171–180 of 226 posts
Beats Opus 4.5 on reasoning you say? Prompt: If A goes to B who then goes to C, can A send something to C? Response: We need to interpret best. The phrase "If A goes to B who then goes to C, can A send something to C?" could be a puzzle about the concept of sending something (like passing a ball) and the relationships. Scenario: A gives something to B, and B passes it on to C. Question: Can A also give the same thing…
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There literally are "no u turn" signage where you are supposed not to do that. They literally put up signs for it. It is not glowing in the sky, and it doesnt need to be, and doesnt help making a point strawmanning it.
> There literally are "no u turn" signage where you are supposed not to do that These signs, you mean? https://en.wikipedia.org/wiki/Prohibitory_traffic_sign#No_U-...
I tried generating the classic pelican svg, but it failed horribly just showing me a rectangle and a black circle...
I think this is predicted? Part of the story is how they were able to preserve core reasoning ability while cutting knowledge like "pelicans have wings." > these findings motivate the Parametric Compression-Coverage Hypothesis, which views verifiable reasoning as compressible into compact reasoning cores, while open-domain knowledge and general-purpose competence require broad parameter coverage over facts, concepts,…
Beats Opus 4.5 on reasoning you say? Prompt: If A goes to B who then goes to C, can A send something to C? Response: We need to interpret best. The phrase "If A goes to B who then goes to C, can A send something to C?" could be a puzzle about the concept of sending something (like passing a ball) and the relationships. Scenario: A gives something to B, and B passes it on to C. Question: Can A also give the same thing…
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> LLM-based Query Quality Filtering. We utilize capable LLMs to assess query quality, filtering out samples with incomplete descriptions, unreasonable conditions, invalid logic, or an inability to effectively assess target knowledge points.
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I think you could probably train a model to consider boolean logic, modal logic, and mathematics reasonably well, but there is still a pretty big leap between that and thinking about things. Even the most basic questions such as put a ball in a cup and place it on a table upside down then pick up the cup and put it in a box. Requires knowledge of things not mentioned in the question (notably gravity). Strict definiti…
>Even the most basic questions such as put a ball in a cup and place it on a table upside down then pick up the cup and put it in a box. That reminds me - this used to be my go-to question for smaller models and on which they would always fail miserably on: A small strawberry is placed in a large cup. The cup is placed upside down on the kitchen table. Someone then lifts the cup as-is and puts it in the microwave. Wh…
What happens if you ask
A small strawberry is placed in a large cup. The cup is placed upside down on a saucer on the kitchen table. Someone then lifts the cup and saucer as-is and puts them in the microwave. Where is the strawberry when the cup is in the microwave?
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I've just started using qwen3.6:35b a couple days ago running on my framework desktop and rather impressed. It runs really well and reminds me of probably the first Claude model I used. It's the first local model that's actually working for me in a coding agent I've tried. Very exciting!
If your framework desktop is the 128G Strix Halo, I recommend giving Qwen 3.5 122B-A10B a shot. This Q5_K_M quant should be near lossless and fit with full 256K context in about 100GB of RAM: https://huggingface.co/AesSedai/Qwen3.5-122B-A10B-GGUF
Edit: specifically Qwen 3.6 27B beats that on coding and agentic workflows.
Looks like we are seeing small but mighty model breakthroughs, outpacing the pure capital firepower of SOTA providers. I love rooting for the little guy, but is it too soon to call it? To play devils advocate, could it just be the benchmarks are not efficient enough to capture success of real developer workflows?
Now, if you ask this model to have a conversation with you, it's gonna fail and be incoherent. But boy, does it sure reason through math problems well.
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I think you could probably train a model to consider boolean logic, modal logic, and mathematics reasonably well, but there is still a pretty big leap between that and thinking about things. Even the most basic questions such as put a ball in a cup and place it on a table upside down then pick up the cup and put it in a box. Requires knowledge of things not mentioned in the question (notably gravity). Strict definiti…
> Even the most basic questions such as put a ball in a cup and place it on a table upside down then pick up the cup and put it in a box. I do not think this is a great example. First, it is not a question. Second, it seems very related to robotics. A model itself cannot put a ball anywhere, it can just call tools and answer in text, image, etc. An LLM seeing "put a x in a y and place it on a z upside down then pick…
That you don't need to have a ball, cup, table, or even the ability to perform physical actions in order to consider where the ball ends up is in-itself required knowledge.
Am I right in thinking this is a tiny model which has been trained well to reason, and that's it? Makes me think of a smart person who doesn't know anything about a given topic, but with the right tools will go and research the heck out of it. I really like the sound of this... why have models train on learning anything when you can just train them how to learn and let them get on with it from something as small as a…
