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.
Transactions on Machine Learning Research , year =
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Metal-Sci is a benchmark and harness for LLM evolutionary optimization of Apple Silicon Metal kernels that uses held-out sizes to detect silent regressions missed by in-distribution scores.
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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Metal-Sci: A Scientific Compute Benchmark for Evolutionary LLM Kernel Search on Apple Silicon
Metal-Sci is a benchmark and harness for LLM evolutionary optimization of Apple Silicon Metal kernels that uses held-out sizes to detect silent regressions missed by in-distribution scores.