arXiv：cs.AI· Qi Feng, Jicong Fan·· 16 小时前
MemPursuit：让长期记忆智能体从"相关"走向"证据充分"
Recall What Is Missing: Composing Evidence from Long-Term Agent Memory
AI 导读
MemPursuit 是一种免训练机制，将长期记忆的读取阶段重构为查询条件下的证据组合，用稀疏凸查询重建的残差作为当前记忆集缺什么的表征代理，无需调用语言模型或 cross-encoder 即可逐层组合证据上下文。
正文
Abstract:Under a limited context budget, a long-term agent must recall not merely memories that are individually relevant, but evidence that is collectively sufficient for the current query. Conventional memory access retrieves, scores, and truncates memories, often spending multiple context slots on redundant facts while leaving complementary evidence behind. We cast the read phase of long-term memory as query-conditioned evidence composition: each memory is valued by what it adds beyond the evidence already selected. We instantiate this operation with MemPursuit, a training-free mechanism that uses the residual of a sparse convex query reconstruction as a representation-space proxy for what the current memory set is missing. By reusing the query and candidate embeddings already available from retrieval, MemPursuit progressively composes nested evidence contexts without invoking a language model or cross-encoder to score each query--memory pair. The effectiveness of our method is verified on 3 popular benchmarks in comparison to strong baselines. In fixed-budget LoCoMo experiments, MemPursuit improves end-to-end accuracy by 7.2 percentage points at Top-10 and 5.5 percentage points at Top-20, with consistent gains across multiple memory frameworks, while achieving a 32.9$\times$ speedup in median reranking latency over fully batched BGE-v2-m3. Our implementation is available at this https URL.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.15642 [cs.AI] |
| (or arXiv:2610.15642v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.15642 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Qi Feng [view email]
[v1]
Sun, 11 Oct 2026 14:18:11 UTC (4,566 KB)
来源：arXiv：cs.AI · arxiv.org