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Universidad Estatal de Milagro

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  1. Multi-conflict detection stops at two parallel branches: the merge loop's checks are now precomputed too, averaging 1.21x on two cores

    Diagnosis of constraint systems needs several minimal conflicts at once, and MergeXplain returns multiple conflicts from one decomposition, but only its two recursive branches had been run in parallel while the merge loop that eliminates boundary-spanning conflicts stayed sequential. ParallelMergeXplain forks the recursion up to a bounded depth and replaces every GetConflict call in the merge loop with a speculative, QXGen-lookahead parallel version. On a 2-core machine it reached a mean wall-clock speed-up of 1.21x (std. 0.20) across all configurations with non-trivial checks, and 1.531x for par full at p=2 on the real SATLIB uf50-218 instance, returning results identical to sequential MergeXplain.

  2. More detailed prompts did not yield better helmet classifiers: the most prescriptive tier dropped two LLMs to about 0.58 accuracy

    ChatGPT, Claude, Gemini and Grok each generated PyTorch helmet-compliance training scripts from three prompt tiers of increasing specificity, run 20 times on 972 balanced images: ten of twelve configurations converged to a 0.957–0.967 accuracy plateau, but the most detailed tier left Gemini and Grok near 0.58, with scripts that ran yet failed to converge. A backbone-by-scheduler ablation traced the failure to the prescribed from-scratch multi-scale network, which collapsed under both schedulers while a pretrained ResNet18 converged under either.

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