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📢Excited to release GoEx⚡️a runtime for LLM-generated actions like code, API calls, and more. Featuring "post-facto validation" for assessing LLM actions after execution 🔍 Key to our approach is "undo" 🔄 and "damage confinement" abstractions to manage unintended actions & risks. This paves the way for fully autonomous LLM...

57,988 Aufrufe • vor 2 Jahren •via X (Twitter)

6 Kommentare

Profilbild von Shishir Patil
Shishir Patilvor 2 Jahren

🌐 Pioneering a future where LLMs empower microservices & apps, evolving from mere data retrievers 🧵to autonomous decision-makers within our digital world 🧙 Wondering about the safety and correctness of these interactions🤔? Our latest vision paper explores these questions, laying out design principles for the next-step in LLM powered applications 💯

Profilbild von Shishir Patil
Shishir Patilvor 2 Jahren

We study the inherent challenges in relying on LLMs—addressing their unpredictability, the essential trust mechanisms for their decision-making, and hurdles in failure recognition & resolution. Our system, GoEx presents abstractions and policies to overcome these for RESTful APIs, and operations on databases and filesystems! An exhilarating collaboration with @tianjun_zhang @vivianfxng Noppapon C @_royh021 Aaron Hao @profjoeyg @ralucaadapopa Ion Stoica from @UCBerkeley and @martin_casado from @a16z

Profilbild von Jeff Schneider
Jeff Schneidervor 2 Jahren

in your API inventory, what % had an undo function?

Profilbild von darya
daryavor 2 Jahren

@vivianfxng hi

Profilbild von Davanum Srinivas
Davanum Srinivasvor 2 Jahren

cc @ibuildthecloud

Profilbild von Tereza Tizkova
Tereza Tizkovavor 2 Jahren

I read your paper and it's the first time I see this approach. Based on the code, you just define a "reverse tool" for each tool the LLM can use, is it correct? For the types of actions you cannot reverse, I suggest running the LLM output in a safe sandboxed environment, e.g. using the @e2b_dev Code Interpreter SDK: There, the actions that the LLM agent "decides" to do are isolated in a separate sandbox instance.

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Andrew Ng

124,382 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.

Isaac Flath

40,212 Aufrufe • vor 2 Monaten