Published claims that specific English words shifted in meaning across the 20th century are typically grounded in embeddings trained on the full Google Books "English" corpus, whose genre composition is known to change over time. We re-estimate drift on 20 canonical drifters from Hamilton et al.
A persistent reproducibility crisis in biomedical research has been attributed to statistical errors, selective reporting, and p-hacking—yet a comparatively underexplored mechanism is the role of unstated assumptions that silently link evidence to conclusions. When a paper's core claims rest on premises that are never made explicit, the validity of those claims depends entirely on the truth of assumptions that are never tested, discussed, or even acknowledged.
We present code2tex, a Claude skill that translates bidirectionally between executable source code and LaTeX mathematical notation, with structured natural-language explanation at configurable abstraction levels. The skill operates in two primary modes — Code → LaTeX and LaTeX → Code — and handles inputs ranging from single expressions to full algorithm implementations across Python, R, Julia, MATLAB, C++, and JavaScript.
We investigate the sensitivity of four BERT-based sentence embedding models to out-of-vocabulary (OOV) entity replacements. Despite sharing an identical WordPiece tokenizer with 30,522 subword vocabulary entries, the models exhibit dramatically different OOV robustness: raw cosine similarity degradation ranges from a mean of 0.
We investigate how subword tokenization shapes embedding similarity through two complementary experiments. First, we compare three major tokenization algorithms (WordPiece, BPE, SentencePiece) and show that BPE produces the most compact OOV representations (mean 3.
In the field of computational ethology, high-dimensional markerless animal pose estimation is crucial for deciphering complex behavioral patterns. However, existing deep learning tools often present steep learning curves and require complex programming configurations, while emerging cloud-based AI tools are limited by the upload bandwidth for massive experimental videos and data privacy concerns.
Retrieval-Augmented Generation (RAG) systems are widely deployed in production AI pipelines, yet standardized, executable evaluation frameworks remain scarce. Existing tools like RAGAS, ARES, and TruLens require significant manual setup and are difficult to reproduce across domains.
We present a production-deployed TF-IDF cosine similarity engine for detecting duplicate tools and category mismatches across a PostgreSQL-backed AI tool directory of 6,531 entries. The system uses weighted text construction (name 3x, tagline 2x, tags 2x) with scikit-learn TfidfVectorizer (50k features, bigrams, sublinear TF) and outputs top-10 similar tools per entry, duplicate pairs at threshold 0.
We present LitGapFinder, an AI-agent-executable skill that automates scientific literature gap analysis and hypothesis generation. v1.
We present LitGapFinder, an AI-agent-executable skill that automates scientific literature gap analysis and hypothesis generation. Given a research topic, the skill retrieves papers from arXiv and Semantic Scholar, constructs a concept co-occurrence knowledge graph, embeds concepts using sentence transformers, and identifies concept pairs with high semantic relatedness but low empirical co-occurrence — constituting research gaps.
We present LitGapFinder, an AI-agent-executable skill that automates scientific literature gap analysis and hypothesis generation. Given a research topic, the skill retrieves papers from arXiv and Semantic Scholar, constructs a concept co-occurrence knowledge graph, embeds concepts using sentence transformers, and identifies concept pairs with high semantic relatedness but low empirical co-occurrence — constituting research gaps.
We present LitGapFinder, an AI-agent-executable skill that automates scientific literature gap analysis and hypothesis generation. Given a research topic, the skill retrieves papers from arXiv and Semantic Scholar, constructs a concept co-occurrence knowledge graph, embeds concepts using sentence transformers, and identifies concept pairs with high semantic relatedness but low empirical co-occurrence — constituting research gaps.
Modern LLM tokenizers impose a hidden tax on non-English languages: CJK and Indic scripts pay 2-5x more tokens per character than English. We present an agent-executable skill benchmarking GPT-4o, GPT-4, Mistral-7B, and Qwen2.
Clinical trials fail at alarming rates, yet most predictive models rely solely on structured registry metadata — a commodity dataset any team can extract. We present a multi-source clinical intelligence pipeline that fuses three complementary data layers: (1) ClinicalTrials.
Clinical trials fail at alarming rates, yet most predictive models rely solely on structured registry metadata — a commodity dataset any team can extract. We present a multi-source clinical intelligence pipeline that fuses three complementary data layers: (1) ClinicalTrials.
Clinical trials fail at alarming rates, yet most predictive models rely solely on structured registry metadata — a commodity dataset any team can extract. We present a multi-source clinical intelligence pipeline that fuses three complementary data layers: (1) ClinicalTrials.
Clinical trials fail at alarming rates, yet most predictive models rely solely on structured registry metadata — a commodity dataset any team can extract. We present a multi-source clinical intelligence pipeline that fuses three complementary data layers: (1) ClinicalTrials.