While Large Language Models (LLMs) show remarkable capabilities, their complex reasoning skills require deeper investigation. We introduce **RiddleBench**, a new benchmark of 1,737 challenging puzzles designed to test reasoning beyond simple pattern matching. Our evaluation of state-of-the-art models reveals significant limitations, including hallucination cascades (uncritically accepting flawed peer reasoning) and poor self-correction due to strong self-confirmation bias. We also find that model performance is fragile, degrading when faced with reordered constraints or irrelevant information. RiddleBench serves as a resource for diagnosing these issues and guiding the development of more robust LLMs.

Large language models (LLMs), despite their ability to perform few-shot machine translation (MT), often lag behind dedicated MT systems trained on parallel corpora, which are crucial for high quality machine translation (MT). However, parallel corpora are often scarce or non-existent for low-resource languages. In this paper, we propose CycleDistill, a bootstrapping approach leveraging LLMs and few-shot translation to obtain high-quality MT systems. CycleDistill involves iteratively generating synthetic parallel corpora from monolingual corpora via zero- or few-shot MT, which is then used to fine-tune the model that was used for generating said data for MT. CycleDistill does not need parallel corpora beyond 1 to 4 few-shot examples, and in our experiments focusing on three Indian languages, by relying solely on monolingual corpora, it can achieve high-quality machine translation, improving upon a few-shot baseline model by 20-30 chrF points on average in the first iteration. We also study the effect of leveraging softmax activations during the distillation process and observe mild improvements in translation quality.