# Data Mining & Modeling

Published articles for Data Mining & Modeling.

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## Bypassing inference bottlenecks: Accelerating complex AI search with Retrieve-for-Train

DevFeed: [Bypassing inference bottlenecks: Accelerating complex AI search with Retrieve-for-Train](<https://devfeed.tech/articles/bypassing-inference-bottlenecks-accelerating-complex-ai-search-with-retrieve-for-train-26972.md>)

Original publisher: [Read original article](<https://research.google/blog/bypassing-inference-bottlenecks-accelerating-complex-ai-search-with-retrieve-for-train/>)

Published: 2026-09-15T20:00:35Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [AI search](<https://devfeed.tech/topics/ai-search.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Google](<https://devfeed.tech/topics/google.md>), [LLMs](<https://devfeed.tech/topics/llms.md>), [Algorithms & Theory](<https://devfeed.tech/topics/algorithms-theory.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-search](<https://devfeed.tech/tags/ai-search.md>), [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [data-mining-modeling](<https://devfeed.tech/tags/data-mining-modeling.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [google](<https://devfeed.tech/tags/google.md>), [icml](<https://devfeed.tech/tags/icml.md>), [icml-2026](<https://devfeed.tech/tags/icml-2026.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [research](<https://devfeed.tech/tags/research.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [rl](<https://devfeed.tech/tags/rl.md>), [search](<https://devfeed.tech/tags/search.md>)

### AI overview

Google Research presents Retrieve-for-Train, a framework that uses offline reinforcement learning to compile reward-aligned query fan-outs into training data for a lightweight diffusion retriever. The approach is intended to produce diverse, complementary, and coherent search-result sets in a single inference pass, reducing reliance on expensive inference-time reasoning.

### Source excerpt

Algorithms & Theory

## The power of collaboration: How we can reduce traffic congestion

DevFeed: [The power of collaboration: How we can reduce traffic congestion](<https://devfeed.tech/articles/the-power-of-collaboration-how-we-can-reduce-traffic-congestion-6894.md>)

Original publisher: [Read original article](<https://research.google/blog/the-power-of-collaboration-how-we-can-reduce-traffic-congestion/>)

Published: 2026-07-07T16:42:08Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [Google](<https://devfeed.tech/topics/google.md>), [Algorithms, Complexity](<https://devfeed.tech/topics/algorithms-complexity.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Routing (disambiguation)](<https://devfeed.tech/topics/routing.md>), [App](<https://devfeed.tech/topics/app.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [algorithms](<https://devfeed.tech/tags/algorithms.md>), [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [climate-sustainability](<https://devfeed.tech/tags/climate-sustainability.md>), [collaboration](<https://devfeed.tech/tags/collaboration.md>), [data-mining-modeling](<https://devfeed.tech/tags/data-mining-modeling.md>), [driving](<https://devfeed.tech/tags/driving.md>), [google](<https://devfeed.tech/tags/google.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [reduce](<https://devfeed.tech/tags/reduce.md>), [routing](<https://devfeed.tech/tags/routing.md>), [transportation](<https://devfeed.tech/tags/transportation.md>), [us](<https://devfeed.tech/tags/us.md>)

### AI overview

Google Research describes a large-scale routing experiment in 10 major US cities. By guiding a small fraction of trips toward alternative routes, the study reports improved overall traffic conditions, faster driving speeds, and reduced emissions.

### Source excerpt

Algorithms & Theory

## Catalyzing scientific impact through global partnerships and open resources

DevFeed: [Catalyzing scientific impact through global partnerships and open resources](<https://devfeed.tech/articles/catalyzing-scientific-impact-through-global-partnerships-and-open-resources-6753.md>)

Original publisher: [Read original article](<https://research.google/blog/catalyzing-scientific-impact-through-global-partnerships-and-open-resources/>)

Published: 2026-05-01T16:37:00Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [Open Source](<https://devfeed.tech/topics/open-source.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Google](<https://devfeed.tech/topics/google.md>), [Transformer architecture](<https://devfeed.tech/topics/transformer-architecture.md>)

Tags: [apis](<https://devfeed.tech/tags/apis.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [australia](<https://devfeed.tech/tags/australia.md>), [collaboration](<https://devfeed.tech/tags/collaboration.md>), [community](<https://devfeed.tech/tags/community.md>), [data-mining-modeling](<https://devfeed.tech/tags/data-mining-modeling.md>), [developers](<https://devfeed.tech/tags/developers.md>), [general-science](<https://devfeed.tech/tags/general-science.md>), [global](<https://devfeed.tech/tags/global.md>), [google](<https://devfeed.tech/tags/google.md>), [health-bioscience](<https://devfeed.tech/tags/health-bioscience.md>), [india](<https://devfeed.tech/tags/india.md>), [japan](<https://devfeed.tech/tags/japan.md>), [open](<https://devfeed.tech/tags/open.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [open-source-models-datasets](<https://devfeed.tech/tags/open-source-models-datasets.md>), [partnerships](<https://devfeed.tech/tags/partnerships.md>), [publications](<https://devfeed.tech/tags/publications.md>), [research](<https://devfeed.tech/tags/research.md>), [science](<https://devfeed.tech/tags/science.md>), [software](<https://devfeed.tech/tags/software.md>), [technology](<https://devfeed.tech/tags/technology.md>), [transformer-architecture](<https://devfeed.tech/tags/transformer-architecture.md>)

### AI overview

Google Research describes an open-science approach centered on responsible research, open-source software, open-access datasets, and partnerships with scientific organizations and consortia worldwide. It highlights shared technologies and datasets, including Transformer architecture and specialized models used across fields such as medicine, genomics, neuroscience, climate, and energy.

