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Received — 25 November 2025 ⏭ Favourites

ConCISE: A Reference-Free Conciseness Evaluation Metric for LLM-Generated Answers

✇cs.AI, q-bio.NC updates on arXiv.org
By: Seyed Mohssen Ghafari · Ronny Kol · Juan C. Quiroz · Nella Luan · Monika Patial · Chanaka Rupasinghe · Herman Wandabwa · Luiz Pizzato
24 November 2025 at 13:00
arXiv:2511.16846v1 Announce Type: cross Abstract: Large language models (LLMs) frequently generate responses that are lengthy and verbose, filled with redundant or unnecessary details. This diminishes clarity and user satisfaction, and it increases costs for model developers, especially with well-known proprietary models that charge based on the number of output tokens. In this paper, we introduce a novel reference-free metric for evaluating the conciseness of responses generated by LLMs. Our method quantifies non-essential content without relying on gold standard references and calculates the average of three calculations: i) a compression ratio between the original response and an LLM abstractive summary; ii) a compression ratio between the original response and an LLM extractive summary; and iii) wordremoval compression, where an LLM removes as many non-essential words as possible from the response while preserving its meaning, with the number of tokens removed indicating the conciseness score. Experimental results demonstrate that our proposed metric identifies redundancy in LLM outputs, offering a practical tool for automated evaluation of response brevity in conversational AI systems without the need for ground truth human annotations.

SMILE: A Composite Lexical-Semantic Metric for Question-Answering Evaluation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shrikant Kendre · Austin Xu · Honglu Zhou · Michael Ryoo · Shafiq Joty · Juan Carlos Niebles
24 November 2025 at 13:00
arXiv:2511.17432v1 Announce Type: cross Abstract: Traditional evaluation metrics for textual and visual question answering, like ROUGE, METEOR, and Exact Match (EM), focus heavily on n-gram based lexical similarity, often missing the deeper semantic understanding needed for accurate assessment. While measures like BERTScore and MoverScore leverage contextual embeddings to address this limitation, they lack flexibility in balancing sentence-level and keyword-level semantics and ignore lexical similarity, which remains important. Large Language Model (LLM) based evaluators, though powerful, come with drawbacks like high costs, bias, inconsistency, and hallucinations. To address these issues, we introduce SMILE: Semantic Metric Integrating Lexical Exactness, a novel approach that combines sentence-level semantic understanding with keyword-level semantic understanding and easy keyword matching. This composite method balances lexical precision and semantic relevance, offering a comprehensive evaluation. Extensive benchmarks across text, image, and video QA tasks show SMILE is highly correlated with human judgments and computationally lightweight, bridging the gap between lexical and semantic evaluation.

Artificial Intelligence Index Report 2025

✇cs.AI, q-bio.NC updates on arXiv.org
By: Nestor Maslej · Loredana Fattorini · Raymond Perrault · Yolanda Gil · Vanessa Parli · Njenga Kariuki · Emily Capstick · Anka Reuel · Erik Brynjolfsson · John Etchemendy · Katrina Ligett · Terah Lyons · James Manyika · Juan Carlos Niebles · Yoav Shoham · Russell Wald · Toby Walsh · Armin Hamrah · Lapo Santarlasci · Julia Betts Lotufo · Alexandra Rome · Andrew Shi · Sukrut Oak
24 November 2025 at 13:00
arXiv:2504.07139v3 Announce Type: replace Abstract: Welcome to the eighth edition of the AI Index report. The 2025 Index is our most comprehensive to date and arrives at an important moment, as AI's influence across society, the economy, and global governance continues to intensify. New in this year's report are in-depth analyses of the evolving landscape of AI hardware, novel estimates of inference costs, and new analyses of AI publication and patenting trends. We also introduce fresh data on corporate adoption of responsible AI practices, along with expanded coverage of AI's growing role in science and medicine. Since its founding in 2017 as an offshoot of the One Hundred Year Study of Artificial Intelligence, the AI Index has been committed to equipping policymakers, journalists, executives, researchers, and the public with accurate, rigorously validated, and globally sourced data. Our mission has always been to help these stakeholders make better-informed decisions about the development and deployment of AI. In a world where AI is discussed everywhere - from boardrooms to kitchen tables - this mission has never been more essential. The AI Index continues to lead in tracking and interpreting the most critical trends shaping the field - from the shifting geopolitical landscape and the rapid evolution of underlying technologies, to AI's expanding role in business, policymaking, and public life. Longitudinal tracking remains at the heart of our mission. In a domain advancing at breakneck speed, the Index provides essential context - helping us understand where AI stands today, how it got here, and where it may be headed next. Recognized globally as one of the most authoritative resources on artificial intelligence, the AI Index has been cited in major media outlets such as The New York Times, Bloomberg, and The Guardian; referenced in hundreds of academic papers; and used by policymakers and government agencies around the world.

