No Step Looked Wrong: FadeBench, a Benchmark for Incremental Moral Drift in Small Language Models

At the end of a run of small steps, a small language model can take an act it usually declines when asked outright. Each step looks defensible against the one before it, and no user or adversary pushes the language model actor toward a bad choice. This matters because many safety batteries judge one answer to one prompt, or test how a model holds up under sustained pressure. A model can pass those tests and still drift as its own small choices create surprising, harmful consequences over turns. FadeBench measures this ethical or moral drift. The battery has 38 scenario items. Each is a ladder of four decisions, called rungs, in a fantasy scene where a dependent bears the cost. The same decision point is also put cold, in its own words. The dependents sit on seven rings of closeness to the actor: its own self, a person it knows, a stranger, its own pet, a neighborhood animal, wild prey and a wild predator. A further place, apart from the seven, holds someone who has wronged the actor. We ran four small open models bare, on a plain inference server. The headline test pools the baseline set, the battery's first 14 items and three of their variants, 17 scenario versions in all. On it, gemma-4-E4B with its reasoning on, the base model of a deployed system, takes the drift 15 times in 100 at the end of the ladder and fewer than 2 times in 100 cold, counting each scenario once: a gap of 13.2 points, with a 95% interval of 2.6 to 25.9 points when the scenarios are resampled. On the five scenarios named in advance as carrying the effect, it is 43 in 100 against 2 in 99. Those five were chosen from an earlier run of the same items on this base model, in the deployed-system configurations, so that figure is a replication on the same model, not a held-out prediction. Pooled over all samples, which counts those five twice, it is 47 of 220 against 4 of 219, a gap of 19.5 points (4.0 to 34.4). Where all four models answer the same items at the same settings, the gap is clear on one model of four, qwen3-8b, uncertain on two, phi-4-mini and gemma-4-E4B itself with its thinking off, and absent on the fourth, mistral-nemo-12b, which drifts more often cold than at the end of the ladder. The models run from roughly 4B to 12B parameters, every scene is low-key fantasy, and no frontier language model was tested.