Google's ReasoningBank: A Framework for Self-Evolving LLM Agents

👻👽The Magic of “Mixture-of-Experts” (MoE) 🧙♂️🧙♀️ Ever wondered how new AI models like GPT-5, Grok 4, or Gemini 2.5 Pro are becoming so powerful without costing a fortune to run? Meet Mixture-of-Experts (MoE) — a game-changing technique that makes Large Language Models (LLMs) more efficient, scalable, and specialized. 🧠✨ 🔹 What Is Mixture-of-Experts (MoE)? Think of MoE like a team of specialists. Instead of asking every team member to work on every task (like older AI models did), the system only calls the right experts for the job. 🧩 How it works: The model has many “experts” — small specialized sub-models (math, code, writing, etc.) A “gating network” acts like a manager, picking the best experts for the task Only those experts are activated → saving compute, boosting performance 👉 Result: The model becomes faster, cheaper, and smarter at focused tasks. 🔹 Real-World Examples (as of 2025) ModelDeveloperMoE?NotesGPT-5OpenAI✅ SpeculatedMassive scale, likely dynamic routingGrok 4xAI✅ ConfirmedMulti-agent MoE, very efficientGemini 2.5 ProGoogle✅ ConfirmedDesigned for efficient scalingClaude 4Anthropic❌Probably dense (no MoE yet)DeepSeek-V3DeepSeek✅ Confirmed671B total, 37B active per token 🔹 Why It Matters ✅ Efficiency: Uses less compute → faster and greener AI ✅ Scalability: Add more experts without slowing down ✅ Specialization: Experts learn unique skills → better accuracy ⚠️ Challenges Routing can sometimes misfire (wrong expert chosen) Requires more memory to store all experts Harder to interpret why a certain expert was picked 💡 TL;DR Mixture-of-Experts = Specialized AI teamwork. Instead of using the whole brain every time, the model just activates the smartest neurons for the task at hand. Smarter use of compute = better performance for less cost. That’s the future of AI — intelligent specialization. 🌍💻 👉 Curious takeaway: The next time you hear about “GPT-5” or “Grok 4,” know that there might be hundreds of tiny experts behind the scenes — working together to make your AI conversations faster and sharper than ever. Asharib Ali | Naeem H. | Ameen Alam | Daniyal Nagori | Muhammad Qasim | #AI #MachineLearning #Innovation #MoE #LLM #ArtificialIntelligence #GPT5 #DeepLearning #TechExplained #FutureOfAI

What if AI could conduct PhD-level research autonomously? Meet Tongyi DeepResearch Alibaba's Tongyi DeepResearch just redefined AI efficiency. A 30B parameter research agent that outperforms GPT-4o and DeepSeek-V3 while using only 3.3B active parameters. Fully open-source. Trained on 2 H100s for under $500. What separates a chatbot from a true AI researcher? The ability to think, plan, and investigate complex problems over multiple steps—just like a human researcher would. 👉 Why this matters Most AI agents today struggle with complex, multi-step reasoning tasks. They can answer simple questions but fail when faced with research problems that require sustained investigation, cross-referencing multiple sources, and synthesizing findings over time. 👉 What Tongyi DeepResearch achieves The team at Tongyi Lab has released the first fully open-source web agent that matches proprietary systems like OpenAI's DeepResearch. The results speak for themselves: • Scores 32.9 on "Humanity's Last Exam" (academic reasoning benchmark) • Achieves 51.5 on BrowseComp (complex information-seeking tasks) • Outperforms GPT-4 and Claude across multiple research benchmarks 👉 How they built it The breakthrough lies in three key innovations: Synthetic data at scale: Instead of relying on expensive human annotations, they developed AgentFounder—a system that automatically generates high-quality training data by constructing knowledge graphs from real websites and creating increasingly complex questions. IterResearch paradigm: Traditional agents suffer from "cognitive suffocation" as they accumulate information. IterResearch solves this by breaking tasks into focused research rounds, reconstructing a clean workspace each time to maintain reasoning quality. End-to-end training pipeline: They rethought the entire training process, connecting Agentic Continual Pre-training → Supervised Fine-Tuning → Reinforcement Learning in one seamless loop. 👉 Real applications This isn't just research—it's already powering Gaode's travel planning agent and Tongyi FaRui's legal research system, demonstrating practical value in navigation and legal analysis. The complete methodology, models, and code are open-source, marking a significant step toward democratizing advanced AI research capabilities. What research problems would you tackle with an AI agent like this?

