# Algorithms & Theory

Published articles for Algorithms & Theory.

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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

## 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

## 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

## 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

## 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

## New framework for auditing machine unlearning

DevFeed: [New framework for auditing machine unlearning](<https://devfeed.tech/articles/new-framework-for-auditing-machine-unlearning-6839.md>)

Original publisher: [Read original article](<https://research.google/blog/new-framework-for-auditing-machine-unlearning/>)

Published: 2026-06-10T17:34:55Z

Content type: article

Language: en

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

Topics: [Algorithms & Theory](<https://devfeed.tech/topics/algorithms-theory.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Responsibility & Safety](<https://devfeed.tech/topics/responsibility-safety.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [compliance](<https://devfeed.tech/tags/compliance.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [models](<https://devfeed.tech/tags/models.md>), [research](<https://devfeed.tech/tags/research.md>), [responsible-ai](<https://devfeed.tech/tags/responsible-ai.md>), [safety](<https://devfeed.tech/tags/safety.md>), [security-privacy-and-abuse-prevention](<https://devfeed.tech/tags/security-privacy-and-abuse-prevention.md>), [statistical-significance](<https://devfeed.tech/tags/statistical-significance.md>), [testing](<https://devfeed.tech/tags/testing.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

Google Research introduces Regularized f-Divergence Kernel Tests, a framework for auditing machine unlearning through black-box statistical comparisons of model outputs. The method is designed to improve sensitivity and flexibility while controlling false positives and reducing false negatives as sample sizes grow.

### Source excerpt

Algorithms & Theory

## Building better AI benchmarks: How many raters are enough?

DevFeed: [Building better AI benchmarks: How many raters are enough?](<https://devfeed.tech/articles/building-better-ai-benchmarks-how-many-raters-are-enough-6752.md>)

Original publisher: [Read original article](<https://research.google/blog/building-better-ai-benchmarks-how-many-raters-are-enough/>)

Published: 2026-03-31T16:16:00Z

Content type: article

Language: en

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

Topics: [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [Human-AI evaluation](<https://devfeed.tech/topics/human-ai-evaluation.md>), [Ground truth / benchmark quality](<https://devfeed.tech/topics/ground-truth-benchmark-quality.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [cost](<https://devfeed.tech/tags/cost.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [research](<https://devfeed.tech/tags/research.md>)

### AI overview

Google Research presents an evaluation framework for machine-learning models that balances the number of rated items with the number of human raters per item. The research addresses reproducibility, human disagreement, benchmark quality, and the cost of collecting evaluation data, arguing that the common practice of using one to five raters per item can miss meaningful disagreement.

### Source excerpt

Algorithms & Theory

## Safeguarding cryptocurrency by disclosing quantum vulnerabilities responsibly

DevFeed: [Safeguarding cryptocurrency by disclosing quantum vulnerabilities responsibly](<https://devfeed.tech/articles/safeguarding-cryptocurrency-by-disclosing-quantum-vulnerabilities-responsibly-6860.md>)

Original publisher: [Read original article](<https://research.google/blog/safeguarding-cryptocurrency-by-disclosing-quantum-vulnerabilities-responsibly/>)

Published: 2026-03-31T02:03:00Z

Content type: article

Language: en

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

Topics: [Cryptocurrency](<https://devfeed.tech/topics/cryptocurrency.md>), [Vulnerabilities](<https://devfeed.tech/topics/vulnerabilities.md>), [Post-quantum cryptography](<https://devfeed.tech/topics/post-quantum-cryptography.md>), [Quantum Computing](<https://devfeed.tech/topics/quantum-computing.md>), [Cryptography](<https://devfeed.tech/topics/cryptography.md>), [Zero-knowledge proof](<https://devfeed.tech/topics/zkp.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [awareness](<https://devfeed.tech/tags/awareness.md>), [coinbase](<https://devfeed.tech/tags/coinbase.md>), [cryptocurrency](<https://devfeed.tech/tags/cryptocurrency.md>), [cryptography](<https://devfeed.tech/tags/cryptography.md>), [ethereum](<https://devfeed.tech/tags/ethereum.md>), [google](<https://devfeed.tech/tags/google.md>), [government](<https://devfeed.tech/tags/government.md>), [migration](<https://devfeed.tech/tags/migration.md>), [post-quantum](<https://devfeed.tech/tags/post-quantum.md>), [quantum](<https://devfeed.tech/tags/quantum.md>), [quantum-computing](<https://devfeed.tech/tags/quantum-computing.md>), [research](<https://devfeed.tech/tags/research.md>), [security-privacy-and-abuse-prevention](<https://devfeed.tech/tags/security-privacy-and-abuse-prevention.md>), [vulnerabilities](<https://devfeed.tech/tags/vulnerabilities.md>), [zero-knowledge](<https://devfeed.tech/tags/zero-knowledge.md>)

