Reflection-Based Memory For Web navigation Agents
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909634670886912 |
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| author | Azam, Ruhana Vempaty, Aditya Jagmohan, Ashish |
| author_facet | Azam, Ruhana Vempaty, Aditya Jagmohan, Ashish |
| contents | Web navigation agents have made significant progress, yet current systems operate with no memory of past experiences -- leading to repeated mistakes and an inability to learn from previous interactions. We introduce Reflection-Augment Planning (ReAP), a web navigation system to leverage both successful and failed past experiences using self-reflections. Our method improves baseline results by 11 points overall and 29 points on previously failed tasks. These findings demonstrate that reflections can transfer to different web navigation tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02158 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reflection-Based Memory For Web navigation Agents Azam, Ruhana Vempaty, Aditya Jagmohan, Ashish Artificial Intelligence Web navigation agents have made significant progress, yet current systems operate with no memory of past experiences -- leading to repeated mistakes and an inability to learn from previous interactions. We introduce Reflection-Augment Planning (ReAP), a web navigation system to leverage both successful and failed past experiences using self-reflections. Our method improves baseline results by 11 points overall and 29 points on previously failed tasks. These findings demonstrate that reflections can transfer to different web navigation tasks. |
| title | Reflection-Based Memory For Web navigation Agents |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2506.02158 |