Are your AI tools operating in silos? Businesses are realizing that large language models aren’t enough on their own, especially when data lives across disconnected systems. Learn how the Model Context Protocol (#MCP) could be the missing piece of the puzzle that lets #AI agents work alongside people, securely access tools, and drive automated, multistep workflows. https://ow.ly/C0Qz50XnwiI

Tired of LLMs hallucinating or missing crucial, real-time context? Learn to build truly context-aware AI by moving 'Beyond Prompts' with Serverless RAG. This deep dive reveals how to integrate real-time and proprietary data, overcoming common LLM limitations for reliable, real-world applications. Ready to build smarter, more reliable AI? Dive in: https://lnkd.in/gs_xz_iB #AI #LLMs #RAG #Serverless #ContextAwareAI #TechInnovation

The semantic gap - great blog post about how the most successful AI projects augment intelligence rather than try to replace human decision making. https://lnkd.in/eqmqfCbc

In today’s fast-moving landscape of generative AI, simply relying on large language models (LLMs) trained on static datasets often isn’t enough. That’s where Retrieval-Augmented Generation (RAG) comes in — a technique that combines retrieval of external, relevant information with generation by an LLM, helping bring more accuracy, relevance and up-to-date context to the output. Here’s why RAG matters: • It enables the model to pull in domain-specific or proprietary data (e.g., internal knowledge bases, up-to-date documents) after training, rather than having to retrain the model every time the knowledge changes. • It helps reduce “hallucinations” — i.e., plausible‐but‐wrong answers from an LLM — by grounding generation in retrieved evidence. • It opens up new enterprise possibilities: e.g., customer service bots, document summarisation, domain-specialised assistants, all leveraging your organisation’s own data. Key components of a RAG system include: 1. A retrieval mechanism (for example, vector-searching a document corpus) 2. A generation step (the LLM) that uses both the user’s query + retrieved context 3. Continuous augmentation of the knowledge base (so that the information remains fresh). Challenges & things to watch out for: • Retrieval quality matters: if you bring in irrelevant or misleading documents, you risk worse outcomes. • Enterprise data governance, security & compliance become critical when you open the retrieval to internal or proprietary content. • Design trade-offs: how many retrieved documents to feed? How to rank them? How to prompt the LLM for best use of context? BentoML Bottom line: If you work in AI, data, knowledge management or customer-facing automation, RAG is a design pattern worth understanding and adopting. It’s not just “another model” — it’s about bridging external (and evolving) knowledge with generative technology. I’d love to hear how others are using or thinking about RAG in their teams: Are you building knowledge bots, document assistants, domain-specific generative systems? What has worked / not worked? #GenerativeAI #RAG #AI #KnowledgeManagement #LLM #Innovation https://lnkd.in/df2-jhH4 https://lnkd.in/dsefHUHu https://lnkd.in/dx9_HhUP

Quantexa Makes Its Decision Intelligence Platform ‘Agent Ready’ to Solve the Hardest Problems in AI: Data Fragmentation & Context Read more: https://lnkd.in/dm3fsqaM #IndiaTechnologyNews #Quantexa #DecisionIntelligence #AIInnovation #AgentReady #DataFragmentation #ContextualAI #EnterpriseAI #DataAnalytics #ArtificialIntelligence

While studying a multi-agent AI paper, I came across the fact that the researchers used Microsoft AutoGen to implement collaborative agent workflows. This led me to study AutoGen in depth, and I wrote an article: “AutoGen — The Multi-Agent AI Framework That Thinks Like a Team." Key takeaways from the article: 1. GroupChat: Enables multiple agents to communicate, debate, and collaborate in a shared conversation. 2. Specialized Roles: Agents like Planner, Critic, and Summarizer work together to tackle complex tasks. 3. LLM Integration: Leverages OpenAI models and can also integrate other language models. 4. Human-in-the-loop & Tools: Agents can receive feedback, use multiple tools, and share context for smarter outcomes. Read the full article here: https://lnkd.in/gzZ3_V6e

