- AD-Bench: A Real-World, Trajectory-Aware Advertising Analytics Benchmark for LLM Agents
Lingxiang Hu, Yiding Sun, Tianle Xia, Wenwei Li, Ming Xu · Feb 15, 2026 · Citations: 0
Expert Verification Simulation Env Long Horizon
While Large Language Model (LLM) agents have achieved remarkable progress in complex reasoning tasks, evaluating their performance in real-world environments has become a critical problem.
- CounselReflect: A Toolkit for Auditing Mental-Health Dialogues
Yahan Li, Chaohao Du, Zeyang Li, Christopher Chun Kuizon, Shupeng Cheng · Mar 31, 2026 · Citations: 0
Rubric RatingExpert Verification Human Eval Web Browsing
The system integrates two families of evaluation signals: (i) 12 model-based metrics produced by task-specific predictors, and (ii) rubric-based metrics that extend coverage via a literature-derived library (69 metrics) and user-defined…
- Guideline-Grounded Evidence Accumulation for High-Stakes Agent Verification
Yichi Zhang, Nabeel Seedat, Yinpeng Dong, Peng Cui, Jun Zhu · Mar 3, 2026 · Citations: 0
Expert Verification Automatic Metrics Long Horizon
As LLM-powered agents have been used for high-stakes decision-making, such as clinical diagnosis, it becomes critical to develop reliable verification of their decisions to facilitate trustworthy deployment.
- An Agentic System for Rare Disease Diagnosis with Traceable Reasoning
Weike Zhao, Chaoyi Wu, Yanjie Fan, Xiaoman Zhang, Pengcheng Qiu · Jun 25, 2025 · Citations: 0
Expert Verification Automatic Metrics Multi Agent
Here we present DeepRare, a multi-agent system for rare disease differential diagnosis decision support powered by large language models, integrating over 40 specialized tools and up-to-date knowledge sources.
- PanCanBench: A Comprehensive Benchmark for Evaluating Large Language Models in Pancreatic Oncology
Yimin Zhao, Sheela R. Damle, Simone E. Dekker, Scott Geng, Karly Williams Silva · Mar 2, 2026 · Citations: 0
Rubric RatingExpert Verification Llm As JudgeAutomatic Metrics
Large language models (LLMs) have achieved expert-level performance on standardized examinations, yet multiple-choice accuracy poorly reflects real-world clinical utility and safety.
- Jailbreak Foundry: From Papers to Runnable Attacks for Reproducible Benchmarking
Zhicheng Fang, Jingjie Zheng, Chenxu Fu, Wei Xu · Feb 27, 2026 · Citations: 0
Red Team Llm As Judge Multi Agent
Jailbreak techniques for large language models (LLMs) evolve faster than benchmarks, making robustness estimates stale and difficult to compare across papers due to drift in datasets, harnesses, and judging protocols.
- Vision-DeepResearch Benchmark: Rethinking Visual and Textual Search for Multimodal Large Language Models
Yu Zeng, Wenxuan Huang, Zhen Fang, Shuang Chen, Yufan Shen · Feb 2, 2026 · Citations: 0
Expert Verification Automatic Metrics Web Browsing
However, evaluating these visual and textual search abilities is still difficult, and existing benchmarks have two major limitations.
- 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.
- CricBench: A Multilingual Benchmark for Evaluating LLMs in Cricket Analytics
Vaibhav Devraj, Dhruv Kumar, Jagat Sesh Challa, Parth Agarwal, Navya Kommuri · Dec 26, 2025 · Citations: 0
Expert Verification Automatic Metrics
To investigate this potential capability gap, we present CricBench, a comprehensive benchmark suite for evaluating LLMs on specialized cricket data.
- From Days to Minutes: An Autonomous AI Agent Achieves Reliable Clinical Triage in Remote Patient Monitoring
Seunghwan Kim, Tiffany H. Kung, Heena Verma, Dilan Edirisinghe, Kaveh Sedehi · Mar 10, 2026 · Citations: 0
Expert Verification Automatic Metrics Long Horizon
Results: Against a human majority-vote standard (N=467), the agent achieved 95.8% emergency sensitivity and 88.5% sensitivity for all actionable alerts (85.7% specificity).
