.

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