Where vs What: Decomposing Structural and Content Failures in LLM-Generated Structured Outputs

arXiv:2608.25358 · cs.AI · Submitted 2026-08-26 · Read on arXiv

cs.AI

Submitted: 2026-08-26

Updated: 2026-08-26

Comments: 13 pages, 4 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Ablation — 27× in this paper · explained in 1 episode
• Accuracy — 23× in this paper · explained in 1 episode
• alpha — 11× in this paper · explained in 1 episode
• Verifiable Rewards — 10× in this paper · explained in 2 episodes
• Core — 9× in this paper · explained in 2 episodes
• Reinforcement Learning — 9× in this paper · explained in 4 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• GRPO — 8× in this paper · explained in 6 episodes
• Fidelity — 7× in this paper · explained in 2 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Structure-Content Decomposition — 5× in this paper
• Failure Modes — 3× in this paper · explained in 3 episodes
• Deep Reinforcement Learning — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• JSON Value Placement Accuracy — 2× in this paper
• Text-to-SQL — 2× in this paper · explained in 3 episodes
• Value Presence — 2× in this paper
• Abstraction — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Agentic Evaluation — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Online Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Schema Compliance Rate — 1× in this paper
• Structural Reasoning — 1× in this paper · explained in 2 episodes

Sources

• JSONSchemaBench: A Rigorous Benchmark of Structured Outputs for Language Models
• DeepSeek-V3 Technical Report
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• AgentBench: Evaluating LLMs as Agents
• Efficient Guided Generation for Large Language Models
• Qwen2.5 Technical Report
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

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