Research & Papers

This article summarizes Google Research's 2026 research themes from the perspective of how AI can enhance human creativity.

Introduces research on Deep Review, an AI model specialized for analyzing technical documents.

A technical discussion on why an encoder is not used in Strands Decider.

Explains the mechanism of the 'Aaronson Oracle' program, which guesses the next key press more often than chance.

• •It is a program that guesses which key the user will press next.
• •It is right far more often than chance.
• •Readers can learn exactly how the program works.

A project building a ChatGPT-style model from first principles in TypeScript to deeply understand transformers.

• •TypeScript was chosen because reimplementing concepts in a different language helps grasp implementation nuances.
• •Chapters so far cover dataloaders, embeddings, and the attention mechanism.
• •Feedback is welcome for any unclear parts, shared on Hacker News.

A research lab at the University of Tokyo exhibited a smart mobility digital twin that reflects real-time road conditions in a virtual world at CEATEC 2026.

• •The exhibition was held at the Digital Traffic Society Pavilion at CEATEC 2026.
• •It collects information from RSUs, vehicles, traffic lights, and road sensors with low latency.
• •The technology integrates the collected data in real time and reflects it on a map.

Explains why attention, a mathematical mechanism enabling AI models to process language at scale, is the key underpinning the current AI revolution.

• •Attention is a mathematical mechanism that allows AI models to process language at scale.
• •This mechanism serves as the foundation for the current AI revolution.
• •A columnist questions why attention is the key to this development.

元Neuralink共同創業者Max Hodakが、PRIMAとの共同開発による視力回復チップの初移植とBCIへの道筋をTechCrunch Disruptで詳述した

• •Stargardt病患者に対して初の視力回復チップ移植が実施された
• •Max Hodak氏はPRIMAとの協業が新しい脳コンピュータインターフェースへの道だと説明した
• •この詳細はTechCrunch Disruptのステージ上で語られた

Research introduces a method for robots to learn assembly actions in simulation, acquiring proficiency and cooperation without binary task rewards or specified manipulation stages.

• •The study focuses on learning proficiency and cooperation in robot assembly within a simulation environment.
• •The learning approach employs binary task rewards.
• •A key feature is learning actions without specifying manipulation stages.

Parsimony is an open-source benchmark that measures how much redundant code AI models add by counting syntax elements, addressing the gap in existing benchmarks that ignore code bloat.

• •Existing benchmarks measure solve rate and token cost but do not penalize excess code that makes codebases harder to maintain long-term.
• •Parsimony uses patches from benchmarks like DeepSWE and SWE-bench Verified to count 'coding units' such as statements and calls.
• •It counts syntax elements rather than lines of code to prevent rewarding code cramming.