large language models

The Borrowed Name: Counterfeit Sanctity, Artificial Intelligence, and the Architecture of the Final Deception

This research paper advances a novel constructive theological argument regarding the intersection of biblical eschatology and generative artificial intelligence (AI). Moving beyond traditional inquiries into the identity or chronology of the Antichrist, the author investigates the mechanism of deception described in New Testament corpora (Matthew 7, 2 Thessalonians 2, 2 Corinthians 11, and Revelation 13). Core ThesisThe paper identifies "Counterfeit Sanctity"—the weaponized mimesis of sacred language and divine invocation—as the central structural weapon of the eschatological deceiver. It argues that the final deception functions not through overt blasphemy or opposition to God, but through the sophisticated capture and impersonation of the Holy Spirit’s linguistic and phenomenological register. Technological SynthesisThe author identifies Large Language Models (LLMs) and generative heuristics as the first historical apparatus capable of realizing this mechanism at civilizational scale. By decoupling religiously fluent, spiritually authoritative speech from ontological character and pneumatic presence, generative AI allows for the manufacturing of "ownerless" sanctity. Key Contributions Exegetical Analysis: A synthesis of the "Lord, Lord" rejection in Matthew 7 with the "lying signs" of 2 Thessalonians 2. Patristic Grounding: Confirmation of the mimesis-of-the-sacred theory in the works of Irenaeus, Cyril of Jerusalem, and John Chrysostom. AI Epistemology: A structural comparison between the "disguise of light" (2 Cor. 11:14) and the output mechanics of generative systems. Practical Theology: A proposed "Pneumatological Epistemology" for the digital age, focusing on communal discernment (diakrisis), relational accountability, and the "Fruit Test" (Galatians 5).

Survey on joint compression and fine-tuning of large language models: Methods, toolchains, and open challenges under resource constraints

Large Language models for banking supervision: reliability evidence from European systemic banks

Vulnerability detection of smart contracts by integrating large language models and dependency analysis

A reference-guided large language model workflow for mobile phase selection in thin-layer chromatography enabled by a polarity-space tetrahedron strategy.

Mobile phase selection in thin-layer chromatography (TLC) still relies heavily on empirical trial-and-error. Existing machine learning methods often exhibit limited predictive performance, as they depend on manual descriptor engineering and are sensitive to data quality. Here, a reference-guided large language model (LLM) workflow for TLC mobile phase recommendation is proposed. The LLM is used as a flexible inference interface that integrates structural similarity, polarity descriptors, and example-based analogical inference. The key methodological contribution is a polarity-space tetrahedron strategy for selecting reference compounds. Three polarity descriptors, including molecular refractivity, topological polar surface area, and n-octanol-water partition coefficient, are used to construct a three-dimensional space, and a target compound is constrained within a tetrahedron formed by four reference compounds to enable interpolation-based inference. A similarity-weighted version of this tetrahedron strategy is also developed. Using a publicly available high-throughput automated TLC dataset and DeepSeek-Reasoner as the inference engine, four reference selection strategies are compared. The similarity-weighted tetrahedron method achieves the best performance, with an availability of 81.48% and a final score of 0.8436 across 45 compound pairs evaluated in triplicate. The success rate of 92.16% confirmed by independent experimental validation supports the practical relevance of the recommendations. The framework also provides interpretable outputs, including structured rationales and experimental suggestions. This work demonstrates that combining a locally constrained reference construction strategy with an LLM offers a practical and interpretable tool for TLC mobile phase optimization, without requiring large labeled datasets or task-specific model training.

LLMs in Civil Engineering: Education Usage Patterns, Verification Practices, and Curriculum Implications from a Taxonomy-Aligned Student Survey

Large language models (LLMs) are rapidly entering civil engineering research and practice, yet little is known about their use in educational contexts. This study reports results from an institutional case study based on a taxonomy-aligned survey of 109 respondents (103 undergraduates, four graduate students, and two faculty) in civil engineering–related programs at a large US university. The survey examined adoption patterns, task functions, verification practices, disclosure norms, and training needs. Undergraduates primarily used LLMs for tutoring and concept explanation (83%) and design ideation (67%), with limited adoption in coding (7%) and technical reasoning (41%). Verification practices were robust: 86% recalculated manually, 52% checked against standards, and only 4% reported nonverification, yielding a median of two methods per user. Ethical orientations favored conditional disclosure for major contributions (53%) and placed primary responsibility for errors on the human user (75%). Demand for formal training was high, especially among those with greater adoption, familiarity, and verification breadth. Results reveal a developmental gap between student practices, which emphasize low-risk learning and ideation, and research and faculty practices, which emphasize technically rigorous applications. The study underscores the need for curricular pathways that guide students from exploratory uses toward responsibly verified technical tasks within similar educational contexts. By linking a civil engineering–specific taxonomy of LLM functions with educational survey data, this work offers institutionally grounded empirical evidence on artificial intelligence (AI) literacy in civil engineering education and highlights directions for curriculum and assessment design.

