让智能体记忆在测试时更安全地进化，避免越用越不靠谱。
TAME: A Trustworthy Test-Time Evolution of Agent Memory with Systematic Benchmarking
- 用执行器和评估器双角色协同管理记忆，动态判断经验可信度。
- 在GPT-5.2 AIME上准确率提升14.6个百分点，同时保持高可信度。
- 首次系统评测记忆误演化问题，适合关注AGI安全的开发者。
测试时记忆演化是推动通用人工智能发展的关键范式，通过积累经验增强复杂推理能力，无需更新模型参数。然而，即使在良性任务演化过程中，智能体的安全对齐仍易受损，这种现象称为记忆误演化。为此，我们构建了Trust-Memevo基准，发现智能体在多任务演化中整体可信度下降。为解决此问题，提出TAME框架，由执行器与评估器共同管理共享记忆库：执行器调用可迁移经验辅助任务求解，评估器则评判每条经验对结果的贡献，并生成可信度反馈以指导后续使用。该执行-评估循环实现记忆的有选择性强化、谨慎复用与持续扩展。实验表明，TAME有效缓解记忆误演化，同时取得优异任务性能。尤其在GPT-5.2 AIME基准上，相比最强现有方法，准确率提升14.6个百分点，并维持良好可信度。
原文摘要 · Abstract (English)
Test-time evolution of agent memory represents a pivotal paradigm for advancing AGI, as it strengthens complex reasoning through experience accumulation without requiring parameter updates. However, even during benign task evolution, agent safety alignment remains vulnerable, a phenomenon known as Agent Memory Misevolution. To evaluate this phenomenon, we construct the Trust-Memevo benchmark and find that agents exhibit an overall decline in trustworthiness across multiple tasks during benign task evolution. To address this issue, we propose TAME, a trust-aware memory evolution framework in which a shared memory bank is jointly governed by an Executor and an Evaluator. The Executor retrieves and applies transferable experiences to support task solving, while the Evaluator assesses the contribution of each utilized experience to the outcome and produces trust-aware feedback to guide subsequent memory use. This executor-evaluator loop enables memory to be selectively reinforced, cautiously reused, and continuously expanded over time. Experiments show that TAME mitigates memory misevolution while achieving strong task performance. In particular, on the GPT-5.2 AIME benchmark, TAME improves accuracy by 14.6 percentage points over the strongest existing method and maintains competitive trustworthiness.
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