- Why Diffusion Language Models Struggle with Truly Parallel (Non-Autoregressive) Decoding?
Pengxiang Li, Dilxat Muhtar, Lu Yin, Tianlong Chen, Shiwei Liu · Feb 26, 2026 · Citations: 0
Automatic Metrics
Across math reasoning benchmarks, NAP yields stronger performance under parallel decoding than DLMs trained on standard long CoT data, with gains growing as parallelism increases.
- Toward Automatic Filling of Case Report Forms: A Case Study on Data from an Italian Emergency Department
Gabriela Anna Kaczmarek, Pietro Ferrazzi, Lorenzo Porta, Vicky Rubini, Bernardo Magnini · Feb 26, 2026 · Citations: 0
Automatic Metrics
We provide an analysis of the data, define the CRF-filling task and metric for its evaluation, and report on pilot experiments where we use an open-source state-of-the-art LLM to automatically execute the task.
- Frequency-Ordered Tokenization for Better Text Compression
Maximilian Kalcher · Feb 26, 2026 · Citations: 0
Automatic Metrics
We present frequency-ordered tokenization, a simple preprocessing technique that improves lossless text compression by exploiting the power-law frequency distribution of natural language tokens (Zipf's law).
- TCM-DiffRAG: Personalized Syndrome Differentiation Reasoning Method for Traditional Chinese Medicine based on Knowledge Graph and Chain of Thought
Jianmin Li, Ying Chang, Su-Kit Tang, Yujia Liu, Yanwen Wang · Feb 26, 2026 · Citations: 0
Automatic Metrics
Additionally, TCM-DiffRAG outperformed directly supervised fine-tuned (SFT) LLMs and other benchmark RAG methods.
- TherapyProbe: Generating Design Knowledge for Relational Safety in Mental Health Chatbots Through Adversarial Simulation
Joydeep Chandra, Satyam Kumar Navneet, Yong Zhang · Feb 26, 2026 · Citations: 0
Expert Verification Simulation Env Multi Agent
As mental health chatbots proliferate to address the global treatment gap, a critical question emerges: How do we design for relational safety the quality of interaction patterns that unfold across conversations rather than the correctness
- TabDLM: Free-Form Tabular Data Generation via Joint Numerical-Language Diffusion
Donghong Cai, Jiarui Feng, Yanbo Wang, Da Zheng, Yixin Chen · Feb 26, 2026 · Citations: 0
Automatic Metrics
Extensive experiments on diverse benchmarks demonstrate the effectiveness of TabDLM compared to strong diffusion- and LLM-based baselines.
- Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA
Wenwei Li, Ming Xu, Tianle Xia, Lingxiang Hu, Yiding Sun · Feb 26, 2026 · Citations: 0
Automatic Metrics
We propose a reinforced co-adaptation framework that jointly optimizes retrieval and generation through two components: (1) Graph-aware Retrieval (GraphRAG), which models entity-relation structure over a high-citation knowledge subgraph for
- Strategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance
Weida Liang, Yiyou Sun, Shuyuan Nan, Chuang Li, Dawn Song · Feb 26, 2026 · Citations: 0
Automatic Metrics
Through a controlled analysis of paired human-written and model-generated solutions, we identify a systematic dissociation between usage and executability: human- and model-derived strategies differ in structured, domain-dependent ways, lea
- Importance of Prompt Optimisation for Error Detection in Medical Notes Using Language Models
Craig Myles, Patrick Schrempf, David Harris-Birtill · Feb 25, 2026 · Citations: 0
Automatic Metrics
We show that automatic prompt optimisation with Genetic-Pareto (GEPA) improves error detection over the baseline accuracy performance from 0.669 to 0.785 with GPT-5 and 0.578 to 0.690 with Qwen3-32B, approaching the performance of medical d
- Dynamic Personality Adaptation in Large Language Models via State Machines
Leon Pielage, Ole Hätscher, Mitja Back, Bernhard Marschall, Benjamin Risse · Feb 25, 2026 · Citations: 0
Simulation Env
This work demonstrates the feasibility of modular, personality-adaptive architectures for education, customer support, and broader human-computer interaction.
- MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models
Boqi Chen, Xudong Liu, Jiachuan Peng, Marianne Frey-Marti, Bang Zheng · Feb 25, 2026 · Citations: 0
Expert Verification Automatic Metrics
Multimodal large language models (MLLMs) have shown great potential in medical applications, yet existing benchmarks inadequately capture real-world clinical complexity.
- SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video
Guanyi Qin, Xiaozhen Wang, Zhu Zhuo, Chang Han Low, Yuancan Xiao · Feb 25, 2026 · Citations: 0
Expert Verification Automatic Metrics
Existing AI systems offer binary safety verification or static detection, ignoring the phase-dependent nature of intraoperative reasoning.
- Following the Diagnostic Trace: Visual Cognition-guided Cooperative Network for Chest X-Ray Diagnosis
Shaoxuan Wu, Jingkun Chen, Chong Ma, Cong Shen, Xiao Zhang · Feb 25, 2026 · Citations: 0
Automatic Metrics
Human-AI collaboration seeks to enhance the reliability of diagnostic models by integrating the behaviors of controllable radiologists.
- Multi-dimensional Assessment and Explainable Feedback for Counselor Responses to Client Resistance in Text-based Counseling with LLMs
Anqi Li, Ruihan Wang, Zhaoming Chen, Yuqian Chen, Yu Lu · Feb 25, 2026 · Citations: 0
Automatic Metrics
Although current NLP research has examined overall counseling quality and general therapeutic skills, it fails to provide granular evaluations of high-stakes moments where clients exhibit resistance.
- Virtual Biopsy for Intracranial Tumors Diagnosis on MRI
Xinzhe Luo, Shuai Shao, Yan Wang, Jiangtao Wang, Yutong Bai · Feb 25, 2026 · Citations: 0
Automatic Metrics
To address these challenges, we construct the ICT-MRI dataset - the first public biopsy-verified benchmark with 249 cases across four categories.
- Adversarial Robustness of Deep Learning-Based Thyroid Nodule Segmentation in Ultrasound
Nicholas Dietrich, David McShannon · Feb 25, 2026 · Citations: 0
Automatic Metrics
Conclusion: Spatial-domain adversarial perturbations in ultrasound segmentation showed partial mitigation with input preprocessing, whereas frequency-domain perturbations were not mitigated by the defenses, highlighting modality-specific ch
- FedVG: Gradient-Guided Aggregation for Enhanced Federated Learning
Alina Devkota, Jacob Thrasher, Donald Adjeroh, Binod Bhattarai, Prashnna K. Gyawali · Feb 24, 2026 · Citations: 0
Automatic Metrics
Extensive experiments on both natural and medical image benchmarking datasets, across diverse model architectures, demonstrate that FedVG consistently improves performance, particularly in highly heterogeneous settings.
- MrBERT: Modern Multilingual Encoders via Vocabulary, Domain, and Dimensional Adaptation
Daniel Tamayo, Iñaki Lacunza, Paula Rivera-Hidalgo, Severino Da Dalt, Javier Aula-Blasco · Feb 24, 2026 · Citations: 0
Automatic Metrics
We introduce MrBERT, a family of 150M-300M parameter encoders built on the ModernBERT architecture and pre-trained on 35 languages and code.
- Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages
Mohammadreza Ghaffarzadeh-Esfahani, Nahid Yousefian, Ebrahim Heidari-Farsani, Ali Akbar Omidvarian, Sepehr Ghahraei · Feb 24, 2026 · Citations: 0
Automatic Metrics
Extracting clinical information from medical transcripts in low-resource languages remains a significant challenge in healthcare natural language processing (NLP).
