# 揭示自进化LLM智能体中的框架更新与框架增益能力

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-28 08:00
- AIHOT 分数：55
- AIHOT 链接：https://aihot.virxact.com/items/cmpvqe8rk00qksluk4mvqwe7p
- 原文链接：https://arxiv.org/abs/2605.30621

## AI 摘要

研究揭示了自进化LLM智能体中两种能力的独立表现。框架更新能力与模型基础能力无关，不同层级模型产生的框架更新所带来的增益相近，如Qwen3.5-9B的更新增益与Claude Opus~4.6相当。框架增益能力与基础能力呈非单调关系：弱模型难以从更新中受益，中等模型受益最大，强模型收益反而低于中等模型。弱模型的失败模式包括无法激活相关构件，或激活后未能遵循指令。研究建议将能力预算投入任务执行智能体而非更新器。

## 正文

LLM agents are increasingly deployed as systems built around editable external harnesses, including prompts, skills, memories and tools, that shape task execution without changing model parameters. Harness self-evolution adapts such agents by updating these harnesses from execution evidence. Yet it remains unclear whether a model's base capability in task-solving predicts its capabilities in harness self-evolution: which models produce useful harness updates, and which actually benefit from them? We analyze two harness self-evolution capabilities: (i) harness-updating, the capability to produce useful persistent harness updates from execution evidence; (ii) harness-benefit, the capability to benefit from updated harnesses during task solving. Our analysis reveals two findings. First, harness-updating is flat in base capability: models from different capability tiers produce harness updates that lead to surprisingly similar gains; even Qwen3.5-9B's updates yield gains comparable to those of Claude Opus~4.6. Second, harness-benefit is non-monotonic in base capability: weak-tier models benefit little from updated harnesses, mid-tier models benefit most, and strong-tier models benefit less than mid-tier. We trace low gains at the weak tier to two failure modes: weak-tier models may fail to activate relevant harness artifacts, or activate them but fail to follow them faithfully. These findings suggest investing capability budget in the task-solving agent rather than the evolver, and targeting harness invocation and long-horizon instruction following in agent training. Our source code is publicly available at https://github.com/A-EVO-Lab/a-evolve/tree/release/harness-evolution.