Scaling Laws for Agent Harnesses via Effective Feedback Compute 事件

Scaling Laws for Agent Harnesses via Effective Feedback Compute arXiv:2605.29682v1 Announce Type: new Abstract: Agent harnesses increasingly determine the performance of language-model systems by deciding how models call tools, receive feedback, verify intermediate states, store memory, and revise solutions. Yet current test-time scaling analyses often parameterize this process by raw expenditure -- tokens, tool calls, operations, wall time, or cost -- which does not distinguish useful feedback