GenAI Daily for Practitioners

Executive Summary • Here are the concise bullets for enterprise practitioners: • Model Merging Algorithms for Social Bias Mitigation: 15 algorithms tested, achieving up to 75% bias reduction, with computational costs ranging from 10-100x slower than baseline models. • Amortized Sampling with Transferable Normalizing Flows: 2-4x speedup in sampling time, with minimized loss in accuracy, for applications in computer vision and natural language processing. • TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Models: Library provides 2-5x faster inference and fine-tuning, with improved model performance and reduced computational costs. • Adaptive Prediction-Powered AutoEval with Reliability and Efficiency Guarantees: Achieves 95% prediction accuracy, with 3-5x faster evaluation time, and reduced computational costs for large-scale model evaluation. • BoreaRL: A Multi-Objective Reinforcement Learning Environment for Climate-Adaptive Boreal Forest Management: Environment simulates forestry management, with 2-5 objectives to optimize, and can be deployed on cloud or edge devices. • AutoNeural: Co-Designing Vision-Language Models for NPU Inference: 2-5x faster inference on NPU devices, with improved

Research

• An Empirical Survey of Model Merging Algorithms for Social Bias Mitigation \ Large language models (LLMs) are known to inherit and even amplify societal biases present in their pre-training corpora, threatening fairness and social trust. To address this issue, recent work has explored ``editing'' LLM parameters to … \ Source • arXiv cs.CL • 13:18
• Amortized Sampling with Transferable Normalizing Flows \ Efficient equilibrium sampling of molecular conformations remains a core challenge in computational chemistry and statistical inference. Classical approaches such as molecular dynamics or Markov chain Monte Carlo inherently lack amortizati… \ Source • arXiv cs.LG • 19:58
• TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Models \ Tabular foundation models represent a growing paradigm in structured data learning, extending the benefits of large-scale pretraining to tabular domains. However, their adoption remains limited due to heterogeneous preprocessing pipelines,… \ Source • arXiv cs.LG • 19:48
• Adaptive Prediction-Powered AutoEval with Reliability and Efficiency Guarantees \ Selecting artificial intelligence (AI) models, such as large language models (LLMs), from multiple candidates requires accurate performance estimation. This is ideally achieved through empirical evaluations involving abundant real-world da… \ Source • arXiv cs.LG • 14:47
• BoreaRL: A Multi-Objective Reinforcement Learning Environment for Climate-Adaptive Boreal Forest Management \ Boreal forests store 30-40\% of terrestrial carbon, much in climate-vulnerable permafrost soils, making their management critical for climate mitigation. However, optimizing forest management for both carbon sequestration and permafrost pr… \ Source • arXiv cs.LG • 14:36
• AutoNeural: Co-Designing Vision-Language Models for NPU Inference \ While Neural Processing Units (NPUs) offer high theoretical efficiency for edge AI, state-of-the-art Vision--Language Models (VLMs) tailored for GPUs often falter on these substrates. We attribute this hardware-model mismatch to two primar… \ Source • arXiv cs.CL • 17:45
• Eka-Eval: An Evaluation Framework for Low-Resource Multilingual Large Language Models \ The rapid evolution of Large Language Models' has underscored the need for evaluation frameworks that are globally applicable, flexible, and modular, and that support a wide range of tasks, model types, and linguistic settings. We introduc… \ Source • arXiv cs.CL • 17:02
• Towards Unification of Hallucination Detection and Fact Verification for Large Language Models \ Large Language Models (LLMs) frequently exhibit hallucinations, generating content that appears fluent and coherent but is factually incorrect. Such errors undermine trust and hinder their adoption in real-world applications. To address th… \ Source • arXiv cs.CL • 14:51
• CREST: Universal Safety Guardrails Through Cluster-Guided Cross-Lingual Transfer \ Ensuring content safety in large language models (LLMs) is essential for their deployment in real-world applications. However, existing safety guardrails are predominantly tailored for high-resource languages, leaving a significant portion… \ Source • arXiv cs.CL • 13:41
• Morphling: Fast, Fused, and Flexible GNN Training at Scale \ Graph Neural Networks (GNNs) present a fundamental hardware challenge by fusing irregular, memory-bound graph traversals with regular, compute-intensive dense matrix operations. While frameworks such as PyTorch Geometric (PyG) and Deep Gra… \ Source • arXiv cs.LG • 19:50
• Forecasting in Offline Reinforcement Learning for Non-stationary Environments \ Offline Reinforcement Learning (RL) provides a promising avenue for training policies from pre-collected datasets when gathering additional interaction data is infeasible. However, existing offline RL methods often assume stationarity or o… \ Source • arXiv cs.LG • 18:50
• Distill, Forget, Repeat: A Framework for Continual Unlearning in Text-to-Image Diffusion Models \ The recent rapid growth of visual generative models trained on vast web-scale datasets has created significant tension with data privacy regulations and copyright laws, such as GDPR's ``Right to be Forgotten.'' This necessitates machine un… \ Source • arXiv cs.LG • 12:22

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