LLM agents exhibit constraint decay with assertion pass rates dropping substantially as structural requirements increase in multi-file backend code generation across web frameworks.
Abc-bench: Benchmarking agentic backend coding in real-world development.arXiv preprint arXiv:2601.11077
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MOSAIC-Bench demonstrates that nine production coding agents achieve 53-86% end-to-end attack success rates on staged innocuous tickets across 10 web substrates and 31 CWE classes, far higher than the 0-20.4% rates seen with direct prompts.
Survey framing LLM agents as model-plus-harness systems, decomposing harness responsibilities, mapping them to tasks, and highlighting open challenges in evaluation, safety, and co-evolution.
citing papers explorer
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Constraint Decay: The Fragility of LLM Agents in Backend Code Generation
LLM agents exhibit constraint decay with assertion pass rates dropping substantially as structural requirements increase in multi-file backend code generation across web frameworks.
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MOSAIC-Bench: Measuring Compositional Vulnerability Induction in Coding Agents
MOSAIC-Bench demonstrates that nine production coding agents achieve 53-86% end-to-end attack success rates on staged innocuous tickets across 10 web substrates and 31 CWE classes, far higher than the 0-20.4% rates seen with direct prompts.
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From Question Answering to Task Completion: A Survey on Agent System and Harness Design
Survey framing LLM agents as model-plus-harness systems, decomposing harness responsibilities, mapping them to tasks, and highlighting open challenges in evaluation, safety, and co-evolution.