A survey that unifies prior work on multi-agent LLM systems via the LIFE framework, mapping dependencies across collaboration, failure attribution, and autonomous self-evolution while identifying cross-stage challenges.
(2026).LLM-Based Agentic Systems for Software Engineering: Challenges and Opportunities
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PerfCodeBench reveals that state-of-the-art LLMs produce functionally correct but significantly slower code than expert-optimized versions on system-level tasks, especially those involving parallelism and GPUs.
Code Broker deploys a five-agent hierarchy that combines LLM semantic analysis with static linting to generate actionable Python code quality reports.
citing papers explorer
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Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems
A survey that unifies prior work on multi-agent LLM systems via the LIFE framework, mapping dependencies across collaboration, failure attribution, and autonomous self-evolution while identifying cross-stage challenges.
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PerfCodeBench: Benchmarking LLMs for System-Level High-Performance Code Optimization
PerfCodeBench reveals that state-of-the-art LLMs produce functionally correct but significantly slower code than expert-optimized versions on system-level tasks, especially those involving parallelism and GPUs.
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Code Broker: A Multi-Agent System for Automated Code Quality Assessment
Code Broker deploys a five-agent hierarchy that combines LLM semantic analysis with static linting to generate actionable Python code quality reports.