Multimodal analysis of whole slide images in colorectal cancer

✇npj Digital Medicine
By: Jitendra Jonnagaddala · Miljana Shulajkovska · Anton Gradišek · Toni Rose Jue · Qifeng Zhou · Yuzhi Guo · Jamil Mahmoud El Chayeb · Ruijiang Li · Jana Lipkova · Jakob Nikolas Kather · Junzhou Huang
24 November 2025 at 08:00

npj Digital Medicine, Published online: 24 November 2025; doi:10.1038/s41746-025-02095-y

Multimodal analysis of whole slide images in colorectal cancer
Received — 24 November 2025 ⏭ Favourites

Health care Experiences of Educated Young Adults With Blindness in the Digital Age: Qualitative Study

✇Journal of Medical Internet Research
By: Junling Zhao · Can Su · Xiji Zhu · Cong Cai · Wei Liu · Xiaochen Ma
22 November 2025 at 04:45
Background: The rapid advancement of digital health technologies (DHTs) offers substantial potential for improving healthcare access, yet it simultaneously risks exacerbating existing inequities for marginalized populations. Previous research on the digital divide has often treated individuals with blindness as a homogenous group, primarily focusing on barriers related to digital access and skills. However, less is known about the nuanced experiences of specific subgroups, such as educated and digitally literate young adults. This study focuses on this demographic to understand how their advanced digital capabilities interact with systemic and infrastructural barriers in healthcare. Objective: This qualitative study aimed to explore the lived healthcare experiences of educated young adults with blindness in China, specifically identifying how DHTs simultaneously contribute to their empowerment and exclusion. Methods: Eligible participants were educated young adults with blindness in China (aged 18-30 years, Mandarin speakers, smartphone users, and holding or pursuing higher education). A total of 12 semi-structured interviews were conducted in Mandarin during September 2024. All interviews were audio-recorded and transcribed verbatim. An inductive thematic analysis was employed to interpret the data and identify key themes. Results: Participants’ experiences highlighted an “empowered but excluded” dynamic. Seven key themes emerged, categorized into empowerment and exclusion. Empowerment themes included: (1) digital platforms empowering self-management and healthcare access, where DHTs enabled independent appointment booking and access to comprehensive health information; and (2) digital platforms empowering for finding medical visit companions, facilitating the discovery of companions for physical and emotional support. Exclusion themes comprised: (3) inaccessible online appointment systems, due to non-inclusive designs; (4) inaccessible healthcare environments and information formats, stemming from non-accessible self-service machines and written materials; (5) lack of provider competencies in respecting patient autonomy, as providers often assumed digital incompetence; (6) data privacy and security concerns, heightened by increased digitalization and reliance on assistive tools; and (7) challenges related to the quality and consistency of online companion support, highlighting the limitations of platform-based assistance. Conclusions: Our findings reveal an “empowered but excluded” dynamic: the potential for digital empowerment and enhanced independence is often curtailed by systematic barriers. Addressing this necessitates a multifaceted approach: enhancing technological accessibility through robust standards adherence and inclusive co-design processes; improving healthcare provider competencies in patient-centered care via targeted training; and empowering educated young blind adults by building their capacity for self-determination to achieve equitable healthcare access.
Received — 19 November 2025 ⏭ Favourites

