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arXiv2026

Cambria: Resource Abstraction for Parametrized Algebraic Effects and Handlers

The algebraic effects and handlers paradigm separates the concerns of the interface and implementation of computational effects in programming languages. We present Cambria, a language that extends this framework to the parametrized setting. Effect signatures may use abstract parameter types that are instantiated by the handler along with the operation implementations. Parameters abstract over resources, such as memory locations or thread IDs, permitting algebraic effects to encode dynamic allocation. They are first-class in the type system but erased at runtime, requiring no coercions or type-directed reduction. We prove parametricity via a step-indexed logical relation, formalizing the abstraction guarantee provided by parametrized handlers. We also establish type safety and classify the annotations needed for completeness of the type inference algorithm. We demonstrate Cambria's practicality with a working implementation and provide examples including local state, Pólya's urn, and concurrent thread management. The last is a parametrized effect whose abstract thread IDs are shared between concurrent computations, going beyond standard instances. Cambria is the first calculus with user-defined resource-allocating effects that guarantees, via parametricity, that client code cannot depend on how a handler represents its resources.

cs.PL↗

arXiv2026

URIUM: A Programming Language for a Practical Open Course on Compiler Design

This paper presents the definition of a simple programming language used as the basis for developing a practical compiler design course. The course explains step by step how to build a compiler, from the initial analysis stages to code generation. The developed compiler generates code for various processors (MIPS, Intel, and RISC-V) and operating systems (MS-Windows and Linux). The course can be adapted to different levels of difficulty and can be used as a starting point for explaining more advanced topics.

cs.PL↗

arXiv2026

FlowCheck: Helping End-Users Specify and Verify Intent in Vibe-Coded Web Apps

Vibe-coded applications often contain silent behavioral failures in which the interface appears functional even though user-visible information does not flow to the expected state or output. We introduce FlowCheck, a constraint language to specify these user-visible information flows directly through the application interface, where constraints can also be displayed and inspected without reading code, and are structured enough for reliable LLM generation. FlowCheck translates the constraints into deterministic CodeQL analyses, and we evaluate it across four applications generated via Claude Code, and compare with three coding models as bug-finding baselines. We find that FlowCheck correctly translates and flags all 30 of our injected constraint violations with no false positives. In contrast, frontier models (Claude Opus 4.7, DeepSeek V3, and Gemini Pro) showed significantly lower accuracy when prompted to find bugs in the same code, with none achieving full accuracy. This approach lets vibe coders state intent in terms of the interface they understand, and checks it deterministically against the code they do not.

cs.SE↗

arXiv2026

Rust's Type Checker Implementation Is Unsound: An Empirical Study on Soundness Bugs in rustc

Rust is claimed to be a type-sound language capable of preventing various undesirable behaviors, including memory bugs. However, rustc, the official Rust compiler, is not immune to defects; it contains soundness bugs, where the compiler accepts programs that should be rejected during type checking. In this work, we present an empirical study of 30 issues that report potential soundness bugs in rustc, collected from the GitHub issue tracker between January 1, 2022 and September 1, 2025. We analyze each issue in depth, focusing on its affected feature, symptom (how the feature is mishandled), consequence (the resulting undesirable behavior), triggering features, community consensus regarding whether it is a bug, and lifecycle, including introduction, discovery, and fix. Furthermore, we investigate existing artifacts, including implementations such as AddressSanitizer, Miri, Chalk, and a-mir-formality, alongside documentation such as the Rust Reference, the FLS, and Rust RFCs to assess their potential as oracles for testing the type soundness of rustc. Our key findings indicate that: (1) Certain soundness bugs, typically triggered by implied bounds or trait objects, compromise memory safety. (2) Sound type checking is challenged by edge cases involving associated types and the interaction between lifetimes and traits. (3) Most bugs persist from the initial introduction of the relevant features and require significant time to be discovered. (4) While AddressSanitizer and Miri can detect soundness bugs that lead to memory bugs, a-mir-formality and Chalk are currently immature despite their potential to identify other bug categories. (5) Existing documentation frequently fails to provide precise explanations of the language semantics.

cs.SE↗

arXiv2026

MIRAGE-CAD: Construction-Mediated Multimodal Generation of Executable CAD Programs

