[HN Gopher] LLM Agent Paper List
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LLM Agent Paper List
The most comprehensive repo out there with all the updated must-
read papers for LLM Agents From the repo - "We start by the
general conceptual framework for LLM-based agents: comprising three
main components: brain, perception, and action, and the framework
can be tailored to suit different applications."
Author : Anil1331
Score : 77 points
Date : 2023-09-23 14:29 UTC (8 hours ago)
(HTM) web link (github.com)
(TXT) w3m dump (github.com)
| djohnston wrote:
| May I humbly request an MVP list? I'm into this stuff but there
| are like 60+ papers here.
| Anil1331 wrote:
| What do you plan to build ? Based on that can suggest relevant
| araes wrote:
| Your comment got me curious, so I made it into a spreadsheet.
|
| There are 265 papers currently.
|
| If you're curious, there's a reason it feels overwhelming.
| Here's a plot of the age of the papers relative to now. Its a
| bit of a wall/tsunami.
|
| https://i.imgur.com/Om7udZR.png
|
| Edit: Because of the shock like character I also postulate
| several hypotheses:
|
| - It will rapidly expand like a fireball, consuming all of the
| "air" of possible nearby topics. - Possible
| secondary: Ignition of other topics as possible fuel to extend
| the publishing "burn." - Possible secondary: Vacuum
| implosion when all nearby topic fuel is expended.
|
| - Cause significant destruction (existing wealth, businesses,
| market segments, ect...)
|
| - Cause many humans to effectively "reflect" due to its
| impenetrability, effectively rendering those humans permanently
| downhill as consumers. - Possible secondary:
| Further shocks as the shock wave bends or reflects off of
| "currently impenetrable" obstructions (topics that do not
| immediately turn into fuel).
|
| - Cause significant secondary (mostly unrelated) growth in
| topics impeded by the current suffocating fuel environment
| (like an old growth forest that has become choked with debris
| and oppresses all growth)
| bthomas wrote:
| Does anybody know a good forum for discussing AI agent design? I
| have a bunch of questions, but too specific for the general LLM
| application communities.
| tikkun wrote:
| Autogpt subreddit and discord, and the babyagi discord
| dang wrote:
| This is not a valid Show HN. Please see
| https://news.ycombinator.com/showhn.html.
|
| I've taken "Show HN" out of the title now.
| catlover76 wrote:
| Can people ELI5 what an LLM or AI "Agent" is? Sometimes when I
| see the term used, it makes sense, and other times there seems to
| be nothing concrete behind it.
|
| [not a comment on OP post]
| DougBTX wrote:
| It is frameworks vs libraries. An agent decides when to call
| your code, or possibly someone else's code.
| localhost wrote:
| I think about agents as being an entity that you can
| collaborate with. Among other things, it needs 1/ memory, 2/
| ability to create and execute long-term plans. One of the best
| papers that I have read on this topic is the Generative Agents
| paper by Park [1]
|
| Today's LLMs are kind of like Dory from Finding Nemo - you have
| to recreate the context every time you do a slightly different
| task, or when the context window for the LLM is no longer
| sufficient to remember previous turns in a conversation.
|
| An agent can sit on the other side of a piece of collaborative
| software that was designed for human collaboration. This is why
| chatbots are the current "killer app" for AI, we already
| understand how to collaborate with other people via chat.
|
| Now imagine what we can do with more sophisticated pieces of
| software like Figma or Excel. Disclosure: I work on the Python
| in Excel feature and we just announced Copilot for Excel a
| couple of days ago. [2] Other modalities that excite me are
| tools like Vision Pro which will be an interesting test bed for
| multi-modal generative agents.
|
| [1] https://arxiv.org/abs/2304.03442
|
| [2] https://twitter.com/john_lam/status/1704886164753363261
| isaacfung wrote:
| LLM has no long term memory(limited by context size or you have
| to do fine tuning which isnt efficient) and can't interact with
| the outside world.
|
| You can augment it with retrieval systems(vector store, graph
| db, sql db + lexical/semantic/graph search) and give it access
| to tools like api, search engine and code interpreter. Then it
| becomes an agent.
| hall0ween wrote:
| From my understanding, an agent is a collection of tools that
| can be navigated by an LLM. It follows an approach called
| reason-action (react) or thought-action-observation. Spit
| balling here, thought means getting a prompt and having a
| response to it (i.e., run particular action). An action
| executes (e.g., executing code in python repl, using a search a
| api, pulling info from wikipedia). The LLM receives some
| feedback (observation) and the process starts over.
| ZunarJ5 wrote:
| Crash course:
|
| LLM: https://writings.stephenwolfram.com/2023/02/what-is-
| chatgpt-...
|
| Agent: https://lilianweng.github.io/posts/2023-06-23-agent/
| aldarisbm wrote:
| there's plenty online, but basically an LLM is a model trained
| on an expansive corpus of data (unstructured, text) and
| basically trained to mimic human language. It can answers
| questions using probabilistic logic.