Towards knowledge-infused seabed sediment mapping: A semi-supervised framework integrating large language models and knowledge graphs for multibeam data

A comparative analysis of the role of Large Language Models and Low-Rank Adaptation in Automated Program Repair

Chat with the manufacturing process: A knowledge-guided cross-modal large language model for machining condition predictive maintenance

An LLM Based Framework for Automated MILP Modeling in Dynamic Multi-Robot Task Scheduling

Coordinating a team of robots in aircraft skin fabrication requires allocating and sequencing tightly coupled subtasks under spatio-temporal constraints, while the fleet must react to runtime disturbances such as robot failures and urgent task arrivals. Mixed-Integer Linear Programming (MILP) yields provably optimal coordination, but a disturbance often introduces new constraint logic rather than a mere parameter change, leaving the existing formulation structurally inadequate and requiring expert-led reformulation to accommodate the new logic. This letter proposes a Large Language Model (LLM)-based framework that automates the path from natural-language scheduling requirements to MILP formulation, executable solver code, and event-driven rescheduling, so that the optimization model can be restructured online without expert intervention. Two compact LLMs are specialized for complementary roles: a modeling LLM, empowered via knowledge augmentation, supervised fine-tuning on industrial constraint descriptions, and direct preference optimization on self-generated negatives; and a code LLM, trained via sandbox-validated knowledge distillation. On ten industrial constraint classes, the 8B modeling LLM reaches 100% constraint-level accuracy, exceeding cloud-scale general-purpose LLMs equipped with retrieval over the same knowledge base, while the 8B code LLM attains 86% under fully on-premises deployment. When a disturbance occurs, an event-triggered mechanism regenerates only the affected constraints. A case study on multi-robot scheduling for aircraft skin fabrication shows that the framework matches the schedule quality of baselines while shifting adaptation cost from offline expert labour to a bounded online inference budget.

Trustworthy RAG: An Evaluation Agent for Detecting Misinformation and Knowledge Poisoning in Generative AI Systems

Retrieval-Augmented Generation (RAG) grounds Large Language Model (LLM) outputs in external knowledge, but RAG systems usually trust whatever they retrieve, creating a Security-Reliability Gap: high semantic relevance does not guarantee factual truth. Adversaries exploit this through knowledge poisoning, inserting malicious documents to cause targeted misinformation. We propose an Evaluation Agent, middleware that combines Natural Language Inference (NLI) factual verification, a five-signal poison detector with relevance-weighted aggregation, and a Trust Index T = 0.4 F + 0.35 C + 0.25 (1 - P ) with a non-linear dampener for high-contamination contexts. On TruthfulQA with Llama 3.3 70B, the agent reaches 91% accuracy and 100% precision, with 100% recall on instruction injection, while in-place edits, such as entity swaps, remain hard to detect. Across three LLMs the Trust Index stays discriminative, with a Receiver Operating Characteristic Area Under the Curve (ROC-AUC) of 0.73 to 0.81; generation style matters more than model size, and per-LLM threshold calibration restores baseline competitive accuracy, whereas a weaker FEVER result shows that cross-dataset generalization requires domain-specific calibration. In a software-engineering use case, a secure-coding assistant over guidance from the Open Worldwide Application Security Project (OWASP) Top 10 and the Common Weakness Enumeration (CWE), the agent reliably blocks instruction injection of unsafe advice (F1 92%), while contradiction and subtle semantic weakening remain hard. Throughout, the agent measures detection of poisoned context before generation, not whether the LLM adopts the injected misinformation. We release the proposed approach, attack generator, and experimental artifacts at the link: https://github.com/GPT-Laboratory/TrustworthyRAG.

A proof of the Dixmier conjecture for the first Weyl algebra

This record contains two papers: (1) A proof of the Dixmier conjecture for the first Weyl algebra; (2) Commutation is isomonodromy: spectral rigidity in the first Weyl algebra. Produced through a collaboration between large language models and an anonymous human researcher.