### AI overview

Google Research describes how future quantum computers could break the elliptic-curve cryptography protecting cryptocurrency with fewer resources than previously estimated. The article recommends transitioning blockchains to post-quantum cryptography and presents zero-knowledge proofs as a way to disclose vulnerabilities responsibly.

### Source excerpt

Algorithms & Theory

## TurboQuant: Redefining AI efficiency with extreme compression

DevFeed: [TurboQuant: Redefining AI efficiency with extreme compression](<https://devfeed.tech/articles/turboquant-redefining-ai-efficiency-with-extreme-compression-6917.md>)

Original publisher: [Read original article](<https://research.google/blog/turboquant-redefining-ai-efficiency-with-extreme-compression/>)

Published: 2026-03-24T19:54:00Z

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>), [quantization](<https://devfeed.tech/topics/quantization.md>), [Compression](<https://devfeed.tech/topics/compression.md>), [large-language-models](<https://devfeed.tech/topics/large-language-models.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [AI search](<https://devfeed.tech/topics/ai-search.md>), [Caching](<https://devfeed.tech/topics/caching.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Algorithms, Complexity](<https://devfeed.tech/topics/algorithms-complexity.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](<https://devfeed.tech/tags/algorithms.md>), [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [cache](<https://devfeed.tech/tags/cache.md>), [compression](<https://devfeed.tech/tags/compression.md>), [efficiency](<https://devfeed.tech/tags/efficiency.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>), [language-models](<https://devfeed.tech/tags/language-models.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [memory](<https://devfeed.tech/tags/memory.md>), [model](<https://devfeed.tech/tags/model.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [performance](<https://devfeed.tech/tags/performance.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [research](<https://devfeed.tech/tags/research.md>), [search](<https://devfeed.tech/tags/search.md>)

### AI overview

Google Research introduces TurboQuant, a theoretically grounded compression algorithm for large language models and vector search. It targets vector-quantization overhead and key-value cache bottlenecks, aiming to reduce model size and memory costs while preserving accuracy.

### Source excerpt

Algorithms & Theory

## Mapping the modern world: How S2Vec learns the language of our cities

DevFeed: [Mapping the modern world: How S2Vec learns the language of our cities](<https://devfeed.tech/articles/mapping-the-modern-world-how-s2vec-learns-the-language-of-our-cities-6835.md>)

Original publisher: [Read original article](<https://research.google/blog/mapping-the-modern-world-how-s2vec-learns-the-language-of-our-cities/>)

Published: 2026-03-24T17:42:00Z

Content type: article

Language: en

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

Topics: [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Earth AI](<https://devfeed.tech/topics/earth-ai.md>), [Google](<https://devfeed.tech/topics/google.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>), [foundation-models](<https://devfeed.tech/topics/foundation-models.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [data](<https://devfeed.tech/tags/data.md>), [earth-ai](<https://devfeed.tech/tags/earth-ai.md>), [embeddings](<https://devfeed.tech/tags/embeddings.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>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [mapping](<https://devfeed.tech/tags/mapping.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [research](<https://devfeed.tech/tags/research.md>)

### AI overview

S2Vec is a self-supervised framework that converts complex geospatial data about the built environment into general-purpose embeddings. The article describes how these embeddings support prediction of socioeconomic and environmental patterns, while noting stronger results for geographic adaptation and remaining limitations on environmental tasks.

