Who Verifies the Verifier? Co-Evolving Inspectable Graders with Self-Improving Agents

arXiv:2610.11464 · cs.AI, cs.CL, cs.LG · Submitted 2026-10-08 · Read on arXiv

cs.AI, cs.CL, cs.LG

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Verifier — 133× in this paper · explained in 2 episodes
• Ablation — 10× in this paper · explained in 1 episode
• LLM judge — 7× in this paper · explained in 2 episodes
• Grounding — 6× in this paper · explained in 3 episodes
• Text-to-SQL — 6× in this paper · explained in 3 episodes
• In International Conference on Learning Representations — 4× in this paper
• Benchmark — 3× in this paper · explained in 2 episodes
• International Conference on Machine Learning — 3× in this paper
• Failure Modes — 2× in this paper · explained in 3 episodes
• LLM-as-a-Judge — 2× in this paper · explained in 3 episodes
• Pruning — 2× in this paper · explained in 4 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Self-Evolving Agents — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Agent-as-Judge — 1× in this paper · explained in 1 episode
• Agentic AI — 1× in this paper · explained in 1 episode
• Agentic System — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Failure Taxonomy — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Hallucination Detection — 1× in this paper · explained in 2 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reward Hacking — 1× in this paper · explained in 8 episodes
• Reward Shaping — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Scaffolding — 1× in this paper · explained in 3 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes

Sources

• Ratchet: How Reliable Must an LLM Judge Be to Retire a Skill?
• Agent Workflow Memory
• EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle
• AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolution
• SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning
• Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills
• From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution
• Experience Compression Spectrum: Unifying Memory, Skills, and Rules in LLM Agents
• SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks
• A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence
• TextGrad: Automatic "Differentiation" via Text
• AlphaEvolve: A coding agent for scientific and algorithmic discovery
• Concrete Problems in AI Safety
• Constitutional AI: Harmlessness from AI Feedback
• A Survey on LLM-as-a-Judge
• Agent-as-a-Judge: Evaluate Agents with Agents
• Hallucination Detection for LLM-based Text-to-SQL Generation via Two-Stage Metamorphic Testing
• ReFoRCE: A Text-to-SQL Agent with Self-Refinement, Consensus Enforcement, and Column Exploration
• Program Synthesis with Large Language Models
• Evaluating Large Language Models Trained on Code

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