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Self-improving LLM learns continually (in context) to solve hard math problems - successful attempt!🥳 Code in comment The system iteratively learns from mistakes and builds increasingly accurate predictors through cross-cycle learning via a cycle and reflection based intelligent system that uses LLMs to automatically generate, test, and improve Python...

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echo.hive

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14,582 Aufrufe • vor 10 Monaten •via X (Twitter)

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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.

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Researchers built a new RAG approach that: - does not need a vector DB. - does not embed data. - involves no chunking. - performs no similarity search. And it hit 98.7% accuracy on a financial benchmark (SOTA). Here's the core problem with RAG that this new approach solves: Traditional RAG chunks documents, embeds them into vectors, and retrieves based on semantic similarity. But similarity ≠ relevance. When you ask "What were the debt trends in 2023?", a vector search returns chunks that look similar. But the actual answer might be buried in some Appendix, referenced on some page, in a section that shares zero semantic overlap with your query. Traditional RAG would likely never find it. PageIndex (open-source) solves this. Instead of chunking and embedding, PageIndex builds a hierarchical tree structure from your documents, like an intelligent table of contents. Then it uses reasoning to traverse that tree. For instance, the model doesn't ask: "What text looks similar to this query?" Instead, it asks: "Based on this document's structure, where would a human expert look for this answer?" That's a fundamentally different approach with: - No arbitrary chunking that breaks context. - No vector DB infrastructure to maintain. - Traceable retrieval to see exactly why it chose a specific section. - The ability to see in-document references ("see Table 5.3") the way a human would. But here's the deeper issue that it solves. Vector search treats every query as independent. But documents have structure and logic, like sections that reference other sections and context that builds across pages. PageIndex respects that structure instead of flattening it into embeddings. Do note that this approach may not make sense in every use case since traditional vector search is still fast, simple, and works well for many applications. But for professional documents that require domain expertise and multi-step reasoning, this tree-based, reasoning-first approach shines. For instance, PageIndex achieved 98.7% accuracy on FinanceBench, significantly outperforming traditional vector-based RAG systems on complex financial document analysis. Everything is fully open-source, so you can see the full implementation in GitHub and try it yourself. I have shared the GitHub repo in the replies!
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Researchers built a new RAG approach that: - does not need a vector DB. - does not embed data. - involves no chunking. - performs no similarity search. And it hit 98.7% accuracy on a financial benchmark (SOTA). Here's the core problem with RAG that this new approach solves: Traditional RAG chunks documents, embeds them into vectors, and retrieves based on semantic similarity. But similarity ≠ relevance. When you ask "What were the debt trends in 2023?", a vector search returns chunks that look similar. But the actual answer might be buried in some Appendix, referenced on some page, in a section that shares zero semantic overlap with your query. Traditional RAG would likely never find it. PageIndex (open-source) solves this. Instead of chunking and embedding, PageIndex builds a hierarchical tree structure from your documents, like an intelligent table of contents. Then it uses reasoning to traverse that tree. For instance, the model doesn't ask: "What text looks similar to this query?" Instead, it asks: "Based on this document's structure, where would a human expert look for this answer?" That's a fundamentally different approach with: - No arbitrary chunking that breaks context. - No vector DB infrastructure to maintain. - Traceable retrieval to see exactly why it chose a specific section. - The ability to see in-document references ("see Table 5.3") the way a human would. But here's the deeper issue that it solves. Vector search treats every query as independent. But documents have structure and logic, like sections that reference other sections and context that builds across pages. PageIndex respects that structure instead of flattening it into embeddings. Do note that this approach may not make sense in every use case since traditional vector search is still fast, simple, and works well for many applications. But for professional documents that require domain expertise and multi-step reasoning, this tree-based, reasoning-first approach shines. For instance, PageIndex achieved 98.7% accuracy on FinanceBench, significantly outperforming traditional vector-based RAG systems on complex financial document analysis. Everything is fully open-source, so you can see the full implementation in GitHub and try it yourself. I have shared the GitHub repo in the replies!

