By Jonathan Kemper
Publication Date: 2026-09-26 10:30:00
A new study from Nvidia researchers tackles these costs not at the model level but at the harness, the control layer between the model and its environment used by systems like Codex, Claude Code, or OpenClaw.
The harness controls how an agent sees states, runs actions, and processes feedback. Most efficiency methods so far have focused on cutting the cost per token through faster attention kernels and serving infrastructure, model compression like quantization, or swapping in cheaper models.
AI explores 152 directions to find leaner control logic
Optimizing the harness is hard in practice because tool usage, context management, verification, and abort logic are all tightly coupled. A change that saves tokens in one place can trigger errors elsewhere or just push costs into a later phase. Typically, humans sift through long execution traces and translate recurring failure patterns into code.
The system, called SoL-Pi, automates that work. A research agent watches another agent’s traces, proposes changes, and tests them in prepared environments. Capability and efficiency checks determine which candidates survive. The approach draws on recursive self-improvement, according to the authors.
