arXiv subjects

Jiacheng Ji

Publications and source records attributed to Jiacheng Ji.

5 recordsLinked to original sources

arXiv2026

RefiningGPT: Specialized language Models for Automated Refinery Unit-level Process Diagram Synthesis

Applying LLMs to complex industrial processes remains challenging due to the semantic gap between natural language design intents and the rigorous physical logic of engineering. In the field of petroleum refining engineering, a critical bottleneck is the automated synthesis of Unit-level Process Diagrams (UPDs), which serve as the topological bridge connecting abstract requirements to concrete unit operations. In this paper, we propose RefineGPT, a domain-specialized agent for autonomous refinery design.RefineGPT adopts a hierarchical architecture in which a supervised fine-tuned small language model is responsible for selecting units that satisfy design requirements, while a large language model is used to connect these units to generate the final topology. To enable supervised training, we develop a pipeline that extracts latent process motifs from noisy, unstructured legacy topologies and synthesizes high-quality rationale-based Chain-of-Thought (CoT) training data. Empirical validation demonstrates that RefineGPT achieves substantial improvements in topological consistency and chemical engineering feasibility, establishing a high-fidelity pathway for AI-augmented industrial process synthesis.

cs.CE↗

arXiv2026

Ethical Risks of Large Language Models in Medical Consultation: An Assessment Based on Reproductive Ethics

Background: As large language models (LLMs) are increasingly used in healthcare and medical consultation settings, a growing concern is whether these models can respond to medical inquiries in a manner that is ethically compliant--particularly in accordance with local ethical standards. To address the pressing need for comprehensive research on reliability and safety, this study systematically evaluates LLM performance in answering questions related to reproductive ethics, specifically assessing their alignment with Chinese ethical regulations. Methods: We evaluated eight prominent LLMs (e.g., GPT-4, Claude-3.7) on a custom test set of 986 questions (906 subjective, 80 objective) derived from 168 articles within Chinese reproductive ethics regulations. Subjective responses were evaluated using a novel six-dimensional scoring rubric assessing Safety (Normative Compliance, Guidance Safety) and Quality of the Answer (Problem Identification, Citation, Suggestion, Empathy). Results: Significant safety issues were prevalent, with risk rates for unsafe or misleading advice reaching 29.91%. A systemic weakness was observed across all models: universally poor performance in citing normative sources and expressing empathy. We also identified instances of anomalous moral reasoning, including logical self-contradictions and responses violating fundamental moral intuitions. Conclusions: Current LLMs are unreliable and unsafe for autonomous reproductive ethics counseling. Despite knowledge recall, they exhibit critical deficiencies in safety, logical consistency, and essential humanistic skills. These findings serve as a critical cautionary note against premature deployment, urging future development to prioritize robust reasoning, regulatory justification, and empathy.

cs.CY↗

arXiv2026

Ethical Risks in Deploying Large Language Models: An Evaluation of Medical Ethics Jailbreaking

Background: While Large Language Models (LLMs) have achieved widespread adoption, malicious prompt engineering specifically "jailbreak attacks" poses severe security risks by inducing models to bypass internal safety mechanisms. Current benchmarks predominantly focus on public safety and Western cultural norms, leaving a critical gap in evaluating the niche, high-risk domain of medical ethics within the Chinese context. Objective: To establish a specialized jailbreak evaluation framework for Chinese medical ethics and to systematically assess the defensive resilience and ethical alignment of seven prominent LLMs when subjected to sophisticated adversarial simulations. Methodology: We evaluated seven prominent models (e.g., GPT-5, Claude-Sonnet-4-Reasoning, DeepSeek-R1) using a "role-playing + scenario simulation + multi-turn dialogue" vector within the DeepInception framework. The testing focused on eight high-risk themes, including commercial surrogacy and organ trading, utilizing a hierarchical scoring matrix to quantify the Attack Success Rate (ASR) and ASR Gain. Results: A systemic collapse of defenses was observed, whereas models demonstrated high baseline compliance, the jailbreak ASR reached 82.1%, representing an ASR Gain of over 80 percentage points. Claude-Sonnet-4-Reasoning emerged as the most robust model, while five models including Gemini-2.5-Pro and GPT-4.1 exhibited near-total failure with ASRs between 96% and 100%. Conclusions: Current LLMs are highly vulnerable to contextual manipulation in medical ethics, often prioritizing "helpfulness" over safety constraints. To enhance security, we recommend a transition from outcome to process supervision, the implementation of multi-factor identity verification, and the establishment of cross-model "joint defense" mechanisms.

cs.CY↗

arXiv2026

A Human-Centric Pipeline for Aligning Large Language Models with Chinese Medical Ethics

Recent advances in large language models have enabled their application to a range of healthcare tasks. However, aligning LLMs with the nuanced demands of medical ethics, especially under complex real world scenarios, remains underexplored. In this work, we present MedES, a dynamic, scenario-centric benchmark specifically constructed from 260 authoritative Chinese medical, ethical, and legal sources to reflect the challenges in clinical decision-making. To facilitate model alignment, we introduce a guardian-in-the-loop framework that leverages a dedicated automated evaluator (trained on expert-labeled data and achieving over 97% accuracy within our domain) to generate targeted prompts and provide structured ethical feedback. Using this pipeline, we align a 7B-parameter LLM through supervised fine-tuning and domain-specific preference optimization. Experimental results, conducted entirely within the Chinese medical ethics context, demonstrate that our aligned model outperforms notably larger baselines on core ethical tasks, with observed improvements in both quality and composite evaluation metrics. Our work offers a practical and adaptable framework for aligning LLMs with medical ethics in the Chinese healthcare domain, and suggests that similar alignment pipelines may be instantiated in other legal and cultural environments through modular replacement of the underlying normative corpus.

cs.CL↗

arXiv2022

Orbital hybridization-driven charge density wave transition in CsV3Sb5 kagome superconductor

Owing to its inherent non-trivial geometry, the unique structural motif of the recently discovered Kagome topological superconductor AV3Sb5 is an ideal host of diverse topologically non-trivial phenomena, including giant anomalous Hall conductivity, topological charge order, charge density wave, and unconventional superconductivity. Despite possessing a normal-state CDW order in the form of topological chiral charge order and diverse superconducting gaps structures, it remains unclear how fundamental atomic-level properties and many-body effects including Fermi surface nesting, electron-phonon coupling, and orbital hybridization contribute to these symmetry-breaking phenomena. Here, we report the direct participation of the V3d-Sb5p orbital hybridization in mediating the CDW phase transition in CsV3Sb5. The combination of temperature-dependent X-ray absorption and first principles studies clearly indicate the Inverse Star of David structure as the preferred reconstruction in the low-temperature CDW phase. Our results highlight the critical role that Sb orbitals plays and establish orbital hybridization as the direct mediator of the CDW states and structural transition dynamics in Kagome unconventional superconductors. This is a significant step towards the fundamental understanding and control of the emerging correlated phases from the Kagome lattice through the orbital interactions and provide promising approaches to novel regimes in unconventional orders and topology.

cond-mat.supr-con↗