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LLMs do not get smarter just by remembering more. EvoLib turns experience into evolving knowledge, taking reusable skills and insights that help models learn and adapt across tasks long after deployment.

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Microsoft Research

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23,059 görüntüleme • 27 gün önce •via X (Twitter)

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Small language models learn to negotiate with SocialRL, PazaBench V2 expands speech AI evaluation across African languages, and EvoLib helps agents turn experience into knowledge. Plus: new methods for more reliable A/B testing and advances in AI-driven precision oncology.
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Small language models learn to negotiate with SocialRL, PazaBench V2 expands speech AI evaluation across African languages, and EvoLib helps agents turn experience into knowledge. Plus: new methods for more reliable A/B testing and advances in AI-driven precision oncology.

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AI isn’t just about models, it’s about agents. Mainfunc’s Genspark platform, powered by Microsoft Azure and eight leading LLMs, is now evolving with Agent 365 and bringing agentic workflows to life. The result? Smarter automation, faster decisions, and real impact for knowledge workers. #MSIgnite

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The ultimate #BackToSchool supplies just dropped! 🎒Get ready for smarter learning with new tools in the Google Gemini! Discover Guided Learning, integrated visuals, and exam prep tools to help students build their knowledge. Learn more:
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The ultimate #BackToSchool supplies just dropped! 🎒Get ready for smarter learning with new tools in the Google Gemini! Discover Guided Learning, integrated visuals, and exam prep tools to help students build their knowledge. Learn more:

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Robot AI brains, aka Vision-Language-Action models, cannot adapt to new tasks as easily as LLMs like Gemini, ChatGPT, or Grok. LLMs can adapt quickly with their in-context learning (ICL) capabilities. But can we inject ICL abilities into a pre-trained VLA like pi0? Yes! Introducing RICL (Retraining for In-Context Learning), our Conference on Robot Learning (CoRL) 2025 paper. Our RICL-pi0 model can adapt to unseen objects, novel motions, and new scenes with just ICL and RAG (retrieval-augmented generation). RICL-pi0 also boosts performance on the long-tail of tasks. A quick 1 minute video summary:
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Robot AI brains, aka Vision-Language-Action models, cannot adapt to new tasks as easily as LLMs like Gemini, ChatGPT, or Grok. LLMs can adapt quickly with their in-context learning (ICL) capabilities. But can we inject ICL abilities into a pre-trained VLA like pi0? Yes! Introducing RICL (Retraining for In-Context Learning), our Conference on Robot Learning (CoRL) 2025 paper. Our RICL-pi0 model can adapt to unseen objects, novel motions, and new scenes with just ICL and RAG (retrieval-augmented generation). RICL-pi0 also boosts performance on the long-tail of tasks. A quick 1 minute video summary:

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Europe's future doesn't happen by accident. Long-term planning turns ideas into action, supporting projects and businesses that strengthen skills, innovation, connectivity and opportunities across the continent. That's why stronger budget needs stronger support for our future.
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Europe's future doesn't happen by accident. Long-term planning turns ideas into action, supporting projects and businesses that strengthen skills, innovation, connectivity and opportunities across the continent. That's why stronger budget needs stronger support for our future.

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Hi Brentford FC slot has just told you how to beat them He doesn’t know how to adapt and deal with long balls and not being allowed to press You know what to do! Same for Aston Villa after that Just go long and get right at that shaky defence 🤣🤣🤣🤣 #GlazersOut
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Hi Brentford FC slot has just told you how to beat them He doesn’t know how to adapt and deal with long balls and not being allowed to press You know what to do! Same for Aston Villa after that Just go long and get right at that shaky defence 🤣🤣🤣🤣 #GlazersOut

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Adapt addressed the situation with his Head Mod and the 'Flock Queens' after he was exposed gatekeeping the comm from them and accusing them of trying to get close to Adapt 😬 “I looked into it and he’s not mod no more that sh*t is not going over here”
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Adapt addressed the situation with his Head Mod and the 'Flock Queens' after he was exposed gatekeeping the comm from them and accusing them of trying to get close to Adapt 😬 “I looked into it and he’s not mod no more that sh*t is not going over here”