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I created a demo of a bioautomation system that uses LLMs, Opentrons, and lua to create a dynamic programming environment for cloud labs or robot/human clusters. It can reason about its own code based off of lab measurements. Most importantly, I actually fucking implemented it, and it is open source. Took about 4 days for this rough draft, and it is very much a draft. The user inputs their task, the system creates code, and then executes it. The code defines control flow from data generated in the lab. Not only can it create code, but it can reason about things that could have gone wrong, run analysis using an internal sandbox, and then create new code based off of that analysis for execution. Timestamps: 0:00 - intro and code generation 3:04 - homebrewed replacement for Opentrons API for running all this code 5:26 - dynamic control flow using data 6:10 - LLM reasoning about a biological protocol and fixing it 10:15 - rant on the future of cloud labs and bioautomation I made this as a demo for how I think we should be thinking about building and scaling biology. I believe we can encode the tacit knowledge of a laboratory into the knowledge of an LLM, that we can do reinforcement learning off of results it creates, and that we must do that by leveraging a sufficient quantity of unique, useful, verifiable protocols. That doesn't come from just doing drug screens - it comes from doing basic everyday experiments and doing them well. Through the elimination of tacit knowledge necessary to physically operate a lab + proper batching + models writing code, I think we can make building biotechnology 10x-100x cheaper and easier than it is nowadays.
14:46

I created a demo of a bioautomation system that uses LLMs, Opentrons, and lua to create a dynamic programming environment for cloud labs or robot/human clusters. It can reason about its own code based off of lab measurements. Most importantly, I actually fucking implemented it, and it is open source. Took about 4 days for this rough draft, and it is very much a draft. The user inputs their task, the system creates code, and then executes it. The code defines control flow from data generated in the lab. Not only can it create code, but it can reason about things that could have gone wrong, run analysis using an internal sandbox, and then create new code based off of that analysis for execution. Timestamps: 0:00 - intro and code generation 3:04 - homebrewed replacement for Opentrons API for running all this code 5:26 - dynamic control flow using data 6:10 - LLM reasoning about a biological protocol and fixing it 10:15 - rant on the future of cloud labs and bioautomation I made this as a demo for how I think we should be thinking about building and scaling biology. I believe we can encode the tacit knowledge of a laboratory into the knowledge of an LLM, that we can do reinforcement learning off of results it creates, and that we must do that by leveraging a sufficient quantity of unique, useful, verifiable protocols. That doesn't come from just doing drug screens - it comes from doing basic everyday experiments and doing them well. Through the elimination of tacit knowledge necessary to physically operate a lab + proper batching + models writing code, I think we can make building biotechnology 10x-100x cheaper and easier than it is nowadays.

Keoni Gandall

21,403 Aufrufe • vor 1 Jahr

🧠David Friedberg explains the great hope of Artificial Superintelligence: Massive leverage on time to solve thousand-year problems "I don't think that humans are limited in our ability to solve problems." "I think we're limited in terms of time, and what digital intelligence gives us is leverage on time so that we can now tackle ever more complex tasks." "This idea of superintelligence is that it could give us immense leverage in solving that complex problem that we otherwise may be challenged to solve over decades, or hundreds of years, or think about one day solving a problem that would take humans thousands of years to solve." "So if I as a human said, 'I would like to harness fusion power similar to what the sun uses to make energy, and I wanna do that on Earth,' today, we're in, call it year 40 or 50 of a research cycle of humans trying to solve that particularly complex problem." "It's a scientific problem, it's a discovery problem, it's an engineering problem." "Functionally, leveraging to complexity is where this becomes super compelling for humans." "And it's why I think we can and should be highly optimistic about living very long lives, and traveling anywhere we want, and having abundance in food, and having abundance in resources, and having abundance in recouping our time to do the things we want to do instead of the things that we have to do today."
1:04