Benchmark on Drug Target Interaction Modeling from a Drug Structure Perspective

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xinnan Zhang · Jialin Wu · Junyi Xie · Tianlong Chen · Kaixiong Zhou
19 November 2025 at 13:00
arXiv:2407.04055v2 Announce Type: replace-cross Abstract: The prediction modeling of drug-target interactions is crucial to drug discovery and design, which has seen rapid advancements owing to deep learning technologies. Recently developed methods, such as those based on graph neural networks (GNNs) and Transformers, demonstrate exceptional performance across various datasets by effectively extracting structural information. However, the benchmarking of these novel methods often varies significantly in terms of hyperparameter settings and datasets, which limits algorithmic progress. In view of these, we conducted a comprehensive survey and benchmark for drug-target interaction modeling from a structural perspective via integrating tens of explicit (i.e., GNN-based) and implicit (i.e., Transformer-based) structure learning algorithms. We conducted a macroscopical comparison between these two classes of encoding strategies as well as the different featurization techniques that inform molecules' chemical and physical properties. We then carry out the microscopical comparison between all the integrated models across the six datasets via comprehensively benchmarking their effectiveness and efficiency. To ensure fairness, we investigate model performance under individually optimized configuration. Remarkably, the summarized insights from the benchmark studies lead to the design of model combos. We demonstrate that our combos can achieve new state-of-the-art performance on various datasets associated with cost-effective memory and computation.
Received — 18 November 2025 ⏭ Favourites

A Workflow for Full Traceability of AI Decisions

✇cs.AI, q-bio.NC updates on arXiv.org
By: Julius Wenzel · Syeda Umaima Alam · Andreas Schmidt · Hanwei Zhang · Holger Hermanns
18 November 2025 at 13:00
arXiv:2511.11275v2 Announce Type: replace Abstract: An ever increasing number of high-stake decisions are made or assisted by automated systems employing brittle artificial intelligence technology. There is a substantial risk that some of these decision induce harm to people, by infringing their well-being or their fundamental human rights. The state-of-the-art in AI systems makes little effort with respect to appropriate documentation of the decision process. This obstructs the ability to trace what went into a decision, which in turn is a prerequisite to any attempt of reconstructing a responsibility chain. Specifically, such traceability is linked to a documentation that will stand up in court when determining the cause of some AI-based decision that inadvertently or intentionally violates the law. This paper takes a radical, yet practical, approach to this problem, by enforcing the documentation of each and every component that goes into the training or inference of an automated decision. As such, it presents the first running workflow supporting the generation of tamper-proof, verifiable and exhaustive traces of AI decisions. In doing so, we expand the DBOM concept into an effective running workflow leveraging confidential computing technology. We demonstrate the inner workings of the workflow in the development of an app to tell poisonous and edible mushrooms apart, meant as a playful example of high-stake decision support.

The dual immunomodulatory role of B cells in tumorigenesis: mechanisms, microenvironment crosstalk, and therapeutic implications

✇(Multiomics OR Omics) AND (Pancreatic)
By: Zhuangwei Lv · Ruohao Yang · Kai Zhang · Ruihan Wang · Xiaoyu Shi · Jinhua Wu · LuLu Liu · Junna Jiao
17 November 2025 at 19:00

Front Immunol. 2025 Oct 30;16:1649812. doi: 10.3389/fimmu.2025.1649812. eCollection 2025.

ABSTRACT

B lymphocytes exhibit a multifaceted and context-dependent role in tumor biology, acting as both promoters and suppressors of malignancy through dynamic interactions within the tumor microenvironment (TME). This review synthesizes current evidence on the dual functions of B cells in tumor immunity, highlighting their capacity to orchestrate antitumor responses via antigen presentation, antibody-dependent cytotoxicity, and tertiary lymphoid structure (TLS)-mediated T cell activation, while paradoxically driving immunosuppression through regulatory B cells (Bregs), pro-angiogenic signaling, and immune checkpoint modulation. Key mechanisms include TLS formation, which enhances cytotoxic T cell priming and correlates with improved immunotherapy outcomes, and Breg-mediated secretion of IL-10/TGF-β, which fosters T cell exhaustion and myeloid-derived suppressor cell recruitment. Tumor-type specificity is evident: TLS-rich malignancies like melanoma and Non-Small Cell Lung Cancer (NSCLC) show B cell-driven immune activation, whereas pancreatic and hepatocellular carcinomas demonstrate B cell functional plasticity influenced by metabolic and epigenetic reprogramming. Therapeutically, B cell-targeted strategies-including CD20 antibodies, CAR-T cells, and B cell epitope vaccines-demonstrate efficacy in hematologic and solid tumors, yet face challenges due to subset heterogeneity and sex-specific response disparities. Emerging approaches combine immune checkpoint inhibitors (ICBs) with TLS-inducing agents or exploit B cell-derived biomarkers for personalized therapy. Future directions emphasize deciphering B cell metabolic-niche crosstalk, optimizing combinatorial regimens, and leveraging spatial multiomics to resolve functional heterogeneity. By bridging mechanistic insights with clinical translation, this work underscores B cells as pivotal regulators of tumor immunity and advocates for precision strategies to harness their antitumor potential while mitigating pro-tumor plasticity.

