DoGBench: Can Agents Meet Expert Standards for User-Facing Documentation?
cs.SE, cs.AI
Submitted: 2026-09-30
Updated: 2026-09-30
Sources
- Process Reward Models for LLM Agents: Practical Framework and Directions
- CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
- Failing to Falsify: Evaluating and Mitigating Confirmation Bias in Language Models
- Let's Verify Step by Step
- CaRT: Teaching LLM Agents to Know When They Know Enough
- Deep Research as Rubric for Reinforcement Learning
- CoDocBench: A Dataset for Code-Documentation Alignment in Software Maintenance
- Do LLM Agents Know How to Ground, Recover, and Assess? A Benchmark for Epistemic Competence in Information-Seeking Agents
- Evaluating Repository-level Software Documentation via Question Answering and Feature-Driven Development
- RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning
- Look Before You Leap: Autonomous Exploration for LLM Agents
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