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版本更新 · v2arXiv:2610.26684

MuLaCover: Flexible Cover Song Generation via Symbolic Tonal Control

Lynn Yi, Zihan Xiong, Junjie Cao, Xiaolong Weng, Yingzhe Ma, Gus Xia, Jinliang Liu, Yuxin Xie 等 20 位作者

原文摘要

A cover song re-renders an existing piece, most commonly by preserving its tonal content -- melody and chord progression -- while reshaping other musical attributes. This suggests a natural formulation for generative cover synthesis with pretrained music language models: explicitly control tonal content, while specifying lyrics and style through text. Existing approaches condition on frame-level features, tightly coupling generation to the reference's timing and structure, and limiting flexibility such as tempo variation, structural rearrangement, or partial conditioning. To this end, we present MuLaCover, a cover generation model that conditions a pretrained text-to-song foundation model on an editable symbolic lead sheet. Our design enables alignment-free control: generated outputs preserve the tonal signature of the source without frame-level alignment, allowing flexible timing, structure, and segment-level generation. We further introduce a training curriculum with partially mismatched audio-symbolic pairs, improving diversity and robustness. We evaluate MuLaCover with objective metrics, standard subjective ratings, and in-depth expert interviews that provide fine-grained diagnostic insights. Experiments show that MuLaCover achieves state-of-the-art performance among open-source systems and is competitive with leading commercial models such as Suno-v5.5. Audio samples and code are available at our project page: https://mulalabs.ai/mulacover/

cs.SD · eess.AS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01982

SurgStream: A Workflow-Anchored Proactive Streaming Framework and Benchmark for Real-Time Surgical Video Understanding

Ruipeng Ma, Qing Xu, Jie Liang, Zhen Chen

原文摘要

Surgical video understanding is essential for intraoperative assistance such as workflow monitoring, instrument preparation and procedural documentation. Delivering such assistance in real time requires a model to jointly perceive the ongoing scene, remember past surgical events, and respond before clinically relevant moments pass. Existing approaches provide limited support for jointly retaining procedural evidence and deciding when to respond in streaming surgical video. In this work, we propose SurgStream, a workflow-anchored proactive streaming framework for real-time surgical video understanding. We first introduce Workflow-Anchored Memory (WAM) to record structured instrument-action-target observations and fold them into phase-level summaries, providing compact and queryable surgical memory. Deadline-aware Transition Learning (DTL) is then devised to supervise response states within clinical windows, enabling timely responses before procedural deadlines. We further introduce SurgStream-Bench, comprising 11.7K streaming interactions across various tasks covering real-time perception, backward memory queries, forward proactive QA, proactive alerts and caption memory. Experiments on SurgStream-Bench show gains over state-of-the-art methods, especially in memory and forward proactive QA. The source code will be available.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36703

RankFormer: Rethinking Time Series Transformers through Representation Capacity Analysis and Progressive Decoding

Peiwang Tang, Cheng HE, Xijie Liang, Defu Lian

原文摘要

Multivariate time series forecasting plays a critical role in Web and cyber-physical systems, where large-scale services and IoT infrastructures generate complex temporal data. Transformer-based models have achieved strong empirical performance in this domain, yet the principles governing the expressive capacity of their learned representations remain insufficiently understood. Moreover, many existing models rely on simplified decoding strategies, including channel-independent modeling and direct projection heads, which limit their ability to capture complex multivariate dynamics. To address these challenges, we propose RankFormer, a novel encoder-decoder architecture for multivariate time series forecasting. We establish a rank-based analysis framework to investigate how tokenization, embedding, and attention designs affect the representation capacity of time series Transformers. Our analysis reveals that patchification increases the potential representation capacity by aggregating multiple temporal observations into each token, while subsequent encoding operations determine how much of this capacity is realized. Guided by these insights, we develop a rank-enhancing encoder with Hourglass Attention to improve rank stability during information interaction. Furthermore, we introduce the Interactive Progressive Decoder (IPD), which improves future trajectory modeling through global-guided cross-channel interaction and progressive refinement. Extensive experiments on real-world long-term forecasting benchmarks demonstrate that RankFormer achieves competitive performance against state-of-the-art methods. Code repository: https://github.com/TangPeiwang/Rankformer.

cs.LG

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.493ACL Anthology · ACL 2026

Musical Score Understanding Benchmark: Evaluating Large Language Models’ Comprehension of Complete Musical Scores

Congren Dai, Yue Yang, Krinos Li, Huichi Zhou University College London, University of London, Shijie Liang, Bo Zhang, Enyang Liu, Ge Jin 等 15 位作者

原文摘要

Understanding complete musical scores entails integrated reasoning over pitch, rhythm, harmony, and large-scale structure, yet the ability of Large Language Models and Vision–Language Models to interpret full musical notation remains insufficiently examined.We introduce Musical Score Understanding Benchmark (MSU-Bench), a human-curated benchmark for score-level musical understanding across textual (ABC notation) and visual (PDF) modalities. MSU-Bench contains 1,800 generative question–answer pairs from works by Bach, Beethoven, Chopin, Debussy, and others, organised into four levels of increasing difficulty, ranging from onset information to texture and form. Evaluations of more than fifteen state-of-the-art models, in both zero-shot and fine-tuned settings, reveal pronounced modality gaps, unstable level-wise performance, and challenges in maintaining multilevel correctness. Fine-tuning substantially improves results across modalities while preserving general knowledge, positioning MSU-Bench as a robust foundation for future research in multimodal reasoning. The benchmark and code are available at https://github.com/Congren-Dai/MSU-Bench.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

版本更新 · v11arXiv:2502.12110Berkeley BAIR

A-MEM：面向LLM智能体的智能体式记忆

A-MEM: Agentic Memory for LLM Agents

Wujiang Xu, Zujie Liang, Kai Mei, Hang Gao, Juntao Tan, Yongfeng Zhang

AI 速读 基于摘要

提出一种借鉴Zettelkasten方法、由智能体动态组织和演化记忆的LLM智能体记忆系统。

cs.CL · cs.HC

arXiv ↗PDF ↗
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While large language model (LLM) agents can effectively use external tools for complex real-world tasks, they require memory systems to leverage historical experiences. Current memory systems enable basic storage and retrieval but lack sophisticated memory organization, despite recent attempts to incorporate graph databases. Moreover, these systems' fixed operations and structures limit their adaptability across diverse tasks. To address this limitation, this paper proposes a novel agentic memory system for LLM agents that can dynamically organize memories in an agentic way. Following the basic principles of the Zettelkasten method, we designed our memory system to create interconnected knowledge networks through dynamic indexing and linking. When a new memory is added, we generate a comprehensive note containing multiple structured attributes, including contextual descriptions, keywords, and tags. The system then analyzes historical memories to identify relevant connections, establishing links where meaningful similarities exist. Additionally, this process enables memory evolution - as new memories are integrated, they can trigger updates to the contextual representations and attributes of existing historical memories, allowing the memory network to continuously refine its understanding. Our approach combines the structured organization principles of Zettelkasten with the flexibility of agent-driven decision making, allowing for more adaptive and context-aware memory management. Empirical experiments on six foundation models show superior improvement against existing SOTA baselines. The source code for evaluating performance is available at https://github.com/WujiangXu/A-mem, while the source code of the agentic memory system is available at https://github.com/WujiangXu/A-mem-sys.