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Yichong Wang

Publications and source records attributed to Yichong Wang.

2 recordsLinked to original sources

Akira: Towards Operating System Architecture for Agile Specialization

Operating Systems (OS) specialization tailors abstractions and privileges to specific use cases, enabling configurable tradeoffs between performance and isolation. However, increasing application diversity makes low-cost and non-invasive agile specialization challenging. First, modular OS designs decompose the system into components and reduce coupling, but lack abstractions separating component implementations from OS configuration. Second, diverse workloads require different isolation strengths, yet existing OSs largely fix isolation models at design time, making isolation adaptation changes costly and invasive. To address these, we present Akira, an OS architecture that enables efficiently adapt OS composition and isolation. Akira introduces the Abstraction and Encapsulation Unit (AEU), which separates component interfaces and isolation requirements from implementations, hiding modular OS internals and enabling non-invasive reconfiguration. Based on the abstraction, Akira introduces the Control Logic Unit (CLU) and the Bridge-based Isolation Unit (BIU) to separately manage component communication and isolation, enabling their independent adaptation without system-wide modifications. Results show that Akira enables rapid and low-cost OS reconfiguration with only a 1.10x slowdown over native FreeRTOS and reduces reconfiguration effort from 1.38 KLOC of intrusive changes to 0.28 KLOC of incremental updates. On Unikraft-based Redis and Nginx, Akira configurations expand the performance-isolation space by up to 10.4x and 7.8x. We publicly release Akira's source code, at https://github.com/JiangRS17/Akira.

cs.OS↗

AkiraRust: Re-thinking LLM-aided Rust Repair Using a Feedback-guided Thinking Switch

Eliminating undefined behaviors (UBs) in Rust programs requires a deep semantic understanding to enable accurate and reliable repair. While existing studies have demonstrated the potential of LLMs to support Rust code analysis and repair, most frameworks remain constrained by inflexible templates or lack grounding in executable semantics, resulting in limited contextual awareness and semantic incorrectness. Here, we present AkiraRust, an LLM-driven repair and verification framework that incorporates a finite-state machine to dynamically adapt its detection and repair flow to runtime semantic conditions. AkiraRust introduces a dual-mode reasoning strategy that coordinates fast and slow thinking across multiple agents. Each agent is mapped to an FSM state, and a waveform-driven transition controller manages state switching, rollback decisions, and semantic check pointing, enabling context-aware and runtime-adaptive repair. Experimental results show that AkiraRust achieves about 92% semantic correctness and delivers a 2.2x average speedup compared to SOTA.

cs.SE↗