### Source excerpt

Algorithms & Theory

## Scheduling in a changing world: Maximizing throughput with time-varying capacity

DevFeed: [Scheduling in a changing world: Maximizing throughput with time-varying capacity](<https://devfeed.tech/articles/scheduling-in-a-changing-world-maximizing-throughput-with-time-varying-capacity-6862.md>)

Original publisher: [Read original article](<https://research.google/blog/scheduling-in-a-changing-world-maximizing-throughput-with-time-varying-capacity/>)

Published: 2026-02-11T10:34:00Z

Content type: article

Language: en

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

Topics: [Algorithms & Theory](<https://devfeed.tech/topics/algorithms-theory.md>), [cloud-infrastructure](<https://devfeed.tech/topics/cloud-infrastructure.md>), [cloud-computing](<https://devfeed.tech/topics/cloud-computing.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [algorithms](<https://devfeed.tech/tags/algorithms.md>), [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [cloud-computing](<https://devfeed.tech/tags/cloud-computing.md>), [cloud-infrastructure](<https://devfeed.tech/tags/cloud-infrastructure.md>), [google](<https://devfeed.tech/tags/google.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [scale](<https://devfeed.tech/tags/scale.md>)

### AI overview

Google Research presents approximation algorithms for scheduling non-preemptive jobs when cloud capacity varies over time. The work targets throughput maximization in volatile environments where interruptions can erase progress.

### Source excerpt

Algorithms & Theory

## Sequential Attention: Making AI models leaner and faster without sacrificing accuracy

DevFeed: [Sequential Attention: Making AI models leaner and faster without sacrificing accuracy](<https://devfeed.tech/articles/sequential-attention-making-ai-models-leaner-and-faster-without-sacrificing-accuracy-6872.md>)

Original publisher: [Read original article](<https://research.google/blog/sequential-attention-making-ai-models-leaner-and-faster-without-sacrificing-accuracy/>)

Published: 2026-02-04T15:14:00Z

Content type: article

Language: en

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

Topics: [Algorithms & Theory](<https://devfeed.tech/topics/algorithms-theory.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Programming](<https://devfeed.tech/topics/programming.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [algorithms](<https://devfeed.tech/tags/algorithms.md>), [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [features](<https://devfeed.tech/tags/features.md>), [google](<https://devfeed.tech/tags/google.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [research](<https://devfeed.tech/tags/research.md>), [scalability](<https://devfeed.tech/tags/scalability.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

Google Research presents Sequential Attention, a greedy and adaptive subset-selection method for making large-scale machine-learning and deep-learning models more efficient. The approach selects useful components such as features, layers, blocks, embedding chunks, or weight entries during a single training run, reducing redundancy while preserving accuracy and limiting additional training cost.

### Source excerpt

Algorithms & Theory

## 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

## Hard-braking events as indicators of road segment crash risk

DevFeed: [Hard-braking events as indicators of road segment crash risk](<https://devfeed.tech/articles/hard-braking-events-as-indicators-of-road-segment-crash-risk-6807.md>)

Original publisher: [Read original article](<https://research.google/blog/hard-braking-events-as-indicators-of-road-segment-crash-risk/>)

Published: 2026-01-13T22:44:00Z

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>), [Statistics](<https://devfeed.tech/topics/statistics.md>), [data](<https://devfeed.tech/topics/data.md>), [Android](<https://devfeed.tech/topics/android.md>), [Network](<https://devfeed.tech/topics/network.md>), [Algorithms, Complexity](<https://devfeed.tech/topics/algorithms-complexity.md>)

Tags: [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [analysis](<https://devfeed.tech/tags/analysis.md>), [android](<https://devfeed.tech/tags/android.md>), [data](<https://devfeed.tech/tags/data.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [google](<https://devfeed.tech/tags/google.md>), [models](<https://devfeed.tech/tags/models.md>), [network](<https://devfeed.tech/tags/network.md>), [product](<https://devfeed.tech/tags/product.md>), [research](<https://devfeed.tech/tags/research.md>), [statistics](<https://devfeed.tech/tags/statistics.md>)

### AI overview

Google Research examines whether hard-braking events collected through Android Auto can indicate crash risk on road segments. Combining anonymized, aggregated hard-braking data with public crash records from California and Virginia, the study finds a statistically significant positive association and identifies hard-braking events as a denser, potentially leading measure for road safety assessment.

