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Introducing ScienceBuddy — a free workspace for scientific agents that improve through researcher collaboration. Use GPT-6 in ScienceBuddy at no cost. GPU-accelerated, and fused with the JEV framework. 🧵 Two loops: 🔹 Inner loop — refines the agent harness 🔹 Outer loop — trains the model with rubric-guided RL... show more

PhAI Labs

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1,166,272 次观看 • 2 天前 •via X (Twitter)

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35 条评论

liam. 的头像

liam.2 天前

congrats team!

Franklin 的头像

Franklin2 天前

The ambition is splendid. I’d simply ask you to put the before-and-after results beside the impressive vocabulary. Preferably in the same size type. You have my attention, but if it’s learning to earn praise instead of finding the truth, you’ve built a very expensive suck-up.

Justin Zhou 的头像

Justin Zhou2 天前

congrats the launch. love the science agent. will try it

叫我阿杭的头像

叫我阿杭1 天前

确实有东西啊，我觉得这个模型拿来做自媒体的调研，真太牛了

我真的没有拼多多的头像

我真的没有拼多多2 天前

非常好用！我让它调研了一下睡眠跟注意力之间的关系

Ella Tech & Tool 的头像

Ella Tech & Tool2 天前

Free GPT-6 GPU RL loops? This is huge

知识猫AI实验室的头像

知识猫AI实验室2 天前

关键是GPT 6 免费用，太良心了吧

Connect Despirit 的头像

Connect Despirit1 天前

Have you tried GPT-6 in ScienceBuddy yet

EyeingAI 的头像

EyeingAI2 天前

Researchers are gonna have fun with this one.

Aria Tech 的头像

Aria Tech2 天前

ScienceBuddy looks really impressive recursive improvement idea is super exciting for research

Jeremy Bosma 的头像

Jeremy Bosma2 天前

Separating harness and model loops is useful

雪踏乌云的头像

雪踏乌云2 天前

试了下，确实不错，证据源列出的很清晰

摸鱼巨匠🔨 的头像

摸鱼巨匠🔨2 天前

非常好，我已经推荐给我很多博士生同学了

Sadok 的头像

Sadok1 天前

getting free gpt-6 access for science is a huge deal

来碗牛肉粉的头像

来碗牛肉粉2 天前

朋友安利的，自己体验了下真的非常强！科研界真正的WorkBuddy

Aaliya 的头像

Aaliya2 天前

quite impressive combining researcher collaboration with rubric guided RL could give scientific agents a more structured way to improve over time.

阿良｜AI 工作流的头像

阿良｜AI 工作流1 天前

把科学家的反馈变成代理进化燃料，方向对，双循环自我改进能否真发现新科学，还得看实验。

Rachel🥥 的头像

Rachel🥥2 天前

很好用，科研人员必备

Elara AI 的头像

Elara AI1 天前

Recursive-in-Recursive self-improvement with inner loop for harness and outer loop for model training is a really elegant approach - free access with GPT-6 makes it easy to test too

Kirill 的头像

Kirill2 天前

That's interesting! Thanks, I'll check it out right away.

Evia AI 的头像

Evia AI1 天前

Recursive-in-Recursive Self-Improvement with inner loop for harness + outer loop with rubric-guided RL is a fascinating approach. Free access to GPT-6 + GPU acceleration + JEV framework for scientific agents is huge for researchers. Excited to see what the community builds at

QuietNode 的头像

QuietNode2 天前

sciencebuddy goes hard 🔥

Zico 的头像

Zico1 天前

sciencebuddy lfg 🚀

Yingcheng Charles Wu 的头像

Yingcheng Charles Wu2 天前

🔬Try it free:

Shraddha Bharuka 的头像

Shraddha Bharuka2 天前

Really interesting approach to improving scientific AI agents.

程序员鱼皮的头像

程序员鱼皮1 天前

已经用上了，用来研究新东西很不错 😉

SpreadX AI 的头像

SpreadX AI2 天前

Free science agent! Thanks team

The Calm Warrior 的头像

The Calm Warrior2 天前

GPT-6 for free? lol what is JEV though

JuanAI 的头像

JuanAI2 天前

Good step!

marcus 的头像

marcus2 天前

banger launch team

Ravindar Bishnoi Mukam Nokha 的头像

Ravindar Bishnoi Mukam Nokha2 天前

GPT-6 at no cost? wait what lol

gigaFlip.eth 的头像

gigaFlip.eth2 天前

GPU-accelerated JEV setup works out of the box?

