SMT-LLM builds a constraint graph from PyPI metadata and AST-derived imports, solves it with Z3, and uses LLM imputation only when needed, resolving 83.6% of HG2.9K snippets versus PLLM's 54.8% while cutting median time by 6.3x and LLM calls by 11x.
The Alberta Workloads for the SPEC CPU 2017 Benchmark Suite
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SPEC CPU 2026 presents a new benchmark suite using open-source apps, expanded multithreading, and Rolling-Round-Robin Rate to address gaps in evaluating heterogeneous multiprogrammed CPU performance.
Attention-augmented LSTMs achieve near-perfect character-level decryption of homophonic ciphers from 1500-1899 English and Swedish texts when all ciphertexts share a known code pool.
citing papers explorer
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Breaking the Dependency Chaos: A Constraint-Driven Python Dependency Resolution Strategy with Selective LLM Imputation
SMT-LLM builds a constraint graph from PyPI metadata and AST-derived imports, solves it with Z3, and uses LLM imputation only when needed, resolving 83.6% of HG2.9K snippets versus PLLM's 54.8% while cutting median time by 6.3x and LLM calls by 11x.
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SPEC CPU: The Next Generation
SPEC CPU 2026 presents a new benchmark suite using open-source apps, expanded multithreading, and Rolling-Round-Robin Rate to address gaps in evaluating heterogeneous multiprogrammed CPU performance.
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Attention-Augmented LSTMs for Automatic Homophonic Ciphertext Decipherment
Attention-augmented LSTMs achieve near-perfect character-level decryption of homophonic ciphers from 1500-1899 English and Swedish texts when all ciphertexts share a known code pool.