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亲爱的朋友们，我希望你们的生活过得很好...
我刚为你们准备了一份礼物 Claim your Gift Enjoy！
你们的良好祝愿者 @Quantrox 

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Infini-gram — The Next Evolution of Data Attribution @OpenLedger takes data transparency to the next level with Infini-gram, a breakthrough framework that replaces rigid, fixed-window n-grams with unbounded dynamic contexts. Instead of limiting analysis to short sequences, Infini-gram searches for the longest possible match from the training data — offering token-level precision across trillion-token datasets. 💡 Key Advantages: • ⚙️ Dynamic Context Selection: Always uses the longest relevant match — no fixed limits. • 🔍 Transparent Attribution: Every token traced to its real source data. • ⚡ Real-Time Efficiency: Works without model access or backpropagation. • 🌐 Scalable to Massive Corpora: Designed for the new era of large, specialized language models. By anchoring every prediction to its exact origin, Infini-gram delivers what AI transparency always promised but never achieved — verifiable, auditable attribution at scale. #OpenLedger $OPEN

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From N-grams to Next-Gen Attribution — How It All Started Before neural networks ruled AI, n-gram models were the backbone of language modeling and attribution. They worked by tracking how often sequences of words (n-grams) appeared in data — making them simple, transparent, and easy to interpret. But they had limits ⛔ • 🧱 Tiny Context Windows: Usually capped at n ≤ 5 — missing long-range dependencies. • 🔍 Data Gaps: Many valid sequences simply never appeared in the training set. • 💾 Explosive Storage: Memory usage grew exponentially with vocabulary size. • 🗣️ No Paraphrase Understanding: Only exact word matches counted — no semantic awareness. Despite their clarity, n-gram models couldn’t keep up with today’s massive, complex datasets. That’s where @OpenLedger innovation steps in — combining transparency with modern scalability through Infini-gram, redefining how we trace data influence in AI. #OpenLedger $OPEN

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Why Gradient-Based Attribution Fails at Scale Traditional AI attribution relies heavily on gradient-based methods — like influence functions or embedding similarity — but these break down when applied to large-scale models. Here’s why: • 🚫 Storage Overload: Tracking gradients and Hessians for trillions of tokens demands impossible compute and memory. • 🔒 Opaque Insights: Embedding metrics blur precision — they show “what” a model knows, not where it learned it from. • 🧩 Limited Access: Many models are black boxes (API-only), blocking gradient-based tracing. • 📜 Lost Context: These methods miss exact token-level origins — crucial for tracing precise phrases or facts. That’s why @OpenLedger moves beyond gradients — building a transparent, scalable attribution framework that actually works for modern AI. #OpenLedger $OPEN

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Why OpenLedger Chose Infini-gram for AI Attribution As AI models scale to billions of parameters, traditional attribution systems simply can’t keep up — they’re too slow, too vague, and too resource-hungry. That’s why @OpenLedger is going all-in on Infini-gram, a breakthrough ∞-gram framework designed for symbolic, scalable, and verifiable data attribution in large language models. Infini-gram uses suffix-array-based indexing to precisely trace which training data influenced each model output — offering true transparency, auditable influence tracking, and real-time attribution efficiency. In short: OpenLedger is redefining how we understand data’s role in AI decisions — turning attribution from a mystery into a measurable science. #OpenLedger $OPEN

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OpenLedger’s Breakthrough: Real-Time Data Influence Tracking The @OpenLedger team just redefined transparency in AI training. Their new closed-form influence method lets models like RoBERTa and LLaMA2 track how each datapoint affects outputs — fast, accurate, and verifiable. ⚙️ Results? 850× faster than traditional Hessian methods Detects mislabeled or noisy data in seconds Enables real-time contributor rewards & audit-ready attribution This is onchain intelligence in action — not theory. AI no longer hides behind black boxes. With $OPEN, every model decision is traceable, and every contributor earns what they deserve. #OpenLedger $OPEN

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