Research lab
Google's research organisation, which publishes papers and blog posts on machine learning, systems, graphics and human-computer interaction.
Google Research is widening experimental access to its Geospatial Reasoning agent, which its report tests in four configurations on two Gemini models. Access runs through Ask Google Earth and an A2A service.
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Google released Deep Review publicly on October 14, extending an agentic verifier tested on more than 16,000 manuscripts to Google Docs and presentations. Google says AI-assisted drafting has outrun human capacity to verify.
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Christopher Monroe's Duke team simulated gluon string breaking on trapped ions and got results similar to earlier neutral-atom and superconducting work. The team found the break starts at the edges, contrary to Schwinger's predictions.
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Google's CAPS workshop report, from more than 50 researchers, proposes a policy engine that checks agent actions against context norms before they run. For teams shipping agents, privacy enforcement would move from manual permission grants to rules a language model generates for each task, a design the authors still present as open research.
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Two decades after Janelia proposed the project, the adult fly connectome is finished. The 1,000-fold efficiency gain credited with making it possible is measured against the institute's own 2008 forecast.
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Google Research now trains federated models inside attested TEEs and logs each access policy to the public Rekor log, with Gboard already switched over. The server-side noise step that users once had to take on trust becomes code an outside auditor can check against open source.
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Google Research says the fully fine-tuned version of its Diffusion Controller posted a 90% win rate over the base image model. The lighter add-on meant for closed models is reported only as beating the industry standard for human preference.
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LLM Gateway's own benchmark found Smart Routing cost 33.2% less than a premium model and 35.6 times as much as a low-cost one that stayed competitive. With the premium saving not statistically established on 80 prompts, a fixed cheap model is the baseline Smart Routing has to beat.
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The wiring of a male fruit fly's brain and nerve cord is now public, and the payload for anyone planning a bigger map is a claimed thousandfold gain in connectome efficiency whose denominator nobody has stated.
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Google's new model trains on sparse station observations instead of six-hour-old physics output, which is what buys the 5 km surface grid. Wind aloft still comes out at 25 km, the same as WeatherNext 2.
Perspective Coverage
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Ramsauer and colleagues proved the equality in 2020, three years after attention was published. It holds for a single retrieval step inside a forward pass, and the capacity figures quoted alongside it come from a different model.
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Google Research has published a C++ instance generator for the middle-mile leg, where one shipment changes vehicles several times and can take a week to arrive. Single-day vehicle routing benchmarks do not describe that.
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Google Research describes a model that assembles the interface as the request arrives, held to a game structure of progressively harder challenges, with more than 30 teacher-reviewed samples out for middle and high school STEM.
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Retrieve-for-Train runs reinforcement learning once against a fixed corpus and distils the winning sub-query sets into a lightweight diffusion model. Adopting it puts a retrain schedule on whoever owns the catalogue.
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A Google XR team fine-tuned Gemma-3 models on data generated answer-first and says they match proprietary LLMs on tools they never trained on. The post's cost and pass-rate claims carry no numbers.
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Google DeepMind says the model resolves temperature at 5 kilometres and updates every hour, and it hands the headline accuracy claim to an outside evaluator, Brightband, whose scores the announcement does not publish.
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The model feeds Search, Maps and Gemini and ships on Google Cloud, so anyone whose product depends on a forecast now has a supplier to choose and an accuracy claim from a startup's benchmark to read closely.
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Google's new forecaster is 330m parameters trained on a trillion time points. In zero-shot multivariate mode it anticipates a 20% promotion lift that a univariate model misses. That is most of what an in-house demand model does.
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Google's new 330 million parameter forecaster takes covariates and related series zero-shot in one forward pass, which puts a lot of bespoke per-SKU plumbing up for review, assuming the accuracy transfers to your own series.
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Google Research argues the weeks in geospatial modelling go into curation rather than modelling, and that an agent can absorb them. The engineering worth copying is that the orchestrating model plans over pointers and never reads the data.
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