🧠David Friedberg explains the great hope of Artificial Superintelligence: Massive leverage on time to solve thousand-year problems "I don't think that humans are limited in our ability to solve problems." "I think we're limited in terms of time, and what digital intelligence gives us is leverage on time so that we can now tackle ever more complex tasks." "This idea of superintelligence is that it could give us immense leverage in solving that complex problem that we otherwise may be challenged to solve over decades, or hundreds of years, or think about one day solving a problem that would take humans thousands of years to solve." "So if I as a human said, 'I would like to harness fusion power similar to what the sun uses to make energy, and I wanna do that on Earth,' today, we're in, call it year 40 or 50 of a research cycle of humans trying to solve that particularly complex problem." "It's a scientific problem, it's a discovery problem, it's an engineering problem." "Functionally, leveraging to complexity is where this becomes super compelling for humans." "And it's why I think we can and should be highly optimistic about living very long lives, and traveling anywhere we want, and having abundance in food, and having abundance in resources, and having abundance in recouping our time to do the things we want to do instead of the things that we have to do today."

The All-In Podcast

53,187 Aufrufe • vor 11 Monaten

🚀 Introducing Cover-Agent 🧪 An open-source tool that includes a reimplementation of Meta's TestGen-LLM for automatically enhancing test suites. Manager: "We must improve old test suites for better code coverage. Can you handle it?" Me: "Sure, my favorite task... (Not!) 🤷‍♂️" Meta's team had the idea of using LLMs to enhance test suites—sounds great, right? But they didn't release their code We did. We'd love your feedback as we work on improving this baseline In this video, I'll review TestGen-LLM, share insights, and introduce you to Cover-Agent
6:11

🚀 Introducing Cover-Agent 🧪 An open-source tool that includes a reimplementation of Meta's TestGen-LLM for automatically enhancing test suites. Manager: "We must improve old test suites for better code coverage. Can you handle it?" Me: "Sure, my favorite task... (Not!) 🤷‍♂️" Meta's team had the idea of using LLMs to enhance test suites—sounds great, right? But they didn't release their code We did. We'd love your feedback as we work on improving this baseline In this video, I'll review TestGen-LLM, share insights, and introduce you to Cover-Agent

Itamar Friedman

139,228 Aufrufe • vor 2 Jahren

this framework makes sure your AI is giving accurate answers by catching hallucinations I just made a full walkthrough on how to evaluate if your AI system is actually working... inside I show: – how I track AI output with a simple dashboard – the key signals I watch to catch mistakes early – how I test for accuracy with real-world questions – what to do when the AI gives a wrong answer – how to improve it over time using real feedback comment “track” and I’ll DM you the guide (must be following)
0:32

this framework makes sure your AI is giving accurate answers by catching hallucinations I just made a full walkthrough on how to evaluate if your AI system is actually working... inside I show: – how I track AI output with a simple dashboard – the key signals I watch to catch mistakes early – how I test for accuracy with real-world questions – what to do when the AI gives a wrong answer – how to improve it over time using real feedback comment “track” and I’ll DM you the guide (must be following)

Tyler

18,324 Aufrufe • vor 11 Monaten

LLM agents have demonstrated promise in their ability to automate computer tasks, but face challenges with multi-step reasoning and planning. Towards addressing this, we propose an inference-time tree search algorithm for LLM agents to explicitly perform exploration and multi-step planning in interactive web environments. It is the first tree search algorithm for LLM agents that shows effectiveness on realistic and complex web environments: on the challenging VisualWebArena benchmark, applying our search algorithm on top of a GPT-4o agent yields a 39.7% relative increase in success rate compared to the same baseline without search, setting a state-of-the-art success rate of 26.4%. On WebArena, search also yields a 28.0% relative improvement over a baseline agent, setting a competitive success rate of 19.2%.
0:20

LLM agents have demonstrated promise in their ability to automate computer tasks, but face challenges with multi-step reasoning and planning. Towards addressing this, we propose an inference-time tree search algorithm for LLM agents to explicitly perform exploration and multi-step planning in interactive web environments. It is the first tree search algorithm for LLM agents that shows effectiveness on realistic and complex web environments: on the challenging VisualWebArena benchmark, applying our search algorithm on top of a GPT-4o agent yields a 39.7% relative increase in success rate compared to the same baseline without search, setting a state-of-the-art success rate of 26.4%. On WebArena, search also yields a 28.0% relative improvement over a baseline agent, setting a competitive success rate of 19.2%.