### Source excerpt

Algorithms & Theory

## Gemini-backed Paper Assistant Tool provides automated feedback for theoretical computer scientists at STOC 2026

DevFeed: [Gemini-backed Paper Assistant Tool provides automated feedback for theoretical computer scientists at STOC 2026](<https://devfeed.tech/articles/gemini-backed-paper-assistant-tool-provides-automated-feedback-for-theoretical-computer-scientists-at-stoc-2026-6788.md>)

Original publisher: [Read original article](<https://research.google/blog/gemini-provides-automated-feedback-for-theoretical-computer-scientists-at-stoc-2026/>)

Published: 2025-12-15T17:37:00Z

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>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [Algorithms & Theory](<https://devfeed.tech/topics/algorithms-theory.md>), [Google](<https://devfeed.tech/topics/google.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Chain-of-thought](<https://devfeed.tech/topics/chain-of-thought.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [chain-of-thought](<https://devfeed.tech/tags/chain-of-thought.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [experimental](<https://devfeed.tech/tags/experimental.md>), [gemini](<https://devfeed.tech/tags/gemini.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [google](<https://devfeed.tech/tags/google.md>), [hallucinations](<https://devfeed.tech/tags/hallucinations.md>), [inference](<https://devfeed.tech/tags/inference.md>), [natural-language-processing](<https://devfeed.tech/tags/natural-language-processing.md>), [research](<https://devfeed.tech/tags/research.md>), [technical](<https://devfeed.tech/tags/technical.md>), [verify](<https://devfeed.tech/tags/verify.md>)

### AI overview

Google Research describes Paper Assistant Tool (PAT), an experimental Gemini-powered system tested for STOC 2026 that gives theoretical computer science authors automated pre-submission feedback. PAT uses inference scaling and multiple reasoning and evaluation traces to identify calculation errors, logic errors, inconsistencies, and other issues in papers.

### Source excerpt

Algorithms & Theory

## Reducing EV range anxiety: How a simple AI model predicts port availability

DevFeed: [Reducing EV range anxiety: How a simple AI model predicts port availability](<https://devfeed.tech/articles/reducing-ev-range-anxiety-how-a-simple-ai-model-predicts-port-availability-6856.md>)

Original publisher: [Read original article](<https://research.google/blog/reducing-ev-range-anxiety-how-a-simple-ai-model-predicts-port-availability/>)

Published: 2025-11-21T16:27:00Z

Content type: article

Language: en

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

Topics: [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Availability](<https://devfeed.tech/topics/availability.md>), [Routing (disambiguation)](<https://devfeed.tech/topics/routing.md>), [real-time](<https://devfeed.tech/topics/real-time.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [availability](<https://devfeed.tech/tags/availability.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [model](<https://devfeed.tech/tags/model.md>), [performance](<https://devfeed.tech/tags/performance.md>), [product](<https://devfeed.tech/tags/product.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [routing](<https://devfeed.tech/tags/routing.md>), [speed](<https://devfeed.tech/tags/speed.md>)

### AI overview

Google Research describes a lightweight linear regression model that predicts whether an electric-vehicle charging port will be available at a specific station at a future time. The model uses real-time charging-network availability data and is designed with its deployment infrastructure to provide fast, low-latency predictions that can improve EV routing, reduce waiting time, and help address range anxiety.

