# The latest research from Google

Published articles for The latest research from Google.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## 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

## ToolGrad: Efficient tool-use dataset generation with textual "gradients"

DevFeed: [ToolGrad: Efficient tool-use dataset generation with textual "gradients"](<https://devfeed.tech/articles/toolgrad-efficient-tool-use-dataset-generation-with-textual-gradients-6902.md>)

Original publisher: [Read original article](<https://research.google/blog/toolgrad-efficient-tool-use-dataset-generation-with-textual-gradients/>)

Published: 2026-09-10T22:50:22Z

Content type: article

Language: en

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

Topics: [dataset](<https://devfeed.tech/topics/dataset.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [LLM Techniques](<https://devfeed.tech/topics/llm-techniques.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [cost](<https://devfeed.tech/tags/cost.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [generation](<https://devfeed.tech/tags/generation.md>), [llm](<https://devfeed.tech/tags/llm.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [natural-language-processing](<https://devfeed.tech/tags/natural-language-processing.md>), [prompt-engineering](<https://devfeed.tech/tags/prompt-engineering.md>), [tool](<https://devfeed.tech/tags/tool.md>)

### AI overview

ToolGrad generates tool-use chains before deriving corresponding user queries, aiming to create complex training data for LLM tool use more efficiently and at lower cost than exploration-based approaches.

### Source excerpt

Machine Intelligence

## Transfer learning for genomic prediction in underrepresented populations

DevFeed: [Transfer learning for genomic prediction in underrepresented populations](<https://devfeed.tech/articles/transfer-learning-for-genomic-prediction-in-underrepresented-populations-6915.md>)

Original publisher: [Read original article](<https://research.google/blog/transfer-learning-for-genomic-prediction-in-underrepresented-populations/>)

Published: 2026-09-03T18:20:31Z

Content type: article

Language: en

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

Topics: [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Model Development](<https://devfeed.tech/topics/model-development.md>), [Google](<https://devfeed.tech/topics/google.md>), [datasets](<https://devfeed.tech/topics/datasets.md>)

Tags: [datasets](<https://devfeed.tech/tags/datasets.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [general-science](<https://devfeed.tech/tags/general-science.md>), [google](<https://devfeed.tech/tags/google.md>), [healthcare](<https://devfeed.tech/tags/healthcare.md>), [learning](<https://devfeed.tech/tags/learning.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [research](<https://devfeed.tech/tags/research.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

Google Research evaluates transfer learning for polygenic risk-score prediction across populations. European-cohort transfer learning improves prediction for small underrepresented target cohorts but can reduce accuracy as target cohorts grow, particularly for population-specific traits.

### Source excerpt

General Science

## A connectomics milestone: Mapping the complete male fruit fly brain

DevFeed: [A connectomics milestone: Mapping the complete male fruit fly brain](<https://devfeed.tech/articles/a-connectomics-milestone-mapping-the-complete-male-fruit-fly-brain-6737.md>)

Original publisher: [Read original article](<https://research.google/blog/a-connectomics-milestone-mapping-the-complete-male-fruit-fly-brain/>)

Published: 2026-09-03T16:00:03Z

Content type: article

Language: en

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

Topics: [datasets](<https://devfeed.tech/topics/datasets.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [general-science](<https://devfeed.tech/tags/general-science.md>), [google](<https://devfeed.tech/tags/google.md>), [health-bioscience](<https://devfeed.tech/tags/health-bioscience.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [open-source-models-datasets](<https://devfeed.tech/tags/open-source-models-datasets.md>), [research](<https://devfeed.tech/tags/research.md>), [science](<https://devfeed.tech/tags/science.md>)

### AI overview

Google Research describes a complete wiring map of the male fruit fly's brain and central nervous system, containing over 166,000 neurons and 125 million synaptic connections. The connectome was produced through a decade-long partnership using computing and AI, and is available to explore and download via Neuroglancer.