- The Mean is the Mirage: Entropy-Adaptive Model Merging under Heterogeneous Domain Shifts in Medical Imaging
Sameer Ambekar, Reza Nasirigerdeh, Peter J. Schuffler, Lina Felsner, Daniel M. Lang · Feb 24, 2026 · Citations: 0
Automatic Metrics
We extensively evaluate our method with state-of-the-art baselines using two backbones across nine medical and natural-domain generalization image classification datasets, showing consistent gains across standard evaluation and challenging
- Scaling View Synthesis Transformers
Evan Kim, Hyunwoo Ryu, Thomas W. Mitchel, Vincent Sitzmann · Feb 24, 2026 · Citations: 0
Automatic Metrics
Across several compute levels, we demonstrate that our encoder-decoder architecture, which we call the Scalable View Synthesis Model (SVSM), scales as effectively as decoder-only models, achieves a superior performance-compute Pareto fronti
- XMorph: Explainable Brain Tumor Analysis Via LLM-Assisted Hybrid Deep Intelligence
Sepehr Salem Ghahfarokhi, M. Moein Esfahani, Raj Sunderraman, Vince Calhoun, Mohammed Alser · Feb 24, 2026 · Citations: 0
Automatic Metrics
Deep learning has significantly advanced automated brain tumor diagnosis, yet clinical adoption remains limited by interpretability and computational constraints.
- PVminer: A Domain-Specific Tool to Detect the Patient Voice in Patient Generated Data
Samah Fodeh, Linhai Ma, Yan Wang, Srivani Talakokkul, Ganesh Puthiaraju · Feb 24, 2026 · Citations: 0
Automatic Metrics
Patient-generated text such as secure messages, surveys, and interviews contains rich expressions of the patient voice (PV), reflecting communicative behaviors and social determinants of health (SDoH).
- CG-DMER: Hybrid Contrastive-Generative Framework for Disentangled Multimodal ECG Representation Learning
Ziwei Niu, Hao Sun, Shujun Bian, Xihong Yang, Lanfen Lin · Feb 24, 2026 · Citations: 0
Automatic Metrics
Accurate interpretation of electrocardiogram (ECG) signals is crucial for diagnosing cardiovascular diseases.
- Prompt-Level Distillation: A Non-Parametric Alternative to Model Fine-Tuning for Efficient Reasoning
Sanket Badhe, Deep Shah · Feb 24, 2026 · Citations: 0
Automatic Metrics
These expressive instructions render the decision-making process transparent, allowing for full human verification of logic, making this approach ideal for regulated industries such as law, finance, and content moderation, as well as high-v
- MIP Candy: A Modular PyTorch Framework for Medical Image Processing
Tianhao Fu, Yucheng Chen · Feb 24, 2026 · Citations: 0
Automatic Metrics
MIPCandy provides a complete, modular pipeline spanning data loading, training, inference, and evaluation, allowing researchers to obtain a fully functional process workflow by implementing a single method, $\texttt{build_network}$, while r
- See and Fix the Flaws: Enabling VLMs and Diffusion Models to Comprehend Visual Artifacts via Agentic Data Synthesis
Jaehyun Park, Minyoung Ahn, Minkyu Kim, Jonghyun Lee, Jae-Gil Lee · Feb 24, 2026 · Citations: 0
Automatic Metrics
Previous artifact-aware methodologies depend on human-labeled artifact datasets, which are costly and difficult to scale, underscoring the need for an automated approach to reliably acquire artifact-annotated datasets.
- Airavat: An Agentic Framework for Internet Measurement
Alagappan Ramanathan, Eunju Kang, Dongsu Han, Sangeetha Abdu Jyothi · Feb 24, 2026 · Citations: 0
Automatic Metrics
We present Airavat, the first agentic framework for Internet measurement workflow generation with systematic verification and validation.
- SoK: Agentic Skills -- Beyond Tool Use in LLM Agents
Yanna Jiang, Delong Li, Haiyu Deng, Baihe Ma, Xu Wang · Feb 24, 2026 · Citations: 0
Simulation Env Tool Use
Agentic systems increasingly rely on reusable procedural capabilities, \textit{a.k.a., agentic skills}, to execute long-horizon workflows reliably.
- OrthoDiffusion: A Generalizable Multi-Task Diffusion Foundation Model for Musculoskeletal MRI Interpretation
Tian Lan, Lei Xu, Zimu Yuan, Shanggui Liu, Jiajun Liu · Feb 24, 2026 · Citations: 0
Automatic Metrics
Our evaluation demonstrates that OrthoDiffusion achieves excellent performance in the segmentation of 11 knee structures and the detection of 8 knee abnormalities.
