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新论文 · v1arXiv:2611.15583

Your Secrets Are Showing: Deep Image Hiding Models Are Surprisingly Easy to Attack

Hao Liu, Jing Yu, Weizhi Liu, Lingling Yuan, Fengyong Li, Keke Gai, Zhaoxia Yin, Xinpeng Zhang

原文摘要

In this work, we ask whether an attacker can go beyond detecting a hidden image and tell what it depicts, even without knowing which model hid it. We show that, for deep image hiding, this is surprisingly easy. To test this, we build an evaluation dataset of stego images generated by 11 hiding models. These models span two families (invertible neural networks and autoencoders), use different backbones (CNN, Transformer, and Mamba), and include both spatial- and frequency-domain hiding methods. We demonstrate that training HiNet [17] in the usual way yields a single decoder that can extract secret content from unseen models across these settings. Both humans and machines can recognize what it reveals. A frequency analysis shows that most models favor mid- and high-frequency embedding. Guided by this finding, we strengthen cross-model extraction by enhancing cover textures and suppressing secret textures during training. The attack extends to out-of-distribution secrets and to multi-image hiding. It even exposes private content hidden by AIRPNet [13], a recent image restoration method that uses steganography for privacy protection. What surprised us most is that decoders from every hiding model we test exhibit this ''universal'' extraction ability to varying degrees. Our code and datasets will be open-sourced.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15464

RSE-TTS: Compact Text-to-Speech through Shared Text-Aware Reference Encoding and Generation

Ming Ma, Mingjie Chen, Yuanzhong Zheng, Hengyao Bao, Hailin Yu, Shijing Yu, Hao Qiu, Gongfan Cao 等 11 位作者

原文摘要

Can a speech generator also serve as an effective voice encoder? We show that a pretrained autoregressive text-to-speech (TTS) backbone can be repurposed for reference compression, improving speaker similarity in subsequent synthesis. Jointly encoding reference speech and its transcript further strengthens this capability. Controlled ablations and activation interventions trace the text benefit through intermediate acoustic representations into the compressed vectors that condition generation. Building on these findings, we introduce RSE-TTS, whose Reconstructive Speaker Encoder (RSE) shares the generation backbone and compresses paired reference text and speech into eight continuous vectors. Sharing reduces encoder-generator parameters by 48% relative to independent pretrained encoding. Large-scale English training yields a 118M-parameter generator that improves both word error rate and speaker similarity over the corresponding official MOSS-TTS-Nano cloning modes on all three English benchmarks. Combined with acoustic continuation, RSE-TTS approaches the speaker similarity of substantially larger systems while achieving lower WER than the evaluated CosyVoice 3 and MOSS-TTS-Local models. Audio samples are available at https://voxnexusai.github.io/RSE-TTS/.

cs.SD · eess.AS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14651

Multiple-Choice Secretary Problem with Predictions

Jing Yuan, Qi Cai, Xin Gao, Shaojie Tang

原文摘要

We study the multiple-choice secretary problem, in which candidates arrive in uniformly random order and the goal is to select at most $k$ candidates to maximize their total value. We consider a setting where predictions of individual candidate values are available before any candidate arrives. Fujii and Yoshida's Learned Kleinberg algorithm achieves competitive loss $O(\min\{\log k/\sqrt{k},\eps\})$, where $k$ is the selection capacity and $\eps$ is the maximum multiplicative prediction error. We remove the logarithmic factor through a sharper analysis of the same switching algorithm with a retuned threshold. The resulting competitive ratio is at least $\max\{0,1-\min\{32/\sqrt{k},4\eps\}\}$. In particular, exact predictions ($\eps=0$) achieve the offline optimum on every arrival order.

cs.DS · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.09568

EvoDesign：通过设计专长演化实现智能体式可编辑图表创建

EvoDesign: Agentic Editable Diagram Creation via Design Expertise Evolution

Tianfu Wang, Leilei Ding, Ziyang Tao, Yi Zhan, Zhiyuan Ma, Wei Wu, Yuxuan Lei, Junyang Wang 等 15 位作者