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MICROSOFT QUIETLY BUILT ONE MEMORY MODULE THAT PLUGS INTO ANY AI AGENT AND CUTS ITS CONTEXT BY UP TO 100X no retraining. no task-specific redesign. you just plug it in Microsoft's insight: agents don't need to remember what happened. they need the knowledge pulled from it - the facts and the reusable skills, not the raw logs so PlugMem stops storing history. it compiles experience into a knowledge graph, then feeds the agent only what matters the result: > up to 1-2 orders of magnitude fewer tokens > highest decision-utility per token of any system tested > one unchanged module beats task-specific memory on chat QA, multi-hop search, and web agents more memory was never the answer. better memory is - and Microsoft just shipped it as a plug-in bookmark this. the agents that win won't remember more, they'll remember smarter.
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MICROSOFT QUIETLY BUILT ONE MEMORY MODULE THAT PLUGS INTO ANY AI AGENT AND CUTS ITS CONTEXT BY UP TO 100X no retraining. no task-specific redesign. you just plug it in Microsoft's insight: agents don't need to remember what happened. they need the knowledge pulled from it - the facts and the reusable skills, not the raw logs so PlugMem stops storing history. it compiles experience into a knowledge graph, then feeds the agent only what matters the result: > up to 1-2 orders of magnitude fewer tokens > highest decision-utility per token of any system tested > one unchanged module beats task-specific memory on chat QA, multi-hop search, and web agents more memory was never the answer. better memory is - and Microsoft just shipped it as a plug-in bookmark this. the agents that win won't remember more, they'll remember smarter.

NO1ennn

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Today, we're launching Rogo Intelligence. One of the accepted costs of knowledge work has always been that experience compounds in people, not software. Teams develop judgment, but that knowledge remains trapped in inboxes, individual memory, and disconnected systems. As more work runs through a single platform, firms are capturing not just outputs, but the reasoning behind them. Increasingly, our customers ask the same question: How do we turn all of that into an advantage? Rogo Intelligence transforms your firm's expertise into reusable institutional memory. As foundation models become increasingly capable, the enduring advantage won't come from the models themselves. It will come from the intelligence your firm builds over time: the workflows behind your best outcomes, the judgment behind your best decisions, and the knowledge created across every deal, investment, and client relationship. We're building to codify your firm's best practices so your institutional edge compounds.
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Today, we're launching Rogo Intelligence. One of the accepted costs of knowledge work has always been that experience compounds in people, not software. Teams develop judgment, but that knowledge remains trapped in inboxes, individual memory, and disconnected systems. As more work runs through a single platform, firms are capturing not just outputs, but the reasoning behind them. Increasingly, our customers ask the same question: How do we turn all of that into an advantage? Rogo Intelligence transforms your firm's expertise into reusable institutional memory. As foundation models become increasingly capable, the enduring advantage won't come from the models themselves. It will come from the intelligence your firm builds over time: the workflows behind your best outcomes, the judgment behind your best decisions, and the knowledge created across every deal, investment, and client relationship. We're building to codify your firm's best practices so your institutional edge compounds.

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Andrej Karpathy just explained the 5 shifts turning LLMs into agentic systems. 00:00 - Memory turns chat into personal AI 06:41 - Multimodal AI reads the world 16:58 - Thinking models solve harder tasks 24:51 - Search makes LLMs live 30:58 - Tools turn LLMs into workers Most people are still treating LLMs like chatbots. Karpathy is showing the full stack: Memory → Vision → Reasoning → Search → Tools Prompting is the old workflow. Agentic systems are the new one. This 40-minute talk is worth more than most paid AI agent courses. Bookmark and watch it before everyone catches up. Then read how to turn LLMs into self-improving agent loops below
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Andrej Karpathy just explained the 5 shifts turning LLMs into agentic systems. 00:00 - Memory turns chat into personal AI 06:41 - Multimodal AI reads the world 16:58 - Thinking models solve harder tasks 24:51 - Search makes LLMs live 30:58 - Tools turn LLMs into workers Most people are still treating LLMs like chatbots. Karpathy is showing the full stack: Memory → Vision → Reasoning → Search → Tools Prompting is the old workflow. Agentic systems are the new one. This 40-minute talk is worth more than most paid AI agent courses. Bookmark and watch it before everyone catches up. Then read how to turn LLMs into self-improving agent loops below

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More and more people are unable to access the resources they need due to digital literacy blocks. Papa has long been helping members adapt to new technology, and they've just announced an enhanced Digital Health Literacy (DHL) offering. The service will help grow virtual care access for dual-eligible, disadvantaged, and low-income members with at-home visits to help them learn the skills they need to access quality health care. Papa has completed more than 54,000 technology visits since January 2022, and their enhanced service will help even more people get the standard of care they deserve 💙

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Introducing The Kyndryl Agentic AI Framework: built to help enterprises scale agentic AI with confidence on-prem, in the cloud or hybrid. Powered by insights from Kyndryl Bridge, our framework deploys self-learning AI agents that adapt in real-time, working alongside human teams to drive smarter operations and faster decisions. Read more about this news: #TheHeartOfProgress

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3D-LLM: Injecting the 3D World into Large Language Models paper page: Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs.
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3D-LLM: Injecting the 3D World into Large Language Models paper page: Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs.

AK

249,798 görüntüleme • 3 yıl önce

Demis Hassabis points out that today’s models don’t learn continuously, they are trained once and then deployed. Unlike humans, they don’t improve from experience or adapt after release. The next major leap, like “AlphaZero,” would involve self-learning systems that discover knowledge on their own. This ability to learn online and evolve in real time is a key missing component on the path to AGI. Solving this will be critical to building truly intelligent systems.
1:42

Demis Hassabis points out that today’s models don’t learn continuously, they are trained once and then deployed. Unlike humans, they don’t improve from experience or adapt after release. The next major leap, like “AlphaZero,” would involve self-learning systems that discover knowledge on their own. This ability to learn online and evolve in real time is a key missing component on the path to AGI. Solving this will be critical to building truly intelligent systems.