- MedCLIPSeg: Probabilistic Vision-Language Adaptation for Data-Efficient and Generalizable Medical Image Segmentation
Taha Koleilat, Hojat Asgariandehkordi, Omid Nejati Manzari, Berardino Barile, Yiming Xiao · Feb 23, 2026 · Citations: 0
Automatic Metrics
Medical image segmentation remains challenging due to limited annotations for training, ambiguous anatomical features, and domain shifts.
- An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models
Cathy Shyr, Yan Hu, Rory J. Tinker, Thomas A. Cassini, Kevin W. Byram · Feb 23, 2026 · Citations: 0
Expert Verification Automatic Metrics
Existing artificial intelligence approaches typically optimize individual components of phenotyping but do not operationalize the full clinical workflow of extracting features from clinical text, standardizing them to Human Phenotype Ontolo
- A Very Big Video Reasoning Suite
Maijunxian Wang, Ruisi Wang, Juyi Lin, Ran Ji, Thaddäus Wiedemer · Feb 23, 2026 · Citations: 0
Simulation Env
We further present VBVR-Bench, a verifiable evaluation framework that moves beyond model-based judging by incorporating rule-based, human-aligned scorers, enabling reproducible and interpretable diagnosis of video reasoning capabilities.
- To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering
Zaifu Zhan, Min Zeng, Shuang Zhou, Yiran Song, Xiaoyi Chen · Feb 23, 2026 · Citations: 0
Automatic Metrics
Two open-source LLMs (Llama-3.1-8B and Qwen-2.5-7B) were evaluated on four biomedical QA benchmarks-HeadQA, MedQA-USMLE, MedMCQA, and PubMedQA.
- AgenticSum: An Agentic Inference-Time Framework for Faithful Clinical Text Summarization
Fahmida Liza Piya, Rahmatollah Beheshti · Feb 23, 2026 · Citations: 0
Human Eval
We present AgenticSum, an inference-time, agentic framework that separates context selection, generation, verification, and targeted correction to reduce hallucinated content.
- Exploring Anti-Aging Literature via ConvexTopics and Large Language Models
Lana E. Yeganova, Won G. Kim, Shubo Tian, Natalie Xie, Donald C. Comeau · Feb 23, 2026 · Citations: 0
Automatic Metrics
Common clustering and topic modeling approaches such as K-means or LDA remain sensitive to initialization and prone to local optima, limiting reproducibility and evaluation.
- Assessing Risks of Large Language Models in Mental Health Support: A Framework for Automated Clinical AI Red Teaming
Ian Steenstra, Paola Pedrelli, Weiyan Shi, Stacy Marsella, Timothy W. Bickmore · Feb 23, 2026 · Citations: 0
Red Team Simulation Env
Large Language Models (LLMs) are increasingly utilized for mental health support; however, current safety benchmarks often fail to detect the complex, longitudinal risks inherent in therapeutic dialogue.
- SHIELD: Semantic Heterogeneity Integrated Embedding for Latent Discovery in Clinical Trial Safety Signals
Francois Vandenhende, Anna Georgiou, Theodoros Psaras, Ellie Karekla · Feb 23, 2026 · Citations: 0
Automatic Metrics
We present SHIELD, a novel methodology for automated and integrated safety signal detection in clinical trials.
- MultiModalPFN: Extending Prior-Data Fitted Networks for Multimodal Tabular Learning
Wall Kim, Chaeyoung Song, Hanul Kim · Feb 23, 2026 · Citations: 0
Automatic Metrics
Recently, TabPFN has gained attention as a foundation model for tabular data.
- Continuous Telemonitoring of Heart Failure using Personalised Speech Dynamics
Yue Pan, Xingyao Wang, Hanyue Zhang, Liwei Liu, Changxin Li · Feb 23, 2026 · Citations: 0
Automatic Metrics Long Horizon
The model's high sensitivity was further corroborated by additional follow-up data, confirming its efficacy in predicting HF deterioration and its potential to secure patient safety in remote, home-based settings.