Recovering an executable parametric CAD program from an observed object is fundamentally ambiguous, because the same final geometry can result from different construction procedures. We study this problem from four types of input: natural-language descriptions, rendered images, point clouds, and STEP/B-Rep geometry. MIRAGE-CAD maps each input to a shared construction representation and mediates program generation through an explicit construction-plan interface. The resulting Python CAD code is executed by an OpenCASCADE kernel to build the solid and export it as STEP. On 2,500 held-out queries per modality, the system achieves 55.4-70.0% build success and 52.3-66.2% STEP export success without retrieval at inference. Controlled comparisons show that strong reconstruction does not depend on expressing the construction representation as text: a decoder conditioned directly on the continuous representation also reconstructs strongly, while an exposure-matched plan-based decoder shows no detected material loss in per-part geometric fidelity. The explicit plan instead provides a readable and separately measurable intermediate representation whose agreement with the reference construction is informative about downstream execution success. Finally, we show that executable validity, geometric fidelity, and parametric responsiveness can diverge substantially and should therefore be evaluated separately.

cs.CV↗

arXiv2026

Beyond Lemma Sharing -- Novel Parallelization Strategies for Property Directed Reachability

Property Directed Reachability (PDR) is a commonly used technique for automated hardware model checking, yet efficiently parallelizing it remains a significant challenge. Existing approaches, such as lemma sharing, often suffer from limited scalability as processor counts increase. In this work, we present two novel sharing-based parallelization strategies, preemptive propagation and ARPOS, and compare their performance with classical lemma sharing. To this end, we develop an asynchronous MPI-based message passing framework for the state-of-the-art rIC3 hardware model checker. Experimental results on the 2025 Hardware Model Checking competition benchmark demonstrate that our preemptive propagation strategy yields a significant performance boost over classical lemma sharing.

cs.DC↗

arXiv2025

Compiler-supported reduced precision and AoS-SoA transformations for heterogeneous hardware

This study evaluates AoS-to-SoA transformations over reduced-precision data layouts for a particle simulation code on several GPU platforms: We hypothesize that SoA fits particularly well to SIMT, while AoS is the preferred storage format for many Lagrangian codes. Reduced-precision (below IEEE accuracy) is an established tool to address bandwidth constraints, although it remains unclear whether AoS and precision conversions should execute on a CPU or be deployed to a GPU if the compute kernel itself must run on an accelerator. On modern superchips where CPUs and GPUs share (logically) one data space, it is also unclear whether it is advantageous to stream data to the accelerator prior to the calculation, or whether we should let the accelerator transform data on demand, i.e.~work in-place logically. We therefore introduce compiler annotations to facilitate such conversions and to give the programmer the option to orchestrate the conversions in combination with GPU offloading. For some of our compute kernels of interest, Nvidia's G200 platforms yield a speedup of around 2.6 while AMD's MI300A exhibits more robust performance yet profits less. We assume that our compiler-based techniques are applicable to a wide variety of Lagrangian codes and beyond.

cs.PL↗

arXiv2023

Dynamic quantum circuit compilation

Quantum computing has shown tremendous promise in addressing complex computational problems, yet its practical realization is hindered by the limited availability of qubits for computation. Recent advancements in quantum hardware have introduced mid-circuit measurements and resets, enabling the reuse of measured qubits and significantly reducing the qubit requirements for executing quantum algorithms. In this work, we present a systematic study of dynamic quantum circuit compilation, a process that transforms static quantum circuits into their dynamic equivalents with a reduced qubit count through qubit-reuse. We establish the first general framework for optimizing the dynamic circuit compilation via graph manipulation. In particular, we completely characterize the optimal quantum circuit compilation using binary integer programming, provide efficient algorithms for determining whether a given quantum circuit can be reduced to a smaller circuit and present heuristic algorithms for devising dynamic compilation schemes in general. Furthermore, we conduct a thorough analysis of quantum circuits with practical relevance, offering optimal compilations for well-known quantum algorithms in quantum computation, ansatz circuits utilized in quantum machine learning, and measurement-based quantum computation crucial for quantum networking. We also perform a comparative analysis against state-of-the-art approaches, demonstrating the superior performance of our methods in both structured and random quantum circuits. Our framework lays a rigorous foundation for comprehending dynamic quantum circuit compilation via qubit-reuse, bridging the gap between theoretical quantum algorithms and their physical implementation on quantum computers with limited resources.

quant-ph↗