#adaptivedynamicprogramming zoekresultaten

"An hour of planning can save you 10 hours of doing." ✨📝 Planned Diffusion 📝 ✨ makes a plan before parallel dLLM generation. Planned Diffusion runs 1.2-1.8× faster than autoregressive and an order of magnitude faster than diffusion, while staying within 0.9–5% AR quality.

New Stanford + SambaNova + UC Berkeley paper proposes quite a revolutionary idea. 🤯 Proves LLMs can be improved by purely changing the input context, instead of changing weights. Introduces a new method called Agentic Context Engineering (ACE). It helps language models…

“何度も適応障害になってしまう” そんなとき次も適応できなかったらどうしようと考えていたけど、実際は「適応する前提」ではなく「選び直せる前提」で設計するのがよかった そして、“積み上げられるもの”を1つ持っておくことが大事。内観と活路をまとめてみました

here are my notes on @fchollet's neat explanation of the differences between deep learning and program synthesis, and the advantages and disadvantages of each, and how they'd fit together to build AGI. in deep learning, your underlying model is a differentiable curve; in program…

Stanford just pulled off something wild 🤯 They made models smarter without touching a single weight. The paper’s called Agentic Context Engineering (ACE), and it flips the whole fine-tuning playbook. Instead of retraining, the model rewrites itself. It runs a feedback loop…

Dynamic programming Goldmine ❤️ Dynamic Programming is one of the most important topic of any tech interview process. Found this really amazing blog on LeetCode covering important topics. A Thread 🧵

Every market evolution starts when someone questions the obvious. Vesting has been broken for decades static, unfair, unresponsive. @Alignerz_ built the next logical step: adaptive vesting that rewards time and conviction. ⏱️ $A26Z

Anchoring execution to valuation can keep teams disciplined across cycles. The Dynamic FDV model scales buyback intensity with valuation, buying more when it counts, and less when the market’s crowded. In practice, this approach helps preserve treasury and lower average cost.

tinyurl.com/27eofcxf A dynamic defense system for Multi-Agent Systems adapts to counter attacks, outperforming static solutions and enhancing AI trustworthiness. Explore this innovation: github.com/ChengcanWu/Mon….

tinyurl.com/28d6ghqy The authors introduce an enhanced decoder Transformer that conditions its outputs on unsupervised random latent variables via a variational method. This approach significantly boosts performance on downstream tasks.

Nice, short post illustrating how simple text (discrete) diffusion can be. Diffusion (i.e. parallel, iterated denoising, top) is the pervasive generative paradigm in image/video, but autoregression (i.e. go left to right bottom) is the dominant paradigm in text. For audio I've…

@ metkun GNGERTI DYNAMIC PROGRAMMING AAAAAAAAA

I still remember the disastrous end of auto-ML in #trading systems years ago.

Introducing Dynamic Optimization, an all-new way of optimizing your published websites. All sites now optimize in seconds, even large ones. And adding pages has no impact on optimization time. Publishing, now even faster. Only in @framer.

We explore a new dimension in scaling reasoning models in Adaptive Parallel Reasoning APR lets LMs learn to orchestrate both serial & parallel compute E2E via supervised training + RL — w/ better efficiency and scalability than long CoT on Countdown 🧵 arxiv.org/abs/2504.15466

🔥 Read our Paper 📚 Inducing Optimality in Prescribed Performance Control for Uncertain Euler–Lagrange Systems 🔗 mdpi.com/2076-3417/13/2… 👨‍🔬 by Christos Vlachos et al. #adaptivedynamicprogramming #optimalcontrol

𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 Goldmine ❤️ Dynamic Programming is one of the most important topic of any tech interview process. Found this really amazing blog on Codeforces covering all important topics. A Thread ❤️

Stop thinking that 'short, clean' prompts work well. 🟠 your LLM can now evolve by learning from every outcome, like a living playbook that gets smarter with use. 1️⃣ Stop fine-tuning your model: use Agentic Context Engineering (ACE) to let the AI reflect on its own output and…

𝗠𝗼𝗻𝗼𝗹𝗶𝘁𝗵 𝗗𝗲𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 We have three types of monoliths. In 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 ones, we have everything bundled together in a layered form. We have also 𝗺𝗼𝗱𝘂𝗹𝗮𝗿 𝗺𝗼𝗻𝗼𝗹𝗶𝘁𝗵𝘀, where we have defined functional boundaries…

Today we're introducing Model Fine-tuning. A new self-serve offering that will soon allow you to customize our models towards your specific use cases and on your own data. From entertainment to robotics, education, life sciences and beyond, our next generation of customizable…