Search “DeepMind”

Google DeepMind paper proposes measuring AGI's economic impact using two thresholds: competence and coordination

Google DeepMind has released the paper "The Economics of Intelligence," proposing the use of two thresholds—competence (single-task capability) and coordination (agent collaboration capability)—to characterize AGI's impact on the economy. The authors argue that AI has already shown changes in leaner startups, flatter organizational structures, and slower hiring for some entry-level positions, but that mass unemployment has not yet occurred; most benchmarks test only a single model, whereas firms compete as teams. The paper also cites a 100-person test in which groups paired for complementarity outperformed randomly paired groups by 32%, showing that AI models sharing the same weaknesses may lose to well-coordinated human teams even if they beat every individual one-on-one.

Paper from Google DeepMind and other institutions: the best AI agent solved 49 of 50 unpublished research-level physics problems

A new paper from Google DeepMind and several top universities shows that the best-performing AI agent in the test solved 49 of 50 unpublished research-level theoretical physics problems, each roughly equivalent to a day's work for a domain expert, at a cost of under $3 per problem. The problems were contributed by experts from their own research, and the agents answered independently, split into two groups—with and without internet access—and were scored only on the final numerical answer. GPT-6-Astra, with internet access in Codex, solved 49; it and Opus 5.5, without internet access in Claude Code, each solved 46, and removing internet access changed accuracy very little, leading the authors to suggest that the models may have memorized a large amount of physics literature.

Google DeepMind paper: Proactive AI agents create stress in some teams

A new Google DeepMind paper finds that the same AI assistant was seen by some employees as a savior and by others as a source of stress, because it acts on its own without being asked. The agent ran for 5 months across more than 20 teams at a large tech company, and researchers interviewed 17 users; it treated outdated documents as current, tagged a VP on 16 bugs, shared a poster draft before the author was ready, and misread sarcastic tone. Some users called it a savior, while others felt surveilled and moved sensitive discussions to chats without the agent; teams that actively chose the agent forgave its early mistakes, while those who felt it was imposed on them did not. The authors recommend obtaining team consent when deploying agents, separating permissions, and providing an easy way to undo actions.

DeepMind has four Gemini agents team up to defeat the Ender Dragon in Minecraft

Google DeepMind had four Gemini 4 Argon agents team up to play Minecraft from scratch, defeating the Ender Dragon after 27 hours of real time, during which they died 605 times, wrote more than 2,700 programs, and produced a total of 63,300 lines of code. The four agents had no human-written programs to rely on; chopping trees, mining, and crafting equipment all required them to write and test their own code, and they also shared experience with each other and reported the locations where teammates died. The article also reviews work from the 2016 Malmo platform and the 2019 MineRL diamond-mining competition to 2022's OpenAI VPT and 2023's Voyager, among other efforts that use Minecraft to study AI.

Google DeepMind beats Minecraft with four Gemini 4 Argon agents

Google DeepMind published the paper "A team of Gemini 4 Argon agents beats Minecraft": four Gemini 4 Argon coding agents autonomously divided labor in a randomly generated Minecraft survival world, accumulating 605 deaths and writing 2,783 programs, defeating the Ender Dragon in 27 hours and 14 minutes. The experiment used Minecraft Java Edition 1.20.4, Easy difficulty, a random map, and no cheats; the agents read the environment via Mineflayer and wrote JavaScript to command characters, with roughly 24,000 model calls and 1.35 billion input tokens over the whole run, 91% of which came from context caching. The authors note that about 22% of game programs contained errors and the server restarted 6 times; the paper did not demonstrate stable completion through repeated tests on multiple random maps, and at discounted API prices the completion cost is estimated at about $520.

Google DeepMind and Cambridge paper: AI agent autonomously discovers exact rules for 11 unsolved Turing machines

A new paper from Google DeepMind and Cambridge shows that a Gemini-based agent can autonomously discover new mathematics, finding and providing exact rules for 11 Busy Beaver Turing machines that previously had no analysis on record. The agent tests edge cases in a simulator, drafts informal proofs, and then completes the proofs in the Lean proof checker; all the relevant rules passed Lean verification. The paper states that this reflects AI moving from answering mathematical questions posed by humans toward open-ended discovery.

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DeepMind AlphaProof Nexus appears in《Science》: autonomously solves 9 Erdős problems

Google DeepMind's automated mathematical proof system AlphaProof Nexus appeared in the October 8 issue of《Science》, autonomously solving 9 of 353 open Erdős problems, two of which had remained open since 1970. The system uses Gemini 3.1 Pro to draft proofs in Lean, with a compiler verifying line by line and feeding back iterations; the reasoning cost per problem is several hundred dollars. The paper's ablation results show that a simplified system that removes complex structures such as the sub-agent tournament and retains only the large model plus Lean feedback can also solve these 9 problems, though the hardest few are more expensive.

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