# ICLR 2026

Published articles for ICLR 2026.

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

## 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

## Diverse reasoning traces teach LLMs to make better decisions

DevFeed: [Diverse reasoning traces teach LLMs to make better decisions](<https://devfeed.tech/articles/diverse-reasoning-traces-teach-llms-to-make-better-decisions-7597.md>)

Original publisher: [Read original article](<https://www.amazon.science/blog/diverse-reasoning-traces-teach-llms-to-make-better-decisions>)

Author: Sheng Jia; Xiao Wang; Shiva Kasiviswanathan

Published: 2026-05-26T15:17:06Z

Content type: article

Language: en

Sources: [Amazon Science homepage](<https://devfeed.tech/sources/amazon-science-homepage.md>)

Topics: [Language models](<https://devfeed.tech/topics/language-models.md>)

Tags: [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [chain-of-thought](<https://devfeed.tech/tags/chain-of-thought.md>), [code-generation](<https://devfeed.tech/tags/code-generation.md>), [conversational-ai](<https://devfeed.tech/tags/conversational-ai.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [iclr](<https://devfeed.tech/tags/iclr.md>), [iclr-2026](<https://devfeed.tech/tags/iclr-2026.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [llms](<https://devfeed.tech/tags/llms.md>), [math-reasoning](<https://devfeed.tech/tags/math-reasoning.md>), [parallel-reasoning](<https://devfeed.tech/tags/parallel-reasoning.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [post-training-optimization](<https://devfeed.tech/tags/post-training-optimization.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [traces](<https://devfeed.tech/tags/traces.md>)

### AI overview

The article presents set-supervised fine tuning and global forking policy optimization to train LLMs on multiple distinct reasoning paths. It reports 5% to 7% single-shot accuracy gains on standard benchmarks.

### Source excerpt

How to train language models to generate diverse, accurate reasoning paths using tokens that control distinct reasoning strategies.

## Making LLMs faster without sacrificing accuracy

DevFeed: [Making LLMs faster without sacrificing accuracy](<https://devfeed.tech/articles/making-llms-faster-without-sacrificing-accuracy-7603.md>)

Original publisher: [Read original article](<https://www.amazon.science/blog/making-llms-faster-without-sacrificing-accuracy>)

Author: Tao Yu; Youngsuk Park

Published: 2026-05-15T13:00:00Z

Content type: article

Language: en

Sources: [Amazon Science homepage](<https://devfeed.tech/sources/amazon-science-homepage.md>)

Topics: [Language models](<https://devfeed.tech/topics/language-models.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [web applications](<https://devfeed.tech/topics/web-applications.md>)

Tags: [architecture](<https://devfeed.tech/tags/architecture.md>), [chinchilla-scaling-law](<https://devfeed.tech/tags/chinchilla-scaling-law.md>), [conversational-ai](<https://devfeed.tech/tags/conversational-ai.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [grouped-query-attention](<https://devfeed.tech/tags/grouped-query-attention.md>), [hyperparameter-optimization](<https://devfeed.tech/tags/hyperparameter-optimization.md>), [iclr-2026](<https://devfeed.tech/tags/iclr-2026.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-efficiency](<https://devfeed.tech/tags/inference-efficiency.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [llm-optimization](<https://devfeed.tech/tags/llm-optimization.md>), [llms](<https://devfeed.tech/tags/llms.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [model-architecture](<https://devfeed.tech/tags/model-architecture.md>), [network-architectures](<https://devfeed.tech/tags/network-architectures.md>), [scaling-laws](<https://devfeed.tech/tags/scaling-laws.md>), [training](<https://devfeed.tech/tags/training.md>), [transformer-architecture](<https://devfeed.tech/tags/transformer-architecture.md>)

### AI overview

The article presents scaling laws that connect LLM architectural choices to the tradeoff between accuracy and efficiency. It describes how these choices can improve inference throughput without reducing accuracy.

### Source excerpt

A new scaling law that relates particular architectural choices to loss helps identify models that improve throughput by up to 47% with no loss of accuracy.

## AI-generated synthetic neurons speed up brain mapping

DevFeed: [AI-generated synthetic neurons speed up brain mapping](<https://devfeed.tech/articles/ai-generated-synthetic-neurons-speed-up-brain-mapping-6748.md>)

Original publisher: [Read original article](<https://research.google/blog/ai-generated-synthetic-neurons-speed-up-brain-mapping/>)

Published: 2026-04-16T12:18:00Z

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>), [AI Models](<https://devfeed.tech/topics/ai-models.md>), [Google](<https://devfeed.tech/topics/google.md>), [Point cloud](<https://devfeed.tech/topics/point-cloud.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [neuron](<https://devfeed.tech/topics/neuron.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [3d](<https://devfeed.tech/tags/3d.md>), [accelerate](<https://devfeed.tech/tags/accelerate.md>), [ai](<https://devfeed.tech/tags/ai.md>), [classification](<https://devfeed.tech/tags/classification.md>), [errors](<https://devfeed.tech/tags/errors.md>), [general-science](<https://devfeed.tech/tags/general-science.md>), [generation](<https://devfeed.tech/tags/generation.md>), [google](<https://devfeed.tech/tags/google.md>), [health-bioscience](<https://devfeed.tech/tags/health-bioscience.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>), [mapping](<https://devfeed.tech/tags/mapping.md>), [model](<https://devfeed.tech/tags/model.md>), [models](<https://devfeed.tech/tags/models.md>), [neuron](<https://devfeed.tech/tags/neuron.md>), [partners](<https://devfeed.tech/tags/partners.md>), [research](<https://devfeed.tech/tags/research.md>), [scale](<https://devfeed.tech/tags/scale.md>), [science](<https://devfeed.tech/tags/science.md>), [synthetic](<https://devfeed.tech/tags/synthetic.md>), [visualization](<https://devfeed.tech/tags/visualization.md>)

### AI overview

Google Research describes how MoGen generates synthetic neuronal shapes to improve AI models that reconstruct brain wiring maps. Adding synthetic training examples reduced reconstruction errors by 4.4%, potentially saving 157 person-years of manual proofreading for a complete mouse brain.

### Source excerpt

General Science

## 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