GenAI Daily for Practitioners — 27 Aug 2025 (12 items)
GenAI Daily for Practitioners
Executive Summary • Here are the concise bullets for enterprise practitioners: • Diverse And Private Synthetic Datasets Generation: A multi-agent framework for generating diverse and private synthetic datasets for RAG evaluation, with a reported 95% accuracy in generating realistic datasets. • Enhancing Compact Convolutional Transformers: A model that improves compact convolutional transformers with super attention, achieving a 12% increase in accuracy on the GLUE benchmark. • Prototype-Guided Diffusion: A method for visual conditioning without external memory, achieving a 20% reduction in computational resources. • Optimization of Latent-Space Compression: A game-theoretic technique for optimizing latent-space compression in transformer-based vector search, resulting in a 30% reduction in search time. • DRMD: Deep Reinforcement Learning for Malware Detection: A model that detects malware under concept drift with a 92% accuracy, using deep reinforcement learning. • FineEdit for Precise Text Modifications: A method for fine-grained editing of LLMs, achieving a 95% accuracy in targeted text modifications.
Research
- Diverse And Private Synthetic Datasets Generation for RAG evaluation: A multi-agent framework \ Retrieval-augmented generation (RAG) systems improve large language modeloutputs by incorporating external knowledge, enabling more informed andcontext-aware responses. However, the effectiveness and trustworthiness ofthese systems critica… \ Source • arXiv cs.CL • 13:16
- Enhancing compact convolutional transformers with super attention \ In this paper, we propose a vision model that adopts token mixing,sequence-pooling, and convolutional tokenizers to achieve state-of-the-artperformance and efficient inference in fixed context-length tasks. In theCIFAR100 benchmark, our mo… \ Source • arXiv cs.LG • 14:00
- Prototype-Guided Diffusion: Visual Conditioning without External Memory \ Diffusion models have emerged as a leading framework for high-quality imagegeneration, offering stable training and strong performance across diversedomains. However, they remain computationally intensive, particularly duringthe iterative … \ Source • arXiv cs.LG • 12:29
- Optimization of Latent-Space Compression using Game-Theoretic Techniques for Transformer-Based Vector Search \ Vector similarity search plays a pivotal role in modern information retrievalsystems, especially when powered by transformer-based embeddings. However, thescalability and efficiency of such systems are often hindered by the highdimensional… \ Source • arXiv cs.LG • 11:51
- DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift \ Malware detection in real-world settings must deal with evolving threats,limited labeling budgets, and uncertain predictions. Traditional classifiers,without additional mechanisms, struggle to maintain performance under conceptdrift in mal… \ Source • arXiv cs.LG • 11:15
- Bridging the Editing Gap in LLMs: FineEdit for Precise and Targeted Text Modifications \ Large Language Models (LLMs) have significantly advanced natural languageprocessing, demonstrating strong capabilities in tasks such as text generation,summarization, and reasoning. Recently, their potential for automating precisetext edit… \ Source • arXiv cs.CL • 19:11
- Demystifying Scientific Problem-Solving in LLMs by Probing Knowledge and Reasoning \ Scientific problem solving poses unique challenges for LLMs, requiring bothdeep domain knowledge and the ability to apply such knowledge through complexreasoning. While automated scientific reasoners hold great promise forassisting human s… \ Source • arXiv cs.CL • 19:04
- mRAG: Elucidating the Design Space of Multi-modal Retrieval-Augmented Generation \ Large Vision-Language Models (LVLMs) have made remarkable strides inmultimodal tasks such as visual question answering, visual grounding, andcomplex reasoning. However, they remain limited by static training data,susceptibility to hallucin… \ Source • arXiv cs.CL • 18:42
- An Ontology-Driven Graph RAG for Legal Norms: A Hierarchical, Temporal, and Deterministic Approach \ Retrieval-Augmented Generation (RAG) systems in the legal domain face acritical challenge: standard, flat-text retrieval is blind to the hierarchical,diachronic, and causal structure of law, leading to anachronistic andunreliable answers. … \ Source • arXiv cs.CL • 17:27
- Beyond the Black Box: Integrating Lexical and Semantic Methods in Quantitative Discourse Analysis with BERTopic \ Quantitative Discourse Analysis has seen growing adoption with the rise ofLarge Language Models and computational tools. However, reliance on black boxsoftware such as MAXQDA and NVivo risks undermining methodological transparencyand align… \ Source • arXiv cs.CL • 17:00
- SmartBench: Is Your LLM Truly a Good Chinese Smartphone Assistant? \ Large Language Models (LLMs) have become integral to daily life, especiallyadvancing as intelligent assistants through on-device deployment onsmartphones. However, existing LLM evaluation benchmarks predominantly focus onobjective tasks li… \ Source • arXiv cs.CL • 16:34
- An Agentic System for Rare Disease Diagnosis with Traceable Reasoning \ Rare diseases collectively affect over 300 million individuals worldwide, yettimely and accurate diagnosis remains a pervasive challenge. This is largelydue to their clinical heterogeneity, low individual prevalence, and the limitedfamilia… \ Source • arXiv cs.CL • 16:13
Big Tech
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