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新论文 · v1arXiv:2611.13807

Relaxing Cross-Modal Correspondence for Multimodal Remote Sensing Pretraining

Xianping Ma, Yu Zhang, Mingzhe Jiang, Wanxin Xie, Qing Zhu, Nan Xu, Xiaokang Zhang, Yuanxin Ye

原文摘要

Multimodal remote sensing pretraining commonly relies on exact instance-level correspondence across modalities, implicitly treating strictly paired observations as a prerequisite for learning transferable representations. In this work, we revisit this assumption by relaxing cross-modal correspondence from the instance level to the semantic level. We introduce Semantic Pairing (SemPair), a pairing-relaxed pretraining strategy that replaces exact instance pairs with class-consistent cross-instance pairs. To characterize the effect of correspondence relaxation, we formulate a latent-factor model that separates cross-modal dependence into shared semantic structure and instance-specific correlation, showing that SemPair preserves the former while marginalizing instance-specific cross-modal correlations in expectation. We further introduce Mixed Pairing (MixPair) as a controlled interpolation between semantic and exact pairing to study correspondence granularity along a continuous spectrum. Extensive experiments consistently show that SemPair achieves transfer performance comparable to exact pairing (ExaPair), while MixPair reveals a non-monotonic relationship between downstream transfer and the instance-pairing ratio. These results demonstrate that exact instance correspondence is not a prerequisite for effective multimodal remote sensing pretraining and establish cross-modal correspondence granularity as an important design dimension for transferable multimodal representation learning. More broadly, this finding suggests a practical route toward scaling multimodal pretraining with reduced dependence on costly instance-aligned data.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.09541

Memory-Efficient Active Perception for Embodied Question Answering in Dynamic Human-Populated Scenes

Xin Lu, Rui Li, Xun Huang, Weixin Li, Jiayuan Li, Chuanqing Zhuang, Zhengda Lu, Jun Xiao

原文摘要

Embodied Question Answering (EQA) couples active visual perception, vision-language reasoning, and sequential decision making, and has traditionally been evaluated in temporally stable environments where visual evidence can be accumulated reliably. In realistic multimedia settings such as dynamic human-populated scenes, however, moving people and transient occlusions introduce human-induced perceptual variations: task-relevant multimodal cues become transient, viewpoint-dependent, and incompletely captured in individual views. Conventional store-then-retrieve strategies generate redundant visual evidence and incur high inference cost, making them ill-suited to such variations. This exposes two core problems for embodied agents: resolving viewpoint-dependent ambiguity, and maintaining compact evidence for efficient inference. To address these challenges, we present DIVRR (Dynamic-Informed View Refinement with Relevance-guided Rotation and adaptive memory selection), a training-free active evidence-acquisition framework for EQA. DIVRR combines question-conditioned room guidance with relevance-triggered view augmentation to guide exploration and additional observation. Selective memory admission uses the same relevance signal as view augmentation to retain high-scoring evidence, reducing stored entries while keeping the retrieved visual context compact. To evaluate EQA under human-induced perceptual variations, we introduce DynHuman-EQA, a dynamic human-populated EQA dataset constructed in Habitat, comprising dynamic and static subsets under shared scene layouts. Experiments on DynHuman-EQA and HM-EQA show that DIVRR outperforms existing baselines in both accuracy and memory efficiency. The gains are most pronounced in dynamic human-populated scenes.

cs.CV · cs.MM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12057

AscendMOEA: An Ascend NPU-Accelerated Platform for Tensorized Multi-Objective Evolutionary Algorithms

