Capabilities · Concept
Scaling Laws for LLM Social Simulation Fidelity
Study finding strong compute scaling in LLM social simulation fidelity across opinion modeling, behavioral simulation, and longitudinal forecasting.
Forecast horizon
Connections
Connections · 3
How this node ties into the rest of the map, and the evidence behind each link.
to · supports 3/10
Science of AI Training Dynamics Scaling law analysis for social simulation fidelity contributes to the broader science of AI training dynamics.
+3 growth to · informs 2/10
Plurality of Human Preferences for AI Systems Scaling laws for social simulation fidelity reveal that opinion and behavioral modeling improve with compute, informing how AI systems represent human preference diversity.
+2 growth to · informs 2/10
Science of AI Training Dynamics Scaling law analysis of social simulation fidelity contributes to the science of AI training dynamics.
+2 growthSignal sources
Signal sources
Dated facts from primary sources in this direction.
Task horizon doubling Mar 2025
The length of software tasks AI agents can do autonomously at 50% reliability has doubled about every 7 months — and since 2024 closer to every ~3 months.
METR → Benchmarks saturating Apr 2025
In one year scores rose by 18.8, 48.9 and 67.3 points on MMMU, GPQA and SWE-bench; real-world software solve rate jumped from 4.4% to 71.7%.
Stanford HAI — AI Index 2025 → Autonomous coding 2025–2026
On SWE-bench Verified (500 real GitHub issues), autonomous coding agents reached ~80–86% by late 2025, up from under 50% in early 2025.
Epoch AI →