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新论文 · v1arXiv:2611.02081

Understanding the relationship between retrospective soundscape attributes and continuous pleasantness and eventfulness ratings

Yuanbo Hou, Kening Guo, Francesco Aletta, Woon-Seng Gan, Dick Botteldooren, Jian Kang

原文摘要

Soundscape studies ask listeners to rate perceived affective quality (PAQ) attributes, such as pleasant and vibrant, after listening to recordings or experiencing acoustic environments. These retrospective ratings summarise an experience during which impressions may change. International Organization for Standardization (ISO) procedures convert these PAQ ratings into Pleasantness and Eventfulness scores. Pleasantness and Eventfulness (P/E) can also be rated directly and continuously during listening, capturing changes in reported perception over time. Retrospective PAQ ratings and continuous direct P/E describe the same soundscape experience from different perspectives. We examine how PAQs relate to direct P/E and how ISO-derived P/E corresponds to direct judgements, using 1,782 paired 30-s trials involving audiovisual recordings from 18 streets. The results show that PAQs retain a predominantly two-dimensional structure. Vibrant and Monotonous lie closer to the Eventfulness-Uneventfulness axis in the direct P/E plane than in the ISO 2025 reference geometry. The full 30-s direct P/E mean has lower calibrated error against ISO-derived P/E than three 10-s means. An empirical mapping using the same 8 PAQs reduces held-out joint root-mean-square error by 4.9% relative to affine-calibrated ISO 2025 scoring. The findings show that retrospective and continuous assessments offer related but distinct soundscape descriptions.

eess.AS · cs.SD

新论文 · v1arXiv:2610.36444

RouteDRAG: Learning to Route and Control Agentic Execution for Multimodal Document RAG

Zekai Chen, Hao Zhu, Yu Liu, Xunkai Li, Zejian Kang, Rui Qian, Quanjiang Li, Rong-Hua Li

原文摘要

Web-based applications increasingly rely on understanding long multimodal documents, where answering questions often requires combining information across pages, tables, and figures. Agentic retrieval-augmented generation addresses these tasks through iterative retrieval and reasoning. However, Agentic execution is not always necessary, introducing additional computation and potentially incorrect answers. Our empirical analysis reveals that Agentic execution provides query-dependent benefits, while additional operations may improve, preserve, or degrade answer accuracy. These findings motivate controlling both Agent invocation and subsequent execution. To address these challenges, we propose RouteDRAG, a learning-based framework comprising four components. Unified Document Index organizes multimodal content into a graph-backed index and derives query representations from initial retrieval. Adaptive Path Routing preserves exact answering and predicts the relative benefit of Agent over shorter execution paths. Dynamic Agent Execution maintains an evolving workspace and refines Planner proposals to control tool use and termination. Sequential Policy Learning first trains operation control from observed outcomes and then trains path routing using the resulting Agent's performance. Across two benchmarks, RouteDRAG achieves an average accuracy improvement of 3.47 percentage points over shorter-path-only execution while assigning only 79.3% of queries to Agent. Notably, it also surpasses uniform Agent execution on both benchmarks while requiring fewer answering calls. Our code is now available at https://github.com/Ereshkigal1/RouteDRAG.

cs.AI