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    <abstract>Agentic systems accumulate persistent memory across sessions, tools, and tasks, and a later request must retrieve from it under two distinct constraints: which memories it is permitted to access, and which are relevant under a limited search budget. Existing memory systems handle access scope in two flawed ways: applying scope after retrieval wastes probe budget on inadmissible memories, while treating scope as a learned ranking feature makes a hard constraint depend on router quality. We present ShardMemo, an agentic memory system built on scope-before-routing: metadata predicates first identify the admissible shards, and a learned router then selects a small number of them for shard-local approximate nearest neighbor retrieval. This separates hard admissibility from learned relevance ranking, so inadmissible shards cannot consume shard-probe budget. We evaluate on LoCoMo, HotpotQA, and ToolBench, covering conversational, long-context, and procedural memory. Under matched supervision and fixed budgets, ShardMemo improves over a learned router baseline by roughly +3 F1 on LoCoMo; in end-to-end LoCoMo evaluation it improves over the strongest evaluated memory baseline by up to +6.8 F1, with gains on HotpotQA and ToolBench.</abstract>
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%0 Conference Proceedings
%T ShardMemo: Scope-Before-Routing for Agentic Memory Retrieval
%A Zhao, Yang
%A Dai, Chengxiao
%A Kou, Mengying
%A Xiu, Yue
%A Niyato, Dusit
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhao-etal-2026-shardmemo
%X Agentic systems accumulate persistent memory across sessions, tools, and tasks, and a later request must retrieve from it under two distinct constraints: which memories it is permitted to access, and which are relevant under a limited search budget. Existing memory systems handle access scope in two flawed ways: applying scope after retrieval wastes probe budget on inadmissible memories, while treating scope as a learned ranking feature makes a hard constraint depend on router quality. We present ShardMemo, an agentic memory system built on scope-before-routing: metadata predicates first identify the admissible shards, and a learned router then selects a small number of them for shard-local approximate nearest neighbor retrieval. This separates hard admissibility from learned relevance ranking, so inadmissible shards cannot consume shard-probe budget. We evaluate on LoCoMo, HotpotQA, and ToolBench, covering conversational, long-context, and procedural memory. Under matched supervision and fixed budgets, ShardMemo improves over a learned router baseline by roughly +3 F1 on LoCoMo; in end-to-end LoCoMo evaluation it improves over the strongest evaluated memory baseline by up to +6.8 F1, with gains on HotpotQA and ToolBench.
%U https://aclanthology.org/2026.findings-emnlp.1806/
%P 38596-38610

Markdown (Informal)

[ShardMemo: Scope-Before-Routing for Agentic Memory Retrieval](https://aclanthology.org/2026.findings-emnlp.1806/) (Zhao et al., Findings 2026)

• ShardMemo: Scope-Before-Routing for Agentic Memory Retrieval (Zhao et al., Findings 2026)

ACL

• Yang Zhao, Chengxiao Dai, Mengying Kou, Yue Xiu, and Dusit Niyato. 2026. ShardMemo: Scope-Before-Routing for Agentic Memory Retrieval. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 38596–38610, Budapest, Hungary. Association for Computational Linguistics.