AssoMem: Scalable Memory QA with Multi-Signal Associative Retrieval
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914089443262464 |
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| author | Zhang, Kai Zhang, Xinyuan Ahmed, Ejaz Jiang, Hongda Kumar, Caleb Sun, Kai Lin, Zhaojiang Sharma, Sanat Oraby, Shereen Colak, Aaron Aly, Ahmed Kumar, Anuj Liu, Xiaozhong Dong, Xin Luna |
| author_facet | Zhang, Kai Zhang, Xinyuan Ahmed, Ejaz Jiang, Hongda Kumar, Caleb Sun, Kai Lin, Zhaojiang Sharma, Sanat Oraby, Shereen Colak, Aaron Aly, Ahmed Kumar, Anuj Liu, Xiaozhong Dong, Xin Luna |
| contents | Accurate recall from large scale memories remains a core challenge for memory augmented AI assistants performing question answering (QA), especially in similarity dense scenarios where existing methods mainly rely on semantic distance to the query for retrieval. Inspired by how humans link information associatively, we propose AssoMem, a novel framework constructing an associative memory graph that anchors dialogue utterances to automatically extracted clues. This structure provides a rich organizational view of the conversational context and facilitates importance aware ranking. Further, AssoMem integrates multi-dimensional retrieval signals-relevance, importance, and temporal alignment using an adaptive mutual information (MI) driven fusion strategy. Extensive experiments across three benchmarks and a newly introduced dataset, MeetingQA, demonstrate that AssoMem consistently outperforms SOTA baselines, verifying its superiority in context-aware memory recall. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10397 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AssoMem: Scalable Memory QA with Multi-Signal Associative Retrieval Zhang, Kai Zhang, Xinyuan Ahmed, Ejaz Jiang, Hongda Kumar, Caleb Sun, Kai Lin, Zhaojiang Sharma, Sanat Oraby, Shereen Colak, Aaron Aly, Ahmed Kumar, Anuj Liu, Xiaozhong Dong, Xin Luna Computation and Language Accurate recall from large scale memories remains a core challenge for memory augmented AI assistants performing question answering (QA), especially in similarity dense scenarios where existing methods mainly rely on semantic distance to the query for retrieval. Inspired by how humans link information associatively, we propose AssoMem, a novel framework constructing an associative memory graph that anchors dialogue utterances to automatically extracted clues. This structure provides a rich organizational view of the conversational context and facilitates importance aware ranking. Further, AssoMem integrates multi-dimensional retrieval signals-relevance, importance, and temporal alignment using an adaptive mutual information (MI) driven fusion strategy. Extensive experiments across three benchmarks and a newly introduced dataset, MeetingQA, demonstrate that AssoMem consistently outperforms SOTA baselines, verifying its superiority in context-aware memory recall. |
| title | AssoMem: Scalable Memory QA with Multi-Signal Associative Retrieval |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2510.10397 |