# Algorithms & Theory

Algorithms and theory is a computer science discipline studying algorithms, computation theory, complexity, and the mathematical foundations of computing.

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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

## 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

## 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

## 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

## 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

## 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

## 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