Language models (LMs) have become one of the most prominent paradigms in modern generative modeling. While making them faster has been the main focus of real-time deployment, speed alone is not enough. Many real-world applications, such as synchronized translation and voice synthesis, also require precise alignment between generation and external signals, both in terms of generation content and timing. We refer to this problem as \textit{frame-synchronous streaming inference}. To address it, we present StreamMUSE, an inference system that performs LM generation in response to an external signal stream within a client-server architecture. The client continuously sends high-frequency inference requests based on the most recent inputs and receives outputs synchronized to the external clock, while the server executes model inference. We demonstrate the framework through a live music accompaniment task, showing how real-time synchronization can be achieved across different deployment environments with varying round-trip latencies. We further model the relationship between system hyperparameters and round-trip latency, and evaluate how different environments affect optimal configurations to achieve real-time performance. Experimental results show a consistent correspondence between system real-time performance and music quality, demonstrating the effectiveness of the proposed framework. The project is open source. Relevant code and the latest updates are available at this https URL.

Coverage-guided kernel fuzzers such as syzkaller rely on edge coverage (trace-pc) as their sole feedback signal. This context-blind approach cannot distinguish execution paths that differ only in argument values -- for example, two invocations of copy_from_user() with different size parameters hit identical basic blocks yet have vastly different security implications. I present TOOLNAME, an LLVM-based instrumentation framework that extends Linux KCOV with data-flow extraction of function arguments and return values. A compiler pass emits lightweight callbacks capturing structured tuples of program counter, argument metadata, and field values at function entry and return. Composite types are automatically decomposed via DWARF DICompositeType metadata with zero source annotation. A lock-free per-task ring buffer delivers records to user space with no interference to existing KCOV or syzkaller infrastructure. I demonstrate dual utility: (1) fuzzers gain state-aware feedback for mutation guidance into value-dependent state transitions, and (2) security analysts obtain deterministic argument records for root-cause analysis without printk or kprobe overhead. Two Rust instrumentation paths are provided: a post-compilation pipeline requiring no rustc modification, and native instrumentation via rustc built against the custom LLVM -- both the only runtime methods for capturing Rust function arguments given that drgn/vmcore fails under -O2 DWARF elision.

In safety-critical autonomous systems, data freshness presents a fundamental design challenge. While the Logical Execution Time (LET) paradigm ensures compositional determinism, it often does so at the cost of injected latency, possibly degrading the age of data on high-frequency control loops. Furthermore, heterogeneous, multi-rate, task dependencies is typically guaranteed inefficiently through oversampling. This paper proposes a Task-based scheduling framework extended with data freshness constraints. Unlike traditional models, scheduling decisions are driven by the lifespan of data. We introduce a formal methodology to decompose Data Dependency Graphs into dominant paths by tracing the strictest data freshness constraints backward from the actuators. Based on this decomposition, we propose an offset search algorithm that synchronizes multi-rate, multi-dependencies, task chains. This approach enforces end-to-end data freshness without the artificial latency of LET buffering, a trade-off between data freshness and execution determinism. We formally prove that this offset-based alignment preserves the 100% schedulability capacity of Global EDF while addressing data freshness guarantees.