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Do Large Language Models (LLM) have leadership? Together with cognitive scientists Tom Griffiths Griffiths Computational Cognitive Science Lab and Natalia Velez Natalia Vélez (natvelali.bsky.social @ 🦋) at Princeton University, we created a multi-LLM-agent system to test if agents work better with a leader. It turns out that LLMs do... show more

Mengdi Wang

5,086 subscribers

41,222 Aufrufe • vor 2 Jahren •via X (Twitter)

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10 Kommentare

Profilbild von Mengdi Wang

Mengdi Wangvor 2 Jahren

Yeah GPT3.5 would be a terrible/bossy leader, but it can work well as a team member when a GPT4 agent leads. The lead agent has to be smart enough!

Profilbild von Mario Giulianelli

Mario Giulianellivor 2 Jahren

@cocosci_lab @natvelali @Princeton Exciting times for those interesting in LLM agent populations :) Shameless plug: here's some exploratory work on personality consistency and linguistic alignment in LLM agents -- two important aspects of what it means to be leaders or followers in humans:

Profilbild von Kuang Xu 许匡

Kuang Xu 许匡vor 2 Jahren

@cocosci_lab @natvelali @Princeton Very cool! Do you have a talk on this?

Profilbild von Mengdi Wang

Mengdi Wangvor 2 Jahren

@cocosci_lab @natvelali @Princeton Not yet. It’s fresh out of the oven

Profilbild von Seth Karten

Seth Kartenvor 2 Jahren

@cocosci_lab @natvelali @Princeton Very cool. Do you have any failure cases where cooperation is difficult?

Profilbild von insom ne ya

insom ne yavor 2 Jahren

@cocosci_lab @natvelali @Princeton maybe we can solve groupthink cases using LLMs as a simulation environment.. 🤔

Profilbild von David Jianhong Wang

David Jianhong Wangvor 2 Jahren

@cocosci_lab @natvelali @Princeton It's a very amazing experiment to demonstrate the collective ability of a group of LLMs. I wonder if the next step could be checking the phenomenon and seeing whether the behaviours are aligned with game theoretical models.

Profilbild von Olaf Geibig flow/acc AI==危机 🍓

Olaf Geibig flow/acc AI==危机 🍓vor 2 Jahren

@cocosci_lab @natvelali @Princeton Very cool experiment. I just wonder why the embodiment is important? Wouldn't leadership be also benficial for working on textual tasks? I would love to expand the experiment to simulate a modern agile and self-organising work setup like at Spotify with guilds, tribes, etc.

Profilbild von Canyu Chen (Seeking Ph.D. 25fall)

Canyu Chen (Seeking Ph.D. 25fall)vor 2 Jahren

Impressive work and nice findings! It is indeed under-explored whether or not LLM agents can behave like humans. We discover the high behavioral alignment between LLM agents and humans regarding the trust behaviors, particularly for GPT-4, in the recent work "Can Large Language Model Agents Simulate Human Trust Behaviors?" 🔗Project website: 🔗link to our post:

Profilbild von Isabella

Isabellavor 2 Jahren

@cocosci_lab @natvelali @Princeton @RayDalio chatgpt for president

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341,204 Aufrufe • vor 1 Jahr

RLM is the most import foundation of my Pi Harness (other than Pi of course). It's seeded with late interaction retrieval results (thanks to @lightonai for pylate). The Agent initiates it with query then.. 𝐒𝐞𝐭𝐮𝐩 A python REPL is created and seeded with: 1. Late interaction search to pre-filter. Instead of doing top 3/5/10, it's top hundreds of documents. This is set into a `context` variable. 2. Python functions are loaded in to do more searches if `context` variable isn't enough. And to make llm calls with cheaper models in parallel batches. 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐋𝐨𝐨𝐩 From there, an LLM iterates in the REPL based on the query. It's just like exploring in a jupyter notebook. The LLM writes prose (like a markdown cell) and code to be run in the REPL each turn. This allows the LLM to sort, filter, and synthesize information. It can fan out and ask smaller models to summarize, combine, contrast, or do anything else to documents to help it understand the data. After several turns the LLM reponds with the final answer. Either because it found the answer, or hit the budget limit. Context as a Python variable, LLM as the programmer, REPL as the runtime. 𝐖𝐡𝐲 𝐃𝐨𝐞𝐬 𝐓𝐡𝐢𝐬 𝐖𝐨𝐫𝐤 1. Richer Shell. Agents (and subagents) work by intermixing code and prose/thinking. But they use static scripts or bash that run and exit and start over each tool call. That's not ideal for exploration and synthesis of data. For that, state is useful to continue building and exploring the data as you learn more. There's a reason jupyter notebooks have been popular with data scientists. 2. Keeps main agent context clean. The better context you have the better the agent will perform (duh!). This means three thing: better human input, less missing search results, and less incorrect search results. Letting the agent iterate allows it to synthesize just what is needed and nothing else. All bad paths or peeks at something that turns out to be irrelevant stays out of main agent context. 3. Stack the good ideas! People often compare late interaction search vs RLM. Or static vs dynamic languages. Or agentic search vs semantic search. But...You can just use them all together for what they're each good at. Use them all for the area they're really great for. Read the full post which has more detail about how and why.

