AI 速览：提出一种支持高效构建与更新的动态帕累托前沿索引 ACTIVE，用于双向量近似最近邻查询。
Existing approximate nearest neighbor search (ANNS) methods generally assume that each entity has a single vector. In many real-world applications, however, an entity is associated with more than one vector, and queries may jointly consider two attributes. With different queries assigning different weights to each attribute, Pareto-frontier-based indices provide an effective solution for dual-vector retrieval by connecting to approximate nearest neighbors under all possible query preferences. However, existing Pareto-frontier-based approaches suffer from substantial inefficiencies in both index construction and dynamic updates, limiting their practical applicability. To address these issues, we propose ACTIVE, a dynamic Pareto-frontier-based DVQ index that supports efficient construction and updates while maintaining high retrieval performance under varying query preferences. It employs angle-constrained active-range computation and connectivity-loss-driven adaptive repair strategies to accelerate node insertion and deletion, respectively. Extensive experiments show that, at comparable (or slightly below) search accuracy, ACTIVE improves insertion throughput by $1.20$--$1.43\times$ over DEG, and deletion throughput by $1.64$--$2.06\times$ over neighbor-reconnection-based repair and by $22.64$--$39.51\times$ over reinsertion-based repair.
Le Zhou, Feng Yao National University of Defense Technology, Fengcai Qiao, Bo Xu, Fangyuan Wang Institute of automation, Chinese academy of science, Chinese Academy of Sciences, Boyan Xu
Recent advances in Large Reasoning Models (LRMs) trained with Long Chain-of-Thought have demonstrated remarkable capabilities in code generation and mathematical reasoning. However, their potential in multi-turn Text-to-SQL tasks remains largely underexplored. Existing approaches typically rely on unstable API-based inference or require expensive fine-tuning on small-scale models. In this work, we present Rose-SQL, a training-free framework that leverages small-scale LRMs through in-context learning to enable accurate context-dependent parsing. We introduce the Role-State, a fine-grained representation that bridges the structural gap between schema linking and SQL generation by serving as a structural blueprint. To handle conversational dependencies, Rose-SQL traces the evolution of Role-State through historical context via structural isomorphism checks, guiding the model to infer the possible SQL composition for the current question through verified interaction trajectories. Experiments on the SParC and CoSQL benchmarks show that, within the Qwen3 series, Rose-SQL outperforms in-context learning baselines at the 4B scale and substantially surpasses state-of-the-art fine-tuned models at the 8B and 14B scales, while showing consistent gains on additional reasoning backbones.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Tianhong Gao, Jundong Shen ByteDance Inc., Jiapeng Wang, Bei Shi ByteDance Inc., Ying Ju, Junfeng Yao, Huiyu Yu
Existing benchmarks and training pipelines for industrial intelligent customer service (ICS) remain misaligned with real-world dialogue requirements, overemphasizing verifiable task success while under-measuring subjective service quality and realistic failure modes, leaving a gap between offline gains and deployable dialogue behavior. We close this gap with a benchmark-to-optimization loop: we first introduce OlaBench, an ICS benchmark spanning retrieval-augmented generation, workflow-based systems, and agentic settings, which evaluates service capability, safety, and latency sensitivity; moreover, motivated by OlaBench results showing state-of-the-art LLMs still fall short, we propose OlaMind, which distills reusable reasoning patterns and service strategies from expert dialogues and applies staged exploration–exploitation reinforcement learning with instance-level rubric-aware guidance to improve model capability. OlaMind surpasses GPT-5.2 and Gemini 3 Pro on OlaBench (83.64 vs. 70.58/70.84) and, in online A/B tests, delivers an average +23.67% issue resolution and -6.6% human transfer rate versus the baseline, bridging offline gains to deployment. Together, OlaBench and OlaMind advance ICS systems toward more anthropomorphic, professional, and reliable deployment. The project page and evaluation are available at https://olamind-olabench.github.io.
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cs.CL · cs.AI
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表