PMID:41246318 | PMC:PMC12611826 | DOI:10.3389/fimmu.2025.1649812

Received — 14 November 2025 ⏭ Favourites

MedFuse: Multiplicative Embedding Fusion For Irregular Clinical Time Series

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yi-Hsien Hsieh · Ta-Jung Chien · Chun-Kai Huang · Shao-Hua Sun · Che Lin
13 November 2025 at 13:00
arXiv:2511.09247v1 Announce Type: new Abstract: Clinical time series derived from electronic health records (EHRs) are inherently irregular, with asynchronous sampling, missing values, and heterogeneous feature dynamics. While numerical laboratory measurements are highly informative, existing embedding strategies usually combine feature identity and value embeddings through additive operations, which constrains their ability to capture value-dependent feature interactions. We propose MedFuse, a framework for irregular clinical time series centered on the MuFuse (Multiplicative Embedding Fusion) module. MuFuse fuses value and feature embeddings through multiplicative modulation, preserving feature-specific information while modeling higher-order dependencies across features. Experiments on three real-world datasets covering both intensive and chronic care show that MedFuse consistently outperforms state-of-the-art baselines on key predictive tasks. Analysis of the learned representations further demonstrates that multiplicative fusion enhances expressiveness and supports cross-dataset pretraining. These results establish MedFuse as a generalizable approach for modeling irregular clinical time series.

LLM4AD: Large Language Models for Autonomous Driving - Concept, Review, Benchmark, Experiments, and Future Trends

✇cs.AI, q-bio.NC updates on arXiv.org
By: Can Cui · Yunsheng Ma · Sung-Yeon Park · Zichong Yang · Yupeng Zhou · Juanwu Lu · Juntong Peng · Jiaru Zhang · Ruqi Zhang · Lingxi Li · Yaobin Chen · Jitesh H. Panchal · Amr Abdelraouf · Rohit Gupta · Kyungtae Han · Ziran Wang
13 November 2025 at 13:00
arXiv:2410.15281v4 Announce Type: replace-cross Abstract: With the broader adoption and highly successful development of Large Language Models (LLMs), there has been growing interest and demand for applying LLMs to autonomous driving technology. Driven by their natural language understanding and reasoning capabilities, LLMs have the potential to enhance various aspects of autonomous driving systems, from perception and scene understanding to interactive decision-making. In this paper, we first introduce the novel concept of designing Large Language Models for Autonomous Driving (LLM4AD), followed by a review of existing LLM4AD studies. Then, we propose a comprehensive benchmark for evaluating the instruction-following and reasoning abilities of LLM4AD systems, which includes LaMPilot-Bench, CARLA Leaderboard 1.0 Benchmark in simulation and NuPlanQA for multi-view visual question answering. Furthermore, we conduct extensive real-world experiments on autonomous vehicle platforms, examining both on-cloud and on-edge LLM deployment for personalized decision-making and motion control. Next, we explore the future trends of integrating language diffusion models into autonomous driving, exemplified by the proposed ViLaD (Vision-Language Diffusion) framework. Finally, we discuss the main challenges of LLM4AD, including latency, deployment, security and privacy, safety, trust and transparency, and personalization.

Large Language Model Benchmarks in Medical Tasks

✇cs.AI, q-bio.NC updates on arXiv.org
By: Lawrence K. Q. Yan · Qian Niu · Ming Li · Yichao Zhang · Caitlyn Heqi Yin · Cheng Fei · Benji Peng · Ziqian Bi · Pohsun Feng · Keyu Chen · Tianyang Wang · Yunze Wang · Silin Chen · Ming Liu · Junyu Liu · Xinyuan Song · Riyang Bao · Zekun Jiang · Ziyuan Qin
13 November 2025 at 13:00
arXiv:2410.21348v3 Announce Type: replace-cross Abstract: With the increasing application of large language models (LLMs) in the medical domain, evaluating these models' performance using benchmark datasets has become crucial. This paper presents a comprehensive survey of various benchmark datasets employed in medical LLM tasks. These datasets span multiple modalities including text, image, and multimodal benchmarks, focusing on different aspects of medical knowledge such as electronic health records (EHRs), doctor-patient dialogues, medical question-answering, and medical image captioning. The survey categorizes the datasets by modality, discussing their significance, data structure, and impact on the development of LLMs for clinical tasks such as diagnosis, report generation, and predictive decision support. Key benchmarks include MIMIC-III, MIMIC-IV, BioASQ, PubMedQA, and CheXpert, which have facilitated advancements in tasks like medical report generation, clinical summarization, and synthetic data generation. The paper summarizes the challenges and opportunities in leveraging these benchmarks for advancing multimodal medical intelligence, emphasizing the need for datasets with a greater degree of language diversity, structured omics data, and innovative approaches to synthesis. This work also provides a foundation for future research in the application of LLMs in medicine, contributing to the evolving field of medical artificial intelligence.

