Reasoning with Sampling: Your Base Model Is Smarter Than You Think
aakaran.github.io·1h·
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SampCert: Verified Foundations for Differential Privacy (PLDI 2025)
dl.acm.org·1h·
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original ↗
wilsoniumite.com·1d·
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Mathematical exploration and discovery at scale
terrytao.wordpress.com·14h·
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Continuous Autoregressive Language Models
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Optimizing filtered vector queries from tens of seconds to single-digit milliseconds in PostgreSQL
clarvo.ai·1d·
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Kosmos: Next-generation AI Scientist
edisonscientific.com·1h·
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Disassembling Terabytes of Random Data with Zig and Capstone to Prove a Point
jstrieb.github.io·1d·
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The Complexity Cliff: Why Reasoning Models Work Right Up Until They Don't
rewire.it·18h·
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Topographical sparse mapping: A training framework for deep learning models
sciencedirect.com·1d·
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Myers Diff for Tab Management: Part 2
deontevanterpool.com·18h·
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I Processed the Internet on a Single Machine to Find Valuable Expired Domains
blog.mbrt.dev·2d·
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Masked Softmax Layers in PyTorch
mcognetta.github.io·3d·
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Improving agent with semantic search Semantic search significantly improves coding agent performance with 12.5% higher accuracy, improves code retention and dec...
cursor.com·18h·
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Google Finance offers Gemini AI tools to stock traders
theverge.com·1h
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AI's capabilities may be exaggerated by flawed tests, according to new study
nbcnews.com·3h·
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Harness the Power of Atlas Search and Vector Search with $RankFusion
mongodb.com·3h·
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Generalizing Test-Time Compute-Optimal Scaling as an Optimizable Graph
huggingface.co·1d·
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Using Coding Agents to Decompile Nintendo 64 Games
blog.chrislewis.au·1d·
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The Means of Prediction: How AI Works (and Who Benefits)
hks.harvard.edu·20h·
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