RLM is the most import foundation of my Pi Harness (other than Pi of course). It's seeded with late interaction retrieval results (thanks to @lightonai for pylate). The Agent initiates it with query then.. 𝐒𝐞𝐭𝐮𝐩 A python REPL is created and seeded with: 1. Late interaction search to pre-filter. Instead of doing top 3/5/10, it's top hundreds of documents. This is set into a `context` variable. 2. Python functions are loaded in to do more searches if `context` variable isn't enough. And to make llm calls with cheaper models in parallel batches. 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐋𝐨𝐨𝐩 From there, an LLM iterates in the REPL based on the query. It's just like exploring in a jupyter notebook. The LLM writes prose (like a markdown cell) and code to be run in the REPL each turn. This allows the LLM to sort, filter, and synthesize information. It can fan out and ask smaller models to summarize, combine, contrast, or do anything else to documents to help it understand the data. After several turns the LLM reponds with the final answer. Either because it found the answer, or hit the budget limit. Context as a Python variable, LLM as the programmer, REPL as the runtime. 𝐖𝐡𝐲 𝐃𝐨𝐞𝐬 𝐓𝐡𝐢𝐬 𝐖𝐨𝐫𝐤 1. Richer Shell. Agents (and subagents) work by intermixing code and prose/thinking. But they use static scripts or bash that run and exit and start over each tool call. That's not ideal for exploration and synthesis of data. For that, state is useful to continue building and exploring the data as you learn more. There's a reason jupyter notebooks have been popular with data scientists. 2. Keeps main agent context clean. The better context you have the better the agent will perform (duh!). This means three thing: better human input, less missing search results, and less incorrect search results. Letting the agent iterate allows it to synthesize just what is needed and nothing else. All bad paths or peeks at something that turns out to be irrelevant stays out of main agent context. 3. Stack the good ideas! People often compare late interaction search vs RLM. Or static vs dynamic languages. Or agentic search vs semantic search. But...You can just use them all together for what they're each good at. Use them all for the area they're really great for. Read the full post which has more detail about how and why.

Isaac Flath

40,212 Aufrufe • vor 3 Monaten

Jensen Huang says the next internet is not for humans. It is for agents. "AI has become agentic, meaning that it can actually do work by itself." "One agent can communicate with another agent and say, 'I have some work to do. Let's team up together and let's do some work.'" "Inside our company... we have hundreds of thousands of agents probably running around right now that are doing work and they're talking to each other and they're solving problems." "All guardrailed, all sandboxed... but they're all working with each other." Then the part most people are not ready for: "The internet that we use today for a billion people will likely mostly be several billion, call it a hundred billion agents working around the clock." Companies talking to companies. Employee agents talking to employee agents. Self-driving cars, robots, manufacturing systems, buildings. "The world is going to be this layer of computing that's going to cocoon the earth, and it's going to be generating intelligence all the time."

Jensen Huang says the next internet is not for humans. It is for agents. "AI has become agentic, meaning that it can actually do work by itself." "One agent can communicate with another agent and say, 'I have some work to do. Let's team up together and let's do some work.'" "Inside our company... we have hundreds of thousands of agents probably running around right now that are doing work and they're talking to each other and they're solving problems." "All guardrailed, all sandboxed... but they're all working with each other." Then the part most people are not ready for: "The internet that we use today for a billion people will likely mostly be several billion, call it a hundred billion agents working around the clock." Companies talking to companies. Employee agents talking to employee agents. Self-driving cars, robots, manufacturing systems, buildings. "The world is going to be this layer of computing that's going to cocoon the earth, and it's going to be generating intelligence all the time."

Karl Mehta

17,931 Aufrufe • vor 1 Monat

Dan Wang explains why it would be harder for the US to rebuild large-scale manufacturing than for China to get better at invention: "By most metrics, Chinese scientists are producing better qualities of work, not just quantity. There's a more stable environment for them to do their science. And conversely, we have not seen that the US is really learning the hard parts of manufacturing really well. The Chinese are learning at a much faster rate to patch up their deficiencies than the Americans have."

