Ksenia Se's picture

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Ksenia Se

Kseniase

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https://www.turingpost.com/

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reacted to fdaudens's post with 🔥 3 days ago

Just tested something this morning that feels kind of game-changing for how we publish, discover, and consume news with AI: connecting Claude directly to the New York Times through MCP. Picture this: You ask Claude about a topic, and it instantly pulls verified and trusted NYT content — no more guessing if the info is accurate. The cool part? Publishers stay in control of what they share via API, and users get fast, reliable access through the AI tools they already use. Instead of scraping random stuff off the web, we get a future where publishers actively shape how their journalism shows up in AI. It’s still a bit technical to set up right now, but this could get super simple soon — like installing apps on your phone, but for your chatbot. And you keep the brand connection, too. Not saying it solves everything, but it’s definitely a new way to distribute content — and maybe even find some fresh value in the middle of this whole news + AI shakeup. Early movers will have a head start. Curious what folks think — could MCPs be a real opportunity for journalism?

reacted to their post with 👍 5 days ago

16 new research on inference-time scaling: For the last couple of weeks a large amount of studies on inference-time scaling has emerged. And it's so cool, because each new paper adds a trick to the toolbox, making LLMs more capable without needing to scale parameter count of the models. So here are 13 new methods + 3 comprehensive studies on test-time scaling: 1. https://huggingface.co/papers/2504.02495 Probably, the most popular study. It proposes to boost inference-time scalability by improving reward modeling. To enhance performance, DeepSeek-GRM uses adaptive critiques, parallel sampling, pointwise generative RM, and Self-Principled Critique Tuning (SPCT) 2. https://huggingface.co/papers/2504.04718 Allows small models to use external tools, like code interpreters and calculator, to enhance self-verification 3. https://huggingface.co/papers/2504.00810 Proposes to train LLMs on code-based reasoning paths to make test-time scaling more efficient, limiting unnecessary tokens with a special dataset and a Shifted Thinking Window 4. https://huggingface.co/papers/2504.00891 Introduces GenPRM, a generative PRM, that uses CoT reasoning and code verification for step-by-step judgment. With only 23K training examples, GenPRM outperforms prior PRMs and larger models 5. https://huggingface.co/papers/2503.24320 SWIFT test-time scaling framework improves World Models' performance without retraining, using strategies like fast tokenization, Top-K pruning, and efficient beam search 6. https://huggingface.co/papers/2504.07104 Proposes REBEL for RAG systems scaling, which uses multi-criteria optimization with CoT prompting for better performance-speed tradeoffs as inference compute increases 7. https://huggingface.co/papers/2503.13288 Proposes a φ-Decoding strategy that uses foresight sampling, clustering and adaptive pruning to estimate and select optimal reasoning steps Read further below 👇 Also, subscribe to the Turing Post https://www.turingpost.com/subscribe

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Scaling test-time compute

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