> 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.
> 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 · 11m 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 · 54m ago
What a phenomenal read, thank you for sharing that.
Noumenon72 · 40m ago
He should submit this to SCP Foundation so you know it's not going to have a plot or a point.
Barbing · 1m 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
esafak · 50m ago
Proving diversity of thought is a good thing. A controversial observation in 2025's USA ;)
Seriously, though, when I embark on a project, I usually ask Gemini to architect and implement the first pass, then iterate with Claude.
recipe19 · 14m 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.
mef · 13m ago
this is a different idea
gnulinux · 2h 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.
ls-a · 2h ago
If you do please report back
sebmellen · 3h 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 · 2h 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.
joshuamoyers · 21m 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.
Flux159 · 2h 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 · 2h ago
It should be pretty simple to do, right? It shouldn't be that hard to abstract out tool calls.
rockwotj · 2h 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.
refulgentis · 2h 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 · 2h ago
Annoying, yes. Tractable, absolutely!
stingraycharles · 2h 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 · 2h ago
Read till the end—what you ask is the last table.
stingraycharles · 2h 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?
BoiledCabbage · 31m 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.
rubycollect4812 · 2h 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.
vFunct · 3h ago
Anyone else try this?
kadushka · 1h 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.
BoorishBears · 2h 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.
CamperBob2 · 1h 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 · 2h 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).
tomrod · 2h ago
Or Minsky's Society of Minds, Dennets Multiple Drafts, Gazzaniga's Social Brain, etc.
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.
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
Seriously, though, when I embark on a project, I usually ask Gemini to architect and implement the first pass, then iterate with Claude.
> 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.
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?
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.
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.
I would say that that’s at least something the alloying should be benchmarked against, which I didn’t find in the article.
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?
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.
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.
Congratulations you just have a very expensive simulation of a Baysian function (ish, close enough that one should get the point).