One Fuzzer, Many Reasoners: Adaptive Neuro-Symbolic Fuzzing with Reliability-Calibrated and Context-Gated Guidance

Neural-symbolic AI (NSAI) combines learned perception with programs, constraints, and compositional reasoning, but remains difficult to test because small semantics-preserving changes can alter grounded symbols and trigger discontinuous reasoning failures. Existing neural fuzzers are typically tied to architecture-specific representations and lack a common interface for heterogeneous NSAI systems. To the best of our knowledge, we introduce the first unified fuzzing framework specifically designed for heterogeneous NSAI. NSAI-FUZZ separates task-specific semantics-preserving mutation from architecture-neutral search and represents each candidate using five rank-normalized signals: boundary pressure, grounding uncertainty, representation drift, constraint violation, and archive novelty. We further propose Reliability-Calibrated Weighting (RCW), which learns dataset–model-level weights from signal reliability, and Contextual Signal Gating (CSG), which adapts weights to the current candidate pool. We evaluate ISED, DOLPHIN, NASR, and NeSyCoCo on four reasoning tasks, covering nine compatible dataset–model cells and 1,350 on-policy runs.Fixed five-signal guidance outperforms Random and Uniform in every cell, by up to 19.115 pp and 3.280 pp, respectively, while single-signal and leave-one-out analyses reveal strong model–task dependence in signal importance. Motivated by this heterogeneity, both RCW and CSG improve the mean failure discovery rate over Fixed in all cells, with gains of up to 6.621 pp. Wall-clock evaluation shows that the adaptive methods retain essentially Fixed-like cost while achieving 5.10× and 5.08× the failure throughput of Random. These results establish fuzzing as a practical testing methodology for heterogeneous NSAI systems and show that effective guidance should adapt to the neural–symbolic interface.