LLMs display clear performance stratification on formal language tasks aligned with Chomsky hierarchy complexity levels, limited by severe efficiency barriers rather than absolute capability.
Codescore: Evaluating code generation by learning code execution
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MASPO jointly optimizes prompts in multi-agent LLM systems via downstream-success evaluation and evolutionary beam search, delivering 2.9 average accuracy gains over prior methods across six tasks.
RuC generates language-agnostic, grammar-based benchmarks for evaluating LLMs on RTL code completion at controllable granularities, demonstrated on SystemVerilog designs from Tiny Tapeout and a RISC-V core where Fill-in-the-Middle prompting performed best.
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MASPO: Joint Prompt Optimization for LLM-based Multi-Agent Systems
MASPO jointly optimizes prompts in multi-agent LLM systems via downstream-success evaluation and evolutionary beam search, delivering 2.9 average accuracy gains over prior methods across six tasks.