Financial numerical reasoning demands rigorous adherence to domain-specific logic and precise evidence foundation. However, large language models (LLMs) are prone to forced generation when confronting ambiguous evidence or complex recursive dependencies, often hallucinating values to bridge information gaps. To address this, we propose graph-bounded financial reasoning (GBFR), a neuro-symbolic framework that imposes semantic and structural constraints via a financial metric knowledge graph (FMKG). Unlike sequential generation paradigms, our approach employs a parallel graph-constrained reasoning algorithm that orchestrates specialized operators to simultaneously explore heterogeneous derivation paths of complex financial metrics. Through cross-path verification, the framework aggregates only semantically consistent results, ensuring reasoning is bounded by available context. Crucially, this approach enables safe abstention by distinguishing genuine data absence from retrieval failure, thereby preventing ungrounded fabrication. To evaluate this capability, we further construct counterfactual samples by perturbing entities, times, and metrics to synthesize unanswerable scenarios. Empirical evaluations on standard benchmarks demonstrate that GBFR significantly outperforms state-of-the-art baselines.

Recent years have witnessed rapid advancements in text-to-music generation using large language models, yielding notable outputs. A critical challenge is understanding users with diverse musical expertise and generating music that meets their expectations, an area that remains underexplored.To address this gap, we introduce the novel task of Professional and Amateur Description-to-Song Generation. This task focuses on aligning generated content with human expressions from varying musical proficiency levels, aiming to produce songs that accurately meet auditory expectations and adhere to musical structural conventions. We utilized the MuChin dataset, which contains annotations from both professionals and amateurs for identical songs, as the source for these distinct description types. We also collected a pre-train dataset of over 1.5 million songs; lyrics were included for some, while for others, lyrics were generated using Automatic Speech Recognition (ASR) models.Furthermore, we propose MuDiT/MuSiT, a single-stage framework designed to enhance human-machine alignment in song generation. This framework employs Chinese MuLan (ChinMu) for cross-modal comprehension between natural language descriptions and auditory musical attributes, thereby aligning generated songs with user-defined outcomes. Concurrently, a DiT/SiT model facilitates end-to-end generation of complete songs audio, encompassing both vocals and instrumentation. We proposed metrics to evaluate semantic and auditory discrepancies between generated content and target music. Experimental results demonstrate that MuDiT/MuSiT outperforms baseline models and exhibits superior alignment with both professional and amateur song descriptions.