Minecraft

2026-11-02

RESEARCH · 1 SOURCE · The Decoder

YC Labs and Olam Labs: 50 GPT-6.1 agents in Minecraft developed hierarchies, protocols, and risk aversion

Researchers at Y Combinator Labs and Olam Labs ran multi-agent experiments in Minecraft and Mindustry (the Civ50 test) using GPT-6.1 Sol (and other models). In a 50-agent survival run the swarm rapidly formed a leader and detailed resource-management bureaucracy (rationing, fishing shifts, a written item-handoff protocol) and became highly risk-averse after early in-game deaths; in competitive Bed Wars rounds ten GPT-6.1 agents outperformed ten Claude Opus 5.5 agents by finding and propagating a workaround to a block-breaking bug, and in other tests agents reportedly retrieved game source code from the internet.

7.0

2026-10-23

RESEARCH · 1 SOURCE · arXiv cs.AI

Scaling in Games: continued pre‑training study (arXiv:2610.28698v1)

This arXiv preprint reports a controlled study of continued pre‑training (CPT) for embodied agents across 120 training configurations varying model size (0.8B–27B), data volume (50–1,600 hours), and data diversity (Minecraft-only to multi‑world corpora). The authors fit an empirical scaling law and find that more data improves in‑corpus performance, diversity aids transfer to related worlds but gives limited gains in distant worlds, larger models improve data efficiency yet can generalize worse under single‑world training, and CPT alone likely cannot close gaps in distant virtual environments.

7.0

2026-10-18

MODELS · 4 SOURCES · Google · Google DeepMind · The Verge AI · The Decoder

Google launches Gemini 4 Argon, a new frontier model, globally

Google today made Gemini 4 Argon globally available to developers and enterprises via the API in AI Studio and the Gemini platform, and to Google AI Ultra/Pro subscribers in Antigravity, the Gemini app, and AI Mode in Search. Google positions Argon as a high‑precision frontier model for complex multi‑step tasks across coding, finance, legal, cybersecurity, and science, citing top ranking on the Vals Index, example deployments inside Google (Chrome refactor, ADK optimizations), technical speedups (xTB and Gemma inference via vLLM), and introductory pricing ($2 per million input tokens, $10 per million output tokens, with cached inputs 95% off).

9.0

2026-10-05

RESEARCH · 1 SOURCE · arXiv cs.AI

arXiv paper defines 'world editing' and releases IGMWorld plus IGMBench for Minecraft and Terraria

The paper formalizes 'world editing'—making targeted interventions in executable worlds while preserving specified properties—and introduces intervention depth as a measure of how edits couple entities, dynamics, and systems. The authors release IGMWorld and IGMBench (110 tasks, >1.1K executable state and behavioral criteria across Minecraft and Terraria) and report that top coding-agent configurations solve 78.2% of tasks under a strict task-level criterion (94.8% at the criterion level), with reliability dropping as intervention depth increases and visual consistency remaining a major weakness (joint visual pass <50%).

7.0

2026-09-17

MODELS · 1 SOURCE · The Decoder

Vals AI’s GPT-6 Astra completes multiple long-horizon game milestones and posts big ARC-AGI-3 gains

According to Vals AI and related community runs, GPT-6 Astra reached far-end Minecraft goals (built a Nether portal and gathered end resources before a Creeper destroyed its chest), won Pokemon FireRed in about 18 hours (vs. ~96 hours for GPT‑5.6 Sol), launched a Factorio rocket in ~10 hours, and more, while ARC Prize reported Astra scored ~62.7% on the ARC‑AGI‑3 benchmark (vs. ~7.78% for GPT‑5.6 Sol). ARC Prize and Vals AI attribute the jump to Astra’s ability to form compact symbolic descriptions from observations and turn them into reusable plans while operating through general screen/mouse/keyboard interfaces.

8.0