Applied & Human-Centered · Computer Science & AI
Programming Languages
The newest papers in Programming Languages that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
81 papers, from 4 September 2026 to 4 November 2026: 81 arXiv preprints.
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Cost-Efficient Theorem Proving via Agent Orchestration in Program Verification
Program verification establishes software correctness through machine-checkable proofs constructed in theorem provers.Code Understanding is a Bottleneck for Coding Agents
Repository benchmarks (e.g., SWE-bench) for coding agents often assume that lines of code edited can predict task difficulty, but such datasets' poor control over code and task types makes it hard…Can LLMs Simulate Novice Programmers' Misconceptions?
We evaluate 13 LLMs on generating, solving, simulating, and diagnosing novice programming misconceptions on code-tracing problems.Cleave: Scaling Tensor Program Optimization via Decoupled Algebraic Search and Operator Scheduling
Optimized kernels such as FlashAttention and FlashDecoding are crucial for accelerating today's large models.Automatically Detecting and Fixing Deadlocks in Go Code with GoDDaR
The Go programming language provides a lightweight abstraction for concurrent programming through goroutines, which are prone to deadlocks.RESOLVE: Language-Agnostic Validation of GPU Kernels Through Testing, Reduction, and Proof
AI systems can now write and optimize production GPU kernels, but validating them remains an important challenge.Hybrid Classical-Quantum Solutions to Accelerate the Adoption of Quantum Computing
Despite the promise and potential of quantum computing, most of the current day developments of quantum computing have several limitations for relevant practical use.VHDL-REPOBENCH: A Repository-Level Benchmark for Evaluating Large Language Models on VHDL Design Generation
Large Language Models (LLMs) are increasingly applied in hardware design automation, demonstrating strong potential in generating and understanding hardware description languages.Pattern-Guided Graph Synthesis for Suppressing Known Defects in DL Compiler Fuzzing
Fuzzing is effective at finding bugs in deep learning (DL) compilers, but existing fuzzers repeatedly trigger faults they have already uncovered.VeriPy Source-Preserving Verification and Compatibility Checking for Python Components
Keeping a Python program and its formal guarantees aligned is a continuing maintenance problem.WAMpy: Efficient Synthesis of Prolog Programs in Python
We present WAMpy, a Python framework optimized for synthesizing Prolog programs.TreeWalker: Partial Evaluation for Grouped Tree-Ensemble Inference
Many inference workloads evaluate a trained tree ensemble on row groups that share feature values: discrete-time survival models expand each patient into $G$ time steps, click-through-rate models…Coco: An Agentic Copilot for the Hardware--Software Co-Design Lifecycle
Co-designing ML models and the accelerators that run them is an unusual reasoning task: architects must draw confident, high-stakes conclusions about systems that do not yet exist, and the pace of…Are you Synthesizing or Recalling? Evaluating LLMs on Algorithmic Code Retrieval
Large language models (LLMs) have demonstrated strong performance in code generation, where success depends on both recalling relevant algorithmic knowledge and reasoning about how to apply it.From Verification Failures to Reusable Guidance for Coding Agents
Coding agents need to establish that a program satisfies a specification and that the specification captures the requested behavior.Automatically Building Machine-Checked Assurance Cases from C Codebases to Requirements
Large language models (LLMs) have shown promise in automating interactive theorem proving, yet verification of real-world C codebases requires more than discharging individual proof goals.DatalogBench: Evaluating Large Language Models on Text-to-Datalog Synthesis
Datalog underpins reasoning tasks such as program analysis, but its programs are hard to write.Towards Certificate-Driven Software Porting: A Self-Improving Agentic Harness for Scientific Program Optimization
The upgrade and rewriting of large scientific codebases has traditionally been a major challenge.Faultless: A Program Equivalence Technique for Validating and Evaluating Neural Decompilers
Neural decompilers are machine learning models which perform the process of decompilation, lifting code from a lower-level language to a higher one.Fewer Assumptions by Design: A Reusable Skill for LLM-Assisted Verus Verification
LLM-assisted Verus verification is a less tedious method to verify Rust implementations, but paired with self-referential structures, e.g., Doubly Linked Lists (DLLs)ânotoriously difficult to…