### Source excerpt

Algorithms & Theory

## Real-time speech-to-speech translation

DevFeed: [Real-time speech-to-speech translation](<https://devfeed.tech/articles/real-time-speech-to-speech-translation-6852.md>)

Original publisher: [Read original article](<https://research.google/blog/real-time-speech-to-speech-translation/>)

Published: 2025-11-19T09:59:00Z

Content type: article

Language: en

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

Topics: [speech-to-speech](<https://devfeed.tech/topics/speech-to-speech.md>), [real-time](<https://devfeed.tech/topics/real-time.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Streaming](<https://devfeed.tech/topics/streaming.md>), [data-processing](<https://devfeed.tech/topics/data-processing.md>), [asr](<https://devfeed.tech/topics/asr.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [asr](<https://devfeed.tech/tags/asr.md>), [data](<https://devfeed.tech/tags/data.md>), [google](<https://devfeed.tech/tags/google.md>), [ml](<https://devfeed.tech/tags/ml.md>), [personalization](<https://devfeed.tech/tags/personalization.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [speech](<https://devfeed.tech/tags/speech.md>), [speech-to-speech](<https://devfeed.tech/tags/speech-to-speech.md>), [streaming](<https://devfeed.tech/tags/streaming.md>)

### AI overview

Google researchers introduce an end-to-end speech-to-speech translation model that translates speech in real time while preserving the original speaker's voice, with a two-second delay. The system uses a streaming architecture, time-synchronized training data, and a scalable data-acquisition pipeline to support more languages and improve conversational naturalness.

### Source excerpt

Algorithms & Theory

## A new quantum toolkit for optimization

DevFeed: [A new quantum toolkit for optimization](<https://devfeed.tech/articles/a-new-quantum-toolkit-for-optimization-6741.md>)

Original publisher: [Read original article](<https://research.google/blog/a-new-quantum-toolkit-for-optimization/>)

Published: 2025-11-13T07:27:00Z

Content type: article

Language: en

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

Topics: [Optimization](<https://devfeed.tech/topics/optimization.md>), [Quantum Computing](<https://devfeed.tech/topics/quantum-computing.md>), [Programming](<https://devfeed.tech/topics/programming.md>), [Algorithms & Theory](<https://devfeed.tech/topics/algorithms-theory.md>), [quantum mechanics](<https://devfeed.tech/topics/quantum-mechanics.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [algorithms](<https://devfeed.tech/tags/algorithms.md>), [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [google](<https://devfeed.tech/tags/google.md>), [lattice](<https://devfeed.tech/tags/lattice.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [quantum](<https://devfeed.tech/tags/quantum.md>), [quantum-computing](<https://devfeed.tech/tags/quantum-computing.md>), [quantum-mechanics](<https://devfeed.tech/tags/quantum-mechanics.md>)

### AI overview

Google Quantum AI researchers describe Decoded Quantum Interferometry, a quantum algorithm designed to find near-optimal solutions for certain optimization problems that are difficult for classical computers. The approach uses interference from quantum mechanics and depends on solving related lattice decoding problems.

### Source excerpt

Algorithms & Theory

## Differentially private machine learning at scale with JAX-Privacy

DevFeed: [Differentially private machine learning at scale with JAX-Privacy](<https://devfeed.tech/articles/differentially-private-machine-learning-at-scale-with-jax-privacy-6760.md>)

Original publisher: [Read original article](<https://research.google/blog/differentially-private-machine-learning-at-scale-with-jax-privacy/>)

Published: 2025-11-12T15:32:00Z

Content type: article

Language: en

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

Topics: [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>), [Algorithms, Complexity](<https://devfeed.tech/topics/algorithms-complexity.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [distributed-training](<https://devfeed.tech/topics/distributed-training.md>), [data](<https://devfeed.tech/topics/data.md>), [Google](<https://devfeed.tech/topics/google.md>), [High-Performance Computing](<https://devfeed.tech/topics/high-performance-computing.md>), [Programming](<https://devfeed.tech/topics/programming.md>)

Tags: [algorithms](<https://devfeed.tech/tags/algorithms.md>), [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [data](<https://devfeed.tech/tags/data.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [distributed-training](<https://devfeed.tech/tags/distributed-training.md>), [google](<https://devfeed.tech/tags/google.md>), [high-performance-computing](<https://devfeed.tech/tags/high-performance-computing.md>), [libraries](<https://devfeed.tech/tags/libraries.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [release](<https://devfeed.tech/tags/release.md>), [research](<https://devfeed.tech/tags/research.md>), [responsible-ai](<https://devfeed.tech/tags/responsible-ai.md>), [scalability](<https://devfeed.tech/tags/scalability.md>), [security-privacy-and-abuse-prevention](<https://devfeed.tech/tags/security-privacy-and-abuse-prevention.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

Google announces JAX-Privacy 1.0, a library for differentially private machine learning built on JAX. The release is intended to help researchers and developers implement, audit, and scale private training workflows for deep learning models using large datasets and distributed training.

