We have 6 of 19 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
This work systematically study how insights from related MLE tasks affect agentic MLE systems, and how their impact depends on insight type and injection method, and introduces MLE-InsightBench, a benchmark of 160 Kaggle competitions across 12 domains.
Scientific software presents a demanding test for computer-using agents based on visual language models (VLMs): completing a research workflow requires interpreting specialized interfaces, manipulating scientific objects, and producing verifiable results. We thus introduce OSWorld-Science, a benchmark and evaluation en...
Many recurring text functions are easy to describe but difficult to implement with rules, while calling a large remote model for every input introduces repeated cost, latency, and dependency on a provider. We present compile by training, which turns a natural-language specification into a reusable neural function. At c...
It is found that a good bug report for an agent overlaps with, but is not identical to, a good report for a human: agents benefit most from concrete, executable, and well-localized information, whereas some qualities long emphasized for human readers, such as natural language steps to reproduce and readable description...
TestEvo-Bench is a benchmark of test and code co-evolution tasks mined from software repositories, with two tracks: in test generation, the agent shall write new tests to capture the new software behavior; in test update, the agent shall adapt failing existing tests to the changed software behavior.
It is confirmed that memorization persists in modern LLMs and is influenced more by a complex interplay of training domain, dataset composition, architectural choices, and content characteristics, rather than parameter count alone.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
