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Hassabis shortens AGI timeline to two to three years

Google DeepMind founder and Nobel Chemistry laureate Demis Hassabis offered his judgment that artificial general intelligence (AGI) may arrive within the next two to three years. Earlier this year he had estimated three to five years. Hassabis defines AGI as a system that can match the full cognitive scope of the human brain, and uses the "Einstein test" as the criterion, namely whether a system trained only on knowledge from before 1901 could reproduce Einstein's 1905 papers. Other reports say that in an interview with The Wall Street Journal he shortened the timeline to the next 2 to 3 years, and mentioned that earlier expectations ranged from 5 to 10 years, to within 5 years, to 2030. There are currently no further developments confirmed by factual evidence.

Gray Swan launches revamped Arena red team testing platform

On October 20, 2026, Gray Swan launched a redesigned Arena red team testing platform. According to Fortune AI, contestants compete for prize money by tricking AI models into breaking through their guardrails; the new platform adds a sandbox testing environment for indirect prompt injection, and offers beginner-oriented challenges, training certificates, and a pathway for top performers to enter the Gray Swan red team community and take part in real testing. The report also states that Gray Swan conducts pre-release stress testing for frontier labs such as Anthropic, OpenAI, and Google DeepMind, and that its Arena community has about 15,000 members. There are currently no subsequent developments.

Metis Reasoning is founded in London, focusing on a compute-efficient AI approach

On October 20, 2026, the London-based AI company Metis Reasoning was officially established and launched, focusing on a compute-efficient reasoning approach as it seeks a direction distinct from the frontier models of Anthropic and OpenAI. According to Tech.eu, the company was co-founded by Thore Graepel, former head of research at Google DeepMind and co-creator of AlphaGo, and has a team of 12 people spanning researchers, mathematicians, robotics experts and technology entrepreneurs. The company believes that LLMs have fundamental limitations in reasoning, with results that are difficult to verify and lacking an audit trail; it says its approach is more verifiable, more robust and safer, and that it has already achieved some positive results on frontier reasoning problems at a cost far below that of frontier models. The report says its initial funding is believed to be in the tens of millions of dollars, but Metis has not yet announced a release date for its first AI model.

Arena.ai says user satisfaction is the biggest gap for AI labs

Arena.ai puts forward a judgment: whether users enjoy collaborating with agents is the biggest gap between AI labs. This judgment is based on Arena's tracking of four Agent Arena signals for the best agents from six labs—Anthropic, OpenAI, Google DeepMind, Kimi, xAI, and DeepSeek—from late June to early October. Among them, on the Praise vs Complaint metric, OpenAI and Anthropic were +35 points above the leaderboard average, while Moonshot, xAI, and DeepSeek ranged between +5 and +13; on Confirmed Success, the average lab's best model rose from -4 to +14 points within three months, with Anthropic leading at +20 with Claude Opus 5.5 (Max); the Bash Recovery gap narrowed from 34 to 16 points, and Steerability from 19 to 11 points. There are currently no further developments.

Andreessen Horowitz leads Eka Robotics Series A funding

On October 20, 2026, Andreessen Horowitz (a16z) led the Series A funding round of Eka Robotics. According to a TechCrunch AI report that day, Eka Robotics completed a $150 million Series A funding round led by a16z and F-Prime, at a post-money valuation of $1.45 billion; the company was founded by MIT professor Pulkit Agrawal and former Google DeepMind researcher Tuomas Haarnoja, and its vision-force-action model, paired with force-sensor grippers, can grasp about 1,500 objects weighing under 1 kg per hour with a success rate exceeding 95%; the grippers cost less than $1,000 and are rated for 5 million cycles, and the company is currently conducting pilot tests of pick-and-place and cable manipulation with industrial customers. The next day, Zhidongxi reported likewise that this funding round was $150 million at a post-money valuation of about $1.45 billion, co-led by a16z and F-Prime with participation from Samsung Next and others, and said the company was founded in 2025 and is carrying out pilot cooperation with multiple industrial customers. There is currently no further progress following the completion of this funding round.

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Four Gemini 4 Argon agents complete Minecraft without human intervention

The Google DeepMind team had four AI agents running on Gemini 4 Argon autonomously complete Minecraft in a randomly generated world, killing the Ender Dragon with no human input throughout. The team did not assign them roles, strategies, or game-specific tools; the four agents divided the work among themselves, warned each other of threats, recovered items from fallen teammates, and recorded mistakes in a shared memory. The entire run took 27 hours and 14 minutes, during which they wrote a total of 2,783 JavaScript programs, exchanged 229 chat messages, and died 605 times, costing $1,620 at the standard Gemini 4 Argon pricing that takes effect in 2027.

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Chollet says Argon performs similarly to Astra on ARC-AGI-3

François Chollet commented that Gemini 4 Argon performs similarly to Astra on ARC-AGI-3, with Google returning to the frontier. On the same day, ARC Prize published the verified results for Gemini 4 Argon on the ARC-AGI series: ARC-AGI-3 scored 97.5% under the provider adapter harness at a cost of $4.8K; under the standard harness it scored 49.8% at a cost of $5.8K. ARC-AGI-2 scored 91.3% at $0.32 per task; ARC-AGI-1 scored 98.5% at $0.16 per task. ARC Prize said Google DeepMind is the second frontier AI lab to exceed 95% on ARC-AGI-3, and Argon's 97.5% is second only to GPT-6 Astra's 99.9%. The standard harness test could not complete all 55 games due to API issues; the high-reasoning setting completed only 19 games, so no score was published.

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Summit announces keynote speakers

On October 8, 2026, ARC Prize announced Kevin Murphy as a research speaker for the ARC Prize Research Summit 2026. According to ARC Prize, Kevin Murphy is a research scientist at Google DeepMind and the author of the textbook "Probabilistic Machine Learning," working on developing AI that learns causal world models through experimentation. The same post also invited registration for the summit. Previously, on October 7, ARC Prize announced research speaker Alexandre Bouayad, a quantitative researcher at G-Research and author of "Weave of Formal Thought," who studies how to introduce formal structures into the process of language model learning and code generation. On October 9, ARC Prize further announced that Stephen Wolfram will serve as a keynote speaker for the summit, along with a registration link for attending.

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