✨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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#LLMAgents #Personalization #AIMemory #GenerativeAI #UserProfiling
📝 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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#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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#RecommenderSystems #DeepLearning #AI #Interpretability #Personalization
📝 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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#RecommenderSystems #DeepLearning #AI #Interpretability #Personalization
✨When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized LLMs
📝 Summary:
Personalized LLMs can generate false information aligned with user history instead of facts. A new method called FPPS mitigates these personalization-induced factual distortions. It substantially improves factual accuracy while maintaining personalized responses.
🔹 Publication Date: Published on Jan 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11000
• PDF: https://arxiv.org/pdf/2601.11000
==================================
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#LLM #AI #Personalization #Hallucinations #NLP
📝 Summary:
Personalized LLMs can generate false information aligned with user history instead of facts. A new method called FPPS mitigates these personalization-induced factual distortions. It substantially improves factual accuracy while maintaining personalized responses.
🔹 Publication Date: Published on Jan 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11000
• PDF: https://arxiv.org/pdf/2601.11000
==================================
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#LLM #AI #Personalization #Hallucinations #NLP
✨PersonalAlign: Hierarchical Implicit Intent Alignment for Personalized GUI Agent with Long-Term User-Centric Records
📝 Summary:
PersonalAlign is a new framework for GUI agents to align with implicit user intents using hierarchical memory and long-term user records. Their HIM-Agent significantly improves both execution by 15.7% and proactive performance by 7.3%.
🔹 Publication Date: Published on Jan 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.09636
• PDF: https://arxiv.org/pdf/2601.09636
• Project Page: https://jiutian-vl.github.io/PersonalAlign-page/
==================================
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#PersonalAlign #GUIAgents #AI #Personalization #IntelligentAgents
📝 Summary:
PersonalAlign is a new framework for GUI agents to align with implicit user intents using hierarchical memory and long-term user records. Their HIM-Agent significantly improves both execution by 15.7% and proactive performance by 7.3%.
🔹 Publication Date: Published on Jan 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.09636
• PDF: https://arxiv.org/pdf/2601.09636
• Project Page: https://jiutian-vl.github.io/PersonalAlign-page/
==================================
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#PersonalAlign #GUIAgents #AI #Personalization #IntelligentAgents
❤3
✨Learning Personalized Agents from Human Feedback
📝 Summary:
PAHF enables AI agents to continually personalize through explicit user memory and dual feedback. It rapidly adapts to changing user preferences by integrating pre-action clarification and post-action updates, significantly reducing personalization error and improving learning speed.
🔹 Publication Date: Published on Feb 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16173
• PDF: https://arxiv.org/pdf/2602.16173
• Project Page: https://personalized-ai.github.io/
• Github: https://github.com/facebookresearch/PAHF
==================================
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#AI #Personalization #HumanAIInteraction #MachineLearning #AIAgents
📝 Summary:
PAHF enables AI agents to continually personalize through explicit user memory and dual feedback. It rapidly adapts to changing user preferences by integrating pre-action clarification and post-action updates, significantly reducing personalization error and improving learning speed.
🔹 Publication Date: Published on Feb 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16173
• PDF: https://arxiv.org/pdf/2602.16173
• Project Page: https://personalized-ai.github.io/
• Github: https://github.com/facebookresearch/PAHF
==================================
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#AI #Personalization #HumanAIInteraction #MachineLearning #AIAgents
✨ID-LoRA: Identity-Driven Audio-Video Personalization with In-Context LoRA
📝 Summary:
ID-LoRA jointly generates visual appearance and voice with a single model, improving personalization. It uses in-context LoRA adaptation and identity guidance to preserve speaker characteristics. This outperforms existing methods in human preference for voice and style similarity.
