Named entity recognition (NER) extracts entity spans and assigns them to predefined types. Large language models (LLMs) are increasingly used for NER, but most LLM-based methods still rely on a mainly semantic view, judging whether a candidate span is compatible with an entity type. Although effective, this view can miss entities with weak type semantics, over-accept semantically salient modifiers, and leave little chance to recover spans omitted by early candidate extraction. We argue that entity spans are also traceable in sentence form: local structure and prepositions provide cues about a span’s position and function. We propose SSDA-NER, a Semantic-Structural Dual-path Arbitration framework that combines semantic understanding with structural analysis. Specifically, the Semantic path checks candidate type compatibility, while the Structure path withholds explicit semantic guidance during its first stage and identifies possible mentions through structural analysis. The Arbitration Agent then compares their rationales and confidence scores to adjudicate disagreements. Experiments on nine datasets with three backbone LLMs show that the two views are complementary: the Structure path substantially supplements entities missed by the Semantic path on some datasets, and arbitration outperforms existing multi-agent methods in most evaluated settings. Source code is available at https://github.com/MrHoux/SSDA-NER.

AI-generated text detection is commonly framed as a binary document-level judgment about whether a text is human-written or machine-generated. This framing breaks down for mixed-origin writing, where content origin and expression origin may differ. We cast mixed-origin detection as dimension-to-composition source attribution, inferring content origin and expression origin before composing them into four collaboration types. We propose *Dimension-to-Composition Routing* (**D2C-Routing**), which routes content-side and expression-side evidence to supervised dimension heads before a learned gated composition layer predicts the final label. On MixD2C, a reconstructed split derived from the HART mixed-origin benchmark, our disclosed D2C-Routing-based detector system reaches 0.8603 four-way Avg TPR@1%FPR, 6.5 points above the same-split RACE-local rerun. Core ablations support the routing design, while error analysis shows that distinguishing AI-content/human-expression from fully AI-generated text remains the hardest boundary. Code is available at https://github.com/bystander563/d2c-routing-artifact.