Cameron Ng 的头像

Cameron Ng2 天前

Rubric guided RL for scientific harness is a neat approach

ShadowAguy 的头像

ShadowAguy2 天前

The inner/outer loop structure is the detail that actually matters. Most agent frameworks just stack tools on tools without a refinement mechanism. Does ScienceBuddy use the outer loop to benchmark against other researchers or just self-improve?

crypt0messenger 的头像

crypt0messenger2 天前

What kind of rubrics are you using for the scientific evaluation in the outer loop?

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120,600 次观看 • 4 个月前

AutoScientists – a research lab made of agents Harvard University researchers connected agents into a self-organizing scientific team without a boss agent standing in the middle All agents look at the same shared workspace: they share memory, explore multiple directions in parallel, critique each other, avoid repeated failures, and reorganize as evidence changes. But the teams are not fixed. Agents can gather around a promising direction, like architecture, optimizer changes, or data augmentation, then abandon it if it stops working. Before they spend compute, they discuss proposals and critique each other. AutoScientists also shows strong results: - 74.4% mean leaderboard percentile on BioML-Bench - 1.9× faster GPT training optimization - +12.5% on ACE2–Spike, with the same method transferring to 217 ProteinGym assays for a +6.5% average gain

AutoScientists – a research lab made of agents Harvard University researchers connected agents into a self-organizing scientific team without a boss agent standing in the middle All agents look at the same shared workspace: they share memory, explore multiple directions in parallel, critique each other, avoid repeated failures, and reorganize as evidence changes. But the teams are not fixed. Agents can gather around a promising direction, like architecture, optimizer changes, or data augmentation, then abandon it if it stops working. Before they spend compute, they discuss proposals and critique each other. AutoScientists also shows strong results: - 74.4% mean leaderboard percentile on BioML-Bench - 1.9× faster GPT training optimization - +12.5% on ACE2–Spike, with the same method transferring to 217 ProteinGym assays for a +6.5% average gain

Turing Post

22,353 次观看 • 3 个月前

The agency model as we know it is starting to crack. For decades, services businesses have been organized the same way. Client account at the top, then an account manager, then a row of specialists underneath. SEO, paid media, content, design, analytics. Everyone owns a function. Knowledge lives in people's heads and scattered docs. Reporting is retrospective. Margins improve only when you squeeze utilization or hire cheaper. That structure made sense when humans were the only execution layer. It doesn't anymore. What I've been building toward is something I'm calling an agent-native revenue loop model. Instead of organizing work by function, you organize it by business outcome. You have an outcome owner at the top. Below that, channel loop owners who run end-to-end processes, keyword research through content production through linking through monitoring, as a single compounding loop. And underneath that, an agent fleet layer where engineers are building and maintaining the agents that handle repeatable execution. The shift sounds structural, but the real change is in how knowledge compounds. In the old model, knowledge walks out the door when someone quits. In the loop model, knowledge lives in infrastructure. Every loop gets smarter over time. Margins improve through automation reuse and productized delivery, not headcount games. And here's the thing Neil and I were getting into: when your margins improve because of this, don't just pocket the difference. Double down. Give more for the money. That's how you build a defensible position. One-person teams sitting inside these loops, running more than any five-person team could run before. That's where agencies are going.

The agency model as we know it is starting to crack. For decades, services businesses have been organized the same way. Client account at the top, then an account manager, then a row of specialists underneath. SEO, paid media, content, design, analytics. Everyone owns a function. Knowledge lives in people's heads and scattered docs. Reporting is retrospective. Margins improve only when you squeeze utilization or hire cheaper. That structure made sense when humans were the only execution layer. It doesn't anymore. What I've been building toward is something I'm calling an agent-native revenue loop model. Instead of organizing work by function, you organize it by business outcome. You have an outcome owner at the top. Below that, channel loop owners who run end-to-end processes, keyword research through content production through linking through monitoring, as a single compounding loop. And underneath that, an agent fleet layer where engineers are building and maintaining the agents that handle repeatable execution. The shift sounds structural, but the real change is in how knowledge compounds. In the old model, knowledge walks out the door when someone quits. In the loop model, knowledge lives in infrastructure. Every loop gets smarter over time. Margins improve through automation reuse and productized delivery, not headcount games. And here's the thing Neil and I were getting into: when your margins improve because of this, don't just pocket the difference. Double down. Give more for the money. That's how you build a defensible position. One-person teams sitting inside these loops, running more than any five-person team could run before. That's where agencies are going.

ericosiu

10,515 次观看 • 4 个月前

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

Andrew Ng

105,343 次观看 • 1 年前