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Haven't gone through full PDF as its very detailed, few things have resonated with me so far.
Basically if a Car A is performing better (be it speed, milage or in general sense) than Car B, then it is not necessarily because its engine. It could be because of better tires, better gearbox, lighter body, better usability of features, etc.
You can implement an AI feature (like AI for BI) in different ways even with the same model - via ReAct-loop, or plan-and-execute, or hybrid. You can make it stateless, stateful, RAG-based, etc. depending upon whether you want to prioritize result accuracy or depth of analysis. You can use LLM to generate either intent (requires lesser reasoning) or the queries itself (requires much more capable model).
Your harness can adapt to the underlying model's native capabilities, or can make up for its absence, e.g. query generation in above example requires your model to have MOE capabilities but intent generation wouldn't.
The conclusions:
Seems fairly intuitive to me, based on feeling. But also fairly kind of obvious; bash-only tooling has higher success for bash-capable models, compared to using predefined tools for models that aren't good at bash? Yeah... They all seem a bit "duh" to me. The final piece of the conclusion is agreeable regardless of how they arrived at it though:
I think lots of people treat the harness/model/prompts combo as interchangeable, but in my experience the quality and efficiently depends heavily on the combo of the harness/model, and using the harness + model made by the same lab, has vastly better experience compared to more "general purpose" (for the lack of a better term) harnesses. Most likely because they use their own traces when training future model iterations.
It might be 'duh' but it means we need a formal list of what each model is good at, and to pick or change harnesses to closer fit the model. Like an llm recipe book. Not just for remote models, but also local ones where how you run the model is critical too.
"Everybody knows foul air causes sickness."
"Duh, of course Mars has canals."
Testing the "obvious", "duh" things is incredibly valuable science. It provides a more solid foundation on which to build because it reduces the assumption space.
not really, there was a recent benchmark with claude and codex and it showed no difference in ability with a harness like pi agent compared to their native harnesses, pi was in fact cheaper per task.
No. The conclusion is that:
bash-capable models + bash-only tools > bash-capable models + predefined tools
In other words, MCP was just a bunch of bullshit that maybe helped a little bit until the models got good at bash, and now it's basically useless.
bash scripts, famously the last word in software engineering. all these castles of sand we've built atop the beautiful, perfect, timeless Bourne Again SHell. all for naught. fools!
This unironically.
The design of pipes and the philosophy of simple composable tools tied together with the Unix shell has proven remarkably difficult to improve upon.
What an intensely wrong comment. The trajectory of all software is the opposite of what you say. Bash is the entry level, everything flees.
At one point the web was bash scripts glued together. Now everything is brought into a runtime. Then brought into virtualized containers to isolate and hide from every other aspect of the operating system.
Eventually the agents will be on a runtime as well. The existing ones just aren't good enough. Rather than forking like made as a way of doing everything, exfiltration into arbitrary executables will be something more tightly controlled.
Organizations need better in-product control and auditing of the operations of the agent. They need it to work across OSes and not depend on the state of the machine, and not conflict with what else the developer is trying to do with it.
This is a complete misunderstanding of why a team would want MCP.
If you're just using bash scripts, where are you putting your enterprise secrets for external systems? How do you cleanly revoke them when a developer leaves your team?
MCP moves execution into a remote environment where it is easy for enterprises to secure access to internal and external systems. OAuth based access makes it easy to audit and revoke tokens. Central HTTP interface makes it trivially easy to monitor and audit.
They solve different problems.
Todo/task-tracking tools (TaskCreate/Get/Update/List, TodoWrite) are no longer available on Opus 4.8, Sonnet 5, Fable 5, Mythos 5, and newer models; set CLAUDE_CODE_ENABLE_TODO_TOOLS=1 to bring them back"
Anthropic appears to agree frontier models don't need in-session planning tools.
https://github.com/anthropics/claude-code/issues/80487
This is done on Nemotron models + mistral, so its not very relevant to the current frontier of cheap chinese models + big models from Claude/GPT. Big miss not having qwen or deepseek in this research.