AI 速读 基于摘要

提出一种基于可操作画布中间表示与设计专长演化记忆的智能体框架，用于生成可编辑图表，并给出 CanvasBench 评测基准。

cs.HC · cs.CL · cs.CV

arXiv ↗PDF ↗
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High-fidelity diagram creation requires coordinated visual-spatial decisions over semantic topology, visual styling, and geometric layout. Existing methods face a representation gap: pixel-based generation offers limited object-level control, while code-based synthesis provides executable structure at the cost of intuitive manipulation. We introduce EvoDesign, an agentic framework for editable diagram creation through an intermediate canvas schema that is both machine-actionable and directly manipulable by users. EvoDesign first constructs a diagram manifest through coordinated structure, style, and layout agents, and then renders the manifest in a diagnostic verification environment. This environment produces objective defect signals and VLM-based critique, enabling localized refinement of the current diagram while also providing evidence for long-term design expertise evolution. The evolution mechanism distills refinement traces into a three-tier design expertise memory, where candidate strategies are linked to the verification evidence that supports them and are promoted into broader guidelines and principles only through repeated cross-context support. We further define CanvasBench, a canvas-recoverable benchmark and evaluation protocol with content, visual, and cognitive dimensions. Experiments across pixel, code-based, agentic, one-shot schema, and canvas-native methods show that EvoDesign provides the strongest overall balance across content fidelity, visual organization, and cognitive usability. Our code is available at https://github.com/GeminiLight/EvoDesign.

版本更新 · v3arXiv:2509.10127

Population-Aligned Persona Generation for LLM-based Social Simulation

Zhengyu Hu, Jianxun Lian, Zheyuan Xiao, Max Xiong, Tianfu Wang, Teng Xiao, Yuxuan Lei, Fengqing Jiang 等 13 位作者

原文摘要

Recent advances in large language models (LLMs) have enabled large-scale, high-fidelity social simulations, creating new opportunities for computational social science. However, constructing persona sets that faithfully reflect real-world population diversity remains a key challenge. Existing studies often emphasize agentic frameworks and simulation environments, while paying less attention to persona generation and the biases introduced by unrepresentative persona sets. In this paper, we propose a systematic framework for synthesizing high-quality, population-aligned persona sets for LLM-driven social simulation. Our approach begins by leveraging LLMs to generate narrative personas from long-term social media data, followed by rigorous quality assessment to filter out low-fidelity profiles. We then apply importance sampling to achieve global alignment with reference psychometric distributions, such as the Big Five personality traits. To address the needs of specific simulation contexts, we further introduce a task-specific module that adapts the globally aligned persona set to targeted subpopulations. Extensive experiments demonstrate that our method significantly reduces population-level bias and enables accurate, flexible social simulation for a wide range of research and policy applications.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37642

Metacognitive Decoding: Efficient Inference for Diffusion Language Agents

Mingyu Zhao, Shuhang Lin, Ligong Han, Xiaojing Yu, Zhenting Wang, Yifei Pang, Yang Zhou, Dimitris N. Metaxas

原文摘要

Diffusion language models offer a promising path to efficient agents through parallel token generation. However, efficient interaction requires adapting both where computation is spent and how much text is generated. We introduce a metacognitive controller that guides diffusion generation according to the needs of the current response and the agent's interaction with the environment. It learns when to finalize tokens and how far generation should extend, reducing overall computation within responses. Our controller, a lightweight plug-in for the diffusion backbone, is trained on agent trajectories to balance correctness and generation cost. This approach connects the agent's generation process to the interaction the agent serves. Experiments across five benchmarks demonstrate that our approach reduces inference FLOPs by 20.6% on average compared with the most efficient baseline, while maintaining competitive task accuracy. Ablation studies further validate the effectiveness of our approach.

cs.CL

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1252ACL Anthology · ACL 2026

PACE: Predictive Adaptive Context Extraction for Long-Horizon LLM Agents

Lei Wei, Xiao Peng Alibaba Group, Tt, Guannan Zhang, Chenhao Jiang, Hongyu Li Alibaba Group, Lanbo Lin, Yuanwu Xu Alibaba Group 等 11 位作者

原文摘要

Large Language Model (LLM) agents struggle with ultra-long-horizon tasks requiring hundreds or thousands of interaction steps. Traditional context management approaches face a fundamental dilemma: preserving complete histories rapidly exhausts context windows and forces crude truncation, while aggressive summarization discards critical information prematurely. We propose Predictive Adaptive Context Extraction (PACE), a novel framework that reconceptualizes context management as a Next Step