Very Long Instruction Word (VLIW) processors can expose substantial instruction-level parallelism (ILP), yet compiler-generated schedules often fail to fully utilize available functional units, leaving performance on the table and forcing costly manual tuning. We present GRASP (Graph Reinforcement Assembly Scheduling Platform), an assembly-level post-pass optimizer that improves VLIW instruction scheduling and packing via graph reinforcement learning. GRASP represents each basic block as an Instruction Dependency Graph (IDG) that explicitly encodes dependence, latency, and machine resource constraints, and trains a GNN-based reinforcement learning agent to iteratively select legal scheduling decisions and construct high-throughput issue packets. By operating after compilation, GRASP requires no source-code changes and does not modify compiler internals, making it easy to integrate into existing toolchains. Across a suite of AI and HPC kernels, GRASP accelerates execution by up to 1.38 × (1.22 × on average), narrowing the gap between compiler output and expert-tuned VLIW code.

MLIR, a modular compiler framework, evolves quickly, with regular updates expanding its dialects and operations across LLVM versions and downstream projects. This fast development reduces the effectiveness of traditional fuzzing tools, which test only a small portion of dialects, require extensive manual work (e.g., nearly ten thousand lines of C++ code), and do not match the update speed of MLIR. To address these challenges, we propose NÜwa, the first LLM-based approach for MLIR fuzzing. Nüwa employs a two-phase strategy: first generating valid operations by encoding constraints into LLMs prompts, then synthesizing multi-operation test cases by learning inter-operation dependencies. To enhance operation coverage, it incorporates high-coverage cases from MLIR's test suite and uses LLM-driven mutations to boost diversity. A self-improvement mechanism enhances the prompts using feedback from highquality test cases, improving the LLMs' understanding of MLIR's complex semantics. Nüwa demonstrates that the generation and mutation process can be fully automated via the intrinsic capabilities of llMs (including in-context learning), while being applicable to MLIR's fast evolution. The experimental study shows that NÜwa outperforms the state-of-the-art tools MLIRSmith and MLIRod, detecting$2.9 \times$more unique bugs and achieving$1.6 \times$greater code coverage. To date, Nüwa has identified 55 bugs in the MLIR framework, with 18 confirmed or fixed.

User and device authentication are vital to the deployment of smart Internet of Things (IoT) devices. Unfortunately, achieving robust authentication on a diverse set of heterogeneous IoT devices remains an open problem. This paper presentsScenario, a generic authentication method to support user-device authentication on a wide range of IoT devices, using RFID-based wireless sensing.Scenarioonly requires attaching an RFID tag on the target device surface. It then uses the unique RFID signal characteristics introduced by the device material and user gestures to perform device and user authentication. We developed a prototype ofScenariousing commercial off-the-shelf devices and applied it to a multi-device smart environment. Experimental results show thatScenariois reliable, giving an average identification accuracy of 97.3% and 96.7% of the device and user authentication stages in diverse environments, respectively.