✨O-Mem: Omni Memory System for Personalized, Long Horizon, Self-Evolving Agents

📝 Summary:
O-Mem, an active user profiling framework, improves LLM agent consistency and personalization. It updates user profiles and outperforms prior SOTA on LoCoMo and PERSONAMEM benchmarks, also boosting response efficiency.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13593
• PDF: https://arxiv.org/pdf/2511.13593

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For more data science resources:
✓ https://t.iss.one/DataScienceT

#LLMAgents #Personalization #AIMemory #GenerativeAI #UserProfiling

✨Extracting Interaction-Aware Monosemantic Concepts in Recommender Systems

📝 Summary:
A Sparse Autoencoder extracts interaction-aware monosemantic concepts from recommender embeddings. Its prediction-aware training aligns these with model predictions, enabling controllable personalization and interpretability.

🔹 Publication Date: Published on Nov 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18024
• PDF: https://arxiv.org/pdf/2511.18024

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For more data science resources:
✓ https://t.iss.one/DataScienceT

#RecommenderSystems #DeepLearning #AI #Interpretability #Personalization