The problem of surface-level pattern mapping represents a critical yet underexplored failure mode in large language model (LLM) reasoning, and is particularly acute in cross-architecture code migration of high-performance libraries. On low-resource, low-level code, insufficient coverage in pretraining data often leads LLMs to rely on superficial name- or type-based correspondences, rather than principled refactorization and reasoning grounded in core functional semantics and architecture-specific optimization intents. This tendency severely hampers the effectiveness of LLMs in complex migration scenarios.To address these challenges, we propose FSCM, a multi-agent framework for cross-architecture migration. FSCM decouples complex implementation details through functional mining and code refactoring, guiding LLMs to focus on invariant semantic anchors across architectures. By mitigating surface-level pattern traps, FSCM improves both functional correctness and performance when targeting emerging architectures. Extensive experiments on the challenging real-world OpenCV library migration tasks demonstrate substantial improvements over state-of-the-art baselines, achieving up to 22% higher correctness rates over Copilot and 43.04x speedup on RISC-V platforms. Code and data are available at: https://anonymous.4open.science/r/code-F8D4.

This paper introduces the approach we adopted for the SemEval-2025 “Food Hazard Detection” task, which aims to predict coarse-grained categories (such as “product category” and “hazard category”) and fine-grained vectors (such as specific products like “ice cream” or hazards like “salmonella”) from noisy, long-tailed text data.To address the issues of dirty data, as well as the severe long-tail distribution of text labels and length in the data, we proposed a pipeline system. This system combines data cleaning, LLM-based enhancement, label resampling, and ensemble learning to tackle data sparsity and label imbalance problems.The two subtasks have strong semantic relatedness. By integrating them into a unified multiturn dialogue framework, we fine-tuned five models using a bagging approach. Ultimately, we achieved good results in both subtasks, ranking 5th (with an F1 score of 80.17% for ST1 and 52.66% for ST2).

This paper presents our approaches for three subtasks in SemEval-2025 Task 10, which focus on entity framing, narrative classification, and narrative extraction in new analysis respectively. We propose a two-stage news analytical framework for both Subtask A and B. In Subtask A (Entity Framing), we design an entity-oriented data processing pipeline to address the issue of redundant information in a news article, and explore effective use of multilingual datasets through sufficient experiments. The system achieves the first place in Bulgarian and the second place in English and Portuguese. In Subtask B (Narrative Classification), a similar narrative-oriented data processing pipeline is adopted to obtain condensed news chunks for each narrative. We conduct in-depth discussion regarding approaches to enhancing both data quality and volume, and explore one-vs-rest classification models and sequence prediction models for multi-label classification tasks. The system ranks first in Bulgarian and second in Russian and Portuguese. In Subtask 3 (Narrative Extraction), we build our system with data augmentation, supervised fine-tuning, and preference-based reinforcement learning. This system achieves the first place in Bulgarian, Russian and Hindi and the second place in Portuguese.

It is well known that deep learning model has huge parameters and is computationally expensive, especially for embedded and mobile devices. Polyphone pronunciations selection is a basic function for Chinese Text-to-Speech (TTS) application. Recurrent neural network (RNN) is a good sequence labeling solution for polyphone pronunciation selection. However, huge parameters and computation make compression needed to alleviate its disadvantage. In contrast to existing quantization with low precision data format and projection layer, we propose a novel method based on shared labels, which focuses on compressing the fully-connected layer before Softmax for models with a huge number of labels in TTS polyphone selection. The basic idea is to compress large number of target labels into a few label clusters, which will share the parameters of fully-connected layer. Furthermore, we combine it with other methods to further compress the polyphone pronunciation selection model. The experimental result shows that for Bi-LSTM (Bidirectional Long Short Term Memory) based polyphone selection, shared labels model decreases about 52% of original model size and accelerates prediction by 44% almost without performance loss. It is worth mentioning that the proposed method can be applied for other tasks to compress the model and accelerate the calculation.