gnm-v3

From tracking hurricanes to optimizing renewable energy grids, @PeterWBattaglia and @fryrsquared explore how WeatherNext 3 is shaping how we model forecasts and prepare in a fast-changing climate. Pod

From tracking hurricanes to optimizing renewable energy grids, @PeterWBattaglia and @fryrsquared explore how WeatherNext 3 is shaping how we model forecasts and prepare in a fast-changing climate. Podcast timecodes: 00:00 Introduction 00:38 Hurricane Melissa 11:50 Why weather https://t.co/JmuAm6NdtE

Our @GoogleResearch Connectomics team, in collaboration with @HHMIJanelia, has released the complete wiring diagram of a male fruit fly’s brain and central nervous system — the largest brain map by nu

Our @GoogleResearch Connectomics team, in collaboration with @HHMIJanelia, has released the complete wiring diagram of a male fruit fly’s brain and central nervous system — the largest brain map by number of proofread neurons to date. So, why do we care so much about a tiny

We’re launching AlphaGenome Atlas: an AI-powered searchable database mapping the predicted impact of all 9 billion possible single-letter DNA changes. Here’s how it could help researchers better under

We’re launching AlphaGenome Atlas: an AI-powered searchable database mapping the predicted impact of all 9 billion possible single-letter DNA changes. Here’s how it could help researchers better understand our biology 🧵 https://t.co/SABFZW5SiR

Introducing WeatherNext 3️⃣— our most advanced global weather AI model yet from @GoogleDeepmind and @GoogleResearch With prediction capabilities that are up to 5x sharper than WeatherNext 2, the model

Introducing WeatherNext 3️⃣— our most advanced global weather AI model yet from @GoogleDeepmind and @GoogleResearch With prediction capabilities that are up to 5x sharper than WeatherNext 2, the model generates a forecast with high spatial resolution in order to catch https://t.co/XNDjd7eoHw

WeatherNext 3 is a major breakthrough in how we forecast global weather. ⛅ Developed with @GoogleResearch, the model learns directly from real-world, real-time observations to give more localized high

WeatherNext 3 is a major breakthrough in how we forecast global weather. ⛅ Developed with @GoogleResearch, the model learns directly from real-world, real-time observations to give more localized highly accurate predictions faster. 🧵 https://t.co/iI8c6uEN4n

Two new Gemini models are here to help scale your AI agents and secure code: 🔘 3.8 Flash: our most intelligent model yet with significant gains from 3.7 Flash across software engineering, agentic tas

Two new Gemini models are here to help scale your AI agents and secure code: 🔘 3.8 Flash: our most intelligent model yet with significant gains from 3.7 Flash across software engineering, agentic tasks, and multi-step reasoning. 🔘 3.8 Flash Cyber: our most capable https://t.co/EEJDIMhRwp

We’re bringing agentic video understanding to our latest Gemini models. They can now analyze videos with better accuracy while using up to 88% fewer tokens. 🧵 https://t.co/ZTjlaLk7tX

Let Confidence Change, Not the Prediction: Prediction-Preserving Repair for Post-hoc Calibration

Post-hoc calibration corrects reported confidence, yet a multiclass calibrator can also change the associated top-1 prediction. Accuracy captures only the net effect of these changes on correctness, not how often predictions change; the Top-1 Prediction Change Rate (TPCR) instead measures this frequency. We propose Calibrator-Output Repair for Top-1 Decision Preservation (CORD), the first post-fit adapter to impose exact prediction preservation by repairing the full calibrated probability vector. From the original and calibrated outputs alone, CORD determines the mass assigned to the original top-1. The calibrated conditional distribution allocates the remaining mass over the other classes, yielding a repaired vector whose own argmax recovers the original prediction. On the calibration split, CORD coordinates the repaired masses to retain the calibrated outputs' mean mass on original predictions whenever attainable. The adapter alters neither the fitted calibrator nor its direct output, fits no additional supervised map, and requires no user- or validation-tuned hyperparameter. Across CIFAR-10/100 and ImageNet-1K, CORD attains zero TPCR by construction and lowers mean ECE, NLL, and Brier relative to the corresponding direct outputs in every dataset; paired gains persist under distribution shift and across calibration-set sizes. CORD thus removes the preservation constraint from calibrator fitting and assigns exact recovery of the original decision to subsequent output repair. Our code is available at https://github.com/labhai/CORD.