The frontier of mathematics is defined by problems whose solutions are not yet known. However, whether language models can meaningfully engage with such problems without human intervention remains unclear. A major obstacle is the lack of large-scale research-level math datasets. To this end, we introduce ResearchMath-14k, a set of $14{,}056$ problems curated from academic sources via a multi-agent pipeline. ResearchMath-14k spans 11 mathematical domains and ranks above existing math datasets on knowledge, novelty, and procedural difficulty. To our knowledge, it is the largest research-level mathematical problem set available for training. We additionally generate $220$K teacher trajectories through targeted prompting, followed by behavioral filtering. Notably, however, generating correct trajectories is nontrivial at this level, and two LLM judges label only $3.7\%$ and $4.3\%$ of sampled ResearchMath training trajectories as correct. Nevertheless, across three model families, full-parameter training on ResearchMath improves performance on graduate- and research-level mathematics benchmarks by $2.1$ points over the starting checkpoints. In comparison, training on existing datasets such as DASD and Nemotron-SFT-Math-v4 changes performance by $0.0$ and $-0.5$ points, respectively. Notably, mixing DASD with ResearchMath yields higher scores than token-matched DASD alone on benchmarks covering olympiad short-form ($+2.0$), graduate- and research-level short-form ($+0.8$), graduate- and research-level symbolic ($+2.6$), and proof evaluation ($+5.7$). Further analysis suggests that research-level mathematical content and greater reasoning diversity may help explain why ResearchMath provides complementary supervision to contemporary datasets. We make ResearchMath-14k publicly available for future works on research-level mathematical reasoning.

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