Ligong Han, Hao Wang, Han Gao, Kai Xu, Akash Srivastava · Mar 26, 2026 · Citations: 0
Automatic Metrics General
← Back to explorer S2D2: Fast Decoding for Diffusion LLMs via Training-Free Self-Speculation Pure and physics-guided deep learning approaches for spatio-temporal groundwater level prediction Integrating Deep RL and Bayesian Inference for ObjectNav in Mobile Robotics When Hate Meets Facts: LLMs-in-the-Loop for Check-worthiness Detection in Hate Speech MolQuest: A Benchmark for Agentic Evaluation of Abductive Reasoning in Chemical Structure Elucidation Does Explanation Correctness Matter? Linking Computational XAI Evaluation to Human Understanding Prompt Attack Detection with LLM-as-a-Judge and Mixture-of-Models FinMCP-Bench: Benchmarking LLM Agents for Real-World Financial Tool Use under the Model Context Protocol AI Security in the Foundation Model Era: A Comprehensive Survey from a Unified Perspective Training LLMs for Multi-Step Tool Orchestration with Constrained Data Synthesis and Graduated Rewards When AI Meets Early Childhood Education: Large Language Models as Assessment Teammates in Chinese Preschools GameplayQA: A Benchmarking Framework for Decision-Dense POV-Synced Multi-Video Understanding of 3D Virtual Agents Schema on the Inside: A Two-Phase Fine-Tuning Method for High-Efficiency Text-to-SQL at Scale BeliefShift: Benchmarking Temporal Belief Consistency and Opinion Drift in LLM Agents Unbiased Reward Modeling from Implicit Feedback for LLM Alignment Quality Over Clicks: Iterative Reinforcement Learning for Early-Stage E-Commerce Query Suggestion Towards Automated Community Notes Generation with Large Vision Language Models for Combating Contextual Deception Learning When to Act: Interval-Aware Reinforcement Learning with Predictive Temporal Structure TREX: Trajectory Explanations for Multi-Objective Reinforcement Learning SecureBreak -- A dataset towards safe and secure models
Tag: General
General papers in the current HFEPX explorer (897 papers).
Papers in tag: 897
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Evaluation Modes
- Automatic Metrics (13)
- Simulation Env (4)
- Human Eval (1)
Human Feedback Types
- Pairwise Preference (4)
- Red Team (3)
- Critique Edit (1)
Required Expertise
- General (20)
Matteo Salis, Gabriele Sartor, Rosa Meo, Stefano Ferraris, Abdourrahmane M. Atto · Mar 26, 2026 · Citations: 0
Expert Verification Automatic Metrics General
João Castelo-Branco, José Santos-Victor, Alexandre Bernardino · Mar 26, 2026 · Citations: 0
Simulation Env General
Nicolás Benjamín Ocampo, Tommaso Caselli, Davide Ceolin · Mar 26, 2026 · Citations: 0
Human EvalAutomatic Metrics General
- We validate it through extensive human evaluation, and show that our LLM-in-the-loop framework reduces human effort without compromising the annotation quality of the data.
Taolin Han, Shuang Wu, Jinghang Wang, Yuhao Zhou, Renquan Lv, Bing Zhao · Mar 26, 2026 · Citations: 0
Automatic MetricsSimulation Env General
- Current scientific evaluation benchmarks predominantly rely on static, single-turn Question Answering (QA) formats, which are inadequate for measuring model performance in complex scientific tasks that require multi-step iteration and…
- To address this gap, we introduce MolQuest, a novel agent-based evaluation framework for molecular structure elucidation built upon authentic chemical experimental data.
Gregor Baer, Chao Zhang, Isel Grau, Pieter Van Gorp · Mar 26, 2026 · Citations: 0
Automatic MetricsSimulation Env General
- Higher correctness is assumed to produce better human understanding, but this link has not been tested experimentally with controlled levels.
- These findings show that not all differences in functional correctness translate to differences in human understanding, underscoring the need to validate functional metrics against human outcomes.
Hieu Xuan Le, Benjamin Goh, Quy Anh Tang · Mar 26, 2026 · Citations: 0
Red Team Llm As Judge General
- 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…
- In this work, we examine whether lightweight, general-purpose LLMs can reliably serve as security judges under real-world production constraints.
Jie Zhu, Yimin Tian, Boyang Li, Kehao Wu, Zhongzhi Liang, Junhui Li · Mar 26, 2026 · Citations: 0
Automatic Metrics General
- This paper introduces FinMCP-Bench, a novel benchmark for evaluating large language models (LLMs) in solving real-world financial problems through tool invocation of financial model context protocols.
- Using this benchmark, we systematically assess a range of mainstream LLMs and propose metrics that explicitly measure tool invocation accuracy and reasoning capabilities.