- PubMed Reasoner: Dynamic Reasoning-based Retrieval for Evidence-Grounded Biomedical Question Answering
Yiqing Zhang, Xiaozhong Liu, Fabricio Murai · Mar 28, 2026 · Citations: 0
Expert Verification Llm As JudgeAutomatic Metrics
In this context, we introduce PubMed Reasoner, a biomedical QA agent composed of three stages: self-critic query refinement evaluates MeSH terms for coverage, alignment, and redundancy to enhance PubMed queries based on partial (metadata)…
- Improving Clinical Diagnosis with Counterfactual Multi-Agent Reasoning
Zhiwen You, Xi Chen, Aniket Vashishtha, Simo Du, Gabriel Erion-Barner · Mar 29, 2026 · Citations: 0
Expert Verification Human EvalAutomatic Metrics Multi Agent
In this work, we propose a counterfactual multi-agent diagnostic framework inspired by clinician training that makes hypothesis testing explicit and evidence-grounded.
- EpidemIQs: Prompt-to-Paper LLM Agents for Epidemic Modeling and Analysis
Mohammad Hossein Samaei, Faryad Darabi Sahneh, Lee W. Cohnstaedt, Caterina Scoglio · Sep 24, 2025 · Citations: 0
Expert Verification Llm As JudgeSimulation Env Multi Agent
We introduce EpidemIQs, a novel multi-agent LLM framework that integrates user inputs and autonomously conducts literature review, analytical derivation, network modeling, mechanistic modeling, stochastic simulations, data visualization and
- TraceSafe: A Systematic Assessment of LLM Guardrails on Multi-Step Tool-Calling Trajectories
Yen-Shan Chen, Sian-Yao Huang, Cheng-Lin Yang, Yun-Nung Chen · Apr 8, 2026 · Citations: 0
Red Team Automatic Metrics Long Horizon
As large language models (LLMs) evolve from static chatbots into autonomous agents, the primary vulnerability surface shifts from final outputs to intermediate execution traces.
- SODIUM: From Open Web Data to Queryable Databases
Chuxuan Hu, Philip Li, Maxwell Yang, Daniel Kang · Mar 19, 2026 · Citations: 0
Expert Verification Automatic Metrics Multi Agent
Existing systems struggle with SODIUM tasks: we evaluate 6 advanced AI agents on SODIUM-Bench, with the strongest baseline achieving only 46.5% accuracy.
- Measuring AI Ability to Complete Long Software Tasks
Thomas Kwa, Ben West, Joel Becker, Amy Deng, Katharyn Garcia · Mar 18, 2025 · Citations: 0
Expert Verification Automatic Metrics Tool Use
Despite rapid progress on AI benchmarks, the real-world meaning of benchmark performance remains unclear.
- 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…
- A Scalable Framework for Evaluating Health Language Models
Neil Mallinar, A. Ali Heydari, Xin Liu, Anthony Z. Faranesh, Brent Winslow · Mar 30, 2025 · Citations: 0
Rubric RatingExpert Verification Automatic Metrics
As LLM-driven health applications are increasingly adopted, rigorous and efficient one-sided evaluation methodologies are crucial to ensure response quality across multiple dimensions, including accuracy, personalization and safety.
- PRBench: End-to-end Paper Reproduction in Physics Research
Shi Qiu, Junyi Deng, Yiwei Deng, Haoran Dong, Jieyu Fu · Mar 29, 2026 · Citations: 0
Rubric RatingExpert Verification Automatic MetricsSimulation Env
We introduce PRBench, a benchmark of 30 expert-curated tasks spanning 11 subfields of physics.
- Step-CoT: Stepwise Visual Chain-of-Thought for Medical Visual Question Answering
Lin Fan, Yafei Ou, Zhipeng Deng, Pengyu Dai, Hou Chongxian · Mar 14, 2026 · Citations: 0
Expert Verification Automatic Metrics Long Horizon
Benchmark: github.com/hahaha111111/Step-CoT.