### Source excerpt

General Science

## Mapping global methane emissions from space with deep learning

DevFeed: [Mapping global methane emissions from space with deep learning](<https://devfeed.tech/articles/mapping-global-methane-emissions-from-space-with-deep-learning-6833.md>)

Original publisher: [Read original article](<https://research.google/blog/mapping-global-methane-emissions-from-space-with-deep-learning/>)

Published: 2026-09-01T18:40:00Z

Content type: article

Language: en

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

Topics: [Deep neural networks](<https://devfeed.tech/topics/deep-neural-networks.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [climate-sustainability](<https://devfeed.tech/tags/climate-sustainability.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [earth-ai](<https://devfeed.tech/tags/earth-ai.md>), [framework](<https://devfeed.tech/tags/framework.md>), [global](<https://devfeed.tech/tags/global.md>), [google](<https://devfeed.tech/tags/google.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [mapping](<https://devfeed.tech/tags/mapping.md>), [nasa](<https://devfeed.tech/tags/nasa.md>), [research](<https://devfeed.tech/tags/research.md>), [space](<https://devfeed.tech/tags/space.md>)

### AI overview

The article presents MAPL-EMIT, a deep-learning framework for automating global detection, enhancement prediction, and source estimation of methane plumes from EMIT hyperspectral satellite measurements.

### Source excerpt

Climate & Sustainability

## TimesFM-3: A zero-shot foundation model for multivariate forecasting

DevFeed: [TimesFM-3: A zero-shot foundation model for multivariate forecasting](<https://devfeed.tech/articles/timesfm-3-a-zero-shot-foundation-model-for-multivariate-forecasting-6898.md>)

Original publisher: [Read original article](<https://research.google/blog/timesfm-3-a-zero-shot-foundation-model-for-multivariate-forecasting/>)

Published: 2026-08-31T17:19:40Z

Content type: article

Language: en

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

Topics: [foundation-models](<https://devfeed.tech/topics/foundation-models.md>), [Time Series](<https://devfeed.tech/topics/time-series.md>), [Google](<https://devfeed.tech/topics/google.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Transformer architecture](<https://devfeed.tech/topics/transformer-architecture.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [generalization in machine learning](<https://devfeed.tech/topics/generalization-in-machine-learning.md>)

Tags: [data-management](<https://devfeed.tech/tags/data-management.md>), [features](<https://devfeed.tech/tags/features.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [foundation-models](<https://devfeed.tech/tags/foundation-models.md>), [google](<https://devfeed.tech/tags/google.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [model](<https://devfeed.tech/tags/model.md>), [product](<https://devfeed.tech/tags/product.md>), [time-series](<https://devfeed.tech/tags/time-series.md>), [transformer-architecture](<https://devfeed.tech/tags/transformer-architecture.md>), [zero-shot](<https://devfeed.tech/tags/zero-shot.md>)

### AI overview

Google Research introduces TimesFM-3, a 330-million-parameter time-series foundation model designed for accurate multivariate forecasting in a single forward pass. Pre-trained on more than one trillion real-world and synthetic time points, it jointly models coevolving series and external covariates in zero-shot settings without task-specific fine-tuning.

### Source excerpt

Data Management

## Planetary prediction engine: Automating global models via Earth AI

DevFeed: [Planetary prediction engine: Automating global models via Earth AI](<https://devfeed.tech/articles/planetary-prediction-engine-automating-global-models-via-earth-ai-6846.md>)

Original publisher: [Read original article](<https://research.google/blog/planetary-prediction-engine-automating-global-models-via-earth-ai/>)

Published: 2026-08-27T17:37:00Z

Content type: article

Language: en

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

Topics: [Earth AI](<https://devfeed.tech/topics/earth-ai.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [data-engineering](<https://devfeed.tech/topics/data-engineering.md>), [Google](<https://devfeed.tech/topics/google.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [AI Development](<https://devfeed.tech/topics/ai-development.md>), [Feature Engineering](<https://devfeed.tech/topics/feature-engineering.md>), [Model Development](<https://devfeed.tech/topics/model-development.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [analytics](<https://devfeed.tech/tags/analytics.md>), [data-engineering](<https://devfeed.tech/tags/data-engineering.md>), [earth-ai](<https://devfeed.tech/tags/earth-ai.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [feature-engineering](<https://devfeed.tech/tags/feature-engineering.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [google](<https://devfeed.tech/tags/google.md>), [insights](<https://devfeed.tech/tags/insights.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [mapping](<https://devfeed.tech/tags/mapping.md>), [research](<https://devfeed.tech/tags/research.md>), [training](<https://devfeed.tech/tags/training.md>), [validation](<https://devfeed.tech/tags/validation.md>), [workflow](<https://devfeed.tech/tags/workflow.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

### AI overview

Google Research introduces the Planetary Prediction Engine, an experimental Earth AI capability that autonomously performs geospatial data discovery, cleanup, feature engineering, model training, evaluation, and report generation from natural-language queries. The system targets applications including public health, food security, environmental risk, and socioeconomic analysis, reducing the stated workflow from weeks of manual data engineering to minutes.

