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Google Releases EmbeddingGemma 2 Paper

Google has released the EmbeddingGemma 2 paper, introducing an Apache 2.0-licensed on-device multimodal embedding model that maps text, code, images, audio, and video into the same vector space. Built on Gemma 4, the model learns from the larger Gemini Embedding 2 and is tuned with contrastive training; with 744M parameters, it outperforms models 2 to 12 times larger on multiple tasks. On the Pixel 11 Pro, the quantized text weights occupy about 191MB of active memory, while the full multimodal model takes about 567MB; the context window is expanded 4 times to 8K tokens, accommodating about 5.5 minutes of audio, 29 images, or 58 video frames. Embedding speech directly scores 69.54 on the MSEB spoken query retrieval benchmark, higher than the 57.59 achieved by transcribing first and then retrieving, and code retrieval on MTEB Code improves from 68.76 in EmbeddingGemma 1 to 78.68. There are currently no further developments.

Google DeepMind paper: Training models to write moral arguments makes them more decisive but does not improve their judgment

Google DeepMind published a paper investigating the effects of training Gemma-4 4B with reinforcement learning to write moral arguments. The results showed that on ambiguous cases in MoralChoice, the proportion of answers that changed due to swapping the order of options dropped from 14.3% to 6.6%, but the proportion of sycophantic agreement with users only went from 45.3% to 44.0%, and accuracy on moral fable questions actually fell from 56.5% to 53.4%. Based on this, the paper recommends that when evaluating advice- or judgment-type tasks, consistency and correctness should be tested separately.

Google DeepMind paper: Training models to write moral arguments makes them more decisive, but their judgments do not improve

Google DeepMind published a paper investigating the effects of training Gemma-4 4B with reinforcement learning to write moral arguments. The results showed that on ambiguous cases in MoralChoice, the proportion of answers that changed due to swapping the order of options dropped from 14.3% to 6.6%, but the proportion of sycophantic agreement with users only went from 45.3% to 44.0%, and accuracy on moral fable questions actually fell from 56.5% to 53.4%. Based on this, the paper recommends that when evaluating advice- or judgment-type tasks, consistency and correctness should be tested separately.

View story:Google DeepMind paper: Training models to write moral arguments makes them more decisive but does not improve their judgment

Kimi integrates with Gmail, Google Drive, and Google Calendar

English text is being prepared.

Yuchen Jin raises the question of whether we are entering a new round of the "Google comeback" cycle

On October 29, 2026, Yuchen Jin posted a question: whether Google has once again entered a new "Google is back" comeback cycle. He said that Google's new model, Gemini 4 Carbon, surpasses Opus 5.5 and GPT-6 Astra on most benchmarks and ranks first on the Artificial Analysis Intelligence Index v5. He also said it remains to be seen whether this performance can shake Anthropic's IPO. At present, this judgment remains at the level of Yuchen Jin's personal question and observation, with no subsequent factual developments yet.

Yuchen Jin raises the question of whether we are entering a new "Google comeback" cycle

On October 29, 2026, Yuchen Jin posted a question: whether Google has once again entered a new "Google is back" comeback cycle. He said that Google's new model, Gemini 4 Carbon, surpasses Opus 5.5 and GPT-6 Astra on most benchmarks and ranks first on the Artificial Analysis Intelligence Index v5. He also said it remains to be seen whether this performance can shake Anthropic's IPO. At present, this judgment remains at the level of Yuchen Jin's personal question and observation, with no subsequent factual developments yet.

View story:Yuchen Jin raises the question of whether we are entering a new round of the "Google comeback" cycle

Google Antigravity adds a plugin marketplace

Google Antigravity adds a plugin marketplace

TestingCatalog says Google has terminated the Jules AI agent

On October 30, 2026, TestingCatalog reported that Google will terminate its AI coding agent Jules. Phoenix Technology reported the same day that Google will gradually shut down Jules, which was launched in December 2024, and will integrate the experience accumulated from the tool into its AI coding platform Antigravity; Jules' official X account stated that Jules was originally an experiment exploring how asynchronous coding agents could help developers ship faster, and that its experience has already influenced multiple Google products. cnBeta reported the same day that Jules will gradually cease operations, less than two years after its launch, and that the team is integrating the experience into Antigravity so that users can both autonomously execute background tasks in remote environments and use an agent-centric development environment. The current status is gradual shutdown and experience integration.