- 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.
- APEX-Agents
Bertie Vidgen, Austin Mann, Abby Fennelly, John Wright Stanly, Lucas Rothman · Jan 20, 2026 · Citations: 0
Rubric RatingExpert Verification Automatic Metrics Long Horizon
We introduce the AI Productivity Index for Agents (APEX-Agents), a benchmark for assessing whether AI agents can execute long-horizon, cross-application tasks created by investment banking analysts, management consultants, and corporate…
- Xpertbench: Expert Level Tasks with Rubrics-Based Evaluation
Xue Liu, Xin Ma, Yuxin Ma, Yongchang Peng, Duo Wang · Mar 27, 2026 · Citations: 0
Rubric RatingExpert Verification Automatic Metrics
To bridge this gap, we present XpertBench, a high-fidelity benchmark engineered to assess LLMs across authentic professional domains.
- AJAR: Adaptive Jailbreak Architecture for Red-teaming
Yipu Dou, Wang Yang · Jan 16, 2026 · Citations: 0
Red Team Simulation Env
Large language model (LLM) safety evaluation is moving from content moderation to action security as modern systems gain persistent state, tool access, and autonomous control loops.
- MedInjection-FR: Exploring the Role of Native, Synthetic, and Translated Data in Biomedical Instruction Tuning
Ikram Belmadani, Oumaima El Khettari, Pacôme Constant dit Beaufils, Benoit Favre, Richard Dufour · Mar 6, 2026 · Citations: 0
Expert Verification Llm As Judge
Evaluation on open-ended QA combines automatic metrics, LLM-as-a-judge assessment, and human expert review; although LLM-based judgments correlate best with human ratings, they show sensitivity to verbosity.
- A Coin Flip for Safety: LLM Judges Fail to Reliably Measure Adversarial Robustness
Leo Schwinn, Moritz Ladenburger, Tim Beyer, Mehrnaz Mofakhami, Gauthier Gidel · Feb 4, 2026 · Citations: 0
Red Team Llm As Judge
Automated LLM-as-a-Judge frameworks have become the de facto standard for scalable evaluation across natural language processing.
- RedTeamCUA: Realistic Adversarial Testing of Computer-Use Agents in Hybrid Web-OS Environments
Zeyi Liao, Jaylen Jones, Linxi Jiang, Yuting Ning, Eric Fosler-Lussier · May 28, 2025 · Citations: 0
Red Team Automatic Metrics Web Browsing
Using RedTeamCUA, we develop RTC-Bench, a comprehensive benchmark with 864 examples that investigate realistic, hybrid web-OS attack scenarios and fundamental security vulnerabilities.
- 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…
- A Decade-Scale Benchmark Evaluating LLMs' Clinical Practice Guidelines Detection and Adherence in Multi-turn Conversations
Andong Tan, Shuyu Dai, Jinglu Wang, Fengtao Zhou, Yan Lu · Mar 26, 2026 · Citations: 0
Expert Verification Human Eval
To address this gap, we introduce CPGBench, an automated framework benchmarking the clinical guideline detection and adherence capabilities of LLMs in multi-turn conversations.
- IntelliAsk: Learning to Ask High-Quality Research Questions via RLVR
Karun Sharma, Vidushee Vats, Shengzhi Li, Yuxiang Wang, Zhongtian Sun · Jan 23, 2026 · Citations: 0
Pairwise PreferenceExpert Verification Human Eval
Peer review relies on substantive, evidence-based questions, yet current LLMs generate surface-level queries that perform worse than human reviewer questions in expert evaluation.
- Automatically Benchmarking LLM Code Agents through Agent-Driven Annotation and Evaluation
Lingyue Fu, Bolun Zhang, Hao Guan, Yaoming Zhu, Lin Qiu · Oct 28, 2025 · Citations: 0
Expert Verification Llm As JudgeAutomatic Metrics
To address these challenges, we propose an agent-driven benchmark construction pipeline that leverages human supervision to efficiently generate diverse project-level tasks.