Zhenyi Wang, Siyu Luan · Mar 25, 2026 · Citations: 0
Red Team General
Cheng Jiayang, Xin Liu, Zhihan Zhang, Haoyang Wen, Zixuan Zhang, Qingyu Yin · Mar 25, 2026 · Citations: 0
Automatic Metrics General
Xingming Li, Runke Huang, Yanan Bao, Yuye Jin, Yuru Jiao, Qingyong Hu · Mar 25, 2026 · Citations: 0
Rubric Rating Automatic Metrics General
- In this paper, we investigate whether AI can serve as a scalable assessment teammate by extracting structured quality indicators and validating their alignment with human expert judgments.
- Our contributions include: (1) TEPE-TCI-370h (Tracing Effective Preschool Education), the first large-scale dataset of naturalistic teacher-child interactions in Chinese preschools (370 hours, 105 classrooms) with standardized ECQRS-EC and…
Yunzhe Wang, Runhui Xu, Kexin Zheng, Tianyi Zhang, Jayavibhav Niranjan Kogundi, Soham Hans · Mar 25, 2026 · Citations: 0
Simulation Env General
- Multimodal LLMs are increasingly deployed as perceptual backbones for autonomous agents in 3D environments, from robotics to virtual worlds.
- We introduce GameplayQA, a framework for evaluating agentic-centric perception and reasoning through video understanding.
Chinmay Soni, Shivam Chourasia, Gaurav Kumar, Hitesh Kapoor · Mar 25, 2026 · Citations: 0
Automatic Metrics General
Praveen Kumar Myakala, Manan Agrawal, Rahul Manche · Mar 25, 2026 · Citations: 0
Pairwise PreferenceCritique Edit Automatic Metrics General
- LLMs are increasingly used as long-running conversational agents, yet every major benchmark evaluating their memory treats user information as static facts to be stored and retrieved.
- We further introduce four novel evaluation metrics: Belief Revision Accuracy (BRA), Drift Coherence Score (DCS), Contradiction Resolution Rate (CRR), and Evidence Sensitivity Index (ESI).
Hao Wang, Haocheng Yang, Licheng Pan, Lei Shen, Xiaoxi Li, Yinuo Wang · Mar 24, 2026 · Citations: 0
Automatic Metrics General
- Despite the success of reinforcement learning from human feedback (RLHF), existing reward modeling methods largely rely on explicit feedback, which is costly to collect and difficult to scale.
- Experiments across diverse LLM backbones and benchmark datasets validate that ImplicitRM learns accurate reward models from implicit feedback and improves performance on downstream RLHF tasks.
Qi Sun, Kejun Xiao, Huaipeng Zhao, Tao Luo, Xiaoyi Zeng · Mar 24, 2026 · Citations: 0
Pairwise Preference General
- Recent approaches mainly optimize generative models using click-through rate (CTR) models to align with user preferences.
- We also introduce EQS-Benchmark, a dataset of 16,949 real-world e-commerce queries for offline training and evaluation.
Jin Ma, Jingwen Yan, Mohammed Aldeen, Ethan Anderson, Taran Kavuru, Jinkyung Katie Park · Mar 23, 2026 · Citations: 0
Automatic Metrics General
- However, its reliance on human contributors limits both the timeliness and scalability.
- Finally, we introduce a new evaluation metric, Context Helpfulness Score (CHS), that aligns with user study outcomes rather than relying on lexical overlap.
Davide Di Gioia · Mar 23, 2026 · Citations: 0
Pairwise Preference Automatic Metrics General
- Autonomous agents operating in continuous environments must decide not only what to do, but when to act.
- High spread indicates a branching, uncertain future and drives the agent to act sooner; low spread signals predictability and permits longer rest intervals.
Dilina Rajapakse, Juan C. Rosero, Ivana Dusparic · Mar 23, 2026 · Citations: 0
Pairwise Preference General
- Multi-Objective Reinforcement Learning (MORL) addresses this limitation by enabling agents to optimize several objectives simultaneously, explicitly reasoning about trade-offs between them.
- Current Explainable Reinforcement Learning (XRL) methods are typically designed for single scalar rewards and do not account for explanations with respect to distinct objectives or user preferences.
Marco Arazzi, Vignesh Kumar Kembu, Antonino Nocera · Mar 23, 2026 · Citations: 0
Red Team General
- To provide a contribution in this scenario, this paper introduces SecureBreak, a safety-oriented dataset designed to support the development of AI-driven solutions for detecting harmful LLM outputs caused by residual weaknesses in security…
- The dataset is highly reliable due to careful manual annotation, where labels are assigned conservatively to ensure safety.