程序设计语言：2025–2026 年度综述

An annual survey of the cs.PL arXiv category, covering research published from September 2025 – August 2026. Built on a close-reading set of 120 papers and organised into 44 chapters, it maps the year by the field's research sub-directions.

Book framework (share of the year's output):

• LLM-Guided Program Synthesis and Verification (15.2%) — This subdirection focuses on leveraging large language models to accelerate program synthesis, invariant generation, and formal verification tasks.
• Type Theory and Dependent Type Systems (14.0%) — Research in this area advances type theory foundations, including dependent types, bidirectional checking, and type-safe calculi for complex software systems.
• LLM-Based Code Generation and Analysis (13.7%) — This cluster explores the application of LLMs for code generation, translation, optimization, and semantic reasoning across various programming tasks.
• Formal Verification Frameworks and Compilers (10.9%) — Work here develops automated verification frameworks, compiler correctness proofs, and compositional methods for verifying tensor and system code.
• Large Language Models for Programming Tasks (10.6%) — This direction investigates the use of large language models for program reasoning, formal proof generation, and optimizing agentic inference systems.
• Logic, Functional, and Choreographic Programming (9.2%) — Research covers the design and analysis of logic, functional, and choreographic programming languages, including their semantics and concurrency models.
• Quantum and Hybrid Programming Systems (9.2%) — This subdirection addresses the compilation, verification, and benchmarking of quantum and hybrid classical-quantum programming languages and frameworks.
• Probabilistic Programming and Rust Verification (7.2%) — Work focuses on verifying probabilistic programs, Rust standard libraries, and tensor programs using hierarchical abstractions and inference techniques.
• Operational and Denotational Semantics (5.2%) — This area studies the formal semantics of programming languages, including operational, denotational, and effect-based models for language design.
• Separation Logic and Program Debugging (4.9%) — Research applies separation logic and concolic execution to verify memory safety, detect data races, and debug complex software systems.

Each sub-direction is covered across three chapters — how it framed its questions, the strength of its key findings, and its open debates and frontiers — so the reader gets a traceable picture of what this part of the field actually produced over the year.