The same behavioral signals in LLM-based software engineering agents correlate with task success in opposite directions across different frameworks, with framework identity explaining more variance than the underlying LLM.
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SmellBench is the first benchmark showing LLM agents resolve 47.7% of architectural code smells while accurately spotting false positives, but aggressive repairs often introduce new smells and degrade overall quality.
ReCodeAgent uses a multi-agent system to translate and validate large code repositories across multiple programming languages, achieving 60.8% higher test pass rates than prior neuro-symbolic and agentic methods on 118 real-world projects.
CoHiKer improves Top-1 fault localization accuracy in the Linux kernel by up to 26% at file level and 57% at method level over prior LLM baselines via contrastive reasoning and hierarchical context analysis.
PROBE turns runtime telemetry from failed software engineering agent runs into evidence-grounded diagnoses and actionable recovery guidance, achieving 65.37% diagnosis accuracy and 21.79% recovery rate on 257 cases.
AADvark extends agent-aided CAD design to dynamic 3D assemblies with movable parts by integrating constraint solvers and visual feedback to create a verification signal for the agent.
The manuscript body introduces PokeGym, a vision-only automated 3D-game benchmark, while the abstract claims a G-EvoMAC method and 60.18% success rate absent from the body.
A human-centered design workshop with journalism practitioners yields an evaluation cookbook and design requirements for contextualized, value-aligned generative AI benchmarks.
Empirical study of 3977 agent trajectories finds Python execution errors correlate with lower success rates on GitHub issues, flags challenging errors, and reports three confirmed bugs in the SWE-Bench platform.
Empirical study finds coding agents produce fewer and less intense tangled refactorings than humans on Multi-SWE-bench; a refactoring-aware refinement improves compilability from 19.34% to 38.33% and resolves 2.79% more issues.
The central challenge in AI-augmented CI/CD is designing authority transfer from humans to agents under constraints, as current systems remain limited to bounded data-plane autonomy backed by external governance.
XARP provides a WebSocket-based remote-procedure system that lets Python code and AI agents control Unity XR clients, with benchmarks and user studies showing faster iteration than conventional XR workflows.
AgentStop uses execution signals to early-terminate failing local LLM agent trajectories, cutting energy use 15-20% with minimal utility loss.
citing papers explorer
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Same Signal, Different Semantics: A Cross-Framework Behavioral Analysis of Software Engineering Agents
The same behavioral signals in LLM-based software engineering agents correlate with task success in opposite directions across different frameworks, with framework identity explaining more variance than the underlying LLM.
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SmellBench: Evaluating LLM Agents on Architectural Code Smell Repair
SmellBench is the first benchmark showing LLM agents resolve 47.7% of architectural code smells while accurately spotting false positives, but aggressive repairs often introduce new smells and degrade overall quality.
-
ReCodeAgent: A Multi-Agent Workflow for Language-agnostic Translation and Validation of Large-scale Repositories
ReCodeAgent uses a multi-agent system to translate and validate large code repositories across multiple programming languages, achieving 60.8% higher test pass rates than prior neuro-symbolic and agentic methods on 118 real-world projects.
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Towards Better Linux Kernel Fault Localization: Leveraging Contrastive Reasoning and Hierarchical Context Analysis
CoHiKer improves Top-1 fault localization accuracy in the Linux kernel by up to 26% at file level and 57% at method level over prior LLM baselines via contrastive reasoning and hierarchical context analysis.
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Debugging the Debuggers: Failure-Anchored Structured Recovery for Software Engineering Agents
PROBE turns runtime telemetry from failed software engineering agent runs into evidence-grounded diagnoses and actionable recovery guidance, achieving 65.37% diagnosis accuracy and 21.79% recovery rate on 257 cases.
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Agent-Aided Design for Dynamic CAD Models
AADvark extends agent-aided CAD design to dynamic 3D assemblies with movable parts by integrating constraint solvers and visual feedback to create a verification signal for the agent.
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Mastering PokeGym: Graph-Guided Multimodal Evolution at Test Time
The manuscript body introduces PokeGym, a vision-only automated 3D-game benchmark, while the abstract claims a G-EvoMAC method and 60.18% success rate absent from the body.
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Towards Real-World Validity in Generative AI Benchmarks: Understanding and Designing Domain-Centered Evaluations for Journalism Practitioners
A human-centered design workshop with journalism practitioners yields an evaluation cookbook and design requirements for contextualized, value-aligned generative AI benchmarks.
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Beyond Final Code: A Process-Oriented Error Analysis of Software Development Agents in Real-World GitHub Scenarios
Empirical study of 3977 agent trajectories finds Python execution errors correlate with lower success rates on GitHub issues, flags challenging errors, and reports three confirmed bugs in the SWE-Bench platform.
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"Refactoring Runaway": Understanding and Mitigating Tangled Refactorings in Coding Agents for Issue Resolution
Empirical study finds coding agents produce fewer and less intense tangled refactorings than humans on Multi-SWE-bench; a refactoring-aware refinement improves compilability from 19.34% to 38.33% and resolves 2.79% more issues.
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From Assistance to Agency: Rethinking Autonomy and Control in CI/CD Pipelines
The central challenge in AI-augmented CI/CD is designing authority transfer from humans to agents under constraints, as current systems remain limited to bounded data-plane autonomy backed by external governance.
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XARP Tools: An Extended Reality Platform for Humans and AI Agents
XARP provides a WebSocket-based remote-procedure system that lets Python code and AI agents control Unity XR clients, with benchmarks and user studies showing faster iteration than conventional XR workflows.
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AgentStop: Terminating Local AI Agents Early to Save Energy in Consumer Devices
AgentStop uses execution signals to early-terminate failing local LLM agent trajectories, cutting energy use 15-20% with minimal utility loss.