Image hiding aims to conceal image-level messages within cover images at the same resolution. Invertible neural networks (INN)-based image hiding has emerged as an important branch. It treats concealing and revealing as a pair of inverse problems on image domain transformation and uses INN's forward and backward processes to address them. Due to architectural constraints, existing INN-based methods suffer from single-scale and single-domain feature extraction and limited nonlinear representation capability, resulting in inferior image quality. To mitigate these limitations, we propose an efficient cross-scale invertible hiding network with the spatial-frequency collaboration and the non-invertible mechanism, termed CrosInv. CrosInv exploits cross-scale and spatial-frequency collaborative features while enhancing nonlinear representation. Specifically, we introduce a cross-scale invertible module that bijectively maps inputs to cross-scale representations. To effectively integrate spatial and frequency information, the cross-scale invertible module employs pixel shuffle, Haar wavelet transformation, and their inverse operations for scale transformation. Furthermore, a non-invertible cross dense module is integrated to enhance the nonlinearity. Comprehensive experiments verify the effectiveness and superiority of the proposed CrosInv.
Zhongyan Ouyang, Weixin Liao, Mingquan Feng, Yehui Tang, Junchi Yan
Neural subspace solvers offer a promising way to accelerate the large sparse linear systems arising from PDE discretizations, but their value depends on generating effective correction directions at low computational overheads. We study this problem from a residual-image viewpoint: a correction subspace is useful when its image under the system operator captures the current residual. Guided by this view, we propose Gate-RINS, which generates multiple correction directions from shared polynomial residual probes and modulates them with a lightweight residual- and coordinate-dependent pointwise gate. We also introduce a hybrid schedule that uses GRANS-style graph controllers for the first correction block and Gate-RINS thereafter, combining an expressive graph based initial step with a lower-cost corrector as the solve progress. Theoretical analysis establishes a unified residual-image and dynamic approximate-inverse perspective for Gate-RINS and G-RANS, showing that a single neural-generated direction can emulate the residual quality of fixed-step GMRES. Numerical experiments on six PDE benchmarks demonstrate that Gate-RINS reaches a relative residual of $10^{-5}$ with 1.6-2.3$\times$ and 2.2-3.0$\times$ less wall-clock time than G-RANS and GMRES respectively. For Helmholtz systems with meshsize up to $10^5$, Gate-RINS also achieves the lowest residual and outperforms a GPU-accelerated parallel GMRES, while GRANS fails to reach relative residual of $10^{-4}$ in the same time budget.
Yuhao Li Beijing University of Posts and Telecommunications, Haifeng Sun, Xuesong Zhang, Shu Yao, Haoyu Zheng, Yvchuan Wang Beijing University of Posts and Telecommunications, Huazheng Wang, Zirui Zhuang Beijing University of Posts and Telecommunications 等 11 位作者
Recent advances in large language models (LLMs) have significantly improved code-generation capabilities, particularly through retrieval-augmented generation (RAG) for private libraries. While RAG leverages API documentation to address the scarcity of private code corpora, its performance critically depends on the quality of retrieved examples. Existing approaches often overlook the intrinsic characteristics of these examples, particularly how factors such as complexity, readability, and correctness impact their effectiveness. In this study, we systematically investigate these three critical aspects—complexity, readability, and correctness—and find that optimal examples should exhibit moderate complexity, semantic correctness, and step-by-step execution patterns. Based on these findings, we propose ComboPrompt, a novel example enhancement method that strategically combines existing API examples to improve complexity, refines code structure for readability, and incorporates automated validation ensuring correctness. Extensive evaluations across five private library benchmarks and different LLMs demonstrate that ComboPrompt achieves up to 22% accuracy improvement over baseline approaches. Code is available at Github.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Jie Huang, Xin Liao, Junjie Wang Institute of Software, Chinese Academy of Sciences, Mingyang Li Institute of Software Chinese Academy of Sciences, Wenshuo Wang ISCAS, Ziyou Jiang Institute of Software Chinese Academy of Sciences, Shoubin Li, Qing Wang
With the rise of short-video platforms, hate speech has evolved from static text and memes into more covert and aggressive hateful video formats, profoundly impacting social dynamics and public sentiment. Existing detection methods typically rely on multimodal feature fusion, which blurs the distinct boundaries of modality-specific information. This leads to the feature dilution problem, where dominant benign modalities often overwhelm sparse, localized hateful cues. To address this, we propose SAGE (Synergistic Adaptive Gating of Experts), a novel framework that shifts the paradigm from blind feature mixing to decision-level arbitration. Mimicking human cognitive processes, SAGE instantiates disentangled experts to rigorously preserve modality-specific semantics, facilitates global expert deliberation for context-aware refinement, and convenes an instance-level tribunal to dynamically arbitrate the final verdict based on evidentiary salience. Extensive experiments on HateMM and MultiHateClip benchmarks demonstrate that SAGE significantly outperforms state-of-the-art methods, achieving accuracy gains of 6.37% to 21.23% and macro-F1 score gains of 6.77% to 28.01%.
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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
Huazheng Wang, Yongcheng Jing, Haifeng Sun, Yingjie Wang, Jingyu Wang, Jianxin Liao, Dacheng Tao Nanyang Technological University
In this paper, we investigate knowledge forgetting in large language models with a focus on its generalisation—ensuring that models forget not only specific training samples but also related implicit knowledge. To this end, we begin by identifying a broader unlearning scope that includes both target data and logically associated samples, including rephrased, subject-replaced, relation-reversed, and one-hop reasoned data. We then conduct a rigorous evaluation of 15 state-of-the-art methods across three datasets, revealing that unlearned models still recall paraphrased answers and retain target facts in their intermediate layers. This motivates us to take a preliminary step toward more generalised implicit knowledge forgetting by proposing PERMU—a novel probability perturbation-based unlearning paradigm. PERMU simulates adversarial unlearning samples to eliminate fact-related tokens from the logit distribution, collectively reducing the probabilities of all answer-associated tokens. Experiments are conducted on a diverse range of datasets, including TOFU, Harry Potter, ZsRE, WMDP, and MUSE, using models ranging from 1.3B to 13B in scale. The results demonstrate that PERMU delivers up to a 50.40% improvement in unlearning vanilla target data while maintaining a 40.73% boost in forgetting implicit knowledge. Our code can be found in the supplementary material.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表