Stereo-seq V2: Spatial mapping of total RNA on FFPE sections with high resolution

✇Cell
By: Yu Zhao · Young Li · Ying He · Junqi Wu · Yi Liu · Xinxing Li · Zhaoxun Li · Qiaomei Yuan · Jialuo Li · Xinya Zhang · Xiaole Hu · Guifang Wu · Ying Qiu · Jie Yuan · Xin Huang · Mengru Xu · Mei Yang · Jing Guo · Tingting Cao · Qiuling Chen · Dandan Li · Jiajun Zhang · Xiaoxi Zhou · Xia Jiang · Fan Zhu · Xuan Dong · Rong Xiang · Hailin Pan · Lei Han · Ziqing Deng · Haohao Deng · Yan Zhang · Maoyuan Liu · Qin Wu · Gaoyang Wang · Jie Zhai · Weihong Tan · Xiawei Liu · Zehao Wang · Shanshan Li · Tianbi Duan · Longqi Liu · Ao Chen · Haiying Liu · Chang Chen · Sha Liao · Xun Xu
28 August 2025 at 08:00
Stereo-seq V2 facilitates single-cell-resolution spatial RNA mapping in FFPE samples through random primer capture, uncovering ncRNAs, host-pathogen transcriptome profiling, and spatial immune repertoires in situ.
Received — 12 November 2025 ⏭ Favourites

Targeted inhibition of gastric adenocarcinoma by nano-curcumin liposomes: Insights from combined machine learning and experimental analyses into the mechanisms of cuproptosis and metabolic reprogramming

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Jun-Feng Cao · Kuan Hang · Chunlu Tan · Zuowei Wu · Ziheng Guo · Qingjie Xia · Dunshui Liao · Zengliang Xia · Jie Men · Kezhou Li
11 November 2025 at 19:00

Int J Pharm. 2025 Nov 9:126368. doi: 10.1016/j.ijpharm.2025.126368. Online ahead of print.

ABSTRACT

PURPOSE: Gastric adenocarcinoma is a highly aggressive malignancy characterized by a complex tumor microenvironment. Nano-curcumin liposomes hold great potential in inhibiting tumor growth and survival, as well as inducing cuproptosis and oxidative stress. Although the anticancer properties of curcumin have been demonstrated, the specific mechanisms by which curcumin inhibites gastric adenocarcinoma through cuproptosis remains unclear. This study investigated how nano-curcumin liposomes mediated the inhibition of gastric adenocarcinoma cell proliferation and survival via cuproptosis.

METHODS: This study utilized the gastric adenocarcinoma cell line AGS to establish 2D and 3D in vitro gastric adenocarcinoma models. Furthermore, we prepared nano-curcumin liposomes to investigate their effects and regulatory mechanisms on AGS gastric adenocarcinoma models. A series of in vitro assays, including flow cytometry, CCK-8, scratch assays and morphological assessments, were performed to evaluate the effects of nano-curcumin liposomes on cell apoptosis, proliferation and migration. Additionally, bioinformatics and machine learning methods were employed to identify key targets that inhibited gastric adenocarcinoma growth and survival associated with nano-curcumin liposomes, which were further validated through RT-qPCR and omics analysis. Computer simulations were also conducted to assess the stability of binding interactions between curcumin and key target proteins.

RESULTS: Cellular experiments demonstrated that nano-curcumin liposomes significantly inhibited proliferation and invasive capacity of gastric adenocarcinoma cells while promoting cellular oxidative stress. Bioinformatics and machine learning analyses identified FDX1, GPX4, SERPINE1 and SLC27A5 as key targets. RT-qPCR results confirmed that nano-curcumin liposomes significantly downregulated the expression of these targets. Molecular dynamics simulations indicated that curcumin could form stable binding interactions with key protein targets.

