This study develops a fairness-calibrated credit-scoring method that combines counterfactual perturbation with group-wise regularization. Using 3.1 million credit files with demographic annotations, the model evaluates whether score outputs remain stable when protected attributes are perturbed in a causal graph. A fairness-adjusted gradient-boosting model is then trained with penalties on group-level prediction disparities. The final model reduces demographic disparity in predicted default probability from 0.112 to 0.034 while maintaining an ROC-AUC of 0.89. Counterfactual-stability checks show that 94.6% of […]

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arXiv:2601.12138v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly integrated into vehicle-based digital assistants, where unsafe, ambiguous, or legally incorrect responses can lead to serious safety, ethical, and regulatory consequences. Despite growing interest in LLM safety, existing taxonomies and evaluation frameworks remain largely general-purpose and fail to capture the domain-specific risks inherent to real-world driving scenarios. In this paper, we introduce DriveSafe, a hierarchical, four-level risk taxonomy designed to systematically characterize safety-critical failure modes of LLM-based […]

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arXiv:2601.12265v1 Announce Type: new Abstract: This study proposes an algorithm titled a statistical firefly algorithm (SFA) for truss topology optimization. In the proposed algorithm, historical results of fireflies’ motions are used in hypothesis testing to limit the motions of fireflies that are suggested by current information exchanges between fireflies only to those that are potentially useful. Hypothesis testing is applied to the mechanism of an ordinary firefly algorithm (FA) without changing its structure. As a result, the implementation […]

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arXiv:2601.03378v1 Announce Type: new Abstract: Repository-level code completion benefits from retrieval-augmented generation (RAG). However, controlling cross-file evidence is difficult because chunk utility is often interaction-dependent: some snippets help only when paired with complementary context, while others harm decoding when they conflict. We propose RepoShapley, a coalition-aware context filtering framework supervised by Shapley-style marginal contributions. Our module ChunkShapley constructs offline labels by (i) single-chunk probing with teacher-forced likelihood to estimate signed, weighted effects, (ii) a surrogate game that captures […]

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I’m training PPO over Hopper environments, I am also randomizing masses for an ablation study and I want to parallelize the different environments to get results faster, but it tells me that running PPO on a GPU is actually worse, so how do I do it? I’m using stable baseline and gymnasium hopper submitted by /u/Santo_Games [link] [comments]

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arXiv:2601.12380v1 Announce Type: cross Abstract: Real-world tabular databases routinely combine continuous measurements and categorical records, yet missing entries are pervasive and can distort downstream analysis. We propose Statistical-Neural Interaction (SNI), an interpretable mixed-type imputation framework that couples correlation-derived statistical priors with neural feature attention through a Controllable-Prior Feature Attention (CPFA) module. CPFA learns head-wise prior-strength coefficients ${lambda_h}$ that softly regularize attention toward the prior while allowing data-driven deviations when nonlinear patterns appear to be present in the data. […]

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arXiv:2601.03274v1 Announce Type: new Abstract: LLM_annotate is a Python package for analyzing the personality of fiction characters with large language models. It standardizes workflows for annotating character behaviors in full texts (e.g., books and movie scripts), inferring character traits, and validating annotation/inference quality via a human-in-the-loop GUI. The package includes functions for text chunking, LLM-based annotation, character name disambiguation, quality scoring, and computation of character-level statistics and embeddings. Researchers can use any LLM, commercial, open-source, or custom, within […]

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