The AI Morning Read November 30 2025 - ViLoMem: Teaching AI to Remember What It Got Wrong

In today's podcast we deep dive into ViLoMem, an Agentic Learner with Grow-and-Refine Multimodal Semantic Memory, introduced in a recent paper to help Multimodal Large Language Models (MLLMs) avoid repeating visual and logical mistakes. This framework addresses the limitation that existing memory systems often lose essential domain knowledge and fail to preserve how visual attention and logical reasoning jointly contribute to solutions, which is fundamentally misaligned with the integrated, multimodal nature of human semantic memory. ViLoMem operates using a dual-stream structure that separately constructs compact, schema-based memory by encoding visual distraction patterns and logical reasoning errors, following a "grow-and-refine" principle to incrementally accumulate and update this knowledge. During retrieval, the model uses image content and question context to drive visual retrieval—producing attention heat maps to show the agent where to look—while text drives the retrieval of logic memory, ensuring the agent is guided by both error streams. This approach consistently improves pass@1 accuracy across multimodal benchmarks, demonstrating gains such as a +6.48 point rise on Math Vision for GPT-4.1, proving that the dual visual and logic memory reliably boosts performance for lifelong learning.