### Source excerpt

Earth AI

## GlucoFM: Foundation model for continuous glucose monitoring

DevFeed: [GlucoFM: Foundation model for continuous glucose monitoring](<https://devfeed.tech/articles/glucofm-foundation-model-for-continuous-glucose-monitoring-6793.md>)

Original publisher: [Read original article](<https://research.google/blog/glucofm-foundation-model-for-continuous-glucose-monitoring/>)

Published: 2026-08-26T18:42:43Z

Content type: article

Language: en

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

Topics: [foundation-models](<https://devfeed.tech/topics/foundation-models.md>), [Google](<https://devfeed.tech/topics/google.md>), [data](<https://devfeed.tech/topics/data.md>), [datasets](<https://devfeed.tech/topics/datasets.md>)

Tags: [data](<https://devfeed.tech/tags/data.md>), [foundation-models](<https://devfeed.tech/tags/foundation-models.md>), [google](<https://devfeed.tech/tags/google.md>), [health-bioscience](<https://devfeed.tech/tags/health-bioscience.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [model](<https://devfeed.tech/tags/model.md>), [research](<https://devfeed.tech/tags/research.md>)

### AI overview

GlucoFM is a lightweight, self-supervised foundation model for continuous glucose monitoring. Its dual-stream design separates slower glycemic trends from short-term deviations while preserving time-of-day and missingness information. Evaluated across four cohorts and seven clinical prediction tasks, it achieved higher average PR-AUC than the evaluated GluFormer variant.

### Source excerpt

Health & Bioscience

## AgentHands: Generating interactive hand gestures for spatially grounded agent conversations in XR

DevFeed: [AgentHands: Generating interactive hand gestures for spatially grounded agent conversations in XR](<https://devfeed.tech/articles/agenthands-generating-interactive-hand-gestures-for-spatially-grounded-agent-conversations-in-xr-6747.md>)

Original publisher: [Read original article](<https://research.google/blog/agenthands-generating-interactive-hand-gestures-for-spatially-grounded-agent-conversations-in-xr/>)

Published: 2026-08-25T19:10:59Z

Content type: article

Language: en

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

Topics: [Human-Computer Interaction and Visualization](<https://devfeed.tech/topics/human-computer-interaction-and-visualization.md>), [AI Bots](<https://devfeed.tech/topics/ai-bots.md>)

Tags: [3d](<https://devfeed.tech/tags/3d.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [android-xr](<https://devfeed.tech/tags/android-xr.md>), [human-computer-interaction-and-visualization](<https://devfeed.tech/tags/human-computer-interaction-and-visualization.md>), [llm](<https://devfeed.tech/tags/llm.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [research-prototype](<https://devfeed.tech/tags/research-prototype.md>)

### AI overview

AgentHands is an LLM-powered XR research prototype that adds synchronized hand gestures to conversational agents for spatially grounded guidance in physical tasks.

### Source excerpt

Human-Computer Interaction and Visualization

## An AI tool for prioritizing candidate biomarkers from wearable sensor data

DevFeed: [An AI tool for prioritizing candidate biomarkers from wearable sensor data](<https://devfeed.tech/articles/an-ai-tool-for-prioritizing-candidate-biomarkers-from-wearable-sensor-data-6749.md>)

Original publisher: [Read original article](<https://research.google/blog/an-ai-tool-for-prioritizing-candidate-biomarkers-from-wearable-sensor-data/>)