Dan Wang explains why it would be harder for the US to rebuild large-scale manufacturing than for China to get better at invention: "By most metrics, Chinese scientists are producing better qualities of work, not just quantity. There's a more stable environment for them to do their science. And conversely, we have not seen that the US is really learning the hard parts of manufacturing really well. The Chinese are learning at a much faster rate to patch up their deficiencies than the Americans have."

Patrick OShaughnessy

150,671 Aufrufe • vor 9 Monaten

ELON: WILL OUR LIVES STILL HAVE MEANING IF COMPUTERS AND ROBOTS OUTPERFORM US? “At some point, as AI and robotics get better, eventually AI will be able to do everything that humans can do. Any task you do will be optional, like a hobby. It does beg the question, will our lives have meaning if the computers and the robots can do everything better than we can? That is a real question. I do wonder about that myself. That's maybe why we need the Neuralink, so we can enhance human capability, so we can keep up with the machines.” Source: Interview with Mark Penn at CES, January 8, 2025

ELON: WILL OUR LIVES STILL HAVE MEANING IF COMPUTERS AND ROBOTS OUTPERFORM US? “At some point, as AI and robotics get better, eventually AI will be able to do everything that humans can do. Any task you do will be optional, like a hobby. It does beg the question, will our lives have meaning if the computers and the robots can do everything better than we can? That is a real question. I do wonder about that myself. That's maybe why we need the Neuralink, so we can enhance human capability, so we can keep up with the machines.” Source: Interview with Mark Penn at CES, January 8, 2025

Mario Nawfal

329,978 Aufrufe • vor 1 Jahr

SITUATION EXPLAINED: A new generation of AI labs are betting that world models are the path to general intelligence that can interact with the real world. We had General Intuition CEO Pim de Witte on MTS to explain what world models are, and why intuitively world models offer a clearer path towards general intelligence than LLMs. For humans, text is a technology for compressing information about the unfolding, three-dimensional world around us and communicating it to other people. When LLMs reason about the world, they do so by reconstructing the world through text. World models work by modeling the three-dimensional world at the architecture level and then allowing agents to interact with that world. Unlike an LLM, which understands space and time through text, world models perform spatial and temporal reasoning natively. General Intuition hopes to use world models to create embodied AI—machines that can reason and act in the real world. But other companies are also pushing the frontier in this space. Odyssey ML today announced a multi-agent world model, Agora-1. Their launch video shows four participants interacting with a real-time world model simulation of GoldenEye, a first-person shooter for the N64. A playable preview of the GoldenEye simulation is available on their site.

SITUATION EXPLAINED: A new generation of AI labs are betting that world models are the path to general intelligence that can interact with the real world. We had General Intuition CEO Pim de Witte on MTS to explain what world models are, and why intuitively world models offer a clearer path towards general intelligence than LLMs. For humans, text is a technology for compressing information about the unfolding, three-dimensional world around us and communicating it to other people. When LLMs reason about the world, they do so by reconstructing the world through text. World models work by modeling the three-dimensional world at the architecture level and then allowing agents to interact with that world. Unlike an LLM, which understands space and time through text, world models perform spatial and temporal reasoning natively. General Intuition hopes to use world models to create embodied AI—machines that can reason and act in the real world. But other companies are also pushing the frontier in this space. Odyssey ML today announced a multi-agent world model, Agora-1. Their launch video shows four participants interacting with a real-time world model simulation of GoldenEye, a first-person shooter for the N64. A playable preview of the GoldenEye simulation is available on their site.

