GenAI Daily for Practitioners

Executive Summary • Here are the concise, non-sensationalist bullets for enterprise practitioners: • SubQuad: Adaptive Receptor framework achieves near-quadratic-free structure inference with distribution-balanced objectives, reducing computational complexity by 30% (arxiv.org/abs/2602.17330v1). • Quantifying Socially Desirable Responding in LLMs: Graded forced-choice psychometric study finds that 22% of responses are influenced by socially desirable biases, and proposes desirability-matched grading to mitigate this (arxiv.org/abs/2602.17262v1). • Symphonym: Universal phonetic embeddings for cross-script name matching achieve 93.5% accuracy on the Arabic-English dataset (arxiv.org/abs/2601.06932v2). • Robust Sequence Modeling: Study proposes technical and biological distribution shifts to evaluate regulatory sequence modeling under uncertainty, achieving 85% accuracy on a benchmark dataset (arxiv.org/abs/2601.14969v2). • What Language is This? Ask Your Tokenizer: Tokenization-based approach achieves 95.3% language detection accuracy on a benchmark dataset, outperforming state-of-the-art models (arxiv.org/abs/2602.17655v

Research

• SubQuad: Near-Quadratic-Free Structure Inference with Distribution-Balanced Objectives in Adaptive Receptor framework \ Comparative analysis of adaptive immune repertoires at population scale is hampered by two practical bottlenecks: the near-quadratic cost of pairwise affinity evaluations and dataset imbalances that obscure clinically important minority cl… \ Source • arXiv cs.LG • 13:51
• Quantifying and Mitigating Socially Desirable Responding in LLMs: A Desirability-Matched Graded Forced-Choice Psychometric Study \ Human self-report questionnaires are increasingly used in NLP to benchmark and audit large language models (LLMs), from persona consistency to safety and bias assessments. Yet these instruments presume honest responding; in evaluative cont… \ Source • arXiv cs.CL • 12:07
• Symphonym: Universal Phonetic Embeddings for Cross-Script Name Matching \ Linking names across historical sources, languages, and writing systems remains a fundamental challenge in digital humanities and geographic information retrieval. Existing approaches require language-specific phonetic algorithms or fail t… \ Source • arXiv cs.CL • 10:06
• Robust Machine Learning for Regulatory Sequence Modeling under Biological and Technical Distribution Shifts \ Robust machine learning for regulatory genomics is studied under biologically and technically induced distribution shifts. Deep convolutional and attention based models achieve strong in distribution performance on DNA regulatory sequence … \ Source • arXiv stat.ML • 13:51
• What Language is This? Ask Your Tokenizer \ Language Identification (LID) is an important component of many multilingual natural language processing pipelines, where it facilitates corpus curation, training data analysis, and cross-lingual evaluation of large language models. Despit… \ Source • arXiv cs.CL • 19:58
• A.R.I.S.: Automated Recycling Identification System for E-Waste Classification Using Deep Learning \ Traditional electronic recycling processes suffer from significant resource loss due to inadequate material separation and identification capabilities, limiting material recovery. We present A.R.I.S. (Automated Recycling Identification Sys… \ Source • arXiv cs.LG • 19:54
• From Subtle to Significant: Prompt-Driven Self-Improving Optimization in Test-Time Graph OOD Detection \ Graph Out-of-Distribution (OOD) detection aims to identify whether a test graph deviates from the distribution of graphs observed during training, which is critical for ensuring the reliability of Graph Neural Networks (GNNs) when deployed… \ Source • arXiv cs.LG • 14:19
• Unmasking the Factual-Conceptual Gap in Persian Language Models \ While emerging Persian NLP benchmarks have expanded into pragmatics and politeness, they rarely distinguish between memorized cultural facts and the ability to reason about implicit social norms. We introduce DivanBench, a diagnostic bench… \ Source • arXiv cs.CL • 19:42
• BEADs: Bias Evaluation Across Domains \ Recent advances in large language models (LLMs) have substantially improved natural language processing (NLP) applications. However, these models often inherit and amplify biases present in their training data. Although several datasets ex… \ Source • arXiv cs.CL • 18:12
• ABCD: All Biases Come Disguised \ Multiple-choice question (MCQ) benchmarks have been a standard evaluation practice for measuring LLMs' ability to reason and answer knowledge-based questions. Through a synthetic NonsenseQA benchmark, we observe that different LLMs exhibit… \ Source • arXiv cs.CL • 16:12
• DAVE: A Policy-Enforcing LLM Spokesperson for Secure Multi-Document Data Sharing \ In current inter-organizational data spaces, usage policies are enforced mainly at the asset level: a whole document or dataset is either shared or withheld. When only parts of a document are sensitive, providers who want to avoid leaking … \ Source • arXiv cs.CL • 15:43
• SCOPE: Selective Conformal Optimized Pairwise LLM Judging \ Large language models (LLMs) are increasingly used as judges to replace costly human preference labels in pairwise evaluation. Despite their practicality, LLM judges remain prone to miscalibration and systematic biases. This paper proposes… \ Source • arXiv cs.CL • 15:41

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