# Evo-Bench：语言模型能否自主改进智能体运行框架？

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-10 08:00
- AIHOT 分数：48
- AIHOT 链接：https://aihot.virxact.com/items/cmso4rcdl0aokrofws8xmwo85
- 原文链接：https://arxiv.org/abs/2608.09096

## AI 摘要

Evo-Bench 发布，成为首个专门评估模型自主进化其运行框架（harness）能力的基准，覆盖搜索、办公和通用智能体三大领域。对九个前沿及开源权重模型的评估显示，顶尖模型在该基准上取得高达 16.6 分的绝对提升，接近人类工程化基线水平。自主进化在通用与搜索任务上表现优于人工框架，但在需要特定处理流程的办公任务上表现不佳。

## 正文

Large Language Models (LLMs) have driven rapid progress in autonomous agents, yet standard evaluations remain confined to static task solving. An emerging frontier is harness evolution---the agent's capacity to autonomously optimize its own operating harness. However, systematically benchmarking this capability remains challenging, as existing evaluations fail to isolate harness improvements from base model strength, prevent task-specific overfitting, or capture long-horizon iterative research. To address these challenges, we introduce Evo-Bench, the first benchmark designed to evaluate models' intrinsic harness-evolving capabilities across Search, Office, and General agent domains. To rigorously isolate this capability, Evo-Bench employs a novel harness-guided construction framework: it leverages auxiliary-task evolution to identify tasks genuinely sensitive to framework improvements, followed by sensitivity-aware stratified splitting to ensure robust cross-suite generalization. Extensive evaluations across nine frontier and open-weight models reveal that top models achieve massive absolute gains reaching 16.6 points, closely approaching state-of-the-art human-engineered baselines. Crucially, while autonomous evolution outpeforms artificial harness in General tasks and excels in Search tasks, it struggles in Office tasks that demand highly specific processing workflows. Furthermore, our analysis exposes critical temporal anomalies like early saturation, while demonstrating that the synthesized harnesses act as highly transferable reasoning structures, consistently boosting diverse policy models.