Testing Catalog reports that Gemini Notebook is about to gain the Connectors feature, but initially only one connector, Google Drive, will be available. Users can search Google Drive files directly in chat and can disable the feature as needed; officials say the connector only retrieves workspace documents, and original files will not be modified. More Connectors are expected to be added later. There is currently no further development progress.
Google has published a new paper proposing the PLE benchmark for entity engineering tasks. The benchmark contains 85 electrical, civil, structural, and mechanical engineering tasks written or verified by practicing engineers. According to a report by Rohan Paul, the strongest agent scores only 50.6% on PLE: Claude Opus 5.5 in Claude Code ranks first among 14 models at 50.6%, consuming 27M tokens and 63 minutes per task, requiring reading about 61 project files and delivering schedules, reports, or 2D/3D drawings. The report also states that in design tasks its output satisfies 91% of constraints, but only 41.3% satisfy all hard constraints. There are currently no follow-up developments.
Google has published a new paper proposing the PLE benchmark for entity engineering tasks. The benchmark contains 85 electrical, civil, structural, and mechanical engineering tasks written or verified by practicing engineers. According to a report by Rohan Paul, the strongest agent scores only 50.6% on PLE: Claude Opus 5.5 in Claude Code ranks first among 14 models at 50.6%, consuming 27M tokens and 63 minutes per task, requiring reading about 61 project files and delivering schedules, reports,
When commenting on Zachary Nado's statement that "Gemini [redacted] hasn't arrived yet," TestingCatalog predicted: this year we will either still be waiting for a large model from Google, or [redacted]. This prediction was published by Testing Catalog on October 29, 2026, and there has been no subsequent development or official response so far.
When commenting on Zachary Nado's statement that "Gemini [redacted] hasn't arrived yet," TestingCatalog predicted: this year we will either still be waiting for a large model from Google, or [redacted]. This prediction was published by Testing Catalog on October 29, 2026, and there has been no subsequent development or official response so far.
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 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
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 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
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.
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.
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 subsequen
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.
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.
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 a
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.
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.
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 s
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.
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.
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 t
Google has published a new paper proposing the PLE benchmark for entity engineering tasks. The benchmark contains 85 electrical, civil, structural, and mechanical engineering tasks written or verified by practicing engineers. According to a report by Rohan Paul, the strongest agent scores only 50.6% on PLE: Claude Opus 5.5 in Claude Code ranks first among 14 models at 50.6%, consuming 27M tokens and 63 minutes per task, requiring reading about 61 project files and delivering schedules, reports, or 2D/3D drawings. The report also states that in design tasks its output satisfies 91% of constraints, but only 41.3% satisfy all hard constraints. There are currently no follow-up developments.
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.
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.