The focus of the study was the different harness approaches and how they scale across model sizes. The fact that they used any particular set of models is irrelevant.
Agree. Harnesses are effective because they interact with the underlying model effectively. If the latest models were fundamentally different, excluding them would be a miss. But I don’t think they are, at least not in ways that would affect these observations.
I think the confounding issue is that by now, millions of sessions of Claude Code and Codex are now in the training set for these models. So they have been trained to work the way these harnesses are configured, and at least in the case of Claude Code the harness itself is greatly stripped down because the model has absorbed it.
Nope. Sorry. Not how this works.
How does it work then?
I'm going to cherry pick one example where newer models are noticeably improving at least in my experience.
What is a noticeable improvement with something that struggles to read a message longer than 200 characters without missing information in the middle, may be a 0.000000001% improvement with a model that... almost never misses info in the first place.
I'm not totally convinced that models are fungible, the claudes/gpts/Gemini all have pretty individual feels when you're working with them. I wouldn't be surprised if the approaches don't scale or even work the same in a poly model setup
that's anecdotal though, right? your subjective feeling of how a model responds to you will greatly influence how you 'feel' about a model and its performance in the same way that a co-worker who you get along with will fuck something up and you'll be more forgiving than when you work with a too-verbose, mansplainer of a co-worker who fucks up
I think until we have actual repeated-use measurements tracked over time (eg consistent prompts used to do the same tasks, count number of hallucinations and errors and bugs over a long period of time) you won't really have any idea of which model is better
I also think of it like a car - some just feel better to drive even if they are materially worse in other measures. until you start measuring the metrics important to you (eg MPG and cost of maintenance over a long period), you have no idea which car is actually better suited for you. and the fact that you can only do so with a limited number of cars (or hours available to work, or money to burn on tokens) means there's no true measure approaching objectivity
Anecdotal or subjective don't mean "wrong." I would 100% agree that claude and chatgpt have different 'styles.' They do have their own patterns, and those patterns are distinguishable.
I have to say, I am starting to hate this line of reasoning. Yes, LLMs move extremely fast and a lot of improvements are done in a short amount of time.
And there might be a point to these arguments, vaguely. However:
There never seems to be - any - kind of counter example or reasoning behind the rationale. You have an in depth and empirical study, done by researchers who, frankly, now their shit (most of the time)
And on the other hand a random internet comment saying "nope" because...the models aren't the latest.
If the latest models really would make a difference, you should at least provide some kind of evidence towards that. As it stands though, every time these comments come up this is missing.
There seems to just be a vaguely defined understanding that "everything changes all the time, and nothing you ever research is transferable to state-of-the-art models"
Which brings me to my second point about these kinds of arguments:
LLM models often - aren't - fundamentally different. Yes, they are vastly more capable. And yes, there are emergent properties. But at their core, they function very much similarly. And for quite a while now, there have not been any of these drastic changes we saw when LLMs first become "good enough" for agentic coding.
I am tired of dismissing empirical evidence and studies every. single. time for reasons without evidence and seemingly a vague sense of "no, but my model is different"
How do Nemotron models + mistral compare to "the current frontier" in your experience?
As far as I can tell, the paper says "bash capable", without ever describing what that means. How would one know whether a given model is "bash capable" or not?
I would have to imagine, that Luna would very much fall into the camp of "bash capable". At which point- it seems to me that adding any tools beyond just Bash requires some rigorous testing and verification that value is being added.
I think it just means “is there some parser program that consumes the LLM token stream and spawns shell processes with the detected command strings”.
I think it’s sort of self-defined. If a model is able to use bash well enough to not need specific tools.
The research seems to agree with you, though. The paper calls out that for “bash capable” models, adding tools to do things bash can already do doesn’t improve performance.