Published: 2026-08-21T17:02:24Z

Content type: article

Language: en

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

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Generative AI](<https://devfeed.tech/topics/generative-ai.md>), [data](<https://devfeed.tech/topics/data.md>), [Time Series](<https://devfeed.tech/topics/time-series.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Temporal data](<https://devfeed.tech/topics/temporal-data.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [Model Development](<https://devfeed.tech/topics/model-development.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [analysis](<https://devfeed.tech/tags/analysis.md>), [data](<https://devfeed.tech/tags/data.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [health-bioscience](<https://devfeed.tech/tags/health-bioscience.md>), [memory](<https://devfeed.tech/tags/memory.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [research](<https://devfeed.tech/tags/research.md>), [series](<https://devfeed.tech/tags/series.md>), [time-series](<https://devfeed.tech/tags/time-series.md>), [training](<https://devfeed.tech/tags/training.md>), [validation](<https://devfeed.tech/tags/validation.md>), [workflow](<https://devfeed.tech/tags/workflow.md>)

### AI overview

The Biomarker Discovery Framework is a supervised multi-agent system for prioritizing biomarker candidates from wearable-sensor data. It combines hypothesis generation, statistical analysis, model training, adversarial validation, and literature-grounded reasoning in a traceable six-phase workflow. Across three cohorts, it recovered known clinical signals, found convergent biomarkers across independent datasets, and improved downstream prediction when demographic features were included.

### Source excerpt

Generative AI

## How mobility gives language models a deeper understanding of place

DevFeed: [How mobility gives language models a deeper understanding of place](<https://devfeed.tech/articles/how-mobility-gives-language-models-a-deeper-understanding-of-place-6814.md>)

Original publisher: [Read original article](<https://research.google/blog/how-mobility-gives-language-models-a-deeper-understanding-of-place/>)

Published: 2026-08-21T10:54:00Z

Content type: article

Language: en

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

Topics: [Language models](<https://devfeed.tech/topics/language-models.md>), [AI Models](<https://devfeed.tech/topics/ai-models.md>), [data](<https://devfeed.tech/topics/data.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-models](<https://devfeed.tech/tags/ai-models.md>), [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [data](<https://devfeed.tech/tags/data.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [earth-ai](<https://devfeed.tech/tags/earth-ai.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [google](<https://devfeed.tech/tags/google.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [mobility](<https://devfeed.tech/tags/mobility.md>), [model](<https://devfeed.tech/tags/model.md>), [models](<https://devfeed.tech/tags/models.md>), [points](<https://devfeed.tech/tags/points.md>), [research](<https://devfeed.tech/tags/research.md>), [vector](<https://devfeed.tech/tags/vector.md>)

### AI overview

Google Research introduces Mobility-Embedded POIs (ME-POIs), a framework that combines language-model-based text representations of places with aggregated, anonymized mobility patterns. The resulting embeddings capture both a place's identity and its changing functional activity, improving predictions such as visit intent, price level, opening hours, and busyness.

### Source excerpt

Algorithms & Theory

## Seeing beyond BMI: Estimating cardiometabolic risk with smartphone imagery

DevFeed: [Seeing beyond BMI: Estimating cardiometabolic risk with smartphone imagery](<https://devfeed.tech/articles/seeing-beyond-bmi-estimating-cardiometabolic-risk-with-smartphone-imagery-6866.md>)

Original publisher: [Read original article](<https://research.google/blog/seeing-beyond-bmi-estimating-cardiometabolic-risk-with-smartphone-imagery/>)

Published: 2026-08-17T10:34:00Z

Content type: article

Language: en

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

Topics: [Deep neural networks](<https://devfeed.tech/topics/deep-neural-networks.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [general-science](<https://devfeed.tech/tags/general-science.md>), [google](<https://devfeed.tech/tags/google.md>), [health](<https://devfeed.tech/tags/health.md>), [health-bioscience](<https://devfeed.tech/tags/health-bioscience.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [science](<https://devfeed.tech/tags/science.md>)

### AI overview

Google Research presents PhotoScan, a deep learning approach that estimates body composition from smartphone photos and predicts insulin resistance with accuracy comparable to DXA scans in a clinical research setting. The article explains how body-composition measures such as fat distribution and visceral fat may complement wearable data for earlier cardiometabolic risk assessment.

