Trends of top arXiv papers this past month Showing views on alphaXiv over time for
– DeepSeek V3
– DeepSeek R1
– s1: Simple test-time scaling
@askalphaxiv
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Top arXiv Paper Trends: DeepSeek and Test-Time Scaling
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alphaXiv: Alternative Platform for AI Research Papers Access
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Check out http://
alphaXiv.org! Alternatively replace the “arXiv” with “alphaXiv” in any arXiv URL. Ex: https://
arxiv.org/abs/2501.17161 -> https://
alphaXiv.org/abs/2501.17161 -
Google AlphaGeometry 2 Achieves 84% on IMO Geometry Problems
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New from Google: AlphaGeometry 2 Gets 84% over all IMO geometry problems from the last 25 years
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How LLMs Learn to Reason: Comprehensive Study Reveals
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Trending on alphaXiv (2/6): The most comprehensive (and refreshingly clear) study on how LLMs learn to reason. A systematic investigation reveals key ingredients: SFT initialization helps, reward shaping stabilizes training, filtered verifiable rewards improve generalization,
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Cursor Uses Gemini 2 Flash for arXiv Paper Analysis
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We used Gemini 2 Flash to build Cursor for arXiv papers
— alphaXiv (@askalphaxiv) 6 février 2025
Highlight any section of a paper to ask questions and “@” other papers to quickly add to context and compare results, benchmarks, etc. pic.twitter.com/2KKTuDuzRhWe used Gemini 2 Flash to build Cursor for arXiv papers Highlight any section of a paper to ask questions and “@” other papers to quickly add to context and compare results, benchmarks, etc.
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alphaXiv Firefox Extension Launches for arXiv Paper Discovery
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The alphaXiv Extension is now live on Firefox! Like papers directly on arXiv and see discussions on relevant papers in your community!
— alphaXiv (@askalphaxiv) 18 janvier 2025
Link to download: https://t.co/OcT79cM6Xq pic.twitter.com/nqdesvSn8IThe alphaXiv Extension is now live on Firefox! Like papers directly on arXiv and see discussions on relevant papers in your community! Link to download: https://
addons.mozilla.org/en-US/firefox/
addon/alphaxiv/
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Understanding Extrapolation: Causal Models for Out-of-Distribution Prediction
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Towards Understanding Extrapolation: a Causal Lens The paper addresses extrapolation, where models predict outcomes for target samples outside the training distribution. It introduces a latent-variable model to formalize when extrapolation is feasible and offers methods to
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Tarsier2: Advanced Video Understanding with Large Vision-Language Models
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Tarsier2: Advancing Large Vision-Language Models from Detailed Description to Comprehensive Understanding Tarsier2 is a state-of-the-art large vision-language model (LVLM) that excels in video description and understanding, improved by scaling pre-training data,
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Inference-Time-Compute Models: Evaluating Faithfulness in AI
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Inference-Time-Compute: More Faithful? A Research Note This research evaluates the faithfulness of Inference-Time-Compute (ITC) models, comparing their ability to articulate cues influencing their answers with non-ITC models. Problem: Faithfulness in Chains of Thought (CoTs) is
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Do Generative Video Models Learn Physical Principles
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Do generative video models learn physical principles from watching videos? This paper explores whether generative video models learn physical principles by analyzing their performance on the Physics-IQ benchmark, which tests understanding of laws like fluid dynamics, optics, and