- AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling
Liang Ding · Mar 22, 2026 · Citations: 0
Demonstrations Human EvalLlm As Judge Long Horizon
LLM agents fail on the majority of real-world tasks -- GPT-4o succeeds on fewer than 15% of WebArena navigation tasks and below 55% pass@1 on ToolBench (Zhou et al., 2024; Qin et al., 2024) -- yet every failed trajectory is routinely…
- 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.
- PoSh: Using Scene Graphs To Guide LLMs-as-a-Judge For Detailed Image Descriptions
Amith Ananthram, Elias Stengel-Eskin, Lorena A. Bradford, Julia Demarest, Adam Purvis · Oct 21, 2025 · Citations: 0
Rubric Rating Human EvalLlm As Judge
In this work, we introduce PoSh, a metric for detailed image description that uses scene graphs as structured rubrics to guide LLMs-as-a-Judge, producing aggregate scores grounded in fine-grained errors (e.g.
- \$OneMillion-Bench: How Far are Language Agents from Human Experts?
Qianyu Yang, Yang Liu, Jiaqi Li, Jun Bai, Hao Chen · Mar 9, 2026 · Citations: 0
Rubric Rating Automatic Metrics Tool Use
To this end, we introduce \OneMillion-Bench \OneMillion-Bench, a benchmark of 400 expert-curated tasks spanning Law, Finance, Industry, Healthcare, and Natural Science, built to evaluate agents across economically consequential scenarios.
- Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization
Qiyao Ma, Dechen Gao, Rui Cai, Boqi Zhao, Hanchu Zhou · Apr 8, 2026 · Citations: 0
Pairwise PreferenceRubric Rating Human EvalAutomatic Metrics
Pluralistic alignment has emerged as a critical frontier in the development of Large Language Models (LLMs), with reward models (RMs) serving as a central mechanism for capturing diverse human values.
- Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation
Mohsen Hariri, Amirhossein Samandar, Michael Hinczewski, Vipin Chaudhary · Oct 5, 2025 · Citations: 0
Rubric Rating Automatic MetricsSimulation Env
We present a principled Bayesian evaluation framework that replaces Pass@k and average accuracy over N trials (avg@N) with posterior estimates of a model's underlying success probability and credible intervals, yielding stable rankings and…
- StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning
Shiyang Li, Zijian Zhang, Winson Chen, Yuebo Luo, Mingyi Hong · Mar 3, 2026 · Citations: 0
Rubric Rating Automatic Metrics Multi Agent
To address the challenge, in this work, we propose StitchCUDA, a multi-agent framework for end-to-end GPU program generation, with three specialized agents: a Planner to orchestrate whole system design, a Coder dedicated to implementing it…
- Beyond Paper-to-Paper: Structured Profiling and Rubric Scoring for Paper-Reviewer Matching
Yicheng Pan, Zhiyuan Ning, Ludi Wang, Yi Du · Apr 7, 2026 · Citations: 0
Rubric Rating Automatic Metrics
To address this gap, we propose P2R, a training-free framework that shifts from implicit paper-to-paper matching to explicit profile-based matching.
- 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.
- When AI Meets Early Childhood Education: Large Language Models as Assessment Teammates in Chinese Preschools
Xingming Li, Runke Huang, Yanan Bao, Yuye Jin, Yuru Jiao · Mar 25, 2026 · Citations: 0
Rubric Rating Automatic Metrics
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.
- QuarkMedBench: A Real-World Scenario Driven Benchmark for Evaluating Large Language Models
Yao Wu, Kangping Yin, Liang Dong, Zhenxin Ma, Shuting Xu · Mar 14, 2026 · Citations: 0
Rubric Rating Automatic Metrics
To bridge this gap, we introduce QuarkMedBench, an ecologically valid benchmark tailored for real-world medical LLM assessment.
- Document Reconstruction Unlocks Scalable Long-Context RLVR
Yao Xiao, Lei Wang, Yue Deng, Guanzheng Chen, Ziqi Jin · Feb 9, 2026 · Citations: 0
Rubric Rating Automatic Metrics
However, it often relies on gold-standard answers or explicit evaluation rubrics provided by powerful teacher models or human experts, which are costly and time-consuming.
- Paper Reconstruction Evaluation: Evaluating Presentation and Hallucination in AI-written Papers
Atsuyuki Miyai, Mashiro Toyooka, Zaiying Zhao, Kenta Watanabe, Toshihiko Yamasaki · Apr 1, 2026 · Citations: 0
Rubric Rating Automatic Metrics
We introduce Paper Reconstruction Evaluation (PaperRecon), an evaluation framework in which an overview (overview.md) is created from an existing paper, after which an agent generates a full paper based on the overview and minimal…
- Rethinking Atomic Decomposition for LLM Judges: A Prompt-Controlled Study of Reference-Grounded QA Evaluation
Xinran Zhang · Mar 30, 2026 · Citations: 0
Rubric Rating Automatic Metrics
Atomic decomposition -- breaking a candidate answer into claims before verifying each against a reference -- is a widely adopted design for LLM-based reference-grounded judges.
- Stabilizing Rubric Integration Training via Decoupled Advantage Normalization
Zelin Tan, Zhouliang Yu, Bohan Lin, Zijie Geng, Hejia Geng · Mar 27, 2026 · Citations: 0
Rubric Rating Automatic Metrics
We propose Process-Aware Policy Optimization (PAPO), a method that integrates process-level evaluation into Group Relative Policy Optimization (GRPO) through decoupled advantage normalization, to address two limitations of existing reward…