TestingCatalog says Google terminates Jules AI agent

On October 30, 2026, TestingCatalog reported that Google will terminate its AI coding agent Jules. Phoenix Technology reported the same day that Google will gradually shut down Jules, which was launched in December 2024, and will integrate the experience accumulated from the tool into its AI coding platform Antigravity; Jules' official X account stated that Jules was originally an experiment exploring how asynchronous coding agents could help developers ship faster, and that its experience has already influenced multiple Google products. cnBeta reported the same day that Jules will gradually cease operations, less than two years after its launch, and that the team is integrating the experience into Antigravity so that users can both autonomously execute background tasks in remote environments and use an agent-centric development environment. The current status is gradual shutdown and experience integration.

View story:TestingCatalog says Google has terminated the Jules AI agent

Google DeepMind paper proposes that AI's economic impact depends on agents' coordination capabilities

On October 30, 2026, Google DeepMind published the paper "The Economics of Intelligence," proposing that the economic impact of AGI be characterized by two thresholds—competence (single-task capability) and coordination (agents' ability to collaborate)—and arguing that the economic impact depends on agents' coordination capabilities rather than on individual models becoming stronger. The paper states that AI has already shown changes in lean startups, flattened organizational structures, and a slowdown in hiring for some entry-level positions, but that large-scale unemployment has not yet occurred; most benchmarks test only a single model, whereas firms compete as teams. The paper cites a 100-person test in which groups with complementary pairings performed 32% better than randomly paired groups. On the same day, Ethan Mollick commented on the research, pointing out that an organization is not merely the sum of its best individuals, that a single agent becoming stronger will not change the economy on its own, and that only agents capable of working in coordination may bring about transformation.

Google DeepMind paper proposes that AI's economic impact depends on agent coordination capabilities

On October 30, 2026, Google DeepMind published the paper "The Economics of Intelligence," proposing that the economic impact of AGI be characterized by two thresholds—competence (single-task capability) and coordination (agents' ability to collaborate)—and arguing that the economic impact depends on agents' coordination capabilities rather than on individual models becoming stronger. The paper states that AI has already shown changes in lean startups, flattened organizational structures, and a slowdown in hiring for some entry-level positions, but that large-scale unemployment has not yet occurred; most benchmarks test only a single model, whereas firms compete as teams. The paper cites a 100-person test in which groups with complementary pairings performed 32% better than randomly paired groups. On the same day, Ethan Mollick commented on the research, pointing out that an organization is not merely the sum of its best individuals, that a single agent becoming stronger will not change the economy on its own, and that only agents capable of working in coordination may bring about transformation.

View story:Google DeepMind paper proposes that AI's economic impact depends on agents' coordination capabilities

Police in Oulu launch criminal investigation into Google data center construction

Police in Oulu, Finland, have launched a criminal investigation into suspected violations of environmental regulations in the construction of a Google data center. According to heise online, the Muhos site is accused of felling too many trees and of deficiencies in drainage and water management. Google subsidiary Tuike Finland has been ordered by the regulatory authority LVV to stop felling, earthworks and blasting, and must submit a water management plan by next week. The report also mentions that Google plans to invest at least 13 billion euros in Finland over the next two years to build digital infrastructure.

Google open-sources the Elephant-Goldfish Model plugin

On October 22, 2026, Google announced the open-sourcing of the Elephant-Goldfish Model (EGM) plugin. The plugin pairs Elephant, which is responsible for conversing with users and writing design documents, with Goldfish, which independently verifies the documents without inheriting the conversation history, to identify ambiguities and omissions in specifications. The plugin was first developed internally at Google for software engineers, and it is said that thousands of employees already use it daily to develop production code; the repository uses the Apache 2.0 license. The first tagged version, v0.1.0, was released on October 27, containing 7 skills in total, and the README provides installation instructions for Antigravity, Claude Code, and Codex; the release notes state that verification across runtime environments has not yet been completed and that it cannot yet be called a stable version.

Google announces that Android XR core development tools have reached stable release

At Android Dev Summit 2026, Google announced that multiple core Android XR development tools have exited preview and entered stable release, including the Jetpack XR SDK core libraries, the Android XR Emulator, and the Android XR Engine Hub. Among them, Jetpack SceneCore, ARCore for Jetpack XR, and the XR Runtime core libraries provide production development APIs, with support for long-term interface stability and backward compatibility; the emulator allows development and debugging for XR glasses, audio glasses, and display-equipped glasses without a physical device; the stable version of the Android XR Engine Hub supports the three major engines Unity, Godot, and Unreal, and can be used to launch performance analysis and view runtime data. On the same day, Google partnered with XREAL to launch the XREAL AURA glasses powered by Android XR.

