RAVEL: Reasoning Agents for Validating and Evaluating LLM Text Synthesis
Large Language Models have evolved from single-round generators into long-horizon agents, capable of complex text synthesis scenarios. However, current evaluation frameworks lack the ability to assess the actual synthesis operations, such as outlining, drafting, and editing. Consequently, they fail to evaluate the actual and detailed capabilities of LLMs. To bridge this gap, we introduce RAVEL, an agentic framework that enables the LLM testers to autonomously plan and execute typical synthesis operations, including outlining, drafting, reviewing, and refining. Complementing this framework, we present C3EBench, a comprehensive benchmark comprising 1,258 samples derived from professional hu...
| Authors | Andrew Zhuoer Feng, Cunxiang Wang, Yu Luo et al. |
|---|---|
| Primary category | cs.CL |
| Published | 28 February 2026 |
| Last updated at source | 28 February 2026 |
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