MTS

11,641 Aufrufe • vor 2 Monaten

Today, we are launching the first publicly available AI Scientist, via the FutureHouse Platform. Our AI Scientist agents can perform a wide variety of scientific tasks better than humans. By chaining them together, we've already started to discover new biology really fast. With the platform, we are bringing these capabilities to the wider community. Watch our long-form video, in the comments below, to learn more about how the platform works and how you can use it to make new discoveries, and go to our website or see the comments below to access the platform. We are releasing three superhuman AI Scientist agents today, each with their own specialization: A general-purpose agent (Crow); An agent to automate literature reviews (Falcon); and An agent to answer the question “Has anyone done X before” (Owl). We are also releasing an experimental agent, Phoenix, that has access to a wide variety of tools for planning experiments in chemistry. More on that below. The three literature search agents (Crow, Falcon, and Owl) have benchmarked superhuman performance. They also have access to a large corpus of full scientific texts, which means that you can ask them more detailed questions about experimental protocols and study limitations that general-purpose web search agents, which usually only have access to abstracts, might miss. Our agents also use a variety of factors to distinguish source quality, so that they don’t end up relying on low-quality papers or pop-science sources. Finally, and critically, we have an API, which is intended to allow researchers to integrate our agents into their workflows. Phoenix is an experimental project we put together recently just to demonstrate what can happen if you give the agents access to lots of scientific tools. It is not better than humans at planning experiments yet, and it makes a lot more mistakes than Crow, Falcon, or Owl. We want to see all the ways you can break it! The agents we are releasing today cannot yet do all (or even most!) aspects of scientific research autonomously. However, as we show in the video, you can already use them to generate and evaluate new hypotheses and plan new experiments way faster than before. Internally, we also have dedicated agents for data analysis, hypothesis generation, protein engineering, and more, and we plan to launch these on the platform in the coming months as well. Within a year or two, it is easy to imagine that the vast majority of desk work that scientists do today will be accelerated with the help of AI agents like the ones we are releasing today. The platform is currently free-to-use. Over time, depending on how people use it, we may implement pricing plans. If you want higher rate limits, especially for research projects, get in touch. Michael Skarlinski, Andrew White 🐦‍⬛, Tyler Nadolski, Remo Storni, James Braza, Ludovico Mitchener, Michaela Hinks, as well as Jason Carman and his team for making such fantastic videos of us!

Today, we are launching the first publicly available AI Scientist, via the FutureHouse Platform. Our AI Scientist agents can perform a wide variety of scientific tasks better than humans. By chaining them together, we've already started to discover new biology really fast. With the platform, we are bringing these capabilities to the wider community. Watch our long-form video, in the comments below, to learn more about how the platform works and how you can use it to make new discoveries, and go to our website or see the comments below to access the platform. We are releasing three superhuman AI Scientist agents today, each with their own specialization: A general-purpose agent (Crow); An agent to automate literature reviews (Falcon); and An agent to answer the question “Has anyone done X before” (Owl). We are also releasing an experimental agent, Phoenix, that has access to a wide variety of tools for planning experiments in chemistry. More on that below. The three literature search agents (Crow, Falcon, and Owl) have benchmarked superhuman performance. They also have access to a large corpus of full scientific texts, which means that you can ask them more detailed questions about experimental protocols and study limitations that general-purpose web search agents, which usually only have access to abstracts, might miss. Our agents also use a variety of factors to distinguish source quality, so that they don’t end up relying on low-quality papers or pop-science sources. Finally, and critically, we have an API, which is intended to allow researchers to integrate our agents into their workflows. Phoenix is an experimental project we put together recently just to demonstrate what can happen if you give the agents access to lots of scientific tools. It is not better than humans at planning experiments yet, and it makes a lot more mistakes than Crow, Falcon, or Owl. We want to see all the ways you can break it! The agents we are releasing today cannot yet do all (or even most!) aspects of scientific research autonomously. However, as we show in the video, you can already use them to generate and evaluate new hypotheses and plan new experiments way faster than before. Internally, we also have dedicated agents for data analysis, hypothesis generation, protein engineering, and more, and we plan to launch these on the platform in the coming months as well. Within a year or two, it is easy to imagine that the vast majority of desk work that scientists do today will be accelerated with the help of AI agents like the ones we are releasing today. The platform is currently free-to-use. Over time, depending on how people use it, we may implement pricing plans. If you want higher rate limits, especially for research projects, get in touch. Michael Skarlinski, Andrew White 🐦‍⬛, Tyler Nadolski, Remo Storni, James Braza, Ludovico Mitchener, Michaela Hinks, as well as Jason Carman and his team for making such fantastic videos of us!

Sam Rodriques

725,230 Aufrufe • vor 1 Jahr

Mati Staniszewski on why ElevenLabs is betting on a cascaded approach for voice agents: “Our approach, as you think about voice agents, conversational agents, is effectively a cascaded approach. You use transcription or speech-to-text, an LLM, text-to-speech, and orchestrate all of that together. Then you have speech-to-speech, which goes directly from speech, and there’s a speech response on the other side. Today, we are optimizing heavily on a cascaded approach. As we work with a lot of the businesses and enterprises, they will need that visibility into what happens. They will want to execute certain tasks on top of that. They want good visibility into each of the steps and great accuracy of all the models. But beyond that, they can abstract away what’s the LLM layer, what’s the intelligence layer, and the integrations are easier in that system. That’s where we are betting a lot of the research work on how you can make that great, and we think we can make that great.” John Collison Mati Staniszewski ElevenLabs

