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The paper introduces Hyperspherical Semantic Trajectory Analysis (HSTA), an unsupervised methodology for tracking technological diffusion using semantic embeddings from academic preprints and patent documents.
This paper presents a deployed pipeline for semantic user profiling that uses per-transaction-pattern LLM inference to reduce costs by three orders of magnitude at a major Japanese bank.
This study uses word embeddings to analyze reduplicative constructions in Mandarin Chinese, revealing semantic and pragmatic differentiation and validating distributional semantics for linguistic investigation.
SAFEGuard is a unified detection framework that uses harmful semantic analysis and fluency measurement to detect optimization-based jailbreak attacks on large language models, outperforming state-of-the-art baselines.
This paper presents the Semantic Signal-Assisted Decision Support (SSADS) framework, which converts return notes into condition factors and signal-quality scores to optimize inspection and recovery allocation in reverse logistics, showing improvements in net recovery value in synthetic benchmark scenarios.
This paper reevaluates the imperfective paradox benchmark for large language models, identifying conceptual and evaluation mis-specifications, and introduces lexically matched minimal pairs to reveal sufficiency bias in models' semantic reasoning.
This paper uses large-scale semantic analysis of over 14,000 publications to map definitions of learner agency and autonomy, revealing three dimensions and a systematic underrepresentation of the sociocultural dimension in existing scales. It argues that current generative AI research in education overly focuses on learning regulation, narrowing the behavioral repertoire for AI-mediated learning environments.
Decypher is a tool that provides deep execution context for agentic coding by performing semantic analysis of codebases, enabling agents to answer queries about code structure and interactions.
Sem-Detect introduces a method to distinguish AI-generated peer reviews from human-written ones by combining textual features with claim-level semantic analysis. It achieves a 25.5% improvement in true positive rate at 0.1% false positive rate over baselines, and shows that LLM-refined human reviews retain distinct semantic signals, with fewer than 3.5% misclassified as AI-generated.
CodeGraph is an open-source tool that pre-builds a semantic knowledge graph for codebases, allowing Claude Code to query the graph instead of scanning files one by one, thereby significantly reducing tool calls (by 92%) and improving exploration speed (by 71%). It supports 19 programming languages and 13 frameworks.
The paper introduces PASA, a robust watermarking algorithm for LLM-generated text that operates at the semantic level using latent embedding spaces to resist semantic-invariant attacks like paraphrasing.
This paper measures the semantic structure and evolution of conspiracy theories using 169.9M Reddit comments from r/politics (2012-2022), introducing the concept of "semantic objects" bounded by semantic neighborhoods to track how conspiracy theory meanings change over time beyond simple keyword-based approaches.
CodeGraph is an open-source tool that creates a pre-indexed knowledge graph of a codebase, enabling Claude Code's exploration agents to query symbol relationships and call graphs instantly, reducing tool calls by up to 96% and exploration time by 77%.