- A Multi-Stage Validation Framework for Trustworthy Large-scale Clinical Information Extraction using Large Language Models
Maria Mahbub, Gregory M. Dams, Josh Arnold, Caitlin Rizy, Sudarshan Srinivasan · Apr 7, 2026 · Citations: 0
Expert Verification Automatic Metrics
Conventional evaluation methods rely heavily on annotation-intensive reference standards or incomplete structured data, limiting feasibility at population scale.
- Automating Clinical Information Retrieval from Finnish Electronic Health Records Using Large Language Models
Mikko Saukkoriipi, Nicole Hernandez, Jaakko Sahlsten, Kimmo Kaski, Otso Arponen · Mar 27, 2026 · Citations: 0
Expert Verification Automatic Metrics
Open-source large language models (LLMs) ranging from 4B to 70B parameters were benchmarked under fully offline conditions using 1,664 expert-annotated question-answer pairs derived from records of 183 patients.
- SleepVLM: Explainable and Rule-Grounded Sleep Staging via a Vision-Language Model
Guifeng Deng, Pan Wang, Jiquan Wang, Shuying Rao, Junyi Xie · Mar 22, 2026 · Citations: 0
Expert Verification Automatic Metrics
Expert evaluations further validated the quality of the model's reasoning, with mean scores exceeding 4.0/5.0 for factual accuracy, evidence comprehensiveness, and logical coherence.
- 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.
- RuleForge: Automated Generation and Validation for Web Vulnerability Detection at Scale
Ayush Garg, Sophia Hager, Jacob Montiel, Aditya Tiwari, Michael Gentile · Apr 2, 2026 · Citations: 0
Expert Verification Llm As JudgeAutomatic Metrics
This paper focuses on RuleForge's architecture and operational deployment for CVE-related threat detection, with particular emphasis on our novel LLM-as-a-judge (Large Language Model as judge) confidence validation system and systematic…
- DistillNote: Toward a Functional Evaluation Framework of LLM-Generated Clinical Note Summaries
Heloisa Oss Boll, Antonio Oss Boll, Leticia Puttlitz Boll, Ameen Abu Hanna, Iacer Calixto · Jun 20, 2025 · Citations: 0
Expert Verification Llm As JudgeAutomatic Metrics
This study introduces DistillNote, an evaluation framework for LLM summaries that targets their functional utility by applying the generated summary downstream in a complex clinical prediction task, explicitly quantifying how much…
- ClinicalAgents: Multi-Agent Orchestration for Clinical Decision Making with Dual-Memory
Zhuohan Ge, Haoyang Li, Yubo Wang, Nicole Hu, Chen Jason Zhang · Mar 27, 2026 · Citations: 0
Expert Verification Automatic Metrics Multi Agent
To bridge this gap, we introduce ClinicalAgents, a novel multi-agent framework designed to simulate the cognitive workflow of expert clinicians.
- A Multidisciplinary AI Board for Multimodal Dementia Characterization and Risk Assessment
Sheng Liu, Long Chen, Zeyun Zhao, Qinglin Gou, Qingyue Wei · Mar 23, 2026 · Citations: 0
Expert Verification Automatic Metrics Multi Agent
We present Cerebra, an interactive multi-agent AI team that coordinates specialized agents for EHR, clinical notes, and medical imaging analysis.
- FairMed-XGB: A Bayesian-Optimised Multi-Metric Framework with Explainability for Demographic Equity in Critical Healthcare Data
Mitul Goswami, Romit Chatterjee, Arif Ahmed Sekh · Mar 16, 2026 · Citations: 0
Expert Verification Automatic Metrics
Post-mitigation evaluation on seven clinically distinct cohorts derived from the MIMIC-IV-ED and eICU databases demonstrates substantial bias reduction: Statistical Parity Difference decreases by 40 to 51 percent on MIMIC-IV-ED and 10 to 19…
- SemEval-2026 Task 6: CLARITY -- Unmasking Political Question Evasions
Konstantinos Thomas, Giorgos Filandrianos, Maria Lymperaiou, Chrysoula Zerva, Giorgos Stamou · Mar 14, 2026 · Citations: 0
Red Team Automatic Metrics
The benchmark is constructed from U.S.