- OptiRepair: Closed-Loop Diagnosis and Repair of Supply Chain Optimization Models with LLM Agents
Ruicheng Ao, David Simchi-Levi, Xinshang Wang · Feb 23, 2026 · Citations: 0
Automatic Metrics
Whether AI agents can perform this task remains untested.
- Reasoning Capabilities of Large Language Models. Lessons Learned from General Game Playing
Maciej Świechowski, Adam Żychowski, Jacek Mańdziuk · Feb 22, 2026 · Citations: 0
Simulation Env
The main results indicate that three of the evaluated models generally perform well across most experimental settings, with performance degradation observed as the evaluation horizon increases (i.e., with a higher number of game steps).
- AgenticRAGTracer: A Hop-Aware Benchmark for Diagnosing Multi-Step Retrieval Reasoning in Agentic RAG
Qijie You, Wenkai Yu, Wentao Zhang · Feb 22, 2026 · Citations: 0
Automatic Metrics Long Horizon
With the rapid advancement of agent-based methods in recent years, Agentic RAG has undoubtedly become an important research direction.
- Whisper: Courtside Edition Enhancing ASR Performance Through LLM-Driven Context Generation
Yonathan Ron, Shiri Gilboa, Tammuz Dubnov · Feb 21, 2026 · Citations: 0
Automatic Metrics Multi Agent
We introduce Whisper: Courtside Edition, a novel multi-agent large language model (LLM) pipeline that enhances Whisper transcriptions without retraining.
- EvalSense: A Framework for Domain-Specific LLM (Meta-)Evaluation
Adam Dejl, Jonathan Pearson · Feb 21, 2026 · Citations: 0
Automatic Metrics
Robust and comprehensive evaluation of large language models (LLMs) is essential for identifying effective LLM system configurations and mitigating risks associated with deploying LLMs in sensitive domains.
- Think$^{2}$: Grounded Metacognitive Reasoning in Large Language Models
Abraham Paul Elenjical, Vivek Hruday Kavuri, Vasudeva Varma · Feb 21, 2026 · Citations: 0
Pairwise Preference Human Eval
We introduce a psychologically grounded metacognitive framework that operationalizes Ann Brown's regulatory cycle (Planning, Monitoring, and Evaluation) as a structured prompting architecture, and study its integration within a lightweight
- DP-RFT: Learning to Generate Synthetic Text via Differentially Private Reinforcement Fine-Tuning
Fangyuan Xu, Sihao Chen, Zinan Lin, Taiwei Shi, Sydney Graham · Feb 20, 2026 · Citations: 0
Automatic Metrics
Differentially private (DP) synthetic data generation plays a pivotal role in developing large language models (LLMs) on private data, where data owners cannot provide eyes-on access to individual examples.
- Vichara: Appellate Judgment Prediction and Explanation for the Indian Judicial System
Pavithra PM Nair, Preethu Rose Anish · Feb 20, 2026 · Citations: 0
Human EvalAutomatic Metrics
Vichara surpasses existing judgment prediction benchmarks on both datasets, with GPT-4o mini achieving the highest performance (F1: 81.5 on PredEx, 80.3 on ILDC_expert), followed by Llama-3.1-8B.
- Agentic Adversarial QA for Improving Domain-Specific LLMs
Vincent Grari, Ciprian Tomoiaga, Sylvain Lamprier, Tatsunori Hashimoto, Marcin Detyniecki · Feb 20, 2026 · Citations: 0
Automatic Metrics
Evaluation on specialized subsets of the LegalBench corpus demonstrates that our method achieves greater accuracy with substantially fewer synthetic samples.
- CUICurate: A GraphRAG-based Framework for Automated Clinical Concept Curation for NLP applications
Victoria Blake, Mathew Miller, Jamie Novak, Sze-yuan Ooi, Blanca Gallego · Feb 20, 2026 · Citations: 0
Expert Verification Automatic Metrics
The framework was evaluated on five lexically heterogeneous clinical concepts against a manually curated benchmark and gold-standard concept sets.