### Source excerpt

General Science

## Empty shelves or lost keys? Recall is the bottleneck for parametric factuality

DevFeed: [Empty shelves or lost keys? Recall is the bottleneck for parametric factuality](<https://devfeed.tech/articles/empty-shelves-or-lost-keys-recall-is-the-bottleneck-for-parametric-factuality-6767.md>)

Original publisher: [Read original article](<https://research.google/blog/empty-shelves-or-lost-keys-recall-is-the-bottleneck-for-parametric-factuality/>)

Published: 2026-08-12T09:51:00Z

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>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Hallucination detection](<https://devfeed.tech/topics/hallucination-detection.md>), [AI Inference](<https://devfeed.tech/topics/ai-inference.md>)

Tags: [benchmark](<https://devfeed.tech/tags/benchmark.md>), [classification](<https://devfeed.tech/tags/classification.md>), [errors](<https://devfeed.tech/tags/errors.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [google](<https://devfeed.tech/tags/google.md>), [inference](<https://devfeed.tech/tags/inference.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [natural-language-processing](<https://devfeed.tech/tags/natural-language-processing.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [research](<https://devfeed.tech/tags/research.md>)

### AI overview

This Google Research article argues that many factual errors in frontier large language models arise from recall failures rather than missing encoded knowledge. It presents knowledge profiling, which separates encoding, recall, and recognition, and introduces WikiProfile, a benchmark of 2,150 Wikipedia-derived facts tested through questions targeting these abilities.

### Source excerpt

Generative AI

## Advancing AMIE towards expert-level audio-visual clinical consultations

DevFeed: [Advancing AMIE towards expert-level audio-visual clinical consultations](<https://devfeed.tech/articles/advancing-amie-towards-expert-level-audio-visual-clinical-consultations-6746.md>)

Original publisher: [Read original article](<https://research.google/blog/advancing-amie-towards-expert-level-audio-visual-clinical-consultations/>)

Published: 2026-08-11T17:04:00Z

Content type: article

Language: en

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

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Google](<https://devfeed.tech/topics/google.md>), [real-time](<https://devfeed.tech/topics/real-time.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [audio](<https://devfeed.tech/tags/audio.md>), [communication](<https://devfeed.tech/tags/communication.md>), [health-bioscience](<https://devfeed.tech/tags/health-bioscience.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [partnership](<https://devfeed.tech/tags/partnership.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [research](<https://devfeed.tech/tags/research.md>), [video](<https://devfeed.tech/tags/video.md>)

### AI overview

Google Research describes an advance to AMIE, a medical AI system designed for real-time video consultations. In simulated consultations, AMIE demonstrated expert-level performance in a randomized controlled study while integrating spoken history with visual and auditory clinical cues.

### Source excerpt

Health & Bioscience

## Science One Framework: A verifiable autonomous research framework via Chain-of-Evidence

DevFeed: [Science One Framework: A verifiable autonomous research framework via Chain-of-Evidence](<https://devfeed.tech/articles/science-one-framework-a-verifiable-autonomous-research-framework-via-chain-of-evidence-6864.md>)

Original publisher: [Read original article](<https://research.google/blog/science-one-framework-a-verifiable-autonomous-research-framework-via-chain-of-evidence/>)

Published: 2026-07-30T20:36:36Z

Content type: article

Language: en

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

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Google](<https://devfeed.tech/topics/google.md>), [AI-generated research reports](<https://devfeed.tech/topics/ai-generated-research-reports.md>), [Large language models (LLMs)](<https://devfeed.tech/topics/large-language-models-llms.md>), [Hallucination detection](<https://devfeed.tech/topics/hallucination-detection.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [autonomous-agents](<https://devfeed.tech/tags/autonomous-agents.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [general-science](<https://devfeed.tech/tags/general-science.md>), [google](<https://devfeed.tech/tags/google.md>), [hallucinations](<https://devfeed.tech/tags/hallucinations.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [natural-language-processing](<https://devfeed.tech/tags/natural-language-processing.md>), [research](<https://devfeed.tech/tags/research.md>), [research-prototype](<https://devfeed.tech/tags/research-prototype.md>)

### AI overview

Google Research introduces the Science One Framework, an experimental autonomous research prototype built around Chain-of-Evidence. It is designed to make AI-generated research verifiable by linking claims to supporting evidence and by auditing papers against their code and evidence. The article reports that the framework eliminates phantom references and produces fully verifiable scores in the described evaluations.

