Capabilities · Trend
Subquadratic LLM Architecture Breakthrough Claims
Startup and research race for post-transformer architectures as the next major LLM efficiency leap.
Forecast horizon
If validated, subquadratic attention mechanisms could substantially reduce inference costs and enable longer context windows at scale.
Connections
Connections · 2
How this node ties into the rest of the map, and the evidence behind each link.
to · drives 3/10
Science of AI Training Dynamics Claims of a subquadratic architecture breakthrough would fundamentally alter the science of AI training dynamics and scaling laws.
+3 growth to · informs 3/10
Science of AI Training Dynamics Subquadratic architecture claims, if verified, would represent a fundamental shift in LLM training dynamics.
+3 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 →