### Source excerpt

Algorithms & Theory

## Introducing Nested Learning: A new ML paradigm for continual learning

DevFeed: [Introducing Nested Learning: A new ML paradigm for continual learning](<https://devfeed.tech/articles/introducing-nested-learning-a-new-ml-paradigm-for-continual-learning-6827.md>)

Original publisher: [Read original article](<https://research.google/blog/introducing-nested-learning-a-new-ml-paradigm-for-continual-learning/>)

Published: 2025-11-07T17:37:22Z

Content type: article

Language: en

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

Topics: [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Network architectures](<https://devfeed.tech/topics/network-architectures.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [LLMs](<https://devfeed.tech/topics/llms.md>), [Google](<https://devfeed.tech/topics/google.md>), [Algorithms & Theory](<https://devfeed.tech/topics/algorithms-theory.md>)

Tags: [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [architectures](<https://devfeed.tech/tags/architectures.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [google](<https://devfeed.tech/tags/google.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>), [optimization](<https://devfeed.tech/tags/optimization.md>), [research](<https://devfeed.tech/tags/research.md>)

### AI overview

Google Research introduces Nested Learning, a machine learning approach for continual learning that represents a model as interconnected, nested optimization problems. The approach aims to reduce catastrophic forgetting by jointly treating model architecture and training rules as multiple optimization levels with distinct information flows and update rates.

### Source excerpt

Algorithms & Theory

## Solving virtual machine puzzles: How AI is optimizing cloud computing

DevFeed: [Solving virtual machine puzzles: How AI is optimizing cloud computing](<https://devfeed.tech/articles/solving-virtual-machine-puzzles-how-ai-is-optimizing-cloud-computing-6876.md>)

Original publisher: [Read original article](<https://research.google/blog/solving-virtual-machine-puzzles-how-ai-is-optimizing-cloud-computing/>)

Published: 2025-10-17T17:56:35Z

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>), [cloud-computing](<https://devfeed.tech/topics/cloud-computing.md>), [Algorithms & Theory](<https://devfeed.tech/topics/algorithms-theory.md>), [data centers](<https://devfeed.tech/topics/data-centers.md>), [servers](<https://devfeed.tech/topics/servers.md>), [Provisioning](<https://devfeed.tech/topics/provisioning.md>)

Tags: [2025](<https://devfeed.tech/tags/2025.md>), [ai](<https://devfeed.tech/tags/ai.md>), [algorithms](<https://devfeed.tech/tags/algorithms.md>), [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [cloud-computing](<https://devfeed.tech/tags/cloud-computing.md>), [distributed-systems-parallel-computing](<https://devfeed.tech/tags/distributed-systems-parallel-computing.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [google](<https://devfeed.tech/tags/google.md>), [packing](<https://devfeed.tech/tags/packing.md>), [provisioning](<https://devfeed.tech/tags/provisioning.md>), [research](<https://devfeed.tech/tags/research.md>), [resource](<https://devfeed.tech/tags/resource.md>), [scale](<https://devfeed.tech/tags/scale.md>), [server](<https://devfeed.tech/tags/server.md>), [servers](<https://devfeed.tech/tags/servers.md>), [software-systems-engineering](<https://devfeed.tech/tags/software-systems-engineering.md>), [virtual-machines](<https://devfeed.tech/tags/virtual-machines.md>)

### AI overview

Google Research presents LAVA, a scheduling system that uses AI models to continuously predict and adapt to virtual machine lifetimes. Its NILAS, LAVA, and LARS algorithms improve VM allocation and rescheduling in large cloud data centers, reducing stranded resources and improving server efficiency.

### Source excerpt

Algorithms & Theory