ZS-MTR-RR: Training-Free Length-Conditioned Reliability Routing of Global and Multi-Trajectory Evidence for Zero-Shot AI-Generated Text Detection

Large language models increasingly produce fluent, human-like text, posing severe challenges to academic integrity, misinformation governance, and content provenance. Zero-shot detectors avoid collecting labeled data for unseen generators, yet existing approaches face a dilemma: document-level statistics compress token probabilities into a single global score, overlooking positional sequence structure; while trajectory-based methods retain positional information but rely on fixed aggregation, failing to adapt the relative contributions of global and trajectory evidence as text length varies. To address these limitations, we propose ZS-MTR-RR, a training-free framework featuring Zero-Shot Multi-Trajectory Representation with Length-Conditioned Reliability Routing. Using paired frozen base and instruction-tuned models, ZS-MTR-RR extracts aligned token log-probability outputs to construct both a full-document proxy-discrepancy path and a multi-trajectory evidence path capturing late-stage stability, proxy tension, and token-reuse structures. Conditioned on scored length as an indicator of available positional support, a deterministic soft gate balances the two paths, converting fixed signal combination into length-conditioned reliability routing without training an additional detector or learned router. We evaluate ZS-MTR-RR on the five-domain EvoBench benchmark and four RealBench-derived transformations. Under the unified EvoBench protocol, ZS-MTR-RR achieves the highest cross-domain average AUROC (reaching 0.8753) among evaluated methods. Ablation and length-stratified analyses show that the two evidence paths provide complementary discriminative signals and support the benefit of length-conditioned routing over fixed fusion on the primary benchmark.