Application profiling is an indispensable technique for many software development tasks, such as code and memory layout optimizations, where optimization decisions are tailored to
specific program profiles. Unfortunately, modern application codebases exhibit highly variant behavior across different inputs, creating challenges for conventional profiling approaches that rely on a single representative execution instance. In this paper, we propose \textbf{Phaedrus}, a new \textit{compiler-assisted deep learning framework} designed to predict dynamic program behavior across varied execution instances, specifically focusing on dynamic function call prediction. These predicted call sequences are subsequently used to guide input-specific compiler optimizations, producing code specialized for each execution instance.

Traditional profile-guided optimization methods struggle with the input-dependent variability of modern applications, where profiling on different inputs yields divergent application behaviors. To address this, Phaedrus proposes two new approaches: \textit{Application Profile Synthesis (Dynamis)}, a profile-less approach where Large Language Models (LLMs) directly infer dynamic functions based on source code & static compiler analysis, bypassing the need for traditional profiling, and \textit{Application Profile Generalization (Morpheus)}, which uses generative models trained on compressed and augmented \textit{Whole Program Path} (WPP) based function profiles to predict application behavior under unseen inputs. Our experiments show that \textit{Phaedrus} accurately identifies the most frequently executed and runtime-dominated hotspot functions, accounting for up to 85–99% of total execution time. Leveraging these predictions, \textit{Phaedrus} enables superior profile-guided optimizations, delivering an average speedup of 6% (up to 25%) and a binary size reduction of 5.19% (up to 19%), without any program execution. In addition, \textit{Phaedrus} reduces WPP function profile sizes by up to $10^{7}\times$.