### Source excerpt

Data Mining & Modeling

## Four ways Google Research scientists have been using Empirical Research Assistance

DevFeed: [Four ways Google Research scientists have been using Empirical Research Assistance](<https://devfeed.tech/articles/four-ways-google-research-scientists-have-been-using-empirical-research-assistance-6778.md>)

Original publisher: [Read original article](<https://research.google/blog/four-ways-google-research-scientists-have-been-using-empirical-research-assistance/>)

Published: 2026-04-29T21:07:00Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [Google](<https://devfeed.tech/topics/google.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Data Mining & Modeling](<https://devfeed.tech/topics/data-mining-modeling.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Monitoring](<https://devfeed.tech/topics/monitoring.md>), [real-time](<https://devfeed.tech/topics/real-time.md>)

Tags: [accelerate](<https://devfeed.tech/tags/accelerate.md>), [ai](<https://devfeed.tech/tags/ai.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [data](<https://devfeed.tech/tags/data.md>), [data-mining-modeling](<https://devfeed.tech/tags/data-mining-modeling.md>), [experiment](<https://devfeed.tech/tags/experiment.md>), [general-science](<https://devfeed.tech/tags/general-science.md>), [generate](<https://devfeed.tech/tags/generate.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [go](<https://devfeed.tech/tags/go.md>), [google](<https://devfeed.tech/tags/google.md>), [insights](<https://devfeed.tech/tags/insights.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [research](<https://devfeed.tech/tags/research.md>), [science](<https://devfeed.tech/tags/science.md>), [software](<https://devfeed.tech/tags/software.md>)

### AI overview

Google Research scientists are using Empirical Research Assistance (ERA) to develop expert-level empirical software and explore AI-assisted scientific discovery across epidemiology, geospatial analysis, cosmology, atmospheric monitoring, and neuroscience. The article highlights ERA's use in computational modeling, interpretable solutions, and real-time forecasts for COVID-19, influenza, and RSV.

### Source excerpt

Data Mining & Modeling

## Introducing GIST: The next stage in smart sampling

DevFeed: [Introducing GIST: The next stage in smart sampling](<https://devfeed.tech/articles/introducing-gist-the-next-stage-in-smart-sampling-6823.md>)

Original publisher: [Read original article](<https://research.google/blog/introducing-gist-the-next-stage-in-smart-sampling/>)

Published: 2026-01-23T17:46:00Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [Algorithms & Theory](<https://devfeed.tech/topics/algorithms-theory.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [data](<https://devfeed.tech/topics/data.md>), [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>), [Algorithm](<https://devfeed.tech/topics/algorithm.md>), [Google](<https://devfeed.tech/topics/google.md>), [NeurIPS](<https://devfeed.tech/topics/neurips.md>)

Tags: [algorithms](<https://devfeed.tech/tags/algorithms.md>), [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [classification](<https://devfeed.tech/tags/classification.md>), [computer-vision](<https://devfeed.tech/tags/computer-vision.md>), [data](<https://devfeed.tech/tags/data.md>), [data-mining-modeling](<https://devfeed.tech/tags/data-mining-modeling.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [diversity](<https://devfeed.tech/tags/diversity.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [google](<https://devfeed.tech/tags/google.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [neurips](<https://devfeed.tech/tags/neurips.md>), [points](<https://devfeed.tech/tags/points.md>), [research](<https://devfeed.tech/tags/research.md>), [systems](<https://devfeed.tech/tags/systems.md>), [training](<https://devfeed.tech/tags/training.md>), [vision](<https://devfeed.tech/tags/vision.md>)

### AI overview

Google Research introduces GIST, an algorithm for selecting a high-quality subset of data for model training. It balances diversity, which reduces redundancy, with utility, which favors relevant and informative data, and provides a mathematical guarantee about solution quality.

### Source excerpt

Algorithms & Theory

## DS-STAR: A state-of-the-art versatile data science agent

DevFeed: [DS-STAR: A state-of-the-art versatile data science agent](<https://devfeed.tech/articles/ds-star-a-state-of-the-art-versatile-data-science-agent-6761.md>)

Original publisher: [Read original article](<https://research.google/blog/ds-star-a-state-of-the-art-versatile-data-science-agent/>)

Published: 2025-11-06T17:50:36Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [Data Science](<https://devfeed.tech/topics/data-science.md>), [Data Mining & Modeling](<https://devfeed.tech/topics/data-mining-modeling.md>), [data-processing](<https://devfeed.tech/topics/data-processing.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [code](<https://devfeed.tech/tags/code.md>), [data-mining-modeling](<https://devfeed.tech/tags/data-mining-modeling.md>), [data-processing](<https://devfeed.tech/tags/data-processing.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [google](<https://devfeed.tech/tags/google.md>), [json](<https://devfeed.tech/tags/json.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [markdown](<https://devfeed.tech/tags/markdown.md>), [natural-language-processing](<https://devfeed.tech/tags/natural-language-processing.md>), [statistics](<https://devfeed.tech/tags/statistics.md>), [verification](<https://devfeed.tech/tags/verification.md>), [visualization](<https://devfeed.tech/tags/visualization.md>)

### AI overview

DS-STAR is a data science agent from Google Cloud that automates tasks including statistical analysis, visualization, and data wrangling across varied data types. It uses file analysis, LLM-based verification, and iterative sequential planning to produce verifiable insights, achieving state-of-the-art results on the DABStep, KramaBench, and DA-Code benchmarks.

### Source excerpt

Data Mining & Modeling