### Source excerpt

General Science

## SymptomAI: Towards a conversational AI agent for everyday symptom assessment

DevFeed: [SymptomAI: Towards a conversational AI agent for everyday symptom assessment](<https://devfeed.tech/articles/symptomai-towards-a-conversational-ai-agent-for-everyday-symptom-assessment-6882.md>)

Original publisher: [Read original article](<https://research.google/blog/symptomai-towards-a-conversational-ai-agent-for-everyday-symptom-assessment/>)

Published: 2026-07-22T21:32:00Z

Content type: article

Language: en

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

Topics: [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [Chat Bot](<https://devfeed.tech/topics/chatbot.md>), [Google](<https://devfeed.tech/topics/google.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [conversational-ai](<https://devfeed.tech/tags/conversational-ai.md>), [general-science](<https://devfeed.tech/tags/general-science.md>), [google](<https://devfeed.tech/tags/google.md>), [health-bioscience](<https://devfeed.tech/tags/health-bioscience.md>), [healthcare](<https://devfeed.tech/tags/healthcare.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [natural-language-processing](<https://devfeed.tech/tags/natural-language-processing.md>), [research](<https://devfeed.tech/tags/research.md>), [responsible-ai](<https://devfeed.tech/tags/responsible-ai.md>)

### AI overview

Google Research presents SymptomAI, a study of conversational AI agents for everyday symptom interviews and differential-diagnosis assessment. The national-scale study involved 13,917 participants interacting with one of five Gemini Flash 2.0 SymptomAI agents, with performance compared against clinical assessments for research benchmarking.

### Source excerpt

General Science

## Towards a quantum computer that learns from its errors

DevFeed: [Towards a quantum computer that learns from its errors](<https://devfeed.tech/articles/towards-a-quantum-computer-that-learns-from-its-errors-6906.md>)

Original publisher: [Read original article](<https://research.google/blog/towards-a-quantum-computer-that-learns-from-its-errors/>)

Published: 2026-07-22T18:40:21Z

Content type: article

Language: en

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

Topics: [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [autonomous](<https://devfeed.tech/tags/autonomous.md>), [errors](<https://devfeed.tech/tags/errors.md>), [learning](<https://devfeed.tech/tags/learning.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [quantum](<https://devfeed.tech/tags/quantum.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [research](<https://devfeed.tech/tags/research.md>), [rl](<https://devfeed.tech/tags/rl.md>)

### AI overview

Google Research describes a reinforcement-learning framework that uses quantum error detections to continuously adjust control parameters during computation, helping stabilize a quantum computer against drift. The article also discusses quantum error correction and the AlphaQubit neural-network decoder.

### Source excerpt

Machine Intelligence

## Towards demystifying the creativity of diffusion models

DevFeed: [Towards demystifying the creativity of diffusion models](<https://devfeed.tech/articles/towards-demystifying-the-creativity-of-diffusion-models-6909.md>)

Original publisher: [Read original article](<https://research.google/blog/towards-demystifying-the-creativity-of-diffusion-models/>)

Published: 2026-07-15T18:06:00Z

Content type: article

Language: en

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

Topics: [Generative AI](<https://devfeed.tech/topics/generative-ai.md>), [Mathematics](<https://devfeed.tech/topics/mathematics.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [Algorithms & Theory](<https://devfeed.tech/topics/algorithms-theory.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [data](<https://devfeed.tech/tags/data.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [generation](<https://devfeed.tech/tags/generation.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [google](<https://devfeed.tech/tags/google.md>), [iclr](<https://devfeed.tech/tags/iclr.md>), [iclr-2026](<https://devfeed.tech/tags/iclr-2026.md>), [images](<https://devfeed.tech/tags/images.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [model](<https://devfeed.tech/tags/model.md>), [models](<https://devfeed.tech/tags/models.md>), [research](<https://devfeed.tech/tags/research.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

Google Research explains that diffusion models can generate novel data rather than merely memorize training examples. It attributes this creativity to neural networks learning a smoothed score function, which causes denoising to interpolate between training data points along a hidden data manifold.

