Medical　image　reconstruction,　particularly　within　the　field　of　MRI,　has　witnessed　a　transformative　advancement　in　recent　years,　primarily　attributed　to　the　integration　of　deep　learning　techniques.　Nevertheless,　these　advanced　deep　reconstruction　models　often　encounter　generalization　difficulties　when　dealing　with　out-of-distribution　samples　from　unseen　domains.　Privacy　regulations　frequently　restrict　access　to　source-domain　training　data,　complicating　efforts　to　retrain　or　fine-tune　models　for　target-domain　test　data　-　a　scenario　frequently　encountered　in　clinical　practice.　In　response　to　these　challenges,　we　propose　a　novel　method:　a　black-box　test-time　adaptation　method　tailored　for　MRI　reconstruction,　regardless　of　noise　variations.　The　proposed　method,　named　ADLER-MRI,　is　grounded　in　prior-informed　implicit　neural　representation　(INR)　learning,　which　capitalizes　on　the　power　of　INR　to　synthesize　image　representations　across　a　spectrum　of　noise　conditions.　ADLER-MRI　adeptly　adjusts　input　representations　by　analyzing　the　outputs　inferred　from　an　opaque　reconstruction　model,　at　test　time.　Our　empirical　investigations,　utilizing　the　IXI　and　fastMRI　datasets　for　test-time　adaptation　in　MRI　reconstruction　with　variable　noise　conditions,　demonstrate　that　ADLER-MRI　surpasses　current　leading　algorithms　in　performance.　©　2024　IEEE.