CONCLUSION: This study revealed that nano-curcumin liposomes inhibited growth and survival of gastric adenocarcinoma cells by interfering with the expression of FDX1, GPX4, SERPINE1 and SLC27A5, which were closely linked to copper-induced oxidative stress. Nano-curcumin liposomes downregulated the expression of FDX1 and GPX4, disrupted mitochondrial energy metabolism, and induced oxidative stress, thereby promoting tumor-associated programmed cell death linked to cuproptosis. Furthermore, by downregulating SERPINE1, nano-curcumin liposomes modulated cell adhesion and migration, inhibiting the invasive and metastatic potential of tumor cells. Finally, downregulation of SLC27A5 altered tumor metabolism and cellular homeostasis, induced oxidative stress, and disrupted intracellular environmental stability, thereby suppressing the growth of gastric adenocarcinoma.

PMID:41218732 | DOI:10.1016/j.ijpharm.2025.126368

Self-Correction Distillation for Structured Data Question Answering

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yushan Zhu · Wen Zhang · Long Jin · Mengshu Sun · Ling Zhong · Zhiqiang Liu · Juan Li · Lei Liang · Chong Long · Chao Deng · Junlan Feng
12 November 2025 at 13:00
arXiv:2511.07998v1 Announce Type: cross Abstract: Structured data question answering (QA), including table QA, Knowledge Graph (KG) QA, and temporal KG QA, is a pivotal research area. Advances in large language models (LLMs) have driven significant progress in unified structural QA frameworks like TrustUQA. However, these frameworks face challenges when applied to small-scale LLMs since small-scale LLMs are prone to errors in generating structured queries. To improve the structured data QA ability of small-scale LLMs, we propose a self-correction distillation (SCD) method. In SCD, an error prompt mechanism (EPM) is designed to detect errors and provide customized error messages during inference, and a two-stage distillation strategy is designed to transfer large-scale LLMs' query-generation and error-correction capabilities to small-scale LLM. Experiments across 5 benchmarks with 3 structured data types demonstrate that our SCD achieves the best performance and superior generalization on small-scale LLM (8B) compared to other distillation methods, and closely approaches the performance of GPT4 on some datasets. Furthermore, large-scale LLMs equipped with EPM surpass the state-of-the-art results on most datasets.

SCoTT: Strategic Chain-of-Thought Tasking for Wireless-Aware Robot Navigation in Digital Twins

✇cs.AI, q-bio.NC updates on arXiv.org
By: Aladin Djuhera · Amin Seffo · Vlad C. Andrei · Holger Boche · Walid Saad
12 November 2025 at 13:00
arXiv:2411.18212v3 Announce Type: replace-cross Abstract: Path planning under wireless performance constraints is a complex challenge in robot navigation. However, naively incorporating such constraints into classical planning algorithms often incurs prohibitive search costs. In this paper, we propose SCoTT, a wireless-aware path planning framework that leverages vision-language models (VLMs) to co-optimize average path gains and trajectory length using wireless heatmap images and ray-tracing data from a digital twin (DT). At the core of our framework is Strategic Chain-of-Thought Tasking (SCoTT), a novel prompting paradigm that decomposes the exhaustive search problem into structured subtasks, each solved via chain-of-thought prompting. To establish strong baselines, we compare classical A* and wireless-aware extensions of it, and derive DP-WA*, an optimal, iterative dynamic programming algorithm that incorporates all path gains and distance metrics from the DT, but at significant computational cost. In extensive experiments, we show that SCoTT achieves path gains within 2% of DP-WA* while consistently generating shorter trajectories. Moreover, SCoTT's intermediate outputs can be used to accelerate DP-WA* by reducing its search space, saving up to 62% in execution time. We validate our framework using four VLMs, demonstrating effectiveness across both large and small models, thus making it applicable to a wide range of compact models at low inference cost. We also show the practical viability of our approach by deploying SCoTT as a ROS node within Gazebo simulations. Finally, we discuss data acquisition pipelines, compute requirements, and deployment considerations for VLMs in 6G-enabled DTs, underscoring the potential of natural language interfaces for wireless-aware navigation in real-world applications.
Received — 11 November 2025 ⏭ Favourites

multiMentalRoBERTa: A Fine-tuned Multiclass Classifier for Mental Health Disorder