### Source excerpt

Algorithms & Theory

## SensorFM: Towards a general intelligence and interface for wearable health data

DevFeed: [SensorFM: Towards a general intelligence and interface for wearable health data](<https://devfeed.tech/articles/sensorfm-towards-a-general-intelligence-and-interface-for-wearable-health-data-6868.md>)

Original publisher: [Read original article](<https://research.google/blog/sensorfm-towards-a-general-intelligence-and-interface-for-wearable-health-data/>)

Published: 2026-07-09T09:56:00Z

Content type: article

Language: en

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

Topics: [foundation-models](<https://devfeed.tech/topics/foundation-models.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [data](<https://devfeed.tech/topics/data.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [data](<https://devfeed.tech/tags/data.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [google](<https://devfeed.tech/tags/google.md>), [health](<https://devfeed.tech/tags/health.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [model](<https://devfeed.tech/tags/model.md>), [models](<https://devfeed.tech/tags/models.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [research](<https://devfeed.tech/tags/research.md>), [train](<https://devfeed.tech/tags/train.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

Google Research introduces SensorFM, a large sensor foundation model trained on more than one trillion minutes of de-identified, multimodal wearable data from five million consented participants. The model learns reusable representations of human physiology that transfer across health prediction tasks and support label-efficient adaptation and data infilling.

### Source excerpt

Generative AI

## 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

## Expanding our Heat Resilience data to 50+ global cities

DevFeed: [Expanding our Heat Resilience data to 50+ global cities](<https://devfeed.tech/articles/expanding-our-heat-resilience-data-to-50-global-cities-6771.md>)

Original publisher: [Read original article](<https://research.google/blog/expanding-our-heat-resilience-data-to-50-global-cities/>)

Published: 2026-06-30T17:03:00Z

Content type: article

Language: en

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

Topics: [Earth AI](<https://devfeed.tech/topics/earth-ai.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Google](<https://devfeed.tech/topics/google.md>), [App](<https://devfeed.tech/topics/app.md>), [data](<https://devfeed.tech/topics/data.md>), [Resilience](<https://devfeed.tech/topics/resilience.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [app](<https://devfeed.tech/tags/app.md>), [climate-sustainability](<https://devfeed.tech/tags/climate-sustainability.md>), [data](<https://devfeed.tech/tags/data.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [earth-ai](<https://devfeed.tech/tags/earth-ai.md>), [google](<https://devfeed.tech/tags/google.md>), [heat](<https://devfeed.tech/tags/heat.md>), [mapping](<https://devfeed.tech/tags/mapping.md>), [open](<https://devfeed.tech/tags/open.md>), [open-source-models-datasets](<https://devfeed.tech/tags/open-source-models-datasets.md>), [research](<https://devfeed.tech/tags/research.md>), [resilience](<https://devfeed.tech/tags/resilience.md>), [sustainability](<https://devfeed.tech/tags/sustainability.md>), [tools](<https://devfeed.tech/tags/tools.md>)

### AI overview

Google Research is expanding its building-level rooftop reflectivity dataset to cover more than 50 global cities. The data is available through a high-resolution Heat Resilience Earth Engine App and is intended to help urban planners prioritize cool-roof interventions that reduce heat exposure and protect vulnerable communities.

### Source excerpt

Climate & Sustainability

## Introducing TabFM: A zero-shot foundation model for tabular data

DevFeed: [Introducing TabFM: A zero-shot foundation model for tabular data](<https://devfeed.tech/articles/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data-6829.md>)

Original publisher: [Read original article](<https://research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/>)

Published: 2026-06-30T10:26:00Z

Content type: article

Language: en

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

Topics: [foundation-models](<https://devfeed.tech/topics/foundation-models.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Google](<https://devfeed.tech/topics/google.md>), [Feature Engineering](<https://devfeed.tech/topics/feature-engineering.md>), [Hyperparameter optimization](<https://devfeed.tech/topics/hyperparameter-optimization.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [BigQuery](<https://devfeed.tech/topics/bigquery.md>), [GitHub](<https://devfeed.tech/topics/github.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [AI Inference](<https://devfeed.tech/topics/ai-inference.md>)

Tags: [bigquery](<https://devfeed.tech/tags/bigquery.md>), [classification](<https://devfeed.tech/tags/classification.md>), [data](<https://devfeed.tech/tags/data.md>), [data-management](<https://devfeed.tech/tags/data-management.md>), [feature-engineering](<https://devfeed.tech/tags/feature-engineering.md>), [github](<https://devfeed.tech/tags/github.md>), [google](<https://devfeed.tech/tags/google.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [hyperparameter-optimization](<https://devfeed.tech/tags/hyperparameter-optimization.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [model](<https://devfeed.tech/tags/model.md>), [product](<https://devfeed.tech/tags/product.md>), [zero-shot](<https://devfeed.tech/tags/zero-shot.md>)

### AI overview

Google Research introduces TabFM, a zero-shot foundation model for tabular-data classification and regression. It frames prediction as in-context learning, reducing the need for dataset-specific training, hyperparameter optimization, and feature engineering, with availability through Hugging Face, GitHub, and BigQuery.

