We study code-to-metric regression: predicting numeric outcomes of code executions, a challenging task due to the open-ended nature of programming languages. While prior methods have resorted to heavy and domain-specific feature engineering, we show that a single unified Regression Language Model (RLM) can simultaneously predict directly from text, (i) the memory footprint of code across multiple high-level languages such as Python and C++, (ii) the latency of Triton GPU kernels, and (iii) the accuracy and speed of trained neural networks represented in ONNX. In particular, a relatively small 300M parameter RLM initialized from T5Gemma, obtains > 0.9 Spearman-rank on competitive programming submissions from APPS, and a single unified model achieves > 0.5 average Spearman-rank across 17 separate languages from CodeNet. Furthermore, the RLM can obtain the highest average Kendall-Tau of 0.46 on five classic NAS design spaces previously dominated by graph neural networks, and simultaneously predict architecture latencies on numerous hardware platforms.

The new Ayasdi Decision Tree MultiClass Predictor provides a single set of rules to justify and explain why certain transactions/claims/genre choices belong to a particular classification group in the Topological Network (using dt.get_rules(), dt.rules, and dt.dot). This is important because users can now understand, at a granular level, the causal factors behind the groupings and is particularly useful when a network has multiple disjoint groups where the user wants to predict in which group a data point belongs.