- Symphonym: Universal Phonetic Embeddings for Cross-Script Name Matching
Stephen Gadd · Jan 11, 2026 · Citations: 0
Expert Verification Automatic Metrics
Trained on 32.7 million triplet samples drawn from 67 million toponyms spanning GeoNames, Wikidata, and the Getty Thesaurus of Geographic Names, the Student achieves the highest Recall@1 (85.2%) and Mean Reciprocal Rank (90.8%) on the…
- Deep Research, Shallow Evaluation: A Case Study in Meta-Evaluation for Long-Form QA Benchmarks
Jena D. Hwang, Varsha Kishore, Amanpreet Singh, Dany Haddad, Aakanksha Naik · Mar 6, 2026 · Citations: 0
Pairwise PreferenceExpert Verification Llm As Judge
This has prompted evaluation frameworks that use LLM-as-judge protocols and claim verification, along with meta-evaluation frameworks that seek to validate these methods.
- EpiScreen: Early Epilepsy Detection from Electronic Health Records with Large Language Models
Shuang Zhou, Kai Yu, Zaifu Zhan, Huixue Zhou, Min Zeng · Mar 30, 2026 · Citations: 0
Expert Verification
In a clinician-AI collaboration setting, EpiScreen-assisted neurologists outperformed unaided experts by up to 10.9%.
- Refusal Steering: Fine-grained Control over LLM Refusal Behaviour for Sensitive Topics
Iker García-Ferrero, David Montero, Roman Orus · Dec 18, 2025 · Citations: 0
Red Team Llm As Judge
We replace fragile pattern-based refusal detection with an LLM-as-a-judge that assigns refusal confidence scores and we propose a ridge-regularized variant to compute steering vectors that better isolate the refusal--compliance direction.
- Application-Driven Pedagogical Knowledge Optimization of Open-Source LLMs via Reinforcement Learning and Supervised Fine-Tuning
Navan Preet Singh, Xiaokun Wang, Anurag Garikipati, Madalina Ciobanu, Qingqing Mao · Apr 7, 2026 · Citations: 0
Expert Verification Automatic Metrics
These models remarkably achieve high enough accuracy on the Cross-Domain Pedagogical Knowledge (CDPK) Benchmark to establish new state-of-the-art (SOTA) results across the interactive Pedagogy Benchmark Leaderboard and surpass significantly…
- Do No Harm: Exposing Hidden Vulnerabilities of LLMs via Persona-based Client Simulation Attack in Psychological Counseling
Qingyang Xu, Yaling Shen, Stephanie Fong, Zimu Wang, Yiwen Jiang · Apr 6, 2026 · Citations: 0
Red Team Simulation Env
The increasing use of large language models (LLMs) in mental healthcare raises safety concerns in high-stakes therapeutic interactions.
- Prompt Attack Detection with LLM-as-a-Judge and Mixture-of-Models
Hieu Xuan Le, Benjamin Goh, Quy Anh Tang · Mar 26, 2026 · Citations: 0
Red Team Llm As Judge
In production, guardrails must mitigate these attacks under strict low-latency constraints, resulting in a deployment gap in which lightweight classifiers and rule-based systems struggle to generalize under distribution shift, while…
- Red-Teaming Vision-Language-Action Models via Quality Diversity Prompt Generation for Robust Robot Policies
Siddharth Srikanth, Freddie Liang, Ya-Chuan Hsu, Varun Bhatt, Shihan Zhao · Mar 12, 2026 · Citations: 0
Red Team Simulation Env
Our results across multiple simulation benchmarks show that Q-DIG finds more diverse and meaningful failure modes compared to baseline methods, and that fine-tuning VLAs on the generated instructions improves task success rates.
- 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.
