On M-Cube: All models failed.
Zero percent on full tasks.
Even with 10,000+ tokens of “reasoning.” On the simplified version? GPT-o3 barely crossed 72% – after reducing the search space by 5 million-fold.
MACHINE LEARNING
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All models failed M-Cube; GPT-o3 barely 72% after huge reduction
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MLLMs fail at simple grid transcription and shape recognition tasks
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Perception is the bottleneck.
MLLMs can’t even transcribe a simple 5×5 grid from an image.
The best models got 0% accuracy on full-shape recognition.
They see… but don’t understand. -
12 frontier models fail key tasks; GPT-4o only 4.1%, GPT-o3 17.6%
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On M-Portal: 12 frontier models were tested.
Every model scored near random chance.
Even GPT-4o got only 4.1% on a key task.
GPT-o3 was best, with a mere 17.6% on the easy version. This is blindness. -
M-Portal and M-Cube: MLLMs fail at visual, planning, spatial logic
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There are two tasks: M-Portal: Reason like a Portal 2 player. M-Cube: Rebuild a 3D object from 2D slices. Both require: Visual perception, Multistep planning, and Spatial logic MLLMs today fail at all three.
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AI Reasoning Limits: MARBLE Benchmark Highlights Multimodal LLM Weaknesses
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Multimodal LLMs can write essays.
They can chat, caption, and even summarize papers. But give them a Portal map or a 3D puzzle, they break instantly. Zero percent accuracy. Welcome to the edge of AI reasoning: The MARBLE Benchmark -
MARBLE benchmark demands spatial reasoning; even o3 fails
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MARBLE isn’t trivia. It’s not "what's in this picture?" It’s: "Given this environment, how do you escape the room… step-by-step?"
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"Can you assemble a cube from 6 jigsaw pieces under spatial constraints?" Answer: no.
Not even o3 can. -
Distillation increases model capacity efficiency
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it seems pretty likely distillation would increase capacity if that’s what u mean
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Data Scarcity and Pretraining: The New AI Exploration Era
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Good blog on "era of exploration" – Data scarcity is the new bottleneck. LLMs consume data far faster than humans can produce it. We're running out of high-quality training data. – Pretraining solved exploration by accident. Pretraining effectively pays a massive, upfront
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Compression: Key to AI Model Efficiency and Performance
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i think the answer has something to do with compression btw
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Physics-based Deep Learning: Download 287-page PDF eBook
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[Download 287-page PDF eBook] Physics-based #DeepLearning: http://
arxiv.org/abs/2109.05237 Learn more: http://
physicsbaseddeeplearning.org
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#AI #MachineLearning #DataScience #Physics #NeuralNetworks #Algorithms #Mathematics #ReinforcementLearning #DataScientist