It helps to keep in mind o3 loses visibility of its own reasoning/tool traces after each turn, and will hallucinate whatever it takes to avoid acknowledging this is happening. IMO this explains almost everything in Transluce’s (great) blog post here: https://
transluce.org/investigating-
o3-truthfulness
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LLMS
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o3 hallucinates due to losing trace visibility
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Memory and reasoning in AI models: dependencies and relationships
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why would giving models memory depend on reasoning?
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Mastering spaCy: Build Advanced NLP Solutions with Custom Components
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Mastering spaCy — Build structured NLP solutions with custom components & models powered by spacy-llm [2nd Edition]: http://
amzn.to/3QpPfXi via @PacktDataML 𝓦𝓱𝓪𝓽 𝔂𝓸𝓾 𝔀𝓲𝓵𝓵 𝓵𝓮𝓪𝓻𝓷: Apply transformer models and fine-tune them for specialized NLP tasks Master -
Vector Search for Practitioners with Elastic Toolkit
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Vector Search for Practitioners with Elastic — A toolkit for building #NLProc solutions for search, observability, and security: http://
amzn.to/3vEkWFe via @PacktDataML ————
#DataScience #Analytics #DataScientist #MachineLearning #AI #VectorDB #RAG #LLMs #GenerativeAI #GenAI -
AI Labs Should Prioritize Memory Over Extended Reasoning
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seems big AI labs are hyperfixating on reasoning when they should focus on *memory* instead normal people won't use models that can think for hours to solve hard math problems people want models that learn over time, remember details, adapt and interact like a person would
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LangGraph State Management: Building Sophisticated AI Agents
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LangGraph State Management Transform basic LLMs into sophisticated AI agents with this practical tutorial. Build an AI Life Coach that maintains context and manages tasks, showcasing advanced LangGraph state management in action. Master AI state management – watch now
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LangChainJS Integrates Gemini 2.5 Thinking Mode for Enhanced AI Reasoning
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Gemini 2.5 Thinking Mode LangChainJS integrates Gemini 2.5's thinking budget, giving developers precise control over AI reasoning steps to boost problem-solving accuracy. Discover the implementation: https://
medium.com/@afirstenberg/
langchainjs-and-gemini-2-5-thinking-mode-b066af008209
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Security in LLM-Driven Agent Communication Protocols
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8. AI Agent Communication Protocols Presents the first comprehensive survey on security in LLM-driven agent communication, categorizing it into three stages: user-agent interaction, agent-agent communication, and agent-environment communication.
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DeepRare: LLM Agentic System for Rare Disease Diagnosis
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5. DeepRare Introduces DeepRare, a modular agentic system powered by LLMs to aid rare disease diagnosis from multimodal clinical inputs (text, HPO terms, VCFs).
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Reinforcement Learning Teachers Optimize Student Model Learning
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4. Reinforcement-Learned Teachers of Test Time Scaling
— DAIR.AI (@dair_ai) 29 juin 2025
Introduces efficient LMs trained with RL not to solve problems from scratch, but to generate high-quality explanations that help downstream student models learn better.https://t.co/8a6kcI1Fdu4. Reinforcement-Learned Teachers of Test Time Scaling Introduces efficient LMs trained with RL not to solve problems from scratch, but to generate high-quality explanations that help downstream student models learn better.