Capabilities · Concept
Orchestration Reward Modeling for Multi-Agent Systems
OrchRM self-supervised framework evaluates multi-agent orchestration quality without human annotations, improving training efficiency by up to 10x and test-time scaling by up to 8%.
Forecast horizon
Orchestration-level reward modeling may become the dominant paradigm for scaling multi-agent system performance.
Connections
Connections · 3
How this node ties into the rest of the map, and the evidence behind each link.
from · drives 4/10
Risks of Mass AI Agent Interactions Risks from mass agent interactions motivate development of orchestration reward modeling to improve coordination quality.
+4 growth to · applies to 3/10
Risks of Mass AI Agent Interactions OrchRM addresses the challenge of training orchestrators for multi-agent systems without costly supervision, relevant to managing mass agent interactions.
+3 growth from · drives 3/10
LLM Agent Performance in Dynamic Environments Poor agent performance in dynamic environments motivates development of better orchestration and reward modeling approaches.
+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 →