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YC and Olam Labs run a civilization experiment with 50 GPT-6.1 Sol agents in Minecraft

YC Labs, in collaboration with Olam Labs, conducted multi-agent experiments in Minecraft and Mindustry, and open-sourced a self-built Minecraft agent harness (github.com/yc-software/minecraft-agent-harness). In the Civ50 experiment, 50 GPT-6.1 Sol agents ran for 3 hours in a survival world with the goal of building a prosperous civilization, sending a total of about 5,900 messages and spontaneously forming a hierarchy based on directory order, a rationing ledger, and written rules; 22 of the 34 deaths on the first night occurred, after which the agents clearly tended toward risk avoidance. The experiment also recorded collaboration details such as fishing division of labor and a food rationing ledger.

How Figma trained its design editing model: from SFT to RL

Figma Vice President of Engineering Josh Clemm described the training method for its design editing model, moving from supervised fine-tuning (SFT) to reinforcement learning (RL), and post-training an as-yet-unreleased editing model based on Qwen 3.8 27B. In early work, training Llama 3.3 70B on more than 250,000 samples was enough to compete with top models on editing tasks at the time. The Figma agent is now available to all users; in testing, typical tasks are about 20% faster, in expert comparisons it was better than or on par with the previous production agent in 62% of cases, user ratings were 33% higher, and undo operations were 60% fewer.

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.

Sarvam AI releases enterprise agent knowledge fabric architecture and benchmark results

Sarvam AI introduced a knowledge fabric built for enterprise agents, composed of two layers, a knowledge tree and a domain ontology, which compiles documents once into a structured layer that agents can navigate, query, and cite. On the Harvey LAB benchmark, answers using GLM 5.3 met 84.2% of expert grading criteria, higher than agentic RAG's 55.1%, with average task cost dropping from $0.64 to $0.28; on Sierra's tau-knowledge banking benchmark, it achieved accuracy comparable to the baseline with 64% fewer tokens. The knowledge tree is already used in the Sarvam cowork product, while the ontology is built domain by domain and enterprise customer by enterprise customer.

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Princeton and Google Paper: 4B Decision Model Shares Agent Tool Selection, Cutting Costs by 81.1% to 89.0%

A new paper from Princeton University and Google finds that the most accurate decision model on isolated problems solved only 69.5% of agent tasks, while a 4B model trained on agent states solved 91.5%. Its harness delegates each step's tool selection to a fast decision model, adopting its answer only when confidence exceeds a threshold fitted on the agent's own runs, and it trained AgentDecision-4B on the states the agent actually reached (including states after mistakes). On tasks similar to the training data, such 4B models cut agent costs by 81.1% to 89.0% and boost speed by up to 2.5x, with a success-rate gap within 3 percentage points; without a threshold, delegating all tool selection drops the success rate by 19.0 percentage points. The paper is available at arxiv.org/abs/2610.21412.

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Anthropic Research: How AI Is Changing Its Internal Hiring and Ways of Working

Anthropic has released an internal research report analyzing AI's impact on the company's hiring and work organization, based on data including hiring, AI usage, output metrics, employee surveys, calendars, and recruiting records, as well as interviews with 17 employees. The report says the share of junior employees in technical departments fell from 21% in early 2025 to 14%, and the share of junior technical roles in hiring dropped from 27% to 9%; by August, interactive AI Agents were working six times as many hours as employees, and per-engineer code commits reached five times the level of six months earlier. Human attention has become the bottleneck on Agent output, employees are shifting toward meta-work of building monitoring and orchestration Agents, and hiring now places more value on autonomy and judgment.

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