- HLE-Verified: A Systematic Verification and Structured Revision of Humanity's Last Exam
Weiqi Zhai, Zhihai Wang, Jinghang Wang, Boyu Yang, Xiaogang Li · Feb 15, 2026 · Citations: 0
Expert VerificationCritique Edit Automatic Metrics
Humanity's Last Exam (HLE) has become a widely used benchmark for evaluating frontier large language models on challenging, multi-domain questions.
- 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…
- SparkMe: Adaptive Semi-Structured Interviewing for Qualitative Insight Discovery
David Anugraha, Vishakh Padmakumar, Diyi Yang · Feb 24, 2026 · Citations: 0
Expert Verification Automatic Metrics Multi Agent
Based on this formulation, we introduce SparkMe, a multi-agent LLM interviewer that performs deliberative planning via simulated conversation rollouts to select questions with high expected utility.
- Modeling Expert AI Diagnostic Alignment via Immutable Inference Snapshots
Dimitrios P. Panagoulias, Evangelia-Aikaterini Tsichrintzi, Georgios Savvidis, Evridiki Tsoureli-Nikita · Feb 26, 2026 · Citations: 0
Expert Verification Automatic Metrics
Human-in-the-loop validation is essential in safety-critical clinical AI, yet the transition between initial model inference and expert correction is rarely analyzed as a structured signal.
- Multi-Objective Alignment of Language Models for Personalized Psychotherapy
Mehrab Beikzadeh, Yasaman Asadollah Salmanpour, Ashima Suvarna, Sriram Sankararaman, Matteo Malgaroli · Feb 17, 2026 · Citations: 0
Pairwise PreferenceExpert Verification Automatic Metrics
While AI systems show therapeutic promise, current alignment approaches optimize objectives independently, failing to balance patient preferences with clinical safety.
- 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.
- 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.
- An Expert Schema for Evaluating Large Language Model Errors in Scholarly Question-Answering Systems
Anna Martin-Boyle, William Humphreys, Martha Brown, Cara Leckey, Harmanpreet Kaur · Feb 24, 2026 · Citations: 0
Expert Verification Automatic Metrics
Current evaluation metrics for testing LLM reliability are primarily automated approaches that prioritize efficiency and scalability, but lack contextual nuance and fail to reflect how scientific domain experts assess LLM outputs in…
- 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.
- LM-Lexicon: Improving Definition Modeling via Harmonizing Semantic Experts
Yang Liu, Jiaye Yang, Weikang Li, Jiahui Liang, Yang Li · Feb 15, 2026 · Citations: 0
Expert Verification Automatic Metrics
By decomposing the definition modeling task into specialized semantic domains, where small language models are trained as domain experts, LM-Lexicon achieves substantial improvements (+7% BLEU score compared with the prior state-of-the-art…
- "Are You Sure?": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systems
Xinfeng Li, Shenyu Dai, Kelong Zheng, Yue Xiao, Gelei Deng · Feb 24, 2026 · Citations: 0
Expert Verification Automatic Metrics
Large language model (LLM) agents are rapidly becoming trusted copilots in high-stakes domains like software development and healthcare.
