Build-bench is the first architecture-aware benchmark that evaluates LLMs on repairing cross-ISA build failures via iterative tool-augmented reasoning, with the best model reaching 63.19% success.
Improving llm-based fault localization with external memory and project context
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SieveFL combines vector retrieval and JaCoCo runtime pruning to cut LLM token use by 49% while achieving 41.8% Top-1 accuracy on 395 Defects4J bugs, outperforming AgentFL.
An empirical study finds that injecting call/inheritance topology as comments improves LLM code agent localization by 2.2pp, shortens trajectories by 1.6 rounds, and halves run-to-run variance on medium repositories via a deterministic anchoring effect.
citing papers explorer
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Can Language Models Go Beyond Coding? Assessing the Capability of Language Models to Build Real-World Systems
Build-bench is the first architecture-aware benchmark that evaluates LLMs on repairing cross-ISA build failures via iterative tool-augmented reasoning, with the best model reaching 63.19% success.
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SieveFL: Hierarchical Runtime-Aware Pruning for Scalable LLM-Based Fault Localization
SieveFL combines vector retrieval and JaCoCo runtime pruning to cut LLM token use by 49% while achieving 41.8% Top-1 accuracy on 395 Defects4J bugs, outperforming AgentFL.
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How Much Static Structure Do Code Agents Need? A Study of Deterministic Anchoring
An empirical study finds that injecting call/inheritance topology as comments improves LLM code agent localization by 2.2pp, shortens trajectories by 1.6 rounds, and halves run-to-run variance on medium repositories via a deterministic anchoring effect.