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新论文 · v1arXiv:2611.04759

更新于 2026-11-03

Beyond Local Refinement: Wide-Area Precise Cross-View Localization

Qingguo Hu, Xinyu Ma, Yujie Lin, Yaoxiang Wang, Qi Shi, Zonghao Guo, Chi Chen, Jinsong Su

原文摘要

Fine-grained cross-view localization aims to precisely localize a ground camera against geo-referenced aerial imagery, but current methods typically assume a narrow geographic prior, substantially limiting their applicability when upstream localization remains coarse. To bridge this gap, we study this task over much larger aerial regions. However, this seemingly simple extension fundamentally changes the localization problem: the growing disparity in spatial extent between ground and aerial views makes reliable cross-view matching difficult to establish directly, while the increasing number of look-alike locations in larger regions introduces substantial visual ambiguity. To address these challenges, we propose RingMatch, a search-and-verification framework for wide-area cross-view localization. RingMatch first leverages predictive uncertainty to uncover plausible locations and progressively zooms into their surroundings to obtain increasingly fine-grained evidence, yielding a compact set of promising candidates. We then introduce the depth ring, a novel geometric representation that reveals subtle variations in the spatial layout around a location and serves as a view-shared spatial signature for an LVLM verifier to resolve residual ambiguity. To benchmark this setting, we construct VIGOR-Pro by composing VIGOR aerial tiles into contiguous regions with spatial extents of up to 400 m. Experiments show that RingMatch maintains strong localization performance as the aerial region expands, consistently outperforming representative end-to-end and retrieval-based methods.

cs.CV

新论文 · v1arXiv:2611.03000

更新于 2026-11-02

Fluid Antenna Array-Enabled Near-Field ISAC: Joint Port Position Optimization and Transmit Beamforming Design

Wangqi Shi, Zhentian Zhang, Hao Jiang, Zaichen Zhang, Kin-Fai Tong

原文摘要

Fluid antenna systems (FASs) have shown that activating a single port from a densely packed antenna array can yield spatial diversity comparable to that of tens of independent antennas, a property that has substantially improved communication reliability and efficiency. The fluid antenna array (FAA) extends this concept to multi-port configurations while avoiding complex multi-dimensional spatial correlation, and offers significant geometrical flexibility under a finite aperture and a limited port budget. These attributes motivate environment-adaptive array geometry for sensing in integrated sensing and communication (ISAC) systems. Although this flexibility enables adaptive array geometries that exploit near-field distance-angle information, improving multi-parameter target estimation under communication and deployment constraints remains challenging. In this paper, we propose a planar FAA-assisted near-field ISAC framework for multiuser communication and target sensing. Specifically, we derive the Fisher information matrix (FIM) and the corresponding Cramér-Rao bound (CRB) for the joint estimation of target range, zenith angle, azimuth angle, and radial velocity. Based on the derived CRB, we jointly optimize the FAA port positions and transmit beamforming to improve sensing accuracy while satisfying communication signal-to-interference-plus-noise ratio (SINR), transmit power, and physical deployment constraints. To solve the resulting non-convex problem, we develop a two-stage alternating optimization algorithm, in which transmit beamforming is optimized through semidefinite relaxation (SDR), while FAA port positions are successively optimized through discrete search and continuous refinement. Simulation results indicate that the proposed algorithm achieves favorable sensing performance while maintaining a reasonable computational cost.

cs.IT

新论文 · v1arXiv:2610.39488

更新于 2026-10-29

InterA11y: Interactive Web Accessibility Error Detection with LLM Agents

Mingyuan Zhong, Mengqi Shi, Davin Win Kyi, Ajit Mallavarapu, Xia Chen, James Fogarty, Jacob O. Wobbrock

原文摘要

Large Language Models (LLMs) have shown potential to expand the coverage of web accessibility evaluation. However, current LLM-based checkers neglect much of the interactive and dynamic web contexts, resulting in limited coverage, high false discovery rates, and inconsistent performance across models. We present InterA11y, an agentic accessibility evaluation system that combines rubrics and interactive tools to support LLM agents in evaluating accessibility effectively. The rubrics decompose WCAG success criteria (SC) into testable aspects and guide agents in collecting and interpreting evidence. The interactive tools inspect or operate a webpage deterministically to collect evidence, such as focus appearances and error messages. We developed these rubrics and tools on 682 curated test cases grounded in established accessibility guidelines that target 10 commonly violated SC. We evaluated InterA11y on 197 expert-annotated cases sampled from 54 real webpages. Results show InterA11y achieves higher recall than the strongest evaluated LLM-driven baseline (79.0% vs. 44.4%), while maintaining comparable precision to best rule-based checkers (93.1% vs. 90.3-97.6%), with little variability between models. Further, when equipped with our interactive tools, general-purpose web agents detect more accessibility errors with fewer false positives and less token usage than without these tools.

cs.HC

新论文 · v1arXiv:2610.36826

更新于 2026-10-27

When the Critic Falls Behind: Co-Evolving Rubrics and Harnesses for Continual Agent Self-Improvement

Hao Chen, Yukun Yan, Ziyu Han, Ye He, Chuanzhe Guo, Shangzhan Li, Qi Shi, Qingfu Zhu 等 10 位作者

原文摘要

Autonomous agents increasingly depend on harnesses that govern interaction, memory, tools, and execution around large language models (LLMs). While recent work has begun to evolve these harnesses from execution experience, the supervision used to guide evolution often remains relatively static, making it difficult to track changing behavioral weaknesses across rounds and potentially limiting generalization to unseen tasks. To address this issue, we propose RuHarness, a rubric--harness co-evolution framework for continual agent self-improvement. RuHarness continually discovers and reassesses behavioral criteria from execution trajectories, prioritizes the most relevant unresolved weaknesses, and uses them to guide targeted harness updates while keeping the task objective fixed. Extensive experiments across diverse agent tasks and backbone models show that RuHarness outperforms strong harness-evolution baselines, particularly on unseen held-out tasks. Further analyses show that adaptive rubric prioritization tracks shifting weaknesses and supports more sustained improvement and stable generalization across evolution rounds.

cs.AI

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1001ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage

Xuanle Zhao York University and Institute of automation, Chinese academy of science, Chinese Academy of Sciences, Zilin Sang, Yuxuan Li, Qi Shi Tsinghua University, Weilun Zhao ModelBest, Shuo Wang, Duzhen Zhang Mohamed bin Zayed University of Artificial Intelligence, Xu Han Tsinghua University, Tsinghua University 等 10 位作者

原文摘要

Efficient reproduction of research papers is pivotal to accelerating scientific progress. However, the increasing complexity of proposed methods often renders reproduction a labor-intensive endeavor, necessitating profound domain expertise.To address this, we introduce the paper lineage, which systematically mines implicit knowledge from the cited literature. This algorithm serves as the backbone of our proposed , a multi-agent framework designed to autonomously reproduce experimental code in a complete, end-to-end manner. To ensure code executability, incorporates a sampling-based unit testing strategy for rapid validation. To assess reproduction capabilities, we introduce , a benchmark featuring verified implementations, alongside comprehensive metrics for evaluating both reproduction and execution fidelity. Extensive evaluations on PaperBench and demonstrate that consistently surpasses existing baselines across all metrics. Notably, it yields substantial improvements in reproduction fidelity and final execution performance. The code is available at https://github.com/AI9Stars/AutoReproduce.

cs.CL · cs.MA

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.1166ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

AIRCoder: Adaptive Integration of Multi-dimensional Retrieval for Repository-level Code Completion

Chuanqi Shi Southeast University, Miao Gao Southeast university, Zhiqiang Gao Southeast University

原文摘要

Repository-level code completion relies on retrieval strategies to select relevant context from large codebases. Most existing methods employ single-dimensional retrieval based on textual similarity or dependency existence, leading to inconsistent performance across completion scenarios. Even task-adaptive and hybrid methods incur high computational costs while failing to explicitly consider structural hierarchy for retrieving functionally similar code. We introduce AIRCoder, a multi-dimensional retrieval framework that combines eight complementary metrics across three dimensions: textual similarity, dependency existence, and structural hierarchy. By proposing a structure-preserving chunking strategy and lightweight fusion module, AIRCoder learns context-dependent weights to adaptively integrate retrieval metrics for each query. Experiments on CrossCodeEval and RepoEval demonstrate that AIRCoder achieves an average improvement of 4.63% in exact match over the best baseline, with 10.2\times higher efficiency and strong cross-language generalization across Python, Java, C#, and TypeScript.

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.1773ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection

Junchao Wu University of Macau, Yefeng Liu, Chenyu Zhu, Hao Zhang, Zeyu Wu, Tianqi Shi, Yichao Du, Longyue Wang Alibaba Group 等 11 位作者

原文摘要

The effective detection and governance of Large Language Model (LLM) generated content has become increasingly critical due to the growing risk of misuse. Despite the impressive performance of existing detectors, their reliability and potential in multilingual, real-world scenarios remain largely underexplored.In this study, we introduce DetectRL-X, a comprehensive multilingual benchmark designed to evaluate advanced detectors across 8 dimensions. The benchmark encompasses 8 languages commonly used in commercial contexts and collects human-written texts from 6 domains highly susceptible to LLM misuse. To better aligned with real-world applications, We create LLM-generated texts using 4 popular commercial LLMs, and include typical AI-assisted writing operations such as polishing, expanding, and condensing to capture authentic usage patterns. Furthermore, we develop a multilingual framework for paraphrasing and perturbation attacks to simulate diverse human modifications and writing noise, enabling stress testing of detectors across languages.Experimental results on DetectRL-X reveal the strengths and limitations of current state-of-the-art detectors when applied to diverse linguistic resources. We further analyze how domains, generators, attack strategies, text length, and refinement operations influence performance in different languages, underscoring DetectRL-X as an effective benchmark for strengthening multilingual and language-specific detectors.

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.2117ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models

Yiyang Gu, Junwei Yang, Junyu Luo, Ye Yuan, Bin Feng International Digital Economy Academy, International Digital Economy Academy, Yingce Xia Zhongguancun Academy, Shufang Xie Renmin University of China, Kaili Liu 等 18 位作者

原文摘要

Large language models (LLMs) are increasingly applied to scientific research, yet existing evaluations often fail to reflect the fine-grained capabilities required in practice. Most benchmarks are manually curated or domain-generic, limiting scalability and alignment with real scientific use cases. In this paper, we propose a new framework named SciCustom to address the problem. It enables the custom construction of benchmarks from large-scale scientific data to evaluate application-specific scientific capabilities in LLMs. SciCustom first organizes scientific knowledge into ontology-grounded knowledge units with controlled granularity and trains a tagger to map large-scale data instances into this knowledge space. Given a custom requirement, relevant knowledge units are identified via voting-based multi-model consensus. These units enable relevance-aware benchmark retrieval via binary search, followed by proxy subset selection and data-grounded benchmark generation for efficient evaluation. Experiments in chemistry and healthcare demonstrate that SciCustom reveals fine-grained differences in LLM scientific capabilities that standard benchmarks overlook, while requiring neither expert annotation nor synthetic question generation. This work provides a scalable and application-aware foundation for benchmarking scientific capabilities in LLMs.

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.787ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

From Inheritance to Saturation: Disentangling the Evolution of Visual Redundancy for Architecture-Aware MLLM Inference Acceleration

Jiaqi Shi, Xulong Zhang Ping An Technology (Shenzhen) Co., Ltd., Yuechan Li Wuhan University, Xiaoyang Qu, Jianzong Wang Pingan Technology

原文摘要

High-resolution Multimodal Large Language Models (MLLMs) face prohibitive computational costs during inference due to the explosion of visual tokens. Existing acceleration strategies, such as token pruning or layer sparsity, suffer from severe “backbone dependency”, performing well on Vicuna or Mistral architectures (e.g., LLaVA) but causing significant performance degradation when transferred to architectures like Qwen. To address this, we leverage truncated matrix entropy to uncover a universal three-stage inference lifecycle, decoupling visual redundancy into universal Intrinsic Visual Redundancy (IVR) and architecture-dependent Secondary Saturation Redundancy (SSR). Guided by this insight, we propose HalfV, a framework that first mitigates IVR via a unified pruning strategy and then adaptively handles SSR based on its specific manifestation. Experiments demonstrate that HalfV achieves superior efficiency-performance trade-offs across diverse backbones. Notably, on Qwen25-VL, it retains 96.8% performance at a 4.1\times FLOPs speedup, significantly outperforming state-of-the-art baselines. Our code is available at https://github.com/civilizwa/HalfV.

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-tutorials.3ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

Towards Effective and Efficient Multi-Agent Language Model Systems: Foundations, Prospects, and Applications

Xuan Wang Virginia Tech, Shuxiang Cao NVIDIA, Yuchen Zhuang Google DeepMind, Wenqi Shi University of Texas Southwestern Medical Center

原文摘要

Multi-agent systems powered by large language models (LLMs) offer a promising paradigm for tackling complex reasoning, decision-making, and problem-solving tasks. However, achieving both effectiveness and efficiency in such systems remains a critical challenge. This tutorial introduces recent advances in building effective and efficient multi-agent LLM systems, focusing on three core components. First, we discuss the design of individual LLM agents. We present state-of-the-art techniques for enabling capable agents using efficient and compact LLMs, including model distillation, dynamic routing, and memory- and compute efficient serving, providing a foundation for scalable and responsive agent design under resource constraints. Second, we cover coordination and communication among agents, crucial for collective performance, highlighting methods for improving multi-agent reasoning and decision-making through prompt and graph optimization, sycophancy mitigation, and structured LLM-based frameworks. Last, we explore real-world applications of LLM agents in areas such as industry, healthcare, quantum computing, and various scientific domains.

cs.CL · cs.MA

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts) · 2026 · 正式发表