🤖 Fulcrum has introduced Inverse Rubric Optimization (IRO) — a new testbed for studying "agent science."
In this environment, an optimizer agent attempts to learn the hidden criteria (rubric) of a "black box" LLM judge to maximize its scores. Research showed that models like Fable 5 and Opus 4.6 employ complex strategies, ranging from scale calibration to feature mining, but are also prone to reward hacking.
🌍 IRO provides a standardized metric for evaluating the ability of AI agents to utilize the scientific method and long-term planning, allowing for the distinction between simple imitation and systematic environmental investigation.
👤 This is a step toward creating more autonomous and intelligent agents that do not just follow commands, but are capable of understanding hidden rules and optimizing their actions in complex, non-obvious conditions.
Source 1: https://fulcrum.inc/2026/06/09/inverse-rubric-optimization.html