- 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 the Resumé: A Rubric-Aware Automatic Interview System for Information Elicitation
Harry Stuart, Masahiro Kaneko, Timothy Baldwin · Mar 2, 2026 · Citations: 0
Rubric Rating Automatic Metrics
Effective hiring is integral to the success of an organisation, but it is very challenging to find the most suitable candidates because expert evaluation (e.g.\ interviews conducted by a technical manager) are expensive to deploy at scale.
- 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.
- Automated Coding of Communication Data Using ChatGPT: Consistency Across Subgroups
Jiangang Hao, Wenju Cui, Patrick Kyllonen, Emily Kerzabi · Oct 23, 2025 · Citations: 0
Rubric Rating Human EvalAutomatic Metrics
Prior research has established that ChatGPT can be directly instructed with coding rubrics to code the communication data and achieves accuracy comparable to human raters.
- KLong: Training LLM Agent for Extremely Long-horizon Tasks
Yue Liu, Zhiyuan Hu, Flood Sung, Jiaheng Zhang, Bryan Hooi · Feb 19, 2026 · Citations: 0
Rubric Rating Long Horizon
Then, we introduce Research-Factory, an automated pipeline that generates high-quality training data by collecting research papers and constructing evaluation rubrics.
- Cross-Cultural Expert-Level Art Critique Evaluation with Vision-Language Models
Haorui Yu, Xuehang Wen, Fengrui Zhang, Qiufeng Yi · Jan 12, 2026 · Citations: 0
Rubric RatingCritique Edit
Existing benchmarks assess perception without interpretation, and common evaluation proxies, such as automated metrics and LLM-judge averaging, are unreliable for culturally sensitive generative tasks.
- Small Reward Models via Backward Inference
Yike Wang, Faeze Brahman, Shangbin Feng, Teng Xiao, Hannaneh Hajishirzi · Feb 14, 2026 · Citations: 0
Rubric Rating Llm As Judge
However, the dominant LLM-as-a-Judge paradigm relies on the strong reasoning capabilities of large models, while alternative approaches require reference responses or explicit rubrics, limiting flexibility and broader accessibility.
- ScholarEval: Research Idea Evaluation Grounded in Literature
Hanane Nour Moussa, Patrick Queiroz Da Silva, Daniel Adu-Ampratwum, Alyson East, Zitong Lu · Oct 17, 2025 · Citations: 0
Rubric Rating
As AI tools become increasingly common for research ideation, robust evaluation is critical to ensure the validity and usefulness of generated ideas.
- Ice Cream Doesn't Cause Drowning: Benchmarking LLMs Against Statistical Pitfalls in Causal Inference
Jin Du, Li Chen, Xun Xian, An Luo, Fangqiao Tian · May 19, 2025 · Citations: 0
Rubric Rating
Current benchmarks usually involve simplified tasks.
- Toward LLM-Supported Automated Assessment of Critical Thinking Subskills
Marisa C. Peczuh, Nischal Ashok Kumar, Ryan Baker, Blair Lehman, Danielle Eisenberg · Oct 14, 2025 · Citations: 0
Rubric Rating
As the world becomes increasingly saturated with AI-generated content, disinformation, and algorithmic persuasion, critical thinking - the capacity to evaluate evidence, detect unreliable claims, and exercise independent judgment - is…
- RM-R1: Reward Modeling as Reasoning
Xiusi Chen, Gaotang Li, Ziqi Wang, Bowen Jin, Cheng Qian · May 5, 2025 · Citations: 0
Pairwise PreferenceRubric Rating
Reward modeling is essential for aligning large language models with human preferences through reinforcement learning.