Google releases EmbeddingGemma 2 paper

Google has released the EmbeddingGemma 2 paper, introducing an Apache 2.0-licensed on-device multimodal embedding model that maps text, code, images, audio, and video into the same vector space. Built on Gemma 4, the model learns from the larger Gemini Embedding 2 and is tuned with contrastive training; with 744M parameters, it outperforms models 2 to 12 times larger on multiple tasks. On the Pixel 11 Pro, the quantized text weights occupy about 191MB of active memory, while the full multimodal model takes about 567MB; the context window is expanded 4 times to 8K tokens, accommodating about 5.5 minutes of audio, 29 images, or 58 video frames. Embedding speech directly scores 69.54 on the MSEB spoken query retrieval benchmark, higher than the 57.59 achieved by transcribing first and then retrieving, and code retrieval on MTEB Code improves from 68.76 in EmbeddingGemma 1 to 78.68. There are currently no further developments.

View story:Google Releases EmbeddingGemma 2 Paper

Kimi integrates with Gmail, Google Drive, and Google Calendar

English text is being prepared.

View story:Kimi integrates with Gmail, Google Drive, and Google Calendar

Finnish police in Oulu launch criminal investigation into Google data center construction

Police in Oulu, Finland, have launched a criminal investigation into suspected violations of environmental regulations in the construction of a Google data center. According to heise online, the Muhos site is accused of felling too many trees and of deficiencies in drainage and water management. Google subsidiary Tuike Finland has been ordered by the regulatory authority LVV to stop felling, earthworks and blasting, and must submit a water management plan by next week. The report also mentions that Google plans to invest at least 13 billion euros in Finland over the next two years to build digital infrastructure.

View story:Police in Oulu launch criminal investigation into Google data center construction

Google announces that Android XR core development tools have reached stable release

At Android Dev Summit 2026, Google announced that multiple core Android XR development tools have exited preview and entered stable release, including the Jetpack XR SDK core libraries, the Android XR Emulator, and the Android XR Engine Hub. Among them, Jetpack SceneCore, ARCore for Jetpack XR, and the XR Runtime core libraries provide production development APIs, with support for long-term interface stability and backward compatibility; the emulator allows development and debugging for XR glasses, audio glasses, and display-equipped glasses without a physical device; the stable version of the Android XR Engine Hub supports the three major engines Unity, Godot, and Unreal, and can be used to launch performance analysis and view runtime data. On the same day, Google partnered with XREAL to launch the XREAL AURA glasses powered by Android XR.

View story:Google announces that Android XR core development tools have reached stable release

Google releases world model Genie 4

On October 27, 2026, Google DeepMind released the world model Genie 4, focused on real-time interactive simulation and introducing native audio to the series for the first time. According to the official introduction, the model is built on capabilities from the Gemini family: Gemini Omni is responsible for making the simulation match the appearance and behavior of the real world, while other Gemini models provide the complex reasoning needed to maintain the simulation's coherence; users can control scenes in real time using natural language, and each world also instantly generates ambient sound, music, and interactive dialogue. The next day, Google DeepMind further explained that Gemini 3.8 Flash breaks down goals in a loop and checks frames to maintain causal coherence across multi-step events, and that Gemini 4 Argon has also been used to jointly simulate tasks with Gemini Robotics; the company said it remains an early experimental model, that object permanence and complex physical interactions still drift during long runs, that all generated content carries a SynthID watermark, and that it is currently being previewed to trusted testers first. On October 28, Ethan Mollick posted that Genie 4 can run DOOM, and tested it generating a game-like environment as it goes, saying the experience is not perfect. On October 29, Peter Yang used Genie 4 to build 5 worlds in real time, arguing that world models are the next big direction for AI after LLMs.

View story:Google releases world model Genie 4

Google open-sources Elephant-Goldfish Model plugin

On October 22, 2026, Google announced the open-sourcing of the Elephant-Goldfish Model (EGM) plugin. The plugin pairs Elephant, which is responsible for conversing with users and writing design documents, with Goldfish, which independently verifies the documents without inheriting the conversation history, to identify ambiguities and omissions in specifications. The plugin was first developed internally at Google for software engineers, and it is said that thousands of employees already use it daily to develop production code; the repository uses the Apache 2.0 license. The first tagged version, v0.1.0, was released on October 27, containing 7 skills in total, and the README provides installation instructions for Antigravity, Claude Code, and Codex; the release notes state that verification across runtime environments has not yet been completed and that it cannot yet be called a stable version.

View story:Google open-sources the Elephant-Goldfish Model plugin