I agree with what this paper generally says...but doesn't it seem like something anyone developing ai systems already do? Learn from past runs to update your skills and system prompt etc? I know I have done this a lot over the last year+.
Maybe I need to give this a more thorough read through, but it sounds largely like what harnesses, or at least, Hermes-agent, are/is already doing. That is. Learning from feedback and creating reusable skills in order to avoid so much reinventing of the wheel, in line with how the user's demands direct things.
Hermes is awesome and perhaps more should be mimicking them as they find paths forward, but is there anything different in kind here going on than they're already doing?
I always wanted to exponentially grow slop, thank you. The less I understand about what's happening, the better. Totally what I as a user want. Thanks G.
Your harness tells agent to do A when it encounters B. When agent encounters C and it doesnt know what to do it does D and you correct it to do E. Harness keeps track of this and makes agent do E next time it encounters C.
It's a plug-and-play framework for recursive agent harnessing. In an open world, agents encounter tasks unseen during the harness design. Harness² is designed for this exact scenario.
Well I wouldn't say "invented", but I'd agree with "incentivized at scale until it became a cancer on the web"
The weird thing is that YouTube seems to have incentivized a lot of good content. Sure there is "scalable slop" like Mr. Beast, but there is also good content on YouTube (Fall of Civilizations podcast, etc.).
Somehow on the web I am not seeing a lot of good content incentivized by ads. I see a lot of WikiHow-type stuff.
It seems like writing moved to Substack and so forth.
Well, as luck have it, Google don't let individuals be individuals if they work for Google, so anything under github.com/google or github.com/google-research could be a weekend hack project from an individual with zero association to Google itself, or the most supported project at Google, everything always go under Google's name if you work for Google. Basically, free-time as the typical person understands it, does not exists if you work for Google.
The disclaimer in the bottom of these project makes the distinction a bit clear, once you got to the bottom of the README:
> Disclaimer This is not an officially supported Google product.
The readme links to the paper (in the badges), which is more helpful in understanding the project: https://arxiv.org/pdf/2610.14224
I agree with what this paper generally says...but doesn't it seem like something anyone developing ai systems already do? Learn from past runs to update your skills and system prompt etc? I know I have done this a lot over the last year+.
I think many of us have already started automating it so that they update alongside code changes.
Maybe I need to give this a more thorough read through, but it sounds largely like what harnesses, or at least, Hermes-agent, are/is already doing. That is. Learning from feedback and creating reusable skills in order to avoid so much reinventing of the wheel, in line with how the user's demands direct things.
Hermes is awesome and perhaps more should be mimicking them as they find paths forward, but is there anything different in kind here going on than they're already doing?
They had Gemini CLI as open-source, used the community for validation, then moved to closed-source Antigravity CLI.
If the harness is closed, why open the "harnessing"?
Honestly the readme image bugs me less than the lack of a single end to end example you can actually run.
Yeah, doesn't help that the "video overview" is just an animated slideshow of benchmarks. I guess that tells us who the intended audience is.
The promo committee?
I always wanted to exponentially grow slop, thank you. The less I understand about what's happening, the better. Totally what I as a user want. Thanks G.
>Harness² is a plug-and-play framework for recursive agent harnessing in an open world, where agents encounter tasks unseen during harness design.
Huh?
Your harness tells agent to do A when it encounters B. When agent encounters C and it doesnt know what to do it does D and you correct it to do E. Harness keeps track of this and makes agent do E next time it encounters C.
It's a plug-and-play framework for recursive agent harnessing. In an open world, agents encounter tasks unseen during the harness design. Harness² is designed for this exact scenario.
I love that even google is using slop images in readmes now
pretty convincing argument to be made that big G invented human web slop AND AI slop, full slop
Well I wouldn't say "invented", but I'd agree with "incentivized at scale until it became a cancer on the web"
The weird thing is that YouTube seems to have incentivized a lot of good content. Sure there is "scalable slop" like Mr. Beast, but there is also good content on YouTube (Fall of Civilizations podcast, etc.).
Somehow on the web I am not seeing a lot of good content incentivized by ads. I see a lot of WikiHow-type stuff.
It seems like writing moved to Substack and so forth.
That YouTube got great content, is that it has a quasi monopoly on video streaming.
Also the reason the noise ratio is high with junk, and now shorts.
Yep. They are the Slopfather, basically.
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Well, as luck have it, Google don't let individuals be individuals if they work for Google, so anything under github.com/google or github.com/google-research could be a weekend hack project from an individual with zero association to Google itself, or the most supported project at Google, everything always go under Google's name if you work for Google. Basically, free-time as the typical person understands it, does not exists if you work for Google.
The disclaimer in the bottom of these project makes the distinction a bit clear, once you got to the bottom of the README:
> Disclaimer This is not an officially supported Google product.