{"data":{"id":"7f6eecb0-7b6a-49b8-9067-bad0a63441c0","title":"On the Equilibrium Between Feasible Zone and Uncertain Model in Safe Exploration","summary":"This research addresses how to safely explore environments using reinforcement learning (RL, a type of AI training where a system learns by trial and error) without causing damage or violating safety rules. The paper introduces safe equilibrium exploration (SEE), a method that balances two competing goals: expanding the area where exploration is allowed (the feasible zone) and building a more accurate model of how the environment works, showing that these two objectives improve each other and can reach an optimal balance without any safety violations.","solution":"N/A -- no mitigation discussed in source.","labels":["research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11419867","publishedAt":"2026-03-03T13:18:19.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-4-5-20251001","classifierPromptVersion":"v3","cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"patchAvailable":null,"disclosureDate":"2026-03-03T13:18:19.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":["safety"],"aiComponentTargeted":"training_data","llmSpecific":false,"classifierConfidence":0.75,"researchCategory":"peer_reviewed","atlasIds":null}}