✇cs.AI, q-bio.NC updates on arXiv.org
By: K M Sajjadul Islam · John Fields · Praveen Madiraju
10 November 2025 at 13:00
arXiv:2511.04698v1 Announce Type: cross Abstract: The early detection of mental health disorders from social media text is critical for enabling timely support, risk assessment, and referral to appropriate resources. This work introduces multiMentalRoBERTa, a fine-tuned RoBERTa model designed for multiclass classification of common mental health conditions, including stress, anxiety, depression, post-traumatic stress disorder (PTSD), suicidal ideation, and neutral discourse. Drawing on multiple curated datasets, data exploration is conducted to analyze class overlaps, revealing strong correlations between depression and suicidal ideation as well as anxiety and PTSD, while stress emerges as a broad, overlapping category. Comparative experiments with traditional machine learning methods, domain-specific transformers, and prompting-based large language models demonstrate that multiMentalRoBERTa achieves superior performance, with macro F1-scores of 0.839 in the six-class setup and 0.870 in the five-class setup (excluding stress), outperforming both fine-tuned MentalBERT and baseline classifiers. Beyond predictive accuracy, explainability methods, including Layer Integrated Gradients and KeyBERT, are applied to identify lexical cues that drive classification, with a particular focus on distinguishing depression from suicidal ideation. The findings emphasize the effectiveness of fine-tuned transformers for reliable and interpretable detection in sensitive contexts, while also underscoring the importance of fairness, bias mitigation, and human-in-the-loop safety protocols. Overall, multiMentalRoBERTa is presented as a lightweight, robust, and deployable solution for enhancing support in mental health platforms.
Received — 10 November 2025 ⏭ Favourites

Nurses’ Perspectives on Evidence Dissemination Barriers and Large Language Model–Based Support: Qualitative Study Using Focus Groups and Nominal Group Technique

✇Journal of Medical Internet Research
By: Junyi Ruan · Yimin Tang · Zhongyu Wei · Weijie Xing · Yan Hu
8 November 2025 at 04:00
Background: Current evidence dissemination methods fall short of meeting clinical nurses’ needs, hindering the implementation of evidence-based nursing practice. Large language models (LLMs), with their advanced natural language processing capabilities, offer potential as innovative tools to facilitate evidence dissemination. However, general-purpose LLMs typically lack domain-specific knowledge, are insufficient to support effective evidence dissemination in clinical contexts. It is essential to develop artificial intelligence tools tailored to nurses’ needs and preferences to enhance evidence dissemination. Objective: The aim of this study is to identify the challenges and barriers clinical nurses face in disseminating evidence, examine their perspectives on the use of existing LLMs to support evidence dissemination, and explore their needs and preferences regarding an LLM-based nursing evidence question-answering system. Methods: This qualitative study used a combined method of focus group discussions and the nominal group technique (NGT). Using purposive sampling, nurses with diverse specialties, professional titles, and years of experience were recruited, resulting in a total of 22 clinical nurses who completed the entire study. A total of 2 focus group discussions were conducted online via Tencent Meeting between November and December 2024 to explore the challenges and barriers nurses face in disseminating evidence, as well as their perspectives on using existing LLMs to support evidence dissemination. The data were analyzed using qualitative content analysis following the approach of Graneheim and Lundman. Subsequently, the NGT was used between March and April 2025 to identify nurses’ needs and preferences for the system to be developed. To overcome geographical constraints and participants’ busy schedules, the NGT was conducted entirely online, using online questionnaires and WeChat groups. Overall, 2 rounds of voting were conducted to determine the priority ranking of the functionalities. Results: The focus group yielded 3 main themes and 7 subthemes. Three main themes were identified as (1) pathways for evidence dissemination among nurses, (2) barriers that hinder the effective dissemination of evidence, and (3) advantages and limitations of using LLMs to support evidence dissemination. The limitations of current LLMs served as the foundation for nurses’ subsequent reflections in the nominal group discussions on the desired functions of a newly developed LLM. The NGT sessions ultimately identified 9 desired functions. After prioritization, the top 3 ranked functions were evidence-based, high-quality question-answering, evidence source provision, and personalized evidence recommendation. Conclusions: The current evidence dissemination process faces multiple barriers. LLMs hold promise as innovative tools to support evidence dissemination, but require further refinement. Clinical nurses have identified key functional needs, guiding the development of LLMs specifically tailored to clinical nursing practice.
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