|
| People have been thinking of a way to make LLMs perform actions
| in the real world, by giving these LLMs "tools".
|
| An LLM with tools that can perform tasks would be an AI Agent
| jondwillis wrote:
| here- try to my buggy, unfinished demo, you'll probably
| understand it: https://waggledance.ai
|
| source is available if you tap the GH icon in the footer.
| thoughtlede wrote:
| I would characterize an LLM Agent to be "a software application
| of LLM" that is typically used to solve a problem that requires
| multiple steps.
|
| The application uses an LLM to breakdown the problem into
| pieces and asks the LLM to choose a tool that is suited to
| handle each piece. The application then invokes the tool to
| solve that piece of the problem. This is iteratively done until
| the original problem is solved or determined to be unsolvable.
|
| There are many agent variations, depending on whether you
| breakdown the problem once at the beginning, or understand what
| to do next based on the outcome of the previous step, or
| constantly evaluate and reprioritize which piece of the problem
| to solve next.
|
| Because you rely on an LLM to understand the problem, if you
| chose an LLM that exhibits good world understanding, and
| designed the LLM Agent well enough, then you can use it to
| solve problems that traditionally required deterministic code.
| itissid wrote:
| Its a fancy term for s piece of code that has some
| context(memory) and can communicate with other AI APIs(aka lang
| chain) including itself. It can retrieve certain information
| from other sources(called vector stores).
|
| What makes it different from other programs is that Agents can
| do probabilistic inference on the closure natural language,
| i.e. they can predict(P(Y|X) as well as generate(P(X, y)). Both
| of these are very hard to do with any _one_ previous model and
| hence with any service that could combine software API +
| Classic ML Model(pre Generative+Discriminative models like
| llm).
| jsemrau wrote:
| The Encyclopedia Autonomica defines Agent as a software that is
| capable of reasoning, using memory, plan its actions, and using
| tools. Or in short:
|
| Agent= Reasoning + Memory + Planning + Tools
| lukebuehler wrote:
| Agents have agency: they do stuff. The way the do it is by
| calling code or writing code and then executing it. They need
| some retrieval system too (often called memory) that selects
| the relevant possible actions for a current context.
|
| I wrote this article recently that outlines what a system needs
| to run agents: https://github.com/lukebuehler/agent-
| os/blob/main/docs/artic...
| madrox wrote:
| Having spent the last three months with Agents I'm starting to
| develop a saying: "Any sufficiently advanced agent is
| indistinguishable from a DSL"
|
| I'm becoming convinced Agents are mostly novelty. They aren't
| Turing complete. They're fun to implement, but any algorithm they
| run to solve a task can't be inspected and can go awry with
| complex logic. You can accomplish the same thing by asking the
| LLM to write code in whatever language, and it's more reliable.
| There is more reluctance to do that though, because the idea of a
| BE environment that runs user generated scripts is scary.
| LASR wrote:
| Curious how you went with this approach.
|
| I too have been playing with agents. But I've explicitly biased
| away from having them do things that can achieved directly with
| code.
|
| Stringing together tool selection into steps with reasoning imo
| is the crucial brain layer. Code is one of the tools. I've also
| gone with a kind of preemptive memory, where the brain gets
| injected with relevant memory automatically, instead of it
| requesting for it. Same for retrieval from the internet.
|
| But wanted to understand how you're thinking about.
| madrox wrote:
| You can string tools together in code as well, and have an
| LLM reinterpret the final results in text if you want.
| There's the middle bit when you ask the Agent to reason that
| works better if you just ask it to write code and give it
| functions to call instead of tools.
| broken_clock wrote:
| What have you been trying to use agents for?
|
| I think most of the actual value created short/medium-term with
| agents will be in entertainment where things going awry is OK
| and no action they take can really be catastrophic.
|
| Some people are already spending a ton of time on character.ai
| (https://beta.character.ai). There are sims-like games like ai-
| town (https://www.convex.dev/ai-town). My friend and I made an
| open-source murder mystery novella with agents
| (https://gron.games).
|
| We found that a lot of the actions they take are pretty low
| stakes, so the correctness issues are much less important (in
| ai-town, they just talk with each other, in our game, they just
| beat you or run away if you interrogate them poorly).
| madrox wrote:
| My current work is using LLMs in fintech, but my background
| is in entertainment and has been where most of side projects
| have gone. LLMs are great generative tools, but Agents are
| reasoning tools. They implicitly execute an algorithm and
| interpret the results. An agent will never reason as well as
| imperative code.
|
| I think this is why agents appear suited to entertainment,
| but this is almost a different topic that I could rant about
| for hours. There's a whimsical element of randomness in what
| the character may do that the programmer does not need to
| explicitly design. However, I think that whimsy has limited
| reach, so while the developer may be more amused by their own
| creation it has limited appeal.
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(page generated 2023-09-23 23:02 UTC)