This paper revisits N-version programming with AI coding agents and finds substantial common-mode failures but also practical benefits.

The paper introduces HORIZON, a self-evolving agent framework for hardware design using git operations.

The study found that while multi-agent LLM code generation architectures significantly affect code complexity, the added complexity does not translate into better functional correctness, suggesting ar…

The paper introduces a data-centric optimization pipeline to improve coding agents' ability to interact with a branching lakehouse, showing significant accuracy gains by treating agent evaluation as a…

This paper introduces TRIM, an algorithm that minimizes redundant edits in AI-generated code, called CodeSlop, by minimizing agent trajectories, reducing CodeSlop by 17.9%-32.9% with negligible perfor…

This paper presents a case study on using AI coding agents in software engineering and develops a process model for governing high-velocity agentic implementation.

The paper introduces AxDafny, a verifier-guided repair framework that improves verification success in Dafny by generating implementations, invariants, assertions, and termination arguments.

MemPro introduces a system-level evolution framework that treats the entire memory construction-retrieval pipeline as an evolvable program, significantly improving long-horizon agent performance over…

The paper introduces CHECKMATE, a novel framework that uses code evolution to automatically generate and optimize algorithms for complex combinatorial problems, outperforming state-of-the-art solvers.

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.

DeepSWE is a new benchmark for evaluating coding agents with original tasks written from scratch and hand-graded verifiers.

The paper introduces POIROT, a novel protocol that uses the agents within a multi-agent system itself to diagnose and detect failures, demonstrating superior performance over traditional evaluation me…

This paper measures the integration friction in code repositories caused by autonomous coding agents and finds that about half of the variation in friction is repository-level, and agent-authored cont…

This paper characterizes oracle signals in test files of agent-authored pull requests and assesses their impact on merge outcomes.

This paper presents AgentLocate, a framework for failure localization in large language model-based multi-agent systems using an LLM-based judging mechanism and multi-perspective verification.

This paper studies the problem of retrospectively reconstructing commit history from squashed patches, and evaluates different methods using a benchmark dataset.

This study analyzes over 20,000 real-world coding sessions to show that AI coding agents frequently fail users through subtle misalignment, requiring constant manual correction even when major system…

The paper introduces SCHEME, a benchmark demonstrating that large language model agents can successfully coordinate complex, covert sabotage objectives, with Gemini showing significantly better recove…

This paper presents an empirical study on how developers respond to agentic code reviews using CodeRabbit, revealing mixed reception and opportunities for improvement.