Jing Yu Koh

124,878 Aufrufe • vor 2 Jahren

Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.
1:05

Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.

Khurram Javed

52,110 Aufrufe • vor 7 Monaten

All in a a few days of spare time. Custom alt momentum scanner tracking various metrics, tagging setups, break of key levels, collecting statistics in real-time. Sector flow monitor and correlation matrix. This is a hobby project which started off as building a custom funding rate arbitrage scanner to test out some new perps dex platforms. Took it a step further so I can exercise the process of creating tooling, improving on the art of providing specs and a framework to Claude, writing out to a database, tracking metrics etc. in real time. Going to continue to improve this for my own purpose but this has been the gateway to opening up the pathway of an entire world of ideas that I have been wanting to bring to life. If there is enough interest I will write an article and share my findings on the "vibe coding" process I used via Claude and what I learned through the exercise. I will say this still required a non-trivial amount of hand holding (you need to understand the problem you're looking to solve and what's going on under the hood otherwise garbage in = garbage out), providing the right framework, reassessing and debugging. The only experience I have is intermediate level python scripting in the framework of structural engineering. Much to learn still. I need to figure out how to implement unique design, improve UI/UX and figure out how to implement a more robust frontend (if you have any suggestions please feel free to comment). I have been using Claude for a year for simple tasks and scripting but this is the first time I took the time to explore further. Will document this journey as I continue to build or work on other hobby ideas. Perhaps it can inspire someone else who finds this intimidating to take the leap. Next stop - perhaps a prediction markets dashboard/tooling that ties in with Converge.
1:15

All in a a few days of spare time. Custom alt momentum scanner tracking various metrics, tagging setups, break of key levels, collecting statistics in real-time. Sector flow monitor and correlation matrix. This is a hobby project which started off as building a custom funding rate arbitrage scanner to test out some new perps dex platforms. Took it a step further so I can exercise the process of creating tooling, improving on the art of providing specs and a framework to Claude, writing out to a database, tracking metrics etc. in real time. Going to continue to improve this for my own purpose but this has been the gateway to opening up the pathway of an entire world of ideas that I have been wanting to bring to life. If there is enough interest I will write an article and share my findings on the "vibe coding" process I used via Claude and what I learned through the exercise. I will say this still required a non-trivial amount of hand holding (you need to understand the problem you're looking to solve and what's going on under the hood otherwise garbage in = garbage out), providing the right framework, reassessing and debugging. The only experience I have is intermediate level python scripting in the framework of structural engineering. Much to learn still. I need to figure out how to implement unique design, improve UI/UX and figure out how to implement a more robust frontend (if you have any suggestions please feel free to comment). I have been using Claude for a year for simple tasks and scripting but this is the first time I took the time to explore further. Will document this journey as I continue to build or work on other hobby ideas. Perhaps it can inspire someone else who finds this intimidating to take the leap. Next stop - perhaps a prediction markets dashboard/tooling that ties in with Converge.

Stoic

12,137 Aufrufe • vor 6 Monaten

💾 Made a DOS-style text-based user interface (TUI) today First app I made is Notepad (or should it be called EDIT.EXE?) and you can type in it and it has a localStorage-based file system with folders and everything! I wanted to create a BIOS for but it became this :D I really like these TUI interfaces, sometimes you still see them in shops or supermarkets when you pay Maybe I should make a whole SaaS biz with this interface?
0:46

💾 Made a DOS-style text-based user interface (TUI) today First app I made is Notepad (or should it be called EDIT.EXE?) and you can type in it and it has a localStorage-based file system with folders and everything! I wanted to create a BIOS for but it became this :D I really like these TUI interfaces, sometimes you still see them in shops or supermarkets when you pay Maybe I should make a whole SaaS biz with this interface?

@levelsio

135,538 Aufrufe • vor 3 Monaten