The paper introduces LACUNA, a novel programming model that allows LLM agents to write code that shapes the runtime environment while maintaining strong type-checking safety guarantees.

This paper introduces decode-time grammars to ensure grammatical and semantic correctness of code generated by large language models, particularly for low-resource programming surfaces.

This paper presents a build-authorized path-effect interface to enforce a boundary for compiler rewrites, ensuring trust and applicability.

LEAF is a dynamic analysis framework for Rust that captures MIR-level semantic information and delivers it as an event stream through Dynamic MIR.

This paper presents a software ecosystem enabling consistent development environments for running workflows across diverse hardware platforms.

Agent libOS introduces a library-OS-inspired runtime substrate that treats LLM agents as schedulable processes, providing explicit capability control and robust auditing for long-running, stateful age…

The paper proposes a generic memory framework for advanced static program analysis, supporting various memory behaviors from different programming languages using Abstract Interpretation theory.

This paper proposes HCP, a Handle-Capability Protocol runtime for MCP-style agent systems, which implements eight execution-control invariants to enhance security while preserving MCP-like workflows.

The paper introduces Heimdall, an automated pipeline that uses LLMs and formal verification to safely and automatically migrate legacy, potentially buggy eBPF programs written in C to memory-safe Rust…

The paper proposes the Communicating Finite Automata (CFA) abstraction and a framework for component program refactoring in large language model inference services, achieving significant speedup and r…

This paper evaluates the performance of large language models on popular benchmarks and finds that only a small percentage of the performant implementations are significantly faster than canonical sol…

This paper proposes a portable inlining-prediction framework for lightweight systems, using production compiler diagnostics, an extractor, and a trained predictor.

The paper demonstrates that current agentic-AI runtimes are fundamentally insecure and architecturally obsolete because they fail to detect critical safety failures, proposing a superior, hardened alt…

The paper proposes a new binary format that embeds compiler-generated metadata into executables, making the binary structure more transparent and enabling reliable analysis, instrumentation, and recom…

This paper explores the use of GPT-4o in game development using a Python/Pygame endless runner, examining refactoring and gameplay feature generation tasks.

SEMBridge is a tagless-final framework that allows a single executable object program to generate multiple program semantics, including weakest-precondition and bounded-checking interpretations, ensur…

The paper introduces Hyperparam, a set of lightweight JavaScript libraries designed to enable direct, model-aware querying of unstructured data (like agent traces) within client-side AI applications.

The paper proposes a novel method to automatically enforce differential privacy in stream-based runtime monitoring specifications by analyzing temporal dependencies and injecting calibrated noise.