Wes Roth

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🏆 Introducing TARS 1/ Your AI Executive Assistant that Turns Natural Language into Action. -> With TARS, you can seamlessly orchestrate multiple AI agents to gather insights, automate tasks, and take actions across platforms — all in one place. -> Build upon the 1st productized multi-agent orchestration (MAO) ecosystem designed for AI Agentic workflow. 🏠 Explore more:
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🏆 Introducing TARS 1/ Your AI Executive Assistant that Turns Natural Language into Action. -> With TARS, you can seamlessly orchestrate multiple AI agents to gather insights, automate tasks, and take actions across platforms — all in one place. -> Build upon the 1st productized multi-agent orchestration (MAO) ecosystem designed for AI Agentic workflow. 🏠 Explore more:

Questflow

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🚂 Introducing Agent Prompt Train 🚂 An open-sourced high-performance proxy that works with Claude Code to help turn it from a single-developer tool into a collaborative development platform. Claude Code changed the game. Now, Agent Prompt Train is here to help teams: ✅ Log every conversation for review ✅ Analyze & optimize prompts with AI ✅ Turn best practices into reusable team commands ✨ The payoff: Smarter prompts, faster learning, and shared knowledge across your whole team. 📖 Read more on the blog & test it out 👇
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🚂 Introducing Agent Prompt Train 🚂 An open-sourced high-performance proxy that works with Claude Code to help turn it from a single-developer tool into a collaborative development platform. Claude Code changed the game. Now, Agent Prompt Train is here to help teams: ✅ Log every conversation for review ✅ Analyze & optimize prompts with AI ✅ Turn best practices into reusable team commands ✨ The payoff: Smarter prompts, faster learning, and shared knowledge across your whole team. 📖 Read more on the blog & test it out 👇

Moonsong Labs

21,484 görüntüleme • 11 ay önce

Robots are getting smarter, but most still fail the same way. They don’t learn from their own mistakes. A new paper proposes something different: a way for robots to self-improve directly from their failures in the real world. It’s called PLD (Probe, Learn, Distill). The idea: instead of collecting endless human demos, let the robot figure out where it fails, learn how to recover, and then distill that knowledge back into its main model. Key takeaways from the research: ✅ Uses residual reinforcement learning to recover from policy failures ✅ Achieves 99% success on LIBERO and 100% on real Franka and YAM arms ✅ Runs hour-long manipulation tasks without human resets ✅ Builds a feedback loop between real-world data and model adaptation Unlike supervised fine-tuning, which relies on humans, PLD learns from the robot’s own experience. By training on its own failure distribution, the model becomes both more efficient and more aligned with the real world. It’s not just a technical shift, it’s a step toward robots that improve themselves through real-world practice. Thanks for sharing, Wenli Xiao !! Paper and demos: —- Weekly robotics and AI insights. Subscribe free:
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Robots are getting smarter, but most still fail the same way. They don’t learn from their own mistakes. A new paper proposes something different: a way for robots to self-improve directly from their failures in the real world. It’s called PLD (Probe, Learn, Distill). The idea: instead of collecting endless human demos, let the robot figure out where it fails, learn how to recover, and then distill that knowledge back into its main model. Key takeaways from the research: ✅ Uses residual reinforcement learning to recover from policy failures ✅ Achieves 99% success on LIBERO and 100% on real Franka and YAM arms ✅ Runs hour-long manipulation tasks without human resets ✅ Builds a feedback loop between real-world data and model adaptation Unlike supervised fine-tuning, which relies on humans, PLD learns from the robot’s own experience. By training on its own failure distribution, the model becomes both more efficient and more aligned with the real world. It’s not just a technical shift, it’s a step toward robots that improve themselves through real-world practice. Thanks for sharing, Wenli Xiao !! Paper and demos: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

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"Character is something you can only get when you experience the things that you do not want." Great athletes are not only shaped by what they achieve. They are shaped by what they survive, learn from, and keep showing up for.
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"Character is something you can only get when you experience the things that you do not want." Great athletes are not only shaped by what they achieve. They are shaped by what they survive, learn from, and keep showing up for.

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The weekends are your opportunity to get ahead You get more free time to work on the life you want to build and not just escape the life you already have. Use that time wisely This ain’t school, the rewards here are real, the knowledge and skills are permanent
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The weekends are your opportunity to get ahead You get more free time to work on the life you want to build and not just escape the life you already have. Use that time wisely This ain’t school, the rewards here are real, the knowledge and skills are permanent

Wealth Squad Chris

26,433 görüntüleme • 1 yıl önce