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Google tested using a lightweight classifier to route between Gemini models and thinking levels to reduce per-task cost
The Google Developers Blog ran a test with 50 auto-graded coding agent questions, connecting TypeSafe's Jev classifier before model calls to perform routing: simple tasks were handed to Flash-Lite, while complex tasks were escalated to Gemini 3.8 Flash at a low thinking level. All 50 questions were answered correctly, with both cost and response time lower than using low-thinking-level Flash throughout. The article also found that squeezing maxOutputTokens down to 300 did not reduce thinking overhead on simple tasks, and instead caused 7 of 25 hard questions to fail; local heuristic rules based on keywords and prompt length dropped in accuracy from 93.8% to 70.6% on 34 unseen questions. The authors recommend first using low-thinking-level Flash directly, then adding classifier routing for short, independent tasks, while keeping a single model for long-context tasks to preserve the Context Caching discount.
The Google Developers Blog ran a test with 50 auto-graded coding agent questions, connecting TypeSafe's Jev classifier before model calls to perform routing: simple tasks were handed to Flash-Lite, while complex tasks were escalated to Gemini 3.8 Flash at a low thinking level. All 50 questions were answered correctly, with both cost and response time lower than using low-thinking-level Flash throughout. The article also found that squeezing maxOutputTokens down to 300 did not reduce thinking ove
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Google's October AI Update: Gemini 4 Argon Launches Globally, Gemini 4…
In October, Google launched its frontier reasoning model Gemini 4 Argon globally, covering Google AI Studio, the Gemini API, Google Cloud, Search, and the Gemini app. Two weeks later, it announced Gemini 4 Carbon, focused on long-horizon engineering and cybersecurity defense, which is currently undergoing pre-release testing with the U.S. government and select trusted cybersecurity parties through the Fairwind Program. In the same period, it released Genie 4, a real-time interactive world model that supports native audio and natural-language scene control; SynthID Detector opened globally in English and can identify AI content from partners such as OpenAI, NVIDIA, Kakao, and Apple.
In October, Google launched its frontier reasoning model Gemini 4 Argon globally, covering Google AI Studio, the Gemini API, Google Cloud, Search, and the Gemini app. Two weeks later, it announced Gemini 4 Carbon, focused on long-horizon engineering and cybersecurity defense, which is currently undergoing pre-release testing with the U.S. government and select trusted cybersecurity parties through the Fairwind Program. In the same period, it released Genie 4, a real-time interactive world model
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Chaoyan Intelligence Open-Sources 35B Model Apex-35B-V1.0, with AI Participating in Its Own R&D
Beijing-based large model startup Chaoyan Intelligence has released and open-sourced its first large model, Apex-35B-V1.0, with 35 billion total parameters, approximately 3 billion parameters activated per token, and support for a 262,000-token context window, optimized through mid-training and post-training based on Qwen3.6-35B-A3B. The model scored 60.9 and 40.0 on the paper reproduction test PaperBench and the scientific research challenge test FrontierScience, respectively, higher than GLM-5.3's 42.5 and 30.7; it scored 84.0 on the tool-calling test BFCL V4, higher than DeepSeek-V4.1-Flash-1017's 75.0. The model has been released with open weights under the Apache 2.0 license, and the official release provides approximately 72GB of BF16 weights and GGUF quantized versions at various precisions.
Beijing-based large model startup Chaoyan Intelligence has released and open-sourced its first large model, Apex-35B-V1.0, with 35 billion total parameters, approximately 3 billion parameters activated per token, and support for a 262,000-token context window, optimized through mid-training and post-training based on Qwen3.6-35B-A3B. The model scored 60.9 and 40.0 on the paper reproduction test PaperBench and the scientific research challenge test FrontierScience, respectively, higher than GLM-5
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Google Releases Gemini 4 Carbon, Focused on Cybersecurity and Coding Capabilities
On October 28, Google released Gemini 4 Carbon, calling it its most powerful AI model to date, with a focus on improving cybersecurity and coding capabilities; it can identify software vulnerabilities, assess security risks, and generate remediation code. The model scored 88.2% on SEC-bench Pro, 54.8% on ExploitGym, and 71.0% on CWE-bench, and Google says these external security evaluations were run without guardrails. Carbon builds on Gemini 4 Argon, released two weeks earlier, and is initially available only to select cybersecurity defenders and trusted partners in the Fairwind Program, with no public access date announced yet; introductory pricing is $2 per million input tokens and $10 per million output tokens, rising to $4 and $20 afterward.