Mati Staniszewski on why ElevenLabs is betting on a cascaded approach for voice agents: “Our approach, as you think about voice agents, conversational agents, is effectively a cascaded approach. You use transcription or speech-to-text, an LLM, text-to-speech, and orchestrate all of that together. Then you have speech-to-speech, which goes directly from speech, and there’s a speech response on the other side. Today, we are optimizing heavily on a cascaded approach. As we work with a lot of the businesses and enterprises, they will need that visibility into what happens. They will want to execute certain tasks on top of that. They want good visibility into each of the steps and great accuracy of all the models. But beyond that, they can abstract away what’s the LLM layer, what’s the intelligence layer, and the integrations are easier in that system. That’s where we are betting a lot of the research work on how you can make that great, and we think we can make that great.” John Collison Mati Staniszewski ElevenLabs

Stripe

17,118 Aufrufe • vor 3 Monaten

#WATCH | Cyprus: On PM Modi's visit to Cyprus, Manish, High Commissioner of India to the Republic of Cyprus, says, "I am trying to push the economic agenda between the India-Cyprus Economic Corridor. We have a policy to become a ‘developed India’ by 2047, and they have ‘Vision 2035’, so there is a synergy there, so we have to see how we can use each other’s strengths and work together to achieve them. I think Cyprus’ advantage is that it can serve as a gateway to the European Union as well as the larger region around it. Cyprus also understands that it has to work with India and it is quite evident that they are reaching out to India to build better relations, to focus more on economic issues, because they know that India is the fastest growing economy in the world. And similarly, if you see, Cyprus is the fastest growing economy in the European Union and they have their own advantages. We have our own strengths and advantages, so we have to work on every aspect of economic engagement…"

#WATCH | Cyprus: On PM Modi's visit to Cyprus, Manish, High Commissioner of India to the Republic of Cyprus, says, "I am trying to push the economic agenda between the India-Cyprus Economic Corridor. We have a policy to become a ‘developed India’ by 2047, and they have ‘Vision 2035’, so there is a synergy there, so we have to see how we can use each other’s strengths and work together to achieve them. I think Cyprus’ advantage is that it can serve as a gateway to the European Union as well as the larger region around it. Cyprus also understands that it has to work with India and it is quite evident that they are reaching out to India to build better relations, to focus more on economic issues, because they know that India is the fastest growing economy in the world. And similarly, if you see, Cyprus is the fastest growing economy in the European Union and they have their own advantages. We have our own strengths and advantages, so we have to work on every aspect of economic engagement…"

ANI

46,372 Aufrufe • vor 1 Jahr

How did a 20-year-old beat Grok. And ChatGPT. And Perplexity. And Gemini? In part one Brayden Levangie laid out how: he built a very different architecture to take AI to a new human level. Cognitive architectures, he calls them. I call them AI's with consciousness. Ones that learn, evolve, build themselves, and do so like we do. Part I is here: In this part two, he goes more into the technology. And this was recorded more than a week ago in my home. Since then his system has gotten way better. An exponentially learning system. It is how we are going to get to AGI.

How did a 20-year-old beat Grok. And ChatGPT. And Perplexity. And Gemini? In part one Brayden Levangie laid out how: he built a very different architecture to take AI to a new human level. Cognitive architectures, he calls them. I call them AI's with consciousness. Ones that learn, evolve, build themselves, and do so like we do. Part I is here: In this part two, he goes more into the technology. And this was recorded more than a week ago in my home. Since then his system has gotten way better. An exponentially learning system. It is how we are going to get to AGI.

Robert Scoble

77,274 Aufrufe • vor 9 Monaten

Introducing RAGs v3 🌟: Build a RAG bot that can also search the web 🌐, to find answers that aren’t immediately in its corpus. Do this all in natural language, not code! 💬 Get a better experience than ChatGPT + Bing ⚡️ We used our integration with Metaphor - a search engine designed for LLMs - to pull in relevant text from the internet. Additionally: - you can now view the tools that the agent is using. - note: web search is only accessible for our OpenAI agent Check out the video below, and get started with the repo today. Repo:

Introducing RAGs v3 🌟: Build a RAG bot that can also search the web 🌐, to find answers that aren’t immediately in its corpus. Do this all in natural language, not code! 💬 Get a better experience than ChatGPT + Bing ⚡️ We used our integration with Metaphor - a search engine designed for LLMs - to pull in relevant text from the internet. Additionally: - you can now view the tools that the agent is using. - note: web search is only accessible for our OpenAI agent Check out the video below, and get started with the repo today. Repo:

LlamaIndex 🦙

76,371 Aufrufe • vor 2 Jahren