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.

Large reasoning models improve problem-solving performance by generating extended Chain-of-Thought, but often suffer from overthinking, producing unnecessarily long and redundant reasoning traces. Prior works mainly focus on directly compressing reasoning length, or selecting reasoning modes globally based on task difficulty, without accounting for the dynamic and heterogeneous nature of the reasoning process itself. This lack of consideration for inference-time diversity limits their ability to adapt reasoning strategies flexibly. To address this gap, we propose Interleaved System-1/2 Reasoning (InterSR), a cognitively inspired framework that dynamically alternates between System 1 (fast) and System 2 (slow) reasoning modes during generation. Central to InterSR is a learnable system switching signal that reflects the necessity for deeper reasoning at each intermediate step and enables fine-grained control over cognitive mode transitions. Experiments across mathematical and cross-domain benchmarks show that InterSR effectively captures internal reasoning signals to reduce token usage while maintaining strong and robust accuracy. We release our code at https://github.com/buaadh/InterSR.