When NOT to use ensembling: Simple factual lookups ("What's the capital of France?") Time-sensitive tasks (response speed matters) Low-stakes decisions (what to eat for lunch) When you're paying per API call and budget is tight Cost: 5x the tokens. Use
MACHINE LEARNING
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Prompt Ensembling Beats Single Prompts for High-Impact Decisions
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When prompt ensembling beats single prompts: High-stakes business decisions ($100K+ impact) Medical/legal advice (where errors cost lives/money) Creative work (logo design, brand names, campaign ideas) Strategic planning (5-year roadmaps, market entry) Code
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Confidence Weighting Strategy for Numerical Estimates and Forecasts
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MERGING STRATEGY 2: Confidence Weighting Best for: Numerical estimates, predictions, forecasts Ask each prompt variation to include confidence score (1-10).
Weight answers by confidence, then average. High confidence answer: 3x weight
Medium: 2x weight
Low: 1x weight -
Stanford prompt ensembling: merging 5 variations improves LLMs
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Stanford researchers just published a prompting technique that makes today’s LLMs behave like better versions of themselves. It’s called “prompt ensembling” and it runs 5 variations of the same prompt, then merges the outputs. Here’s how it works
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Rolv’s AI performance breakthrough proving itself with faster math
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Months ago I shared Rolv with you and he shared that he had a major performance breakthrough for running AIs. His breakthrough is proving itself. Will share the final results when it's up, but his math is working out to speed things up.
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AlphaGenome Advanced Genomics Model Published in Nature
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AlphaGenome is our latest & most advanced genomics model published in @Nature today including making the model & weights available to academic researchers. Can’t wait to see what the research community will do with it. Congrats to the team on our newest front cover! #AI4Science
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Implementing Genetic Algorithms for Optimization and Simulation
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Hey coders and computational scientists! If you have never implemented Genetic Algorithms for your simulation, parameter search, and optimization problems & challenges, then you have really missed out. Get this book. I used GAs in my galaxy collisions simulation research as a
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TED Talk Released: Competition Stifling AI Breakthroughs Discussion
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People can finally hear my full thoughts on this now that my TED Talk has been released today!!! ted.com/talks/llion_jones_ho… Sakib (@zsakib_) Funny that both Ilya Sutskever (@ilyasut), and Llion Jones (@YesThisIsLion) the co-inventor of the Transformer architecture, are sick of Transformers Both @SSI and @SakanaAILabs give their researchers freedom to pursue wild ideas beyond token stacking Even Jim Simons’ of RenTech, the only hedge fund ever to consistently return >>40%+ annually (sometimes even 152%) let their researchers spend HALF their time on working on whatever they wanted!! Reminds me of the foundations of Physics in the 90s and even now: everyone works on unfalsifiable “theories of everything” like string theory (bro you can’t even test it what are you doing?) Diversity of thought (i.e. research freedom) is how we get true innovation — https://nitter.net/zsakib_/status/1993883878361067988#m
→ View original post on X — @_yutaroyamada, 2026-01-29 04:28 UTC
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Dream to Control: Learning Behaviors by Latent Imagination
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#PaperADay 13
2020: DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION https://danijar.com/project/dreamer/
… More than doubled the performance of PlaNet, and beat the state-of-the-art model-free algorithm of the day that used many more environment steps. PlaNet (#PaperADay 12) wasn't -
Unified Theory of Neural Network Reprogrammability Explained
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What if all the ways we adapt AI models are actually the same thing? Researchers from the University of Melbourne, Southeast University, and IBM present a unified theory: "Neural Network Reprogrammability." They show that methods like prompt tuning and in-context learning all