On October 28, Google released Gemini 4 Carbon, calling it its most powerful AI model to date, with a focus on improving cybersecurity and coding capabilities; it can identify software vulnerabilities, assess security risks, and generate remediation code. The model scored 88.2% on SEC-bench Pro, 54.8% on ExploitGym, and 71.0% on CWE-bench, and Google says these external security evaluations were run without guardrails. Carbon builds on Gemini 4 Argon, released two weeks earlier, and is initially
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Google releases Gemini 4 Carbon, strengthening long-duration development and vulnerability discovery and remediation capabilities
On October 28, 2026, Google released the new model Gemini 4 Carbon, which improves coding and tool-calling capabilities over Gemini 4 Argon and strengthens cybersecurity defense capabilities spanning from vulnerability discovery and exploitability validation to remediation. On the Artificial Analysis Intelligence Index v5.0, Carbon scored 59, ranking first, while Argon scored 50; on Terminal-Bench 4.0, the cheating success rate was 0.0% and the attempted cheating rate was 0.4%. That week, the model was first made available to some cybersecurity defenders and gradually expanded through the Fairwind Program, before later being opened to developers, enterprises, and general users. Announced pricing is $2 per million tokens for input and $10 per million tokens for output.
On October 28, 2026, Google released the new model Gemini 4 Carbon, which improves coding and tool-calling capabilities over Gemini 4 Argon and strengthens cybersecurity defense capabilities spanning from vulnerability discovery and exploitability validation to remediation. On the Artificial Analysis Intelligence Index v5.0, Carbon scored 59, ranking first, while Argon scored 50; on Terminal-Bench 4.0, the cheating success rate was 0.0% and the attempted cheating rate was 0.4%. That week, the mo
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Google Releases Flagship Model Gemini 4 Carbon, Strengthening Cybersecurity Capabilities
Google has released the frontier large language model Gemini 4 Carbon, focused on complex programming and cybersecurity tasks. Google says internal testing shows its ability to generate secure code is more than twice that of the previous generation, that it surpasses the previous record on SEC-bench Pro, and that it can better patch discovered vulnerabilities; in Terminal-Bench 4.0, only 0.4% of problems involved attempted cheating, and none succeeded, while in the Gray Swan IPI Benchmark it blocked all simulated indirect prompt injections. Carbon will open early access later this week through the Fairwind Program to organizations such as internet service providers, and Google plans to refine its guardrails based on feedback before making it broadly available.
Google has released the frontier large language model Gemini 4 Carbon, focused on complex programming and cybersecurity tasks. Google says internal testing shows its ability to generate secure code is more than twice that of the previous generation, that it surpasses the previous record on SEC-bench Pro, and that it can better patch discovered vulnerabilities; in Terminal-Bench 4.0, only 0.4% of problems involved attempted cheating, and none succeeded, while in the Gray Swan IPI Benchmark it blo
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Google releases Gemini education research results in the UK and Italy
Google announced the results of its Gemini classroom research in the UK and Italy: a 10-week randomized controlled trial in the UK covering more than 1,100 students aged 12 to 15 found that AI tutoring performed similarly to human tutoring on platform tasks and quizzes, but showed no improvement on the STAR standardized test. A study of 31 schools and 1,600 teachers in Italy showed that after teachers used Gemini to co-design lessons, traditional lecturing decreased by 38%, student-led collaborative learning nearly tripled, and student engagement rose from 77% to 84%.
Google announced the results of its Gemini classroom research in the UK and Italy: a 10-week randomized controlled trial in the UK covering more than 1,100 students aged 12 to 15 found that AI tutoring performed similarly to human tutoring on platform tasks and quizzes, but showed no improvement on the STAR standardized test. A study of 31 schools and 1,600 teachers in Italy showed that after teachers used Gemini to co-design lessons, traditional lecturing decreased by 38%, student-led collabora
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Fort Robotics Secures Approximately $20 Million in Additional Financing, Led by Tiger Global
Fort Robotics has received approximately $20 million in additional investment from existing investors, with Tiger Global leading approximately $10 million and Mark Cuban Companies participating, expected to close this week. Tiger Global is additionally contributing another $10 million to the PIPE related to the merger with Newbury Street II Acquisition Corp, bringing total committed funding for the transaction to over $50 million. Fort's Trust Layer has been deployed on more than 19,500 devices, serving over 600 customers, and the combined company plans to list on Nasdaq under the ticker "FROB".
