We propose the Linguistics Olympiad Benchmark for Structured Evaluation on Reasoning, or LOBSTER, a linguistically-informed benchmark designed to evaluate large language models (LLMs) on complex linguistic puzzles of the International Linguistics Olympiad (IOL). Unlike prior benchmarks that focus solely on final answer accuracy, our benchmark provides concrete evaluation protocols and rich typological metadata across over 90 low-resource and cross-cultural languages alongside the puzzles. Through systematic evaluations of state-of-the-art models on multilingual abilities, we demonstrate that LLMs struggle with low-resource languages, underscoring the need for such a benchmark. Experiments with various models on our benchmark showed that IOL problems remain a challenging task for reasoning models, though there are ways to enhance the performance—for example, iterative reasoning outperforms single-pass approaches in both final answers and explanations. Our benchmark offers a comprehensive foundation for advancing linguistically grounded, culturally informed, and cognitively plausible reasoning in LLMs.

This paper presents a comprehensive approach for the Formosa Speech Recognition Challenge 2025 (FSR-2025), targeting automatic speech recognition (ASR) for the under-resourced Dapu and Zhao’an dialects of Taiwanese Hakka. Our method integrates data augmentation and robustness techniques, including SpecAugment, dialect-aware special tokens, text-to-speech (TTS) augmentation, noise/reverberation mixing, and speed perturbation, to mitigate data scarcity and domain mismatch. Experiments on the official FSR-2025 datasets show consistent improvements in both character error rate (CER) and word error rate (WER). Extensive ablation studies further confirm that each component contributes positively. These results offer a practical path toward robust ASR for under-resourced Hakka dialects and suggest broader applicability to other low-resource languages.