RecurSE: Bounded Recursive Self-Evaluation for LLM Rubric Judges
cs.CL
Submitted: 2026-08-25
Updated: 2026-08-25
Terminology
Sources
- HealthBench: Evaluating Large Language Models Towards Improved Human Health
- Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops
- On the Expressive Power of Tree-Structured Probabilistic Circuits
- EvoLM: Self-Evolving Language Models through Co-Evolved Discriminative Rubrics
- GPQA: A Graduate-Level Google-Proof Q&A Benchmark
- Reinforcement Learning from Meta-Evaluation: Aligning Language Models Without Ground-Truth Labels
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- RLSR: Reinforcement Learning from Self Reward
- Gemma 4 Technical Report
- Co-Evolving LLM Evaluators and Policies via DynamicRubric
- Self-Taught Evaluators
- Grad2Reward: From Sparse Judgment to Dense Rewards for Improving Open-Ended LLM Reasoning
- More Convincing, Not More Correct: Self-Play Reward Hacking of Reference-Free LLM Judges
Related papers
- Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
- Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
- Subliminal Steering: Stronger Encoding of Hidden Signals
- MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
- The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
- Untangling the Mechanisms of Misleading Context in Medical Question Answering