Phoenix-bench shows agentic AI systems lose 37-58% resolved rate when moving from SWE-bench Verified to hardware tasks because bugs spread across parallel modules via signal flow, with testbench feedback lifting performance by 42-45% while file-level oracles add only 1.4%.
arXiv preprint arXiv:2503.21710 , year=
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AgenticSZZ reframes bug-inducing commit identification as temporal knowledge graph search navigated by an LLM agent, reporting F1 scores of 0.47-0.79 and up to 34% improvement over prior SZZ methods on three datasets.
Refining bug reports with hierarchical evidence from an unverified agent trajectory plus repository review raises Pass@1 on SWE-Bench Lite from 41% to 59.67% (GPT-5-mini) and similarly for other agents.
DUALVIEW is a dual-modal framework using Module Coupling, Function Call, Class Hierarchy, and Program Dependence graphs to enable persistent structural reasoning for agentic issue resolution, reporting gains on SWE-bench Pro and Verified.
PatchFusion uses deterministic atomic evidence fusion on candidate patches to outperform ranking, test-filtering, and LLM-judge selectors on SWE-bench and Defects4J pools.
RepoRescue creates a benchmark of 315 repositories and shows LLM agents rescue up to 41.5% with runtime enforcement and 62.7% when combining systems, with hardest cases requiring cross-file changes.
Agent-CoEvo is a multi-agent LLM framework that coevolves code patches and test patches to resolve repository-level issues, outperforming fixed-test baselines on SWE-bench Lite and SWT-bench Lite.
Embedding dynamic analysis into an LLM repair agent yields a claimed 79.4% resolution rate on SWE-bench Verified while cutting token use by about 25%.
A multi-granularity program graph with intra-procedural def-use edges and a data-flow slicing tool raises SWE-agent resolve rate on SWE-bench Lite by 4.7 points under a fixed open-source backbone.
citing papers explorer
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Is Agentic AI Ready for Real-World Hardware Engineering? A Deep Dive with Phoenix-bench
Phoenix-bench shows agentic AI systems lose 37-58% resolved rate when moving from SWE-bench Verified to hardware tasks because bugs spread across parallel modules via signal flow, with testbench feedback lifting performance by 42-45% while file-level oracles add only 1.4%.
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AgenticSZZ: Temporal Knowledge Graph-Guided Agentic Bug-Inducing Commit Identification
AgenticSZZ reframes bug-inducing commit identification as temporal knowledge graph search navigated by an LLM agent, reporting F1 scores of 0.47-0.79 and up to 34% improvement over prior SZZ methods on three datasets.
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Bug Report Specification Refinement with Trajectory Guidance for Automated Program Repair
Refining bug reports with hierarchical evidence from an unverified agent trajectory plus repository review raises Pass@1 on SWE-Bench Lite from 41% to 59.67% (GPT-5-mini) and similarly for other agents.
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Beyond Textual Repository Exploration: Dual-Modal Structural Reasoning for Agentic Issue Resolution
DUALVIEW is a dual-modal framework using Module Coupling, Function Call, Class Hierarchy, and Program Dependence graphs to enable persistent structural reasoning for agentic issue resolution, reporting gains on SWE-bench Pro and Verified.
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A Single Patch Is Not Enough: Deterministic Fusion of Repair Candidates
PatchFusion uses deterministic atomic evidence fusion on candidate patches to outperform ranking, test-filtering, and LLM-judge selectors on SWE-bench and Defects4J pools.
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RepoRescue: An Empirical Study of LLM Agents on Whole-Repository Compatibility Rescue
RepoRescue creates a benchmark of 315 repositories and shows LLM agents rescue up to 41.5% with runtime enforcement and 62.7% when combining systems, with hardest cases requiring cross-file changes.
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Beyond Fixed Tests: Repository-Level Issue Resolution as Coevolution of Code and Behavioral Constraints
Agent-CoEvo is a multi-agent LLM framework that coevolves code patches and test patches to resolve repository-level issues, outperforming fixed-test baselines on SWE-bench Lite and SWT-bench Lite.
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Dynamic analysis enhances issue resolution
Embedding dynamic analysis into an LLM repair agent yields a claimed 79.4% resolution rate on SWE-bench Verified while cutting token use by about 25%.
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ARISE: A Repository-level Graph Representation and Toolset for Agentic Program Repair and Fault Localization
A multi-granularity program graph with intra-procedural def-use edges and a data-flow slicing tool raises SWE-agent resolve rate on SWE-bench Lite by 4.7 points under a fixed open-source backbone.
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