While knowledge distillation has seen widespread use in pre-training and instruction tuning, its application to aligning language models with human preferences remains underexplored, particularly in the more realistic cross-tokenizer setting. The incompatibility of tokenization schemes between teacher and student models has largely prevented fine-grained, white-box distillation of preference information. To address this gap, we propose Cross-Tokenizer Preference Distillation (CTPD), the first unified framework for transferring human-aligned behavior between models with heterogeneous tokenizers. CTPD introduces three key innovations: (1) Aligned Span Projection, which maps teacher and student tokens to shared character-level spans for precise supervision transfer; (2) a cross-tokenizer adaptation of Token-level Importance Sampling (TIS-DPO) for improved credit assignment; and (3) a Teacher-Anchored Reference, allowing the student to directly leverage the teacher’s preferences in a DPO-style objective. Our theoretical analysis grounds CTPD in importance sampling, and experiments across multiple benchmarks confirm its effectiveness, with significant performance gains over existing methods. These results establish CTPD as a practical and general solution for preference distillation across diverse tokenization schemes, opening the door to more accessible and efficient alignment of language models.

In the digital era, the internet provides rapid and convenient access to vast amounts of information. However, much of this information remains unverified, particularly with the increasing prevalence of falsified numerical data, leading to public confusion and negative societal impacts. To address this issue, we developed ViNumFCR, a first dataset dedicated to fact-checking numerical information in Vietnamese. Comprising over 10,000 samples collected and constructed from online newspaper across 12 different topics. We assessed the performance of various fact-checking models, including Pretrained Language Models and Large Language Models, alongside retrieval techniques for gathering supporting evidence. Experimental results demonstrate that the XLM-R_Large model achieved the highest accuracy of 90.05% on the fact-checking task, while the combined SBERT + BM25 model attained a precision of over 97% on the evidence retrieval task. Additionally, we conducted an in-depth analysis of the linguistic features of the dataset to understand the factors influencing the performance models. The ViNumFCR dataset is publicly available to support further research.

This study aims to contribute to a deeper understanding of the interplay between customer experience perceptions (including trendiness, aesthetic quality, information quantity, personalization quality, entertainment, and accuracy) and engagement within the context of AI-based content recommendation systems on short-video platforms, with a focus on the mediating role of empathy and self-congruence. 781 online responses were collected in Vietnam, and a structural equation model was utilized to analyze the data. The findings show a significant positive impact of AI-based content recommendation experience perceptions on empathy and self-congruence. The link between empathy and customer engagement and the association between self-congruence and customer engagement were found to be positive. This study validates six dimensions used to measure customer experience perceptions of AI recommendations on short-video social media platforms and examines the knock-on effects of this concept on the psychological nuances and behavioral outcomes of consumers. Both theoretical and managerial implications are discussed.

Online health communities (OHCs) offer the promise of connecting with supportive peers. Forming these connections first requires finding relevant peers—a process that can be time-consuming. Peer recommendation systems are a computational approach to make finding peers easier during a health journey. By encouraging OHC users to alter their online social networks, peer recommendations could increase available support. But these benefits are hypothetical and based on mixed, observational evidence. To experimentally evaluate the effect of peer recommendations, we conceptualize these systems as health interventions designed to increase specific beneficial connection behaviors. In this paper, we designed a peer recommendation intervention to increase two behaviors: reading about peer experiences and interacting with peers. We conducted an initial feasibility assessment of this intervention by conducting a 12-week field study in which 79 users of CaringBridge.org received weekly peer recommendations via email. Our results support the usefulness and demand for peer recommendation and suggest benefits to evaluating larger peer recommendation interventions. Our contributions include practical guidance on the development and evaluation of peer recommendation interventions for OHCs.

MOTIVATION: Researchers now have access to large volumes of genome sequences for comparative analysis, some generated by the plethora of public sequencing projects and, increasingly, from individual efforts. It is not possible, or necessarily desirable, that the public genome browsers attempt to curate all these data. Instead, a wealth of powerful tools is emerging to empower users to create their own visualizations and browsers. RESULTS: We introduce a pipeline to easily generate collections of Web-accessible UCSC Genome Browsers interrelated by an alignment. It is intended to democratize our comparative genomic browser resources, serving the broad and growing community of evolutionary genomicists and facilitating easy public sharing via the Internet. Using the alignment, all annotations and the alignment itself can be efficiently viewed with reference to any genome in the collection, symmetrically. A new, intelligently scaled alignment display makes it simple to view all changes between the genomes at all levels of resolution, from substitutions to complex structural rearrangements, including duplications. To demonstrate this work, we create a comparative assembly hub containing 57 Escherichia coli and 9 Shigella genomes and show examples that highlight their unique biology. AVAILABILITY AND IMPLEMENTATION: The source code is available as open source at: https://github.com/glennhickey/progressiveCactus The E.coli and Shigella genome hub is now a public hub listed on the UCSC browser public hubs Web page.

SUMMARY: Track data hubs provide an efficient mechanism for visualizing remotely hosted Internet-accessible collections of genome annotations. Hub datasets can be organized, configured and fully integrated into the University of California Santa Cruz (UCSC) Genome Browser and accessed through the familiar browser interface. For the first time, individuals can use the complete browser feature set to view custom datasets without the overhead of setting up and maintaining a mirror. AVAILABILITY AND IMPLEMENTATION: Source code for the BigWig, BigBed and Genome Browser software is freely available for non-commercial use at http://hgdownload.cse.ucsc.edu/admin/jksrc.zip, implemented in C and supported on Linux. Binaries for the BigWig and BigBed creation and parsing utilities may be downloaded at http://hgdownload.cse.ucsc.edu/admin/exe/. Binary Alignment/Map (BAM) and Variant Call Format (VCF)/tabix utilities are available from http://samtools.sourceforge.net/ and http://vcftools.sourceforge.net/. The UCSC Genome Browser is publicly accessible at http://genome.ucsc.edu.