aspg-39-4466-1792222400 20261017073320 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Neutrosophic and Information Fusion NIF 2836-7863 2026 6 1 Erina Kovachiskaya Faculty of Information Technology and Robotics, Vitebsk State Technological University, Belarus Reliable multi-source AI requires a distinction between two non-equivalent failure modes: within-source ambiguity and between-source contradiction. This paper formulates dual-indeterminacy neutrosophic fusion (DINF), in which calibrated posteriors ps ∈ ΔK−1 are transformed into class-wise triples Nk = ⟨Tk, Ik,Fk⟩. Source weights combine chance-corrected validation reliability, normalized entropy, and a robust Jensen–Shannon consensus discount. Intrinsic ambiguity U and class-specific contradiction Ck are retained separately and joined by Ik =U +Ck −UCk. The decision distribution qk ∝ Tk exp(−λIk) therefore penalizes conflict without collapsing neutrosophic evidence into a probability simplex. Boundedness, permutation invariance, consensus preservation, conflict monotonicity, log-odds sensitivity, and missing-source neutrality are established. Repeated experiments on three public benchmarks examine clean data, 30% source dropout, 30% confident contradiction, and 40% mixed failure. Under contradiction, DINF achieves macro-F1 0.910, NLL 0.445, and selective accuracy 0.939 at approximately 90% coverage; the corresponding logarithmic-pool values are 0.896, 0.469, and 0.929. The results identify robust agreement discounting and explicit contradiction-sensitive indeterminacy as complementary mechanisms for calibrated failure-aware fusion. 2026 26 33 4466 https://creativecommons.org/licenses/by/4.0/ 10.54216/NIF.060104 https://americaspg.com/journal/39/article/4466 https://americaspg.com/storage/articles/manuscripts/01KYGJYH3V44T3SHMKAAV508NP.pdf