arXiv:2608. 08447v1 Announce Type: cross Abstract: Multilingual reasoning models are commonly evaluated by whether they arrive at the correct answer, but not by whether they preserve the intended language while reasoning and responding.
arXiv:2607. 06327v1 Announce Type: cross Abstract: Uncertainty estimation (UE) enables LLM-powered systems to recognize when to abstain, yet existing research has predominantly focused on English.
arXiv:2609.37104v1 Announce Type: cross Abstract: Open-source reasoning models provide unequal access to reasoning capability across languages. When a model can solve a problem but cannot deliver a c...
The study investigates the performance gap between native-language reasoning and English-pivoted reasoning in large language models. By creating extensive multilingual reasoning datasets and fine‑tuning specialists on Qwen/Qwen3-8B-Base, the authors find that the native reasoning gap is much smaller (1.9–3.5%) than previously reported. They analyze weight‑space changes, discover a language‑agnostic reasoning core in the middle layers, and propose a Layer Swap technique that transfers these mid‑layer updates from an English specialist to native specialists, effectively closing most of the gap while maintaining native chain‑of‑thought output.
arXiv:2608. 15964v1 Announce Type: cross Abstract: Language-specific competency (LSC) is the phenomenon of a language model performing better or worse depending on the language of the prompt.
arXiv:2604.04720v2 Announce Type: replace Abstract: Large Reasoning Models (LRMs) still exhibit large performance gaps between English and other languages, yet much current work assumes these gaps ca...
arXiv:2607. 09999v1 Announce Type: cross Abstract: We show that post-training quantization can silently alter how large language models reason even when task accuracy is preserved.
The paper investigates how multilingual large language models can be guided to reason more reliably in low- to mid-resource languages by selecting appropriate language modes during inference. Experiments with LLaMA and Qwen models show that using English context can correct errors from non‑English comprehension, but adding redundant bilingual context can cause interference. To balance this trade‑off, the authors propose Reliability‑Aware Adaptive Inference (RAAI), a training‑free test‑time framework that routes prompts based on Expected Calibration Error and gates reasoning with a mid‑layer Risk Index, achieving up to 37.7% accuracy gains and reduced calibration error on low‑resource languages.
arXiv:2509. 14704v3 Announce Type: replace Abstract: Benchmark saturation and training-data contamination increasingly obscure whether reported gains in large language models (LLMs) reflect genuine advances in reasoning or familiarity with recurring patterns in benchmark problems.
arXiv:2607. 20520v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evaluated on mathematical problem solving, yet prior work often treats representationally equivalent formulations as interchangeable and conflates reasoning errors with interface failures.
LogicSkills is a benchmark designed to isolate three core logical abilities in large language models: formal symbolization, countermodel construction, and validity assessment. The dataset draws items from the two-variable fragment of first‑order logic without identity, presented in both English and a Carrollian nonce‑word language, and all instances are solver‑verified with Z3. Results show that conventional instruction‑tuned LLMs excel at validity assessment but struggle with symbolization and countermodel construction, whereas recent reasoning‑tuned models perform well across all tasks, indicating a more systematic logical skill profile.
arXiv:2605.29637v2 Announce Type: replace Abstract: Large language models often exhibit a substantial gap between their performance in English and in lower-resourced languages on equivalent knowledge...
arXiv:2608. 10444v1 Announce Type: cross Abstract: Large language models (LLMs) have made substantial progress on reasoning tasks that require increasingly long and complex inferential chains.