发表机构
Shenzhen University; Guangzhou University; The Chinese University of Hong Kong（深圳大学; 广州大学; 香港中文大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SemDHT提出一种基于两层语义草图和认证发布键的索引，在精确键DHT上实现高效语义发现，显著减少查找次数和发布开销，并提升查询速度。
AI 中文摘要
智能体可能需要通过外部智能体端点或服务API暴露的能力。当请求者尚未绑定到提供者时，它必须发现与其任务和接口要求匹配的已发布能力。在精确键分布式哈希表（DHT）上，广泛检索会传输大量候选列表，而选择性检索可能遗漏相关提供者或需要更多复制和查找。开放式发布还允许提供者夸大其暴露范围，除非发布边界可强制执行。我们提出SemDHT，一种在精确键DHT上发现智能体可访问能力的认证语义索引。两层语义草图使用粗粒度单元对邻近描述符进行分组，并使用残差码缩小候选选择范围。提供者在有界的派生键集合上发布，而请求者在查找预算内先探测精确键，再探测更广泛的召回键。锚定委员会认证每个描述符的发布键集合，使存储服务和请求者能够强制执行描述符到键的一致性。在真实API描述符和任务查询上，SemDHT相对于精确嵌入空间邻居实现了0.955的recall@10，相对于ToolBench相关性标签实现了0.947的recall@10。在具有受控密度增强的语料库上，它在召回率0.95时匹配了在DHT上调整过的局部敏感哈希（LSH）的候选暴露，同时查找次数减少7.7倍，并将发布扇出从16减少到10。部署在相同区域和跨区域200节点云覆盖网络上的Go/libp2p原型重放了299个互联网查询。在并行探测和冷证书缓存条件下，SemDHT相对于LSH分别实现了3.64倍和4.11倍的平均完成时间加速。
英文摘要
Agents may need capabilities exposed through external agent endpoints or service APIs. When a requester is not already bound to a provider, it must discover advertised capabilities matching its task and interface requirements. Over exact-key distributed hash tables (DHTs), broad retrieval transfers large candidate lists, whereas selective retrieval may miss relevant providers or require more replication and lookups. Open publication also lets providers inflate their exposure unless publication bounds are enforceable. We present SemDHT, a certified semantic index for discovering agent-accessible capabilities over exact-key DHTs. A two-layer semantic sketch uses coarse cells to group nearby descriptors and residual codes to narrow candidate selection. Providers publish at a bounded set of derived keys, while requesters probe precision keys before broader recall keys within a lookup budget. Anchor committees certify each descriptor's publication-key set, enabling storage services and requesters to enforce descriptor-to-key consistency. On real API descriptors and task queries, SemDHT achieves recall@10 of 0.955 against exact embedding-space neighbors and 0.947 against ToolBench relevance labels. On a corpus with controlled density augmentation, it matches the candidate exposure of tuned locality-sensitive hashing (LSH) over a DHT at recall 0.95 with 7.7x fewer lookups and reduces publication fan-out from 16 to 10. A Go/libp2p prototype deployed on same-region and cross-region 200-peer cloud overlays replays 299 Internet queries. With parallel probes and cold certificate caches, SemDHT achieves mean completion-time speedups of 3.64x and 4.11x over LSH, respectively.
Comments29 pages, 10 figures, 13 tables