Qianlong Dang, Zhiyang Zhang, Baosheng Li, Tianqing Zhu, Junhu Ruan

原文摘要

Multi-objective evolutionary optimization increasingly involves large populations, expensive problem evaluations, and diverse algorithmic structures. These workloads expose the limited parallelism of loop-based implementations, while rewrites for accelerator execution can alter selection order, tie handling, and population updates. AscendMOEA is presented as a scalable platform that executes multi-objective evolutionary algorithms (MOEAs) on Huawei Ascend neural processing units (NPUs) and conventional central processing units (CPUs). The platform represents the decision vectors, objectives, constraints, and auxiliary states as tensors and provides reusable interfaces for algorithms, problems, population operators, quality indicators, monitoring, and repeated experiments. Dense computations are executed in batches, such as dominance, distance, variation, and problem-evaluation calculations. Short recurrences with causal dependencies retain their prescribed order and consume precomputed tensor results, which preserves the behavior of the original algorithms. AscendMOEA contains 18 algorithms, 155 benchmark and application problems, 19 public operator interfaces, and 11 quality indicators. Experiments on eight Ascend NPUs and 192 physical CPU cores evaluate operator throughput, end-to-end runtime, memory consumption, numerical agreement, and implementation ablation. Tensorized operators obtain a median acceleration of 1.58 times on the NPU. At large population scales, complete algorithms achieve a median acceleration of 5.76 times while retaining the recorded quality indicators. Moreover, the results identify the workload scale and ordered computation that determine whether accelerator execution is beneficial. AscendMOEA is open-sourced at https://github.com/dqlme/AscendMOEA.

cs.NE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10095

Adaptive Budgeted Data Acquisition for Robust Off-Policy Evaluation from Multiple Loggers

Amitakshar Biswas, Ruoqing Zhu

原文摘要

We consider off-policy evaluation when additional data can be purchased from several existing logging policies (or loggers) with heterogeneous costs per observation. While existing work on multiple loggers presumes a fixed number of observations from each logger, we study how to allocate a limited budget across them sequentially to evaluate a finite class of target policies. We propose Adaptive Greedy, an algorithm that purchases observations sequentially from the affordable logger that has the most cost-normalized variance reduction across the target policies. It turns out that each such purchase is an 'approximate conditional-gradient step' for the corresponding design problem. Under mild assumptions, we establish that the empirical allocation converges almost surely to the optimal design at an explicit rate. We also derive the asymptotic normality of the final pooled estimator and show that it attains the semiparametric efficiency bound. Simulations and a semi-synthetic application based on the AIDS Clinical Trials Group Study 175 show that Adaptive Greedy outperforms standard allocation strategies, remains accurate if stopped early, and matches the final accuracy of repeated integer programming at a fraction of its computational cost.

stat.ML · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07420

Adaptive and High-Fidelity Matching Algorithm for Retrieval-Based Accent Conversion

Xinran Wang, Ning Chen, Yixin Luo, Hongqing Zhu, Li Tian

原文摘要

Accent conversion (AC) aims to transform accent-related acoustic characteristics of the source speech to a target accent while preserving linguistic content and speaker identity. Unlike conventional generative AC models, the retrieval-based ones, which employ k-nearest neighbors (kNN) to directly retrieve acoustic features from target-accent speech and so do not need source-accent speech during training, are more effective. However, existing retrieval-based AC frameworks face three issues: limited intelligibility due to incomplete phoneme coverage from sequential matching pool construction, limited adaptability to source speech variations, and low spectral continuity from coarse-grained style modulation. To address these limitations, we propose Anisotropy-Aware Adaptive k-Nearest Neighbor Accent Conversion (AA-kNN-AC), an enhanced retrieval-based AC model. First, we design a phoneme coverage-based matching pool construction strategy, employing greedy selection with diversity-aware scoring to achieve balanced and near-complete phonetic representation under limited target accent data. Second, we propose an anisotropy-aware adaptive kNN retrieval mechanism that dynamically determines the neighborhood size according to the singular value decomposition (SVD)-based anisotropy analysis, enhancing adaptability to source speech variations. Third, we present a progressive Adaptive Instance Normalization (AdaIN)-based fine-grained decoder by integrating temporal up-sampling within AdaIN blocks, enhancing spectral continuity of the converted speech. Experimental results on the L2-ARCTIC and VCTK datasets demonstrate that the proposed method outperforms existing retrieval-based and generative baselines in both objective and subjective evaluations, achieving high intelligibility, speaker similarity, and naturalness. Demo page is available at https://aa-knn-ac.github.io/demo/.

cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39575

Hidden Phase Transition in Single-Polymer Stretching under Elongational Flow

Aiqing Zhu, Fushuai Wang, Eleonore Vissol-Gaudin, Qianxiao Li, Beatrice W. Soh

原文摘要

Using machine learning, we identify a second transition in polymer chain stretching driven by planar elongational flow, beyond the well-known coil-stretch transition. We achieve this by developing a conditional extension of Universal and Identifiable Dissipative Dynamics (UIDD) that jointly learns reduced stochastic dynamics from microscopic trajectories across flow strengths and yields the corresponding stationary nonequilibrium free energy landscapes. As the flow strength increases, the evolving landscape exhibits the expected coil-stretch transition, and then a previously unrecognized dynamical transition. In the latter, the stretched states persist, but the distribution along a folding coordinate becomes bimodal when conditioned on small end-to-end separation. Large-deviation analysis of a simplified analytical chain model reproduces both transitions and shows that they are phase transitions in the long-chain limit. Single-molecule DNA experiments provide qualitative support for the dynamical transition through the predicted reversal of the folding response.