- Condition-Gated Reasoning for Context-Dependent Biomedical Question Answering
Jash Rajesh Parekh, Wonbin Kweon, Joey Chan, Rezarta Islamaj, Robert Leaman · Feb 20, 2026 · Citations: 0
Automatic Metrics
Existing benchmarks do not evaluate such conditional reasoning, and retrieval-augmented or graph-based methods lack explicit mechanisms to ensure that retrieved knowledge is applicable to given context.
- Using LLMs for Knowledge Component-level Correctness Labeling in Open-ended Coding Problems
Zhangqi Duan, Arnav Kankaria, Dhruv Kartik, Andrew Lan · Feb 19, 2026 · Citations: 0
Human Eval
Human evaluation further demonstrates substantial agreement between LLM and expert annotations.
- Bridging the Domain Divide: Supervised vs. Zero-Shot Clinical Section Segmentation from MIMIC-III to Obstetrics
Baris Karacan, Barbara Di Eugenio, Patrick Thornton · Feb 19, 2026 · Citations: 0
Automatic Metrics
Clinical free-text notes contain vital patient information.
- Small LLMs for Medical NLP: a Systematic Analysis of Few-Shot, Constraint Decoding, Fine-Tuning and Continual Pre-Training in Italian
Pietro Ferrazzi, Mattia Franzin, Alberto Lavelli, Bernardo Magnini · Feb 19, 2026 · Citations: 0
Automatic Metrics
Large Language Models (LLMs) consistently excel in diverse medical Natural Language Processing (NLP) tasks, yet their substantial computational requirements often limit deployment in real-world healthcare settings.
- Diverse Word Choices, Same Reference: Annotating Lexically-Rich Cross-Document Coreference
Anastasia Zhukova, Felix Hamborg, Karsten Donnay, Norman Meuschke, Bela Gipp · Feb 19, 2026 · Citations: 0
Automatic Metrics
Cross-document coreference resolution (CDCR) identifies and links mentions of the same entities and events across related documents, enabling content analysis that aggregates information at the level of discourse participants.
- What Makes a Good Doctor Response? An Analysis on a Romanian Telemedicine Platform
Adrian Cosma, Cosmin Dumitrache, Emilian Radoi · Feb 19, 2026 · Citations: 0
Expert Verification Automatic Metrics
As platforms increasingly rely on patient ratings and feedback, clinicians face growing pressure to maintain satisfaction scores, even though these evaluations often reflect communication quality more than clinical accuracy.
- ReIn: Conversational Error Recovery with Reasoning Inception
Takyoung Kim, Jinseok Nam, Chandrayee Basu, Xing Fan, Chengyuan Ma · Feb 19, 2026 · Citations: 0
Automatic Metrics
Conversational agents powered by large language models (LLMs) with tool integration achieve strong performance on fixed task-oriented dialogue datasets but remain vulnerable to unanticipated, user-induced errors.
- BanglaSummEval: Reference-Free Factual Consistency Evaluation for Bangla Summarization
Ahmed Rafid, Rumman Adib, Fariya Ahmed, Ajwad Abrar, Mohammed Saidul Islam · Feb 18, 2026 · Citations: 0
Automatic Metrics
However, most existing evaluation metrics overlook Bangla, a widely spoken yet under-resourced language, and often depend on reference summaries.
- Scaling Open Discrete Audio Foundation Models with Interleaved Semantic, Acoustic, and Text Tokens
Potsawee Manakul, Woody Haosheng Gan, Martijn Bartelds, Guangzhi Sun, William Held · Feb 18, 2026 · Citations: 0
Automatic Metrics
Current audio language models are predominantly text-first, either extending pre-trained text LLM backbones or relying on semantic-only audio tokens, limiting general audio modeling.
- Quecto-V1: Empirical Analysis of 8-bit Quantized Small Language Models for On-Device Legal Retrieval
Subrit Dikshit · Feb 18, 2026 · Citations: 0
Automatic MetricsSimulation Env
The rapid proliferation of Large Language Models (LLMs) has revolutionized Natural Language Processing (NLP) but has simultaneously created a "resource divide." State-of-the-art legal intelligence systems typically rely on massive parameter