Fort Robotics has received approximately $20 million in additional investment from existing investors, with Tiger Global leading approximately $10 million and Mark Cuban Companies participating, expected to close this week. Tiger Global is additionally contributing another $10 million to the PIPE related to the merger with Newbury Street II Acquisition Corp, bringing total committed funding for the transaction to over $50 million. Fort's Trust Layer has been deployed on more than 19,500 devices,
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Physical Intelligence in Talks for at Least $1 Billion in New Funding, Valuation Could Reach $20 Billion
According to Bloomberg, U.S. robotics AI company Physical Intelligence is in talks for a new funding round of at least $1 billion, with existing investors Avenir and Lux Capital considering leading the round, and a post-money valuation expected at about $20 billion. The company was founded in 2024 and was valued at $5.6 billion in its November funding round last year; it currently has no commercial revenue and has not mass-produced robot products. Its technical approach is to build on VLM by introducing action generation capabilities to train VLA models, and it has iterated through π0, π0.5, and π0.7; this month it launched a beta version of a robot foundation model API, offering model fine-tuning and inference services to external developers.
According to Bloomberg, U.S. robotics AI company Physical Intelligence is in talks for a new funding round of at least $1 billion, with existing investors Avenir and Lux Capital considering leading the round, and a post-money valuation expected at about $20 billion. The company was founded in 2024 and was valued at $5.6 billion in its November funding round last year; it currently has no commercial revenue and has not mass-produced robot products. Its technical approach is to build on VLM by int
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Google previews Genie 4 world model, which can generate 3D environments with audio
Google previews the Genie 4 world model, which can generate realistic three-dimensional environments based on natural language descriptions and supports editing operations such as changing the weather and adding objects with follow-up prompts. Compared with the previous generation, Genie 4 is better at rendering complex details such as fluid motion, can also generate accompanying audio such as crashing waves, and makes virtual characters move naturally and be controllable through real-time interaction. Internally, the model calls Gemini Omni to ensure visual realism and Gemini 3.8 Flash to maintain inter-frame consistency. It is currently being tested with a small number of early users and will later be offered through the Google AI Ultra subscription, the Gemini API, and Google AI Studio.
Google previews the Genie 4 world model, which can generate realistic three-dimensional environments based on natural language descriptions and supports editing operations such as changing the weather and adding objects with follow-up prompts. Compared with the previous generation, Genie 4 is better at rendering complex details such as fluid motion, can also generate accompanying audio such as crashing waves, and makes virtual characters move naturally and be controllable through real-time inter
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Google Releases Gemini 4 Carbon, Topping the Artificial Analysis Intelligence Index but Not Yet Publicly Available
On October 28, Google released Gemini 4 Carbon, which topped the Artificial Analysis Intelligence Index v5.0 with a score of 59, surpassing Claude Opus 5.5's 58 and GPT-6 Astra's 57 to reclaim first place. The model is currently available only to a small number of cybersecurity defense personnel; when asked when ordinary developers and enterprises will be able to use it, officials responded only with "as soon as possible." On the GraphWalks ultra-long-context test, Carbon achieved 96.2% F1, far above GPT-6 Astra's 71.8% and Claude Opus 5.5's 66.8%; introductory pricing is $2 per million input tokens and $10 per million output tokens, the same as the previous-generation Argon.
On October 28, Google released Gemini 4 Carbon, which topped the Artificial Analysis Intelligence Index v5.0 with a score of 59, surpassing Claude Opus 5.5's 58 and GPT-6 Astra's 57 to reclaim first place. The model is currently available only to a small number of cybersecurity defense personnel; when asked when ordinary developers and enterprises will be able to use it, officials responded only with "as soon as possible." On the GraphWalks ultra-long-context test, Carbon achieved 96.2% F1, far
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Elorian Releases DecoyCount Benchmark: Frontier Models All Score Below 50% on Visual Counting Accuracy
Elorian and Mercor jointly released the DecoyCount benchmark, which tests whether models truly count objects or rely on visual shortcuts. Frontier models all scored below 50% accuracy, with the highest being GPT-6 Astra at 43.17%. Elorian says its visual reasoning model Glimpse scored 50.48% in a single run on 1,660 questions, making it the only model to exceed 50%, but this result comes from internal testing and did not use an agent or tool framework, whereas the six models in the paper each took the average of three runs. Elorian CEO Andrew Dai said the benchmark examines both vision and reasoning, and can distinguish whether a model failed to see the objects or miscounted them.
