Agentic Learner with Grow-and-Refine Multimodal Semantic Memory
📰 ArXiv cs.AI
Learn how to implement a Grow-and-Refine Multimodal Semantic Memory for agentic learners to improve reasoning and problem-solving capabilities
Action Steps
- Implement a multimodal semantic memory module using a graph-based architecture to store and refine knowledge
- Use a grow-and-refine approach to update the memory module with new information and remove outdated knowledge
- Integrate the memory module with a large language model to enable agentic learning and improve reasoning capabilities
- Test the agentic learner on multimodal problem-solving tasks to evaluate its performance and identify areas for improvement
- Apply the grow-and-refine approach to refine the memory module and adapt to new tasks and domains
Who Needs to Know This
AI researchers and engineers working on multimodal learning and memory-augmented agents can benefit from this knowledge to improve their models' performance and reduce repetition of mistakes
Key Insight
💡 Agentic learners with multimodal semantic memory can improve reasoning and problem-solving capabilities by storing and refining knowledge in a graph-based architecture
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Key Takeaways
Learn how to implement a Grow-and-Refine Multimodal Semantic Memory for agentic learners to improve reasoning and problem-solving capabilities
Full Article
Title: Agentic Learner with Grow-and-Refine Multimodal Semantic Memory
Abstract:
arXiv:2511.21678v2 Announce Type: replace Abstract: MLLMs exhibit strong reasoning on isolated queries, yet they operate de novo -- solving each problem independently and often repeating the same mistakes. Existing memory-augmented agents mainly store past trajectories for reuse. However, trajectory-based memory suffers from brevity bias, gradually losing essential domain knowledge. More critically, even in truly multimodal problem-solving settings, it records only a single-modality trace of pas
Abstract:
arXiv:2511.21678v2 Announce Type: replace Abstract: MLLMs exhibit strong reasoning on isolated queries, yet they operate de novo -- solving each problem independently and often repeating the same mistakes. Existing memory-augmented agents mainly store past trajectories for reuse. However, trajectory-based memory suffers from brevity bias, gradually losing essential domain knowledge. More critically, even in truly multimodal problem-solving settings, it records only a single-modality trace of pas
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