WikiTrace 证据链式记忆系统减轻大语言模型智能体记忆退化
Don't Just Trust Your Memory: Evidence-Linked Memory Reduces Memory-Induced Degradation in LLM Agents
"WikiTrace 通过将每个蒸馏经验与其来源轨迹和推理链关联，使大语言模型智能体在行动前验证和修正记忆，在五个智能体基准测试中提升性能并减少记忆导致的退化。该系统将每个经验作为精简洞察存储，并链接到其轨迹成功或失败的原因摘要及原始轨迹，智能体在执行新任务时可通过链接工具补充缺失细节或修正洞察，验证显示其优于现有记忆方法及仅使用相同洞察的基线。"
Abstract
WikiTrace links each distilled lesson to its source trajectory and rationale, enabling LLM agents to verify and correct memories before acting, improving performance and reducing memory-induced degradation across agentic benchmarks.
External memory enables LLM agents to improve from experience by distilling trajectories of past tasks into concise lessons for new tasks. However, memory can also lead to memory-induced degradation, where an agent fails on tasks it would solve without memory, often because it follows a lesson that is incorrect, or misapplies or misreads a valid one. This is because lessons are inferred from trajectories labeled only with task success or failure, yet are typically provided without the trajectories they came from, leaving the agent no way to check them. To address this, we introduce WikiTrace, an evidence-linked memory system designed to let agents check each lesson against its original trajectory. Specifically, motivated by the finding that a trajectory often still holds the evidence needed to catch an erroneous lesson, WikiTrace stores each lesson as a concise insight linked to a summary of why its trajectory succeeded or failed and to the raw trajectory itself. Then, on a new task, the agent starts from all insights and, whenever one lacks details needed to act, conflicts with another, or comes from a failed task, descends through its links with terminal tools to fill in missing details or correct the insight before acting. We validate WikiTrace on five agentic benchmarks, showing that it consistently outperforms existing memory approaches and a baseline given the same insights alone, while also reducing memory-induced degradation. Our code is available at https://github.com/yukyeongleee/wikitrace
View arXiv page View PDF Project page GitHub 4 Add to collection
Get this paper in your agent:
hf papers read 2611.14320
Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash
Models citing this paper 0
No model linking this paper
Cite arxiv.org/abs/2611.14320 in a model README.md to link it from this page.
Datasets citing this paper 0
No dataset linking this paper
Cite arxiv.org/abs/2611.14320 in a dataset README.md to link it from this page.
Spaces citing this paper 0
No Space linking this paper
Cite arxiv.org/abs/2611.14320 in a Space README.md to link it from this page.
Collections including this paper 1
来源: Hugging Face Daily Papers · huggingface.co