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新论文 · v1arXiv:2611.14042

Resilient Integrated Access and Backhaul Network Planning via Graph Neural Network Curriculum Learning

Jie Zhang, Haochen Sun, Mingzhang Zhou, Paul D. Mitchell, Hamed Ahmadi, Qiang Ni

原文摘要

Integrated Access and Backhaul (IAB) is a key enabler for cost-effective millimeter-wave (mmWave) densification, yet practical deployments are strongly constrained by the spatial availability of fiber-connected IAB donors. Variations in donor topologies reshape both the initial coverage footprint and the feasible multi-hop backhaul expansion patterns, causing planning policies trained on a single topology to generalize poorly. This paper studies cross-topology generalization for resilience-aware IAB deployment under coverage and transport feasibility requirements. We formulate IAB deployment as a constrained Markov decision process that couples access coverage satisfaction with multi-hop transport feasibility, including hop-by-hop rate-budget degradation and a minimum parent-connectivity requirement for fault tolerance. To solve the resulting large discrete planning problem, we propose a graph-based actor-critic approach that uses a Graph Attention Network v2 (GATv2) encoder with Proximal Policy Optimization (PPO), and we develop a multi-stage curriculum learning strategy that progressively increases constraint difficulty and scenario diversity for stable training and transferable policies. In a 60 GHz urban simulation over a 1000 m x 1000 m area with multiple donor layouts, the trained policy satisfies the minimum coverage requirement of 98% with fewer deployed IAB nodes than learning and heuristic baselines, and maintains higher coverage under progressive backhaul failures. The results demonstrate that curriculum-trained graph-based reinforcement learning can improve deployment efficiency and resilience while transferring across heterogeneous IAB donor topologies without retraining.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11823

AscSpMM: Efficient SpMM on Ascend NPUs via Pipelined Vector-Cube Collaboration

Fei Li, Haoxin Li, Kehong Liu, Yanzi Zhou, Weiguo Wu, Shiqiang Nie, Jinyu Wang, Song Liu

原文摘要

Neural processing units (NPUs) provide high compute density and energy efficiency through specialized matrix engines and software-managed on-chip memory. However, their libraries primarily target dense computation. Sparse matrix-dense matrix multiplication (SpMM), a key kernel in graph analytics, sparse neural networks, and scientific computing, requires index-driven operand selection, indirect addressing, scattered memory accesses, and load balancing. These requirements conflict with the fixed shapes and contiguous layouts preferred by NPU matrix engines. GPU Tensor Core SpMM methods cannot be directly transferred to NPUs with decoupled vector and matrix engines. We present AscSpMM, which separates irregular operand preparation from regular matrix computation through a parameterized Vector-CSR representation. The vector engine decodes indices and gathers and reorganizes dense operands, while the Cube engine performs matrix multiply-accumulate operations. A double-slot ring buffer enables dependency-aware asynchronous pipelining, while operand reuse and runtime-adaptive tiling reduce cross-engine stalls. On an Ascend 910B3, AscSpMM achieves geometric-mean speedups of 2.36x over GEMM, 19.92x over an existing SpMM implementation, and 5.82x over cuSPARSE on an A100. It reduces matrix-input storage and peak HBM footprint by 48.24% and 22.98% on average, while remaining competitive with specialized GPU Tensor Core SpMM kernels. The code is available at https://github.com/LfLab-AI/Ascend-NPU-SpMM.

cs.AR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37231

TaskBridge: Technical Report for SIGIR 2026 eCommerce Challenge

Zhiheng Fu, Yupeng Hu, Zixu Li, Zhiwei Chen, Qinlei Huang, Shaokun Wang, Han Liu, Fan Liu 等 10 位作者

原文摘要

The SIGIR 2026 eCommerce Challenge Query Reformulation Task evaluates the reformulation of task-oriented queries into expressions suitable for product retrieval, followed by BM25-based retrieval. These queries often imply product needs through activity-level semantics, creating a lexical gap with catalog descriptions. Systems must preserve task intent while adapting the number of reformulations to the Automatic and Interactive protocols. We propose TaskBridge, a catalog-anchored framework comprising Catalog Semantic Anchoring, Unified Five-Candidate Rewriting, and Protocol-Aware Selection. It grounds queries in catalog vocabulary, generates five complementary reformulations, and selects one query for Automatic retrieval or up to four for Interactive retrieval using BM25 scores, query-product reranking, and weighted Jaccard overlap. The selected queries are independently executed using BM25 to produce results in the required six-column format. Experiments demonstrate improved retrieval effectiveness, with multiple complementary queries providing additional gains in the Interactive setting. TaskBridge achieved first place in the SIGIR 2026 eCommerce Challenge.

cs.IR

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1121ACL Anthology · ACL 2026

TEMA: Anchor the Image, Follow the Text for Multi-Modification Composed Image Retrieval

Zixu Li Shandong University, Yupeng Hu, Zhiheng Fu, Zhiwei Chen Shandong University, Yongqi Li Hong Kong Polytechnic University, Liqiang Nie Harbin Institute of Technology (Shenzhen) and Shandong University

原文摘要

Composed Image Retrieval (CIR) is an important image retrieval paradigm that enables users to retrieve a target image using a multimodal query that consists of a reference image and modification text. Although research on CIR has made significant progress, prevailing setups still rely simple modification texts that typically cover only a limited range of salient changes, which induces two limitations highly relevant to practical applications, namely Insufficient Entity Coverage and Clause-Entity Misalignment. In order to address these issues and bring CIR closer to real-world use, we construct two instruction-rich multi-modification datasets, M-FashionIQ and M-CIRR. In addition, we propose TEMA, the Text-oriented Entity Mapping Architecture, which is the first CIR framework designed for multi-modification while also accommodating simple modifications. Extensive experiments on four benchmark datasets demonstrate that TEMA’s superiority in both original and multi-modification scenarios, while maintaining an optimal balance between retrieval accuracy and computational efficiency. Our codes and constructed multi-modification dataset (M-FashionIQ and M-CIRR) are available at https://github.com/lee-zixu/ACL26-TEMA/

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1669ACL Anthology · ACL 2026

PersonalAlign: Hierarchical Implicit Intent Alignment for Personalized GUI Agent with Long-Term User-Centric Records

Yibo Lyu Harbin Institute of Technology(Shenzhen), Gongwei Chen Harbin Institute of Technology (Shenzhen), Rui Shao Harbin Institute of Technology, Weili Guan, Liqiang Nie Harbin Institute of Technology (Shenzhen) and Shandong University

原文摘要

While GUI agents have shown strong performance under explicit and completion instructions, real-world deployment requires aligning with users’ more complex implicit intents. In this work, we highlight Hierarchical Implicit Intent Alignment for Personalized GUI Agent (PersonalAlign), a new agent task that requires agents to leverage long-term user records as persistent context to resolve omitted preferences in vague instructions and anticipate latent routines by user state for proactive assistance. To facilitate this study, we introduce AndroidIntent, a benchmark designed to evaluate agents’ ability in resolving vague instructions and providing proactive suggestions through reasoning over long-term user records. We annotated 775 user-specific preferences and 215 routines from 20k long-term records across different users for evaluation. Furthermore, we introduce Hierarchical Intent Memory Agent (HIM-Agent), which maintains a continuously updating personal memory and hierarchically organizes user preferences and routines for personalization. Finally, we evaluate a range of GUI agents on AndroidIntent, including GPT-5, Qwen3-VL, and UI-TARS, further results show that HIM-Agent significantly improves both execution and proactive performance by 15.7% and 7.3%.

cs.CL

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2069ACL Anthology · ACL 2026

Parallel Test-Time Scaling for Latent Reasoning Models

Runyang You Hong Kong Polytechnic University, Yongqi Li Hong Kong Polytechnic University, Meng Liu Shandong University and Shandong Jianzhu University, Wenjie Wang University of Science and Technology of China, Liqiang Nie Harbin Institute of Technology (Shenzhen) and Shandong University, Wenjie Li The Hong Kong Polytechnic University, The Hong Kong Polytechnic University

原文摘要

Parallel test-time scaling (TTS) is a pivotal approach for enhancing large language models (LLMs), typically by sampling multiple token-based chains-of-thought in parallel and aggregating outcomes through voting or search. Recent advances in latent reasoning, where intermediate reasoning unfolds in continuous vector spaces, offer a more efficient alternative to explicit Chain-of-Thought, yet whether such latent models can similarly benefit from parallel TTS remains open, mainly due to the absence of sampling mechanisms in continuous space, and the lack of probabilistic signals for advanced trajectory aggregation. This work enables parallel TTS for latent reasoning models by addressing the above issues. For sampling, we introduce two uncertainty-inspired stochastic strategies: Monte Carlo Dropout and Additive Gaussian Noise. For aggregation, we design a Latent Reward Model (LatentRM) trained with step-wise contrastive objective to score and guide latent reasoning. Extensive experiments and visualization analyses show that both sampling strategies scale effectively with compute and exhibit distinct exploration dynamics, while LatentRM enables effective trajectory selection. Together, our explorations open a new direction for scalable inference in continuous spaces. Code and checkpoint are included as supplementary materials.GitHub Project: https://github.com/ModalityDance/LatentTTS

cs.CL

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2095ACL Anthology · ACL 2026

TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection

Fuqiang Niu, Zini Chen, Zhiyu Xie, Hu Huang University of Science and Technology of China, Qing Liao Harbin Institute of Technology and Pengcheng Laboratory, Qianlong Wang Shenzhen Technology University, Genan Dai Shenzhen Technology University, Bowen Zhang Shenzhen Technology University

原文摘要

Political user-level stance detection is vital for analyzing polarization, yet progress is hindered by the scarcity of high-quality benchmarks integrating linguistic and social signals. Existing datasets, largely relying on noisy heuristic or distant supervision, limit model robustness and generalizability. To address this, we introduce TwiUSD, a large-scale, expert-annotated benchmark for political user-level stance detection with explicit social network structure. TwiUSD comprises 16,211 users and 47,757 tweets, labeled by domain experts using a protocol that integrates both user content and followee signals, ensuring high-quality annotations (kappa > 0.9). Building upon TwiUSD, we propose MRFG, a Multi-scale Relevance Filtering and Graph-aware framework that leverages large language models to filter stance-relevant followee content and adaptively routes features based on structural informativeness. This design enables robust stance prediction by jointly modeling semantic and relational cues. Extensive experiments show that MRFG significantly outperforms strong baselines, highlighting the importance of relevance filtering and structure-aware modeling.

cs.CL

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.530ACL Anthology · ACL 2026

Evolving Sparsity: Leveraging Token Importance Dynamics for Efficient LLM Decoding with Sparse Attention

Ruizi Han Harbin Institute of Technology, Miao Zhang Harbin Institute of Technology (Shenzhen), Ziyue Qiao Great Bay University, Liqiang Nie Harbin Institute of Technology (Shenzhen) and Shandong University

原文摘要

Efficient long-context inference remains a major challenge for large language models (LLMs), as the cost of attention computation during auto-regressive decoding grows linearly with the context length. Recent sparse attention methods attempt to reduce the computational burden by selecting a subset of tokens at each step, while most rely on static importance scores that are repeatedly computed over the entire cache, overlooking the relational dynamics of the decoding process. In this work, we revisit sparse attention in LLMs and propose to model token importance as a dynamic process that evolves over decoding steps and propagates through model layers. To efficiently measure token importance, we propose two lightweight mechanisms: (1) Cross-Step Accumulation, which incrementally maintains long-term, query-agnostic importance via decayed accumulation of sparse attention scores, avoiding recomputing the importance of decoded tokens; and (2) Cross-Layer Propagation, which leverages the model’s intrinsic Retrieval Heads to compute query-aware indices and efficiently propagate them across layers; Together, these mechanisms preserve both stable context memory and adaptive query relevance while reduce redundant computation. We evaluate our approach on PG-19, RULER, LongBench, and mathematical reasoning benchmarks using models employing Multi-Head and Grouped-Query Attention. Under varying KV cache budgets, our method consistently outperforms prior sparse attention baselines, approaches full attention performance in most settings, and achieves speedups of up to 5.36\times for attention latency and 2.33\times for end-to-end decoding. Our code is available at: https://github.com/iLearn-Lab/ACL26-EvoSparse.

cs.CL

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