Vaguely the same result as RAG. Unless you’re in specific domains, you won’t beat handing the agent a shell and grep.
Awesome work!
I tried to get my lawyer-mom switched to Linux a bit ago, and she loved it in generally, but none of the Office competitors had good enough compat to work.
The only other thing keeping her on Windows is Adobe PDF pro, since it can do OCR where, when you edit it, it reflow the text in a font that matches the scanned in one to look like the original. (I also got weird "This feels like it enables fraud vibes" from this, but, no, turns out it's a totally common workflow for lawyers to need to do this ... I hate it.)
Cool to see 1/2 of the problems keeping her on Windows solved.
Cool study, we definitely need more principled studies on the role of harnesses. I'd also say that there aren't too many benchmarks where the more complicated harnesses consistently outperform extremely simple agents. But I'm also biased, because I wrote https://github.com/swe-agent/mini-swe-agent/ , which is probably the most minimal agent out there (it started as just 100 lines, all included), and it's used in a lot of benchmarks like DeepSWE, terminalbench, programbench (seems like it's still top of the ranking for TB3, but wasn't evaluated with the best models on TB4).
I'd really love to see more studies about effectiveness of AI in general. As in, what works best and how to use it and such.
Because I feel that the technology and space is - so - hyped and fast moving that a lot of cultish feeling rituals seem to pop up, none of which are backed by evidence. Anthropic openly recommend giving the agents.md file an architectural overview of the code, and the one time this was studied they found the opposite - that the agents.md file is best for concrete commands about how to build stuff and such, and - not - huge overviews. This was, and still is, the official recommendation from Anthropic as far as I can tell.
And then there are the benchmarks, how feel vague and not concrete, and everyone kind of knows they're not the best cuz you can't just assign these tools one fixed number ( for multiple reasons ), but everyone still looks at them and compares them.
People share skills and superpowers and plugins and mcps and very, very few of them have and kind of proof they do much at all.
It all feels a bit weird to me, and I've been on the lookout for exactly these kinds of studies more lately, because I think having this research, even if not done on the exact newest models or not the exact, newest thing, are still - vastly - superior to the alternative.
At least 3 times last month I was asked to review a change in a .md file used by agents. And I am like: "yeah I guess it makes sense?"
It feels we need to write unit tests for this stuff, but even how to do so in reasonable time and complexity seems difficult.
It is way more than 100 lines. Why keep advertising something that is no longer the case?
I think they are referring to the file https://github.com/SWE-agent/mini-swe-agent/blob/main/src/mi...
190 lines with comments included so close enough
Which is the actual agent? Everything else is just glue code to connect to different LLM providers, etc
Minimal agents also allow room for more focused add-on tools/infra. I'm working on a context management layer[1] and it's very difficult to do well. In our benchmarking, we've observed simple harnesses like Stirrup[2] outperforming more elaborate ones.
[1] https://www.induction.ai/docs/context-management [2] https://github.com/ArtificialAnalysis/Stirrup
nice work and paper, seeing more harness benchmarks emerge and we definitely need more. I ran mouse on the Frontier Harness benchmark and scored the highest pass rate, however I'm not convinced the results there actually translate to meaning the "best" harness in practice.
Always looking for more harness evals, although I'm going broke running them across all these models and tasks.
Quite inline with what I had found with my Claude code sessions over the last year. I wrote about this a few months ago.
https://rahulmax.com/notes/how-i-keep-the-ai-bill-down/
In their case, context management pays off more the tighter your window. Their gap between managing and not managing is 35.7 points of success rate at 32k and 2.7 points at 128k. My version of that was a rule I stick to, as much as I can. I checkpoint a session at about 25-30% of the window, write the state out to a PROGRESS.md and a JSON file of the requirements, and start fresh. This restart costs me 30 seconds, since a bloated session doesn't get any cheaper the longer you stay in it.
Also worth knowing that the models are Nemotron-3 and Mistral-Medium, not the frontier models most people here are paying for.