today at 5:02 AM
Reads like an ad for Microsoft and its model because it's an article about a decision model that doesn't mention Jev or other decision models at all.
today at 8:42 AM
Exactly my thought. I read the whole article, did Cmd +F for Jev - and felt uneasy on did the author did any research at all
today at 5:24 AM
No pelicans riding bicycles on this one, eh?
today at 8:11 AM
Could one reverse the test? Feed ai generated svgs into the model and ask it to decide which of 10 animals (dog, cat, pelican, crocodile, etc) doing which of 10 actions (moonwalking, paddleboarding, sitting, riding a bicycle, etc.).
today at 8:25 AM
AlexNet (2012) can do the first part :)
today at 5:57 AM
Instead the right app is hotdog / not hotdog
today at 5:38 AM
Pelicans are deciding.
today at 5:47 AM
Pelicans deciding if they are going to ride a bicycle.
today at 8:30 AM
Bikes with integrated decision models deciding which pelican needs a ride the most.
This made me realize it's been a long time since I did some brainstorming and how I missed it.
today at 5:39 AM
Yeah the most usesless and stupidest bench .. I don't know why LLM Community thinks its good idea to use that.. It can't work on decision systems.
today at 5:51 AM
It's a meme. Simon Willison started doing it because, I'm paraphrasing, it's something that (at the time) was just sort of at the edge of what you could expect an LLM to be able to do, and also it's somewhat of an impossible task since, of course, Pellicans can't ride bicycles. From there, he started writing about it along with every major LLM release he covered. His coverage is generally well regarded (hi, Simon!) and so it sticks around. It's never been intended as a serious benchmark.
today at 6:32 AM
The 3D visualizations have always made it starkly apparent the difference in quality between quantizations and thinking.
The pelican meme is so much better than benchmaxxed stuff. Unfortunately we are likely now in pelicanmaxxing territory.
today at 7:27 AM
already , i self hosted Deekseek 4.1 Flash , Qwen 3.8 Flash Next , Glm 5.3 flash , tested it , all benchmaxxed to the point that all of them can draw that well , much better than other logos .
today at 7:25 AM
Sorry I didn't knew it was started here and , lol I didn't know it was viralized by Simon of django! Now these days , many LLMs are benchmaxxed to it.
today at 6:54 AM
> Yeah the most usesless and stupidest bench ..
I recall that when this benchmark came out there were concerns that AI companies were gaming benchmarks to fake their progress. The purpose was to find something silly and outlandish that AI companies would definitely not have trained their models on, and thus have a way to check a model's capabilities.
A pelican riding a bicycle is pretty random. That was the whole point.
today at 4:58 AM
I haven't evaluated this one, but for what I have, it seem the pplx-decider-v1.2 (and its -flash variant) are pretty good. Anyone evaluated all these open-source decision models on their own tasks? (I evaluated on MonitoringBench).
today at 5:03 AM
I didn't look specifically at open-source but I evaluated as many decision models as I could on openrouter against how good are they at detecting phishing, which isn't the best use of these models but I was interested to see how they compared. https://emdashcyber.com/phishnchips-decision-models/
today at 6:07 AM
I had my first use case recently, and did some very non-systematic benchmarking of models on OpenRouter.
The only one that came close to Jev itself was the one from respan (respan/span-02), which is also open source and cheaper too.
today at 7:34 AM
Have you checked mammoth-1/elephant-1 models? They are benchmarking quite well compared and are currently free on openrouter. I currently use mammoth-1 for a small internal app and it works flawlessly for what I am doing.
https://huggingface.co/baselabs/mammoth-1
today at 5:13 AM
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