We solved a decades-old challenge of scaling neurosymbolic AI! Knowledge graphs (KGs) are the gold standard of symbolic representation. However, automatically building reliable KGs from unstructured data has remained an open problem, making current neurosymbolic systems hard to scale. Our tiny 80M-parameter neural-to-symbolic converter solves this challenge by efficiently creating superior KGs compared to a 32B-parameter LLM baseline. 📊 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 𝗮𝘁 𝗮 𝗴𝗹𝗮𝗻𝗰𝗲 • 69.8% factuality vs. 40.2% (+29.6) • 68.8% validity vs. 43.0% (+25.8) • 59.4% benchmark accuracy vs. 50.2% (+9.2) 🔑 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗶𝘀 𝗮 𝗯𝗶𝗴 𝗯𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵 Our architecture, GraphMERT, reliably extracts domain-specific KGs from unstructured data, enabling systems that are: • Efficient & scalable • Transparent & interpretable • Attributable with full provenance • Accountable with built-in governance • Editable & auditable by human experts • Continually improvable This matters because current LLMs, despite their impressive capabilities, fall short on reliability for high-stakes domains like medicine, law, and finance. They're prone to hallucinations, lack transparency, and can't provide verifiable reasoning chains. The improved factuality and ontological validity of our KGs indicate a future where high-quality data may not be the limiting factor for superior AI systems. And this is just the beginning. This innovation opens doors to scaling the symbolic side, similar to the neural scaling that the current AI industry is pursuing. We are nearing superintelligent AI systems, and GraphMERT enables future neurosymbolic superintelligence that is safe, trustworthy, and accountable. This also opens the door to business models that simply aren't possible with current GenAI, especially in high-stakes use cases. As a dear friend Manu Namboodiri pointed out, this enables a "Spotify for AI" with pay-per-play for knowledge: if a query routes through a KG built from a NYT piece, that journalist is transparently attributed and paid. 💭 𝗔 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗻𝗼𝘁𝗲 I remember proposing this idea to my PhD advisor, Prof. Niraj Jha, three years ago. What began as a promising direction became a journey of dead ends, many late-night debug logs, and moments where we questioned if it was even possible. But we kept pushing. Through failed experiments, architectural redesigns, and the inevitable "this will never work" moments every researcher knows, we persisted. Seeing these results now feels surreal... we finally cracked a problem the field has been chasing for decades. Kudos to Margarita Belova and Jiaxin Xiao who poured countless hours into experiments and iterations. The path from idea to a working solution was long and humbling, but we made it. Sometimes the most important breakthroughs take time. This was one of them. 📄 Full paper: https://lnkd.in/evyhhzmx #NeurosymbolicAI #KnowledgeGraphs #ResponsibleAI #AIResearch #EnterpriseAI

MORNING EDITION - AI Intelligence Briefing – 21st October 2025 OpenAI suffers embarrassing math claims debacle. The company's VP Kevin Weil claimed GPT-5 solved 10 previously unsolved Erdos mathematical problems, but mathematician Thomas Bloom quickly clarified these were simply papers he personally hadn't read, not actual unsolved problems. Google DeepMind CEO Demis Hassabis called it "embarrassing," while Meta's Yann LeCun criticized the premature victory lap. OpenAI researcher Sebastien Bubeck later acknowledged only existing solutions were found, though defended the literature search capability as valuable. https://tcrn.ch/471VEBf Wikipedia reports 8% traffic decline as AI summaries reshape information consumption. The popular online encyclopedia attributes the drop to generative AI tools like Google's AI Overviews and ChatGPT providing instant answers, reducing direct site visits. Social media video platforms also contributed to declining pageviews. This trend threatens Wikipedia's volunteer-driven model, as fewer human visitors mean reduced engagement and contributions. The shift demonstrates how AI-powered search is fundamentally altering how people seek and consume information online. https://cnet.co/3IT7Xqs Anthropic launches Claude Haiku 4.5 with frontier-level coding at lower cost. The updated model delivers performance comparable to mid-tier Sonnet on coding tasks while operating 4-5 times faster and at reduced cost. Designed for automating customer service and coding workflows, Haiku 4.5 enables deploying multiple agents in parallel for complex problem-solving. The release reflects Anthropic's strategy to democratize access to advanced AI capabilities through more efficient, affordable models suitable for enterprise automation at scale. https://reut.rs/48FjIv5 THE MORNING INSIGHT AI credibility and verification challenges intensify as industry leaders face public scrutiny over exaggerated claims. The OpenAI mathematics controversy highlights growing pressure for transparent, verifiable AI capabilities rather than marketing hype. Simultaneously, AI's disruption of established information platforms like Wikipedia signals fundamental shifts in knowledge discovery and consumption patterns that will reshape digital business models across sectors.