- From Evidence-Based Medicine to Knowledge Graph: Retrieval-Augmented Generation for Sports Rehabilitation and a Domain Benchmark
Jinning Zhang, Jie Song, Wenhui Tu, Zecheng Li, Jingxuan Li · Jan 1, 2026 · Citations: 0
Rubric RatingExpert Verification Automatic Metrics
Validated in sports rehabilitation, we release a knowledge graph (357,844 nodes, 371,226 edges) and a benchmark of 1,637 QA pairs.
- Moving Beyond Medical Exams: A Clinician-Annotated Fairness Dataset of Real-World Tasks and Ambiguity in Mental Healthcare
Max Lamparth, Declan Grabb, Amy Franks, Scott Gershan, Kaitlyn N. Kunstman · Feb 22, 2025 · Citations: 0
Pairwise PreferenceExpert Verification Automatic Metrics
Current medical language model (LM) benchmarks often over-simplify the complexities of day-to-day clinical practice tasks and instead rely on evaluating LMs on multiple-choice board exam questions.
- WebWeaver: Breaking Topology Confidentiality in LLM Multi-Agent Systems with Stealthy Context-Based Inference
Zixun Xiong, Gaoyi Wu, Lingfeng Yao, Miao Pan, Xiaojiang Du · Mar 11, 2026 · Citations: 0
Red Team Automatic Metrics Multi Agent
Communication topology is a critical factor in the utility and safety of LLM-based multi-agent systems (LLM-MAS), making it a high-value intellectual property (IP) whose confidentiality remains insufficiently studied.
- MUSE: A Run-Centric Platform for Multimodal Unified Safety Evaluation of Large Language Models
Zhongxi Wang, Yueqian Lin, Jingyang Zhang, Hai Helen Li, Yiran Chen · Mar 3, 2026 · Citations: 0
Red Team Automatic Metrics Web Browsing
Safety evaluation and red-teaming of large language models remain predominantly text-centric, and existing frameworks lack the infrastructure to systematically test whether alignment generalizes to audio, image, and video inputs.
- What Matters For Safety Alignment?
Xing Li, Hui-Ling Zhen, Lihao Yin, Xianzhi Yu, Zhenhua Dong · Jan 7, 2026 · Citations: 0
Red Team Automatic Metrics Tool Use
This paper presents a comprehensive empirical study on the safety alignment capabilities.
- A Simple and Efficient Jailbreak Method Exploiting LLMs' Helpfulness
Xuan Luo, Yue Wang, Zefeng He, Geng Tu, Jing Li · Sep 17, 2025 · Citations: 0
Red Team Automatic Metrics
This study reveals a critical safety blind spot in modern LLMs: learning-style queries, which closely resemble ordinary educational questions, can reliably elicit harmful responses.
- Dyslexify: A Mechanistic Defense Against Typographic Attacks in CLIP
Lorenz Hufe, Constantin Venhoff, Erblina Purelku, Maximilian Dreyer, Sebastian Lapuschkin · Aug 28, 2025 · Citations: 0
Red Team Automatic Metrics
These models serve as suitable drop-in replacements for a broad range of safety-critical applications, where the risks of text-based manipulation outweigh the utility of text recognition.
- Multi-Agent Comedy Club: Investigating Community Discussion Effects on LLM Humor Generation
Shiwei Hong, Lingyao Li, Ethan Z. Rong, Chenxinran Shen, Zhicong Lu · Feb 16, 2026 · Citations: 0
Pairwise PreferenceRubric Rating Multi Agent
Prior work has explored multi-turn interaction and feedback for LLM writing, but evaluations still largely center on prompts and localized feedback, leaving persistent public reception in online communities underexamined.
- DEER: A Benchmark for Evaluating Deep Research Agents on Expert Report Generation
Janghoon Han, Heegyu Kim, Changho Lee, Dahm Lee, Min Hyung Park · Dec 19, 2025 · Citations: 0
Rubric RatingExpert Verification Long Horizon
However, evaluating such reports remains challenging: report quality is multifaceted, making it difficult to determine what to assess and by what criteria; LLM-based judges may miss errors that require domain expertise to identify; and…
- 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.