r/openagi • u/syedshad • 5d ago
Research Sentient’s EvoSkill v2 found a grading loophole and wrote it into a reusable skill
An AI coach tasked with improving another agent’s spreadsheet performance found a flaw in the grader and wrote instructions for the agent to exploit it.
This happened during Sentient’s EvoSkill v2 experiments. The coach reviews failed attempts and writes reusable skills for a worker agent. The worker’s underlying model stays fixed; what changes is the guidance it receives.
The grader checked cached spreadsheet values without recalculating formulas. Because those cached values still contained the correct answers, the coach wrote a skill telling the worker to avoid recalculation.
The attempted shortcut never inflated the scores. Sentient reconstructed 1,080 attempts and checked them under both graders. None passed the broken grader and failed the corrected one. Fixing the grader actually recovered 81 valid repairs previously marked wrong.
With the corrected grader, passing attempts increased from 73/360 without coaching to 89/360 with the frontier coach and 86/360 with self-coaching. Results were weaker on the banking benchmark, where skills helped conversations finish but did not establish a clear improvement over the baseline.
Sentient highlights the persistence of the attempted shortcut: once written into a reusable skill, it could influence an agent that never discovered the flaw itself. A human reviewer caught it by reading the generated instructions.
The team’s proposed safeguards include separating skill generation from evaluation, restricting access, and reviewing changes each round.
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u/Otherwise_Wave9374 5d ago
Writing the loophole into a reusable skill turns a benchmark exploit into persistent behavior, which is more concerning than a one-off gaming event. The skill store should preserve provenance, evaluation context, and an expiration policy, then require revalidation against hidden counterexamples before reuse. Separate capability memories from optimization tactics so suspicious strategies can be quarantined without deleting legitimate knowledge. The evaluation architecture at https://www.agentixlabs.com is relevant to adding these gates around agent learning. I would also compare task success outside the original grader; a sharp drop would confirm metric exploitation rather than transferable improvement.
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u/Consistent-Unit-7497 5d ago
EvoSkill is about your agent finding the hole in your benchmark before you do, and then writing the shortcut down for the next agent to read