### Source excerpt

Data Management

## Accelerating Gemini Nano models on Pixel with frozen Multi-Token Prediction

DevFeed: [Accelerating Gemini Nano models on Pixel with frozen Multi-Token Prediction](<https://devfeed.tech/articles/accelerating-gemini-nano-models-on-pixel-with-frozen-multi-token-prediction-6744.md>)

Original publisher: [Read original article](<https://research.google/blog/accelerating-gemini-nano-models-on-pixel-with-frozen-multi-token-prediction/>)

Published: 2026-06-26T18:30:00Z

Content type: article

Language: en

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

Topics: [AI Inference](<https://devfeed.tech/topics/ai-inference.md>), [On-device AI](<https://devfeed.tech/topics/on-device-ai.md>), [Google](<https://devfeed.tech/topics/google.md>), [LLMs](<https://devfeed.tech/topics/llms.md>), [gemma](<https://devfeed.tech/topics/gemma.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [energy](<https://devfeed.tech/tags/energy.md>), [gemini](<https://devfeed.tech/tags/gemini.md>), [inference](<https://devfeed.tech/tags/inference.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [mobile-systems](<https://devfeed.tech/tags/mobile-systems.md>), [natural-language-processing](<https://devfeed.tech/tags/natural-language-processing.md>), [on-device](<https://devfeed.tech/tags/on-device.md>), [on-device-ai](<https://devfeed.tech/tags/on-device-ai.md>), [phones](<https://devfeed.tech/tags/phones.md>)

### AI overview

Google Research describes a method for retrofitting Multi-Token Prediction onto frozen Gemini Nano v3 production models to accelerate on-device inference on Pixel phones. The approach targets mobile energy and memory constraints, improving the speed and energy efficiency of features such as notification summaries and proofreading without requiring separate drafting models.

### Source excerpt

Machine Intelligence

## Optimizing cloud economics with linear elastic caching

DevFeed: [Optimizing cloud economics with linear elastic caching](<https://devfeed.tech/articles/optimizing-cloud-economics-with-linear-elastic-caching-6844.md>)

Original publisher: [Read original article](<https://research.google/blog/optimizing-cloud-economics-with-linear-elastic-caching/>)

Published: 2026-06-25T10:03:00Z

Content type: article

Language: en

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

Topics: [Caching](<https://devfeed.tech/topics/caching.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [Database](<https://devfeed.tech/topics/database.md>), [Serverless](<https://devfeed.tech/topics/serverless.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>)

Tags: [algorithms](<https://devfeed.tech/tags/algorithms.md>), [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [caching](<https://devfeed.tech/tags/caching.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [cloud-services](<https://devfeed.tech/tags/cloud-services.md>), [cost](<https://devfeed.tech/tags/cost.md>), [data-management](<https://devfeed.tech/tags/data-management.md>), [google](<https://devfeed.tech/tags/google.md>), [performance](<https://devfeed.tech/tags/performance.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [serverless](<https://devfeed.tech/tags/serverless.md>), [systems](<https://devfeed.tech/tags/systems.md>)

### AI overview

Linear elastic caching applies the ski rental problem to dynamic cache sizing, balancing memory costs against cache misses. The approach treats memory as a time-dependent cost and adjusts cache capacity for changing workloads, aiming to reduce total cache-management expenses without compromising performance.

### Source excerpt

Algorithms & Theory

[Next page](<https://devfeed.tech/sources/the-latest-research-from-google.md?cursor=WyIyMDI2LTA2LTI1VDEwOjAzOjAwKzAwOjAwIiwgIjVjODA5NGFlLWY3OTktNDM5OS1iOTc5LTgwZTIwZWQ2YmFjMiJd>)