Elorian and Mercor jointly released the DecoyCount benchmark, which tests whether models truly count objects or rely on visual shortcuts. Frontier models all scored below 50% accuracy, with the highest being GPT-6 Astra at 43.17%. Elorian says its visual reasoning model Glimpse scored 50.48% in a single run on 1,660 questions, making it the only model to exceed 50%, but this result comes from internal testing and did not use an agent or tool framework, whereas the six models in the paper each to
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Google releases Gemini 4 Carbon, tops the Artificial Analysis leaderboard with 59 points
Google released Gemini 4 Carbon, which scored 59 points on the Artificial Analysis Intelligence Index v5.0 leaderboard, surpassing Claude Opus 5.5's 58 points and GPT-6 Astra's 57 points. The model achieved 88.2%, 54.8%, and 71.0% on SEC-bench Pro, ExploitGym, and CWE-bench, respectively, and Google says it can handle engineering problems in large codebases continuously for several days. The model this week is first being made available to some cybersecurity defenders, then expanded to trusted partners through the Fairwind Program, with no timeline yet for general users; initial pricing is $2 per million input tokens and $10 per million output tokens.
Google released Gemini 4 Carbon, which scored 59 points on the Artificial Analysis Intelligence Index v5.0 leaderboard, surpassing Claude Opus 5.5's 58 points and GPT-6 Astra's 57 points. The model achieved 88.2%, 54.8%, and 71.0% on SEC-bench Pro, ExploitGym, and CWE-bench, respectively, and Google says it can handle engineering problems in large codebases continuously for several days. The model this week is first being made available to some cybersecurity defenders, then expanded to trusted p
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Riley Goodside Says Prompt Engineering Is Dead: Ordinary Users Can Just Ask Directly
Riley Goodside, once regarded as the world's first officially titled prompt engineer, has publicly declared that "prompt engineering is dead"—ordinary users can ask whatever they want, however they want. He added that the workload of engineers who write underlying prompts for systems, fine-tune small models, and embed prompts into applications has not decreased; what has died is only the prompt-crafting skill that ordinary users were once required to have. Chainguard used a single sentence—"find remotely exploitable high-severity vulnerabilities in PHP"—to get its self-built system to reproduce known vulnerabilities and verify official fixes.
Riley Goodside, once regarded as the world's first officially titled prompt engineer, has publicly declared that "prompt engineering is dead"—ordinary users can ask whatever they want, however they want. He added that the workload of engineers who write underlying prompts for systems, fine-tune small models, and embed prompts into applications has not decreased; what has died is only the prompt-crafting skill that ordinary users were once required to have. Chainguard used a single sentence—"find
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AI-driven cyberattacks erupt intensively; industry insiders call October a "Black October" for cybersecurity
Cailianshe reported that in recent weeks hackers have massively deployed AI agents and automated tools to launch attacks, and industry insiders describe this month as Black October in the cybersecurity world. A major vulnerability was exposed in PHP, and as many as 70% of websites worldwide may face the risk of being hacked. The U.S. National Security Agency and the FBI warned enterprises to shut down their websites if they cannot patch immediately, and some government websites in Canada, Belgium, France and the Netherlands have proactively gone offline. Shane Fry, chief technology officer of security firm RunSafe Security, said AI can weaponize a vulnerability into attack code within hours, whereas in the past this took weeks or even months.
Cailianshe reported that in recent weeks hackers have massively deployed AI agents and automated tools to launch attacks, and industry insiders describe this month as Black October in the cybersecurity world. A major vulnerability was exposed in PHP, and as many as 70% of websites worldwide may face the risk of being hacked. The U.S. National Security Agency and the FBI warned enterprises to shut down their websites if they cannot patch immediately, and some government websites in Canada, Belgiu
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What exactly are automakers aiming for by building humanoid robots?
Automakers building humanoid robots aim to incubate new businesses using the funding, real factory tasks, and supply chain capabilities of their automotive operations—first validating them in manufacturing and store scenarios, then pursuing external customers. Tesla's Optimus has already begun production at the Fremont factory, with external delivery targets still around the end of 2027, and plans to build 10,000 to 20,000 units for training; Figure 02 has accumulated more than 1,250 hours of operation at BMW's factory, and a forearm failure prompted a wrist structure change in the third generation. Hyundai controls Boston Dynamics, XPeng develops IRON in-house, and Mojia leverages Chery's channels—the approaches differ, but whether internal trials can translate into sustained paying external customers remains to be verified.
Automakers building humanoid robots aim to incubate new businesses using the funding, real factory tasks, and supply chain capabilities of their automotive operations—first validating them in manufacturing and store scenarios, then pursuing external customers. Tesla's Optimus has already begun production at the Fremont factory, with external delivery targets still around the end of 2027, and plans to build 10,000 to 20,000 units for training; Figure 02 has accumulated more than 1,250 hours of op
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