cond-mat.soft · cs.LG · physics.comp-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34829

Logic Hijacking of RAG: Single-Document Poisoning via Local Optimization

Hangyi Zhao, Bonan Zhang, Fusen Guo, Jiaheng Wei, Chao Chen, Tianqing Zhu

原文摘要

Single-document corpus poisoning under black-box access poses a severe threat to Retrieval-Augmented Generation (RAG). However, existing attacks predominantly rely on explicit factual refutations or imperative instruction overrides. Modern aligned LLMs often arbitrate such conflicts conservatively, rejecting overt contradictions in favor of parametric memory or clean retrieved context while triggering input guardrails. We propose MONOLOGUE, a single-document poisoning framework based on Logic Hijacking without explicit refutation. Rather than disputing the ground truth, our method concedes it as an authoritative historical baseline before introducing the update, weaving a grounded record-correction narrative (e.g., dated institutional reattributions) that presents the counterfactual target as the most recent update. To prevent suspicious live writes, payloads are refined entirely offline via local multi-dimensional gated optimization, coupling automated text quality audits with surrogate behavioral verification. Evaluations across three benchmarks, three retrieval backends, and four open-weight generator regimes show that our method achieves the strongest overall attack efficacy among the evaluated single-document poisoning attacks.

cs.AI

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1095ACL Anthology · ACL 2026

Beyond Meta-Reasoning: Metacognitive Consolidation for Self-Improving LLM Reasoning

Ziqing Zhuang, Linhai Zhang, Jiasheng Si Qilu University of Technology (Shandong Academy of Sciences), Deyu Zhou Southeast University, Yulan He King's College London, University of London

原文摘要

Large language models (LLMs) have demonstrated strong reasoning capabilities, and as existing approaches for enhancing LLM reasoning continue to mature, increasing attention has shifted toward meta-reasoning as a promising direction for further improvement. However, most existing meta-reasoning methods remain episodic: they focus on executing complex meta-reasoning routines within individual instances, but ignore the accumulation of reusable meta-reasoning skills across instances, leading to recurring failure modes and repeatedly high metacognitive effort. In this paper, we introduce Metacognitive Consolidation, a novel framework in which a model consolidates metacognitive experience from past reasoning episodes into reusable knowledge that improves future meta-reasoning. We instantiate this framework by structuring instance-level problem solving into distinct roles for reasoning, monitoring, and control to generate rich, attributable meta-level traces. These traces are then consolidated through a hierarchical, multi-timescale update mechanism that gradually forms evolving meta-knowledge. Experimental results demonstrate consistent performance gains across benchmarks and backbone models, and show that performance improves as metacognitive experience accumulates over time.

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.833ACL Anthology · ACL 2026

Seeing No Evil: Blinding Large Vision-Language Models to Safety Instructions via Adversarial Attention Hijacking

Jingru Li China University of Geoscience, Wei Ren China University of Geosciences, Tianqing Zhu City University of Macau

原文摘要

Large Vision-Language Models (LVLMs) rely on attention-based retrieval of safety instructions to maintain alignment during generation. Existing attacks typically optimize image perturbations to maximize harmful output likelihood, but suffer from slow convergence due to gradient conflict between adversarial objectives and the model’s safety-retrieval mechanism. We propose Attention-Guided Visual Jailbreaking, which circumvents rather than overpowers safety alignment by directly manipulating attention patterns. Our method introduces two simple auxiliary objectives: (1) suppressing attention to system-prompt tokens and (2) anchoring generation on adversarial image features. This simple yet effective push-pull formulation reduces gradient conflict by 45% and achieves 94.4% attack success rate on Qwen-VL (vs. 68.8% baseline) with 40% fewer iterations. At tighter perturbation budgets (\epsilon=8/255), we maintain 59.0% ASR compared to 45.7% for standard methods. Mechanistic analysis reveals a failure mode we term safety blindness: successful attacks suppress system-prompt attention by 80%, causing models to generate harmful content not by overriding safety rules, but by failing to retrieve them.

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