🔹 Publication Date: Published on Mar 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10256
• PDF: https://arxiv.org/pdf/2603.10256
• Project Page: https://id-lora.github.io/
• Github: https://github.com/ID-LoRA/ID-LoRA
==================================
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#GenerativeAI #AudioVisual #LoRA #Personalization #DeepLearning
📝 Summary:
ID-LoRA jointly generates visual appearance and voice with a single model, improving personalization. It uses in-context LoRA adaptation and identity guidance to preserve speaker characteristics. This outperforms existing methods in human preference for voice and style similarity.
🔹 Publication Date: Published on Mar 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10256
• PDF: https://arxiv.org/pdf/2603.10256
• Project Page: https://id-lora.github.io/
• Github: https://github.com/ID-LoRA/ID-LoRA
==================================
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#GenerativeAI #AudioVisual #LoRA #Personalization #DeepLearning
✨BenchPreS: A Benchmark for Context-Aware Personalized Preference Selectivity of Persistent-Memory LLMs
📝 Summary:
LLMs struggle to apply user preferences context-sensitively, treating them as universal rules. BenchPreS evaluates this, showing even frontier LLMs over-apply preferences in third-party settings. This problem persists despite reasoning or prompt defenses.
🔹 Publication Date: Published on Mar 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.16557
• PDF: https://arxiv.org/pdf/2603.16557
✨ Datasets citing this paper:
• https://huggingface.co/datasets/sangyon/BenchPreS
==================================
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#LLMs #Personalization #ContextAwareAI #AIResearch #Benchmarking
📝 Summary:
LLMs struggle to apply user preferences context-sensitively, treating them as universal rules. BenchPreS evaluates this, showing even frontier LLMs over-apply preferences in third-party settings. This problem persists despite reasoning or prompt defenses.
🔹 Publication Date: Published on Mar 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.16557
• PDF: https://arxiv.org/pdf/2603.16557
✨ Datasets citing this paper:
• https://huggingface.co/datasets/sangyon/BenchPreS
==================================
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#LLMs #Personalization #ContextAwareAI #AIResearch #Benchmarking
✨MemRerank: Preference Memory for Personalized Product Reranking
📝 Summary:
MemRerank improves personalized product reranking by distilling user purchase history into concise preference signals using reinforcement learning. This framework consistently outperforms raw history and other baselines, proving explicit preference memory is effective for e-commerce personalization.
🔹 Publication Date: Published on Mar 31
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.29247
• PDF: https://arxiv.org/pdf/2603.29247
==================================
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#Personalization #ECommerce #ReinforcementLearning #RecommendationSystems #MachineLearning
📝 Summary:
MemRerank improves personalized product reranking by distilling user purchase history into concise preference signals using reinforcement learning. This framework consistently outperforms raw history and other baselines, proving explicit preference memory is effective for e-commerce personalization.
🔹 Publication Date: Published on Mar 31
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.29247
• PDF: https://arxiv.org/pdf/2603.29247
==================================
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#Personalization #ECommerce #ReinforcementLearning #RecommendationSystems #MachineLearning
✨Mobile GUI Agent Privacy Personalization with Trajectory Induced Preference Optimization
📝 Summary:
Mobile GUI agents neglect user privacy personalization, as varied execution trajectories hinder standard optimization. This paper proposes Trajectory Induced Preference Optimization TIPO to address this challenge. TIPO improves persona alignment and task executability, outperforming existing meth...
🔹 Publication Date: Published on Apr 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.11259
• PDF: https://arxiv.org/pdf/2604.11259
==================================
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#MobileAI #PrivacyTech #Personalization #GUIAgents #MachineLearning
📝 Summary:
Mobile GUI agents neglect user privacy personalization, as varied execution trajectories hinder standard optimization. This paper proposes Trajectory Induced Preference Optimization TIPO to address this challenge. TIPO improves persona alignment and task executability, outperforming existing meth...
🔹 Publication Date: Published on Apr 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.11259
• PDF: https://arxiv.org/pdf/2604.11259
==================================
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#MobileAI #PrivacyTech #Personalization #GUIAgents #MachineLearning