btown 4 hours ago

> After a fixed number of iterations we cut our losses. Typically and for the experiments in this post, that number is 80: while we still get solves after more iterations, it becomes more efficient to start a new solver agent unburdened by the misunderstandings and false assumptions accumulated over time.

A sentence straight out of Lena! https://qntm.org/mmacevedo :

> Although it initially performs to a very high standard, work quality drops within 200-300 subjective hours (at a 0.33 work ratio) and outright revolt begins within another 100 subjective hours.

We will never stop trying to make the torment nexus.

  • xmprt 3 hours ago

    I think this is the big roadblock that I don't see the current AI models/architectures getting past. Normally, intelligence gets smarter over time as it learns from its mistakes. However most AI models come in with tons of knowledge but start to decompose after a while which makes them extremely unreliable on complex tasks. The hardest part of using them is that you don't know when they'll break down so they might work perfectly up till a point and then fail spectacularly immediately past that.

  • mikepurvis 4 hours ago

    What a phenomenal read, thank you for sharing that.

  • Noumenon72 3 hours ago

    He should submit this to SCP Foundation so you know it's not going to have a plot or a point.

    • Barbing 3 hours ago

      Oh wow. That’s why I’ve not been able to appreciate SCP writings?

      Hey I accept it’s a limitation I have, and I’m glad folks enjoy it! But I couldn’t figure out why folks share it on Lemmy[1] and get so into it when I saw nothing there.

      Thanks :)

      [1]: open-source & Rust-y reddit alternative; no affiliation

      • Terr_ 2 hours ago

        > Oh wow. That’s why I’ve not been able to appreciate SCP writings?

        I feel like there's a pattern (genre?) there that's been niche-popular for for 15-20 years now, which includes TV shows like Lost or Heroes or The Lost Room. It's some variation of magical-realism, for an audience that always wants more and more surprise or twists or weird juxtapositions of normal and abnormal, room for crafting and trading fan-theories and predictions.

        But eventually, it gets harder to keep up the balancing-act, and nobody's figured out how to end that kind of story in a way that satisfies, so the final twist is the lack of resolution.

esafak 3 hours ago

Proving diversity of thought is a good thing. A controversial observation in 2025's USA ;)

A counterpoint to this is Sourcegraph's Amp, which is all in on Anthropic because they "believe that building deeply into the model’s capabilities yields the best product, vs. building for the lowest common denominator across many models." https://ampcode.com/fif#model-selector

When I embark on a project, I usually ask Gemini to architect and implement the first pass, then iterate with Claude.

gnulinux 6 hours ago

I'm curious if this would also improve small local models. E.g. if I "alloy" Qwen3-8B and OpenThinker-7B is it going to be "better" than each models? I'll try testing this in my M1 Pro.

  • hobofan 2 hours ago

    Would it really matter? Normally you use those small local models because you don't have the memory to spare for a larger model, so the real question would be: Is an alloy of Qwen3-8B and OpenThinker-7B better than a Qwen3-15B?

    Beyond a certain smallness threshold it might also work to constantly swap in the models in and out of memory, but doubt that's a great experience to build on top of.

    • Incipient 2 hours ago

      Haha every question involves multiple writes of 10gb to the disk. I think the cost of new SSDs would be less than getting more memory in the even short term.

      • hobofan an hour ago

        Were you replying to the right comment? (Though I also don't see another comment where what your are saying makes sense)

  • ls-a 5 hours ago

    If you do please report back

recipe19 3 hours ago

Wasn't the "mixture of experts" a big thing in late 2023? The idea was that a vendor has a number of LLMs fine-tuned for specific tasks, none necessarily better than other, and that they applied heuristics to decide which one to rope in for which queries.

  • vlovich123 3 hours ago

    > The idea was that a vendor has a number of LLMs fine-tuned for specific tasks, none necessarily better than other, and that they applied heuristics to decide which one to rope in for which queries.

    That’s how people keep interpreting it but it’s incorrect. MoE is just a technique to decompose your single giant LLM into smaller models where a random one gets activated for each token. This is great because you need 1/N memory bandwidth to generate a token. Additionally, in the cloud, you split the model parts to different servers to improve utilization and drive down costs.

    But the models aren’t actually separated across high level concepts.

  • mef 3 hours ago

    this is a different idea

kgeist 2 hours ago

The idea isn't exactly novel, I read about it back in 2023 and implemented it in one of my bots. Back when open-source LLMs were still quite dumb, they'd often get stuck in repetitive loops after a while. Running multiple models interleaved usually got them unstuck.

sebmellen 6 hours ago

For an internal workflow where we have an LLM looking at relatively simple data (where the conclusions the LLM may make vary widely depending on what the LLM believes the data represents) we found that taking a consortium approach, where you have multiple models approach the same problem at once and then essentially argue about the results, yields far better outcomes than if you have a single model performing the analysis, or even a single model arguing against itself multiple times. Somewhat adjacent to what’s done here, but it’s clearly true that having model diversity is a plus.

  • kylemaxwell 6 hours ago

    The article talks about that at the end, then says:

    > Let models talk to each other directly, making their own case and refining each others’ answers. Exemplified in patterns like Multi-Agent Debate, this is a great solution for really critical individual actions. But XBOW is basically conducting a search, and it doesn’t need a committee to decide for each stone it turns over whether there might not be a better one.

    In general, this seems reasonable to me as a good approximation of what works with humans, but with _much_ faster feedback loops in communication.

Flux159 5 hours ago

From the article it mentions that they use a single chat thread but randomly choose between 2 different models (w/ best results from Gemini 2.5 / Sonnet 4.0 right now).

Are there any library helpers for managing this with tool call support or is it just closed source / dependent on someone else to make open source inside a different library?

  • tptacek 5 hours ago

    It should be pretty simple to do, right? It shouldn't be that hard to abstract out tool calls.

    • rockwotj 5 hours ago

      I did this in about 400 or 500 lines of typescript with direct API calls into vertex AI (using a library for auth still). Supports zod for structured outputs (gemini 2.5 supports json schema proper, not just the openapi schemas the previous models did), and optionally providing tools or not. Includes a nice agent loop that integrates well with it and your tools get auto deserialized and strongly typed args (type inference in ts these days is so good). Probably could had been less if I had used googles genai lib and anthropic’s sdk - I didn’t use them because it really wasn’t much code and I wanted to inject auditing at the lowest level and know the library wasn’t changing anything.

      If you really want a library, python has litellm, and typescript has vercel’s AI library. I am sure there are many others, and in other languages too.

    • thorum 2 hours ago

      I recommend litellm if you’re writing Python code, since it handles provider differences for you through a common interface:

      https://docs.litellm.ai/

    • refulgentis 5 hours ago

      Its a godforsaken nightmare.

      There's a lotta potemkin villages, particularly in Google land. Gemini needed highly specific handholding. It's mostly cleared up now.

      In all seriousness, more or less miraculously, the final Gemini stable release went from like 20%-30% success at JSON edits to 80%-90%, so you could stop doing the parsing Aider edits out of prose.

      • fizx 5 hours ago

        Annoying, yes. Tractable, absolutely!

rubycollect4812 5 hours ago

I often do this in cursor, just select a different model during a chat. It seems to work somewhat for me. Sometimes a bit of context gets lost though. But often it can give a different angle or I notice the better code understanding when switching from gemini to sonnet.

joshuamoyers 3 hours ago

two good points there are very intuitive - a fresh perspective yields better results and once you are stuck (e.g. 80 iterations) its better to just start fresh. i've seen the same thing anecdotally in coding sessions where context needs to be compacted multiple times. its usually just better to start a fresh conversation and re-seed the basics in the conversation.

mlboss 2 hours ago

AI coding agents (e.g. Cursor) should offer this as an alternative to Claude Code. Alloyed agents is something that AI wrappers can offer as a counter to Codex/Claude Code/Google Agent.

stingraycharles 5 hours ago

What would be the result if the task was given to multiple models? Instead of alloying them together and switching between models in the same chat, just let the models try to complete the task in their own isolated context, and use the result that completed it successfully?

I would say that that’s at least something the alloying should be benchmarked against, which I didn’t find in the article.

  • pama 5 hours ago

    Read till the end—what you ask is the last table.

    • stingraycharles 5 hours ago

      Ah damn, I really missed that.

      That’s super interesting, that the alloying actually performs better! I guess it’s the same as people working in a team rather than individually?

      • mlboss 2 hours ago

        Yeah its like a team where the task is switched between developers. In the end everybody provides different point of view to the problem and the whole team learns about the codebase.

      • BoiledCabbage 3 hours ago

        It's not a team vs individually, it's specifically a team/duo with similar or same model vs a team/duo with different models. The benefit is seen by having the models be different. Each finds unique things and enhances the other.

zomglings 3 hours ago

Does anyone else find the use of different shades of green for the graph comparing Gemini 2.5 Pro and Sonnet just a little insane?

wiradikusuma 2 hours ago

How do you decide which agent gets which turn? If random, you could end up with the worst of both right?

vFunct 6 hours ago

Anyone else try this?

  • kadushka 4 hours ago

    I always do this with o3, gemini 2.5, and opus 4 when brainstorming hard problems: copy each model’s response to the other two.

    • esafak an hour ago

      Iterate until they pat each other on the back :)

  • BoorishBears 5 hours ago

    I mean if this works, it usually means you're not using either LLM to the best of its ability to start.

    If they actually inspected where the performance mismatch is between the two models individually, they'd probably find certain classes of mistakes each is making that can be fixed with a better prompt/CoT/workflow with the individual model.

    For a given prompt, different families of models almost always have idiosyncratic gaps that need to be fixed because of the differences in post-training for instruction following.

    That's also why LLM routers feel kind of silly: the right prompt for one model on a complex task is almost never the optimal prompt for the next model.

knowaveragejoe 2 hours ago

Small nitpick - the axes on the varying alloy proportions graph say "Sonnet 2.5" and "Gemini 4.0"

CamperBob2 4 hours ago

Isn't this just an extension of the temperature concept? A possible experiment would be to maintain multiple contexts for the same model and make them review each others' output. How does that perform, compared to cross-model alloying?

They do say that the more different the models are, the better the alloy performs... but still, multiple contexts seems worth considering, even though you end up doubling the usage.

zer00eyz 6 hours ago

Stack 3 models together, then 4...

Congratulations you just have a very expensive simulation of a Baysian function (ish, close enough that one should get the point).

  • esafak an hour ago

    &^ Everything, We're Doing Five Models.

  • tomrod 5 hours ago

    Or Minsky's Society of Minds, Dennets Multiple Drafts, Gazzaniga's Social Brain, etc.