Separable measurements augmented with classical feedforward suffice to certify full network nonlocality and minimal network nonclassicality while enabling device-independent randomness quantification.
Ldb: A large language model debugger for code generation
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
CodeEvolve uses runtime-guided target selection and MCTS-augmented LLM evolution to optimize real Java and Apex code, reporting 15.22x average speedup on seven hotspots while preserving correctness.
citing papers explorer
-
Network Nonlocality with Separable Measurements
Separable measurements augmented with classical feedforward suffice to certify full network nonlocality and minimal network nonclassicality while enabling device-independent randomness quantification.
-
CodeEvolve: LLM-Driven Evolutionary Optimization with Runtime-Enriched Target Selection for Multi-Language Code Enhancement
CodeEvolve uses runtime-guided target selection and MCTS-augmented LLM evolution to optimize real Java and Apex code, reporting 15.22x average speedup on seven hotspots while preserving correctness.