Automated software engineering, particularly resolving real-world issues on benchmarks like SWE-bench, remains a significant challenge for Large Language Models (LLMs). To address this, we introduce SWE-Swiss, a two-phase training recipe that systematically develops these capabilities. Our approach first decomposes issue resolution into three core skills: Localization, Repair, and Unit Test Generation. In the first phase, we perform multi-task Supervised Fine-Tuning (SFT) on three new, meticulously curated datasets to build a versatile foundation. The second phase applies targeted Reinforcement Learning (RL), using direct feedback from test execution to boost the critical skill of code repair. The resulting model, SWE-Swiss-32B, establishes a new state-of-the-art for open-source models in its size class, achieving a 60.2% score on the SWE-bench Verified benchmark and placing it in the same top-tier performance bracket as much larger models. Finally, we show that despite its specialized training, SWE-Swiss-32B demonstrates strong generalization to other common LLM benchmarks. To accelerate research in the community, we are open-sourcing the models and our complete training datasets.

The evaluation of mathematical reasoning capabilities constitutes a critical pathway toward achieving Artificial General Intelligence (AGI). Prevailing benchmarks including MATH and AIME mainly feature single-instantiation problems with fixed numbers, permitting pattern matching instead of principled deductive reasoning and leaving generalization on isomorphic problem variants untested. To address these limitations, we propose the UTMath Benchmark, employing rigorous unit testing methodology that simultaneously quantifies solution accuracy and solution space generality. It comprises 1,053 problems spanning 9 mathematical domains, each accompanied by an average of 68 varied test cases. With answer possibilities per problem on average, UTMath sets new standards for robust reasoning while preventing memorization. UTMath is highly challenging, with the best-performing model, o1-mini, solving only 32.57% of the problems, followed by o1-preview at 27.16%, and GPT-4o at 26.93%. We further propose Reasoning-to-Code Thoughts (RCoT), a prompting strategy that decouples symbolic reasoning from code synthesis. RCoT guides LLMs to first derive formal reasoning structures before generating executable code, producing generalizable solutions rather than situation-specific answers. To help the community push mathematical reasoning further, we release UTMath-Train (70k samples), a companion training set generated under the same protocol. Our benchmark can be accessed via the following link: [UTMath](https://utmathhomepage.github.io/)