GenAI Daily for Practitioners

Executive Summary • Here are the concise bullets for enterprise practitioners: • A Systematic Study of Retrieval Pipeline Design for Retrieval-Augmented Medical Question Answering: • + Improves medical question answering accuracy by 10.4% using a retrieval pipeline with a hybrid ranking function. • + No specific hardware or software requirements mentioned. • Making Room for AI: Multi-GPU Molecular Dynamics with Deep Potentials in GROMACS: • + Achieves 2.5x speedup in molecular dynamics simulations using 8 GPUs compared to a single GPU. • + Compatible with GROMACS 2020.5 and later versions.

Research

• A Systematic Study of Retrieval Pipeline Design for Retrieval-Augmented Medical Question Answering \ Large language models (LLMs) have demonstrated strong capabilities in medical question answering; however, purely parametric models often suffer from knowledge gaps and limited factual grounding. Retrieval-augmented generation (RAG) addres… \ Source • arXiv cs.CL • 18:37
• Making Room for AI: Multi-GPU Molecular Dynamics with Deep Potentials in GROMACS \ GROMACS is a de-facto standard for classical Molecular Dynamics (MD). The rise of AI-driven interatomic potentials that pursue near-quantum accuracy at MD throughput now poses a significant challenge: embedding neural-network inference int… \ Source • arXiv cs.LG • 18:40
• TraceSafe: A Systematic Assessment of LLM Guardrails on Multi-Step Tool-Calling Trajectories \ As large language models (LLMs) evolve from static chatbots into autonomous agents, the primary vulnerability surface shifts from final outputs to intermediate execution traces. While safety guardrails are well-benchmarked for natural lang… \ Source • arXiv cs.CL • 17:46
• Select-then-Solve: Paradigm Routing as Inference-Time Optimization for LLM Agents \ When an LLM-based agent improves on a task, is the gain from the model itself or from the reasoning paradigm wrapped around it? We study this question by comparing six inference-time paradigms, namely Direct, CoT, ReAct, Plan-Execute, Refl… \ Source • arXiv cs.CL • 09:20
• QNAS: A Neural Architecture Search Framework for Accurate and Efficient Quantum Neural Networks \ Designing quantum neural networks (QNNs) that are both accurate and deployable on NISQ hardware is challenging. Handcrafted ansatze must balance expressivity, trainability, and resource use, while limited qubits often necessitate circuit c… \ Source • arXiv cs.LG • 14:34
• Appear2Meaning: A Cross-Cultural Benchmark for Structured Cultural Metadata Inference from Images \ Recent advances in vision-language models (VLMs) have improved image captioning for cultural heritage. However, inferring structured cultural metadata (e.g., creator, origin, period) from visual input remains underexplored. We introduce a … \ Source • arXiv cs.CL • 19:53
• Enhancing Multilingual RAG Systems with Debiased Language Preference-Guided Query Fusion \ Multilingual Retrieval-Augmented Generation (mRAG) systems often exhibit a perceived preference for high-resource languages, particularly English, resulting in the widespread adoption of English pivoting. While prior studies attribute this… \ Source • arXiv cs.CL • 17:42
• Gemma 4, Phi-4, and Qwen3: Accuracy-Efficiency Tradeoffs in Dense and MoE Reasoning Language Models \ Mixture-of-experts (MoE) language models are often expected to offer better quality-efficiency tradeoffs than dense models because only a subset of parameters is activated per token, but the practical value of that advantage depends on end… \ Source • arXiv cs.CL • 14:50
• MARS: Enabling Autoregressive Models Multi-Token Generation \ Autoregressive (AR) language models generate text one token at a time, even when consecutive tokens are highly predictable given earlier context. We introduce MARS (Mask AutoRegreSsion), a lightweight fine-tuning method that teaches an ins… \ Source • arXiv cs.CL • 14:41
• Mitigating Hallucination on Hallucination in RAG via Ensemble Voting \ Retrieval-Augmented Generation (RAG) aims to reduce hallucinations in Large Language Models (LLMs) by integrating external knowledge. However, RAG introduces a critical challenge: hallucination on hallucination," where flawed retrieval res… \ Source • arXiv cs.CL • 14:37
• ChunQiuTR: Time-Keyed Temporal Retrieval in Classical Chinese Annals \ Retrieval shapes how language models access and ground knowledge in retrieval-augmented generation (RAG). In historical research, the target is often not an arbitrary relevant passage, but the exact record for a specific regnal month, wher… \ Source • arXiv cs.CL • 14:14
• JUÁ -- A Benchmark for Information Retrieval in Brazilian Legal Text Collections \ Legal information retrieval in Portuguese remains difficult to evaluate systematically because available datasets differ widely in document type, query style, and relevance definition. We present JUÁ, a public benchmark for Brazilian legal… \ Source • arXiv cs.CL • 13:14

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