9 citations · 9 across the 13 of their papers we have counts for
15 papers
Large Language Model Agents for Evidence Based Genetic Disease Severity Classification
Tohid Ghasemnejad, Ahmadreza Argha, Mark Grosser +7
Disease severity classification for genetic conditions is subjective and labor-intensive, creating bottlenecks in genomic screening, where commercial panels vary widely in size and…
Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation
Sally Chen, Roxana Zahedi, Lucy Chhuo +12
Single-cell and spatial foundation models promise transferable biological representations, yet their generality remains largely untested across modalities, biological domains and a…
Transcriptomic Models for Immunotherapy Response Prediction Show Limited Cross-cohort Generalisability
Yuheng Liang, Lucy Chhuo, Ahmadreza Argha +9
Immune checkpoint inhibitors (ICIs) have transformed cancer therapy; yet substantial proportion of patients exhibit intrinsic or acquired resistance, making accurate pre-treatment…
Beyond Quantity: Trajectory Diversity Scaling for Code Agents
Guhong Chen, Chenghao Sun, Cheng Fu +16
As code large language models (LLMs) evolve into tool-interactive agents via the Model Context Protocol (MCP), their generalization is increasingly limited by low-quality synthetic…
RxSafeBench: Identifying Medication Safety Issues of Large Language Models in Simulated Consultation
Jiahao Zhao, Luxin Xu, Minghuan Tan +4
Numerous medical systems powered by Large Language Models (LLMs) have achieved remarkable progress in diverse healthcare tasks. However, research on their medication safety remains…
Structuring Reasoning for Complex Rules Beyond Flat Representations
Zhihao Yang, Ancheng Xu, Jingpeng Li +11
Large language models (LLMs) face significant challenges when processing complex rule systems, as they typically treat interdependent rules as unstructured textual data rather than…
SemanticST: Spatially Informed Semantic Graph Learning for Clustering, Integration, and Scalable Analysis of Spatial Transcriptomics
Roxana Zahedi, Ahmadreza Argha, Nona Farbehi +4
Spatial transcriptomics (ST) technologies enable gene expression profiling with spatial resolution, offering unprecedented insights into tissue organization and disease heterogenei…
STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation
Jiaming Li, Yukun Chen, Ziqiang Liu +10
Stories are central to human culture, serving to share ideas, preserve traditions, and foster connections. Automatic story generation, a key advancement in artificial intelligence…
Expanding before Inferring: Enhancing Factuality in Large Language Models through Premature Layers Interpolation
Dingwei Chen, Ziqiang Liu, Feiteng Fang +6
Large Language Models (LLMs) demonstrate remarkable capabilities in text understanding and generation. However, their tendency to produce factually inconsistent outputs, commonly r…
Interpretable graph-based models on multimodal biomedical data integration: A technical review and benchmarking
Alireza Sadeghi, Farshid Hajati, Ahmadreza Argha +3
Integrating heterogeneous biomedical data including imaging, omics, and clinical records supports accurate diagnosis and personalised care. Graph-based models fuse such non-Euclide…
xJailbreak: Representation Space Guided Reinforcement Learning for Interpretable LLM Jailbreaking
Sunbowen Lee, Shiwen Ni, Chi Wei +7
Safety alignment mechanism are essential for preventing large language models (LLMs) from generating harmful information or unethical content. However, cleverly crafted prompts can…
PersonaMath: Boosting Mathematical Reasoning via Persona-Driven Data Augmentation
Jing Luo, Longze Chen, Run Luo +12
While closed-source Large Language Models (LLMs) demonstrate strong mathematical problem-solving abilities, open-source models still face challenges with such tasks. To bridge this…
ETAGE: Enhanced Test Time Adaptation with Integrated Entropy and Gradient Norms for Robust Model Performance
Afshar Shamsi, Rejisa Becirovic, Ahmadreza Argha +3
Test time adaptation (TTA) equips deep learning models to handle unseen test data that deviates from the training distribution, even when source data is inaccessible. While traditi…
Lower Layers Matter: Alleviating Hallucination via Multi-Layer Fusion Contrastive Decoding with Truthfulness Refocused
Dingwei Chen, Feiteng Fang, Shiwen Ni +6
Large Language Models (LLMs) have demonstrated exceptional performance across various natural language processing tasks. However, they occasionally generate inaccurate and counterf…
Deep Learning in Spatially Resolved Transcriptomics: A Comprehensive Technical View
Roxana Zahedi Nasab, Mohammad Reza Eftekhariyan Ghamsari, Ahmadreza Argha +6
Spatially resolved transcriptomics (SRT) has evolved rapidly through various technologies, enabling scientists to investigate both morphological contexts and gene expression profil…