Meta 发布 ProPerAgent harness，主动智能体准确率从 15% 提至 38%
Meta 的 ProPerAgent 在不训模型的情况下把主动智能体准确率翻倍还多，关键是在记忆传给模型前先过滤一次，值得看看细节。
Meta 推出 ProPerAgent harness，把主动智能体在标准记忆基线下最高 15% 的准确率提升到 38%，全程不改动模型权重。其做法是先用模型识别视野中的物体，再用检索模块在过往经历、偏好与约束构成的图里搜索相关记忆，随后由一次独立的 LLM 调用判断这些记忆是否值得此刻介入。消融实验显示去掉该过滤步骤会让准确率从 37.3% 降到 24.0%，在需要保持沉默的片段上，不必要干预降到 15.3%，而标准 RAG 高达 77.3% 或 84.0%。用 Qwen3.8-27B 时 harness 无需训练即达 37.5%，再做 RL 训练（惩罚过早与重复介入）后准确率升至 42.4%。
Recommended work from Meta on harnesses for proactive agents.
The ProPerAgent harness raises accuracy from at most 15% with standard memory baselines to 38%, without changing the model's weights.
But this success requires both a useful response and correct timing.
The harness controls how personal memories reach the model as it observes the user's surroundings.
First, the model identifies objects in view. A retrieval module uses those objects to search a graph of past experiences, preferences, and constraints.
A separate LLM call then checks whether the retrieved memories justify offering help now. It filters out distracting or already resolved information and returns one concise piece of evidence, or nothing. The model uses that evidence and the current view to decide whether to speak or stay silent.
That filtering step matters. Removing it drops accuracy from 37.3% to 24.0% in an ablation. Across the full harness, unnecessary interventions on clips that require silence fall to 15.3%, compared with 77.3% or 84.0% for standard RAG in the two evaluation modes.
The harness also reaches 37.5% with Qwen3.8-27B without training. Additional RL training on that model raises accuracy to 42.4% by penalizing premature and repeated interventions.
For proactive assistants, the harness needs to check whether a memory warrants an interruption before passing it to the model.
Paper: https://t.co/fLvcHtmWpK
Chat with Paper: https://t.co/mexKsft27L
