Hottest paper on AlphaXiv Language Models are Injective and Hence Invertible Every prompt maps to a unique hidden state and can be exactly reconstructed with this paper’s algorithm SIPIT. This means the model’s internal activations are the full prompt in disguise!!
@askalphaxiv
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Recursive Language Models for Near-Infinite Context Agents
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One of our best talks yet. Thanks @a1zhang for the amazing presentation + Q&A on Recursive Language Models! If you're interested in how we can get agents to handle near-infinite contexts, this one is a must. Watch the recording here!
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Stanford Method Detects Copied Fine-Tuned AI Models
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Someone stole your model & u can’t prove it? This Stanford paper just showed that you can find out if a model is a copy or finetuned based on your model with just its generated text So if someone yoinks DeepSeek-v3.2 and finetunes it, it’ll leave statistical traces!
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Personalized arXiv feeds powered by AI recommendations
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Introducing personalized arXiv feeds 🚀
— alphaXiv (@askalphaxiv) 30 octobre 2025
Rate 5 papers, get a feed tailored to your research that learns what you care about
Find the papers you need in seconds, not hours pic.twitter.com/3evDOcBKiOIntroducing personalized arXiv feeds Rate 5 papers, get a feed tailored to your research that learns what you care about Find the papers you need in seconds, not hours
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Linearizer Collapses Diffusion Model Training to Single Step
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Who Said Neural Networks Aren’t Linear?? In this paper, authors are able to collapse the training of diffusion models down to only 1 step by introducing Linearizer, which sandwiches a linear matrix A between two invertible neural networks!
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Visualizing Llama 3.1 Tensor Operations Through GGML Debug Output
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The three.js visualization parses the output of llama-cpp's ggml debug output (of unsloth llama 3.1) to directly obtain all the tensor calculations happening under the hood. Operations (MUL_MAT, ROPE, RESHAPE, ADD) are grouped into query, key, value, MLP, and residual stream
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LlaMA Tensor Trace Tool for Model Analysis
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Fly through LlaMA here! https://
alphaxiv.org/labs/tensor-tr
ace
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3D Illustrated Transformer: Interactive LLaMA Learning Tool
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Introducing the Illustrated Transformer in 3D 🚀
— alphaXiv (@askalphaxiv) 29 octobre 2025
Fly through LLaMA like never before. See every tensor and operation in motion.
Click any component to reveal the exact lines of code that run it.
A new way to learn and teach LLMs. Try it out in the link below 👇 pic.twitter.com/pBpEZyse2vIntroducing the Illustrated Transformer in 3D Fly through LLaMA like never before. See every tensor and operation in motion. Click any component to reveal the exact lines of code that run it. A new way to learn and teach LLMs. Try it out in the link below
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DeepSeek-OCR OmniDocBench: Document Recognition Benchmark
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Detailed report + code: https://
github.com/alphaXiv/DeepS
eek-OCR-OmniDocBench/blob/main/REPORT.md
… Datasets page: http://
alphaxiv.org/datasets/shang
hai-ai-laboratory/omnidocbench
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alphaXiv Resources Tab Now Available for Research Papers
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Check out http://
alphaXiv.org and click on the resources tab for any paper!