Tired of agents that mindlessly follow patterns? 🤖💡 We introduce "Agentic Knowledgeable Self-awareness", enabling agent to dynamically assess situations and strategically use resources! Paper: huggingface.co/papers/2504.0… Code: github.com/zjunlp/KnowSelf 🧠 Key Idea: Agents learn to recognize when they need to reflect, when to seek knowledge, and when to act directly. This avoids overfitting to planning patterns and reduces unnecessary knowledge usage. 🔍 How It Works: 1️⃣ Data-Driven Training: Special tokens mark fast, slow, and knowledgeable thinking. 2️⃣ Two-Stage Learning: Supervised fine-tuning RPO loss for robust self-awareness. 3️⃣ Inference: Agents generate tokens to reflect or query knowledge based on context. 📈 Results: Outperforms baselines with minimal knowledge usage! Breaks pattern overfitting and enhances generalization! Scales efficiently with model size and training data! 🌐 Future: KnowSelf paves the way for smarter, more efficient agents. Maybe in the future, we can train models to develop stronger agentic self-awareness through reinforcement learning powerful verifier engineering. #AI #LLMs #AgentPlanning #SelfAwareness #NLP #agent #KnowledgeAugmentation

Ensuring your Large Language Models constantly deliver accurate #AI outcomes can be challenging. Our blog discusses how Retrieval-Augmented Generation impacts your #LLM. Explore how #RAG works, #RAGbenefits, and #RAGusecases. tenupsoft.com/blog/unlocking… #NLP #KnowledgeAugmentation

Looking forward to discussing this with @barnesgc and our NSW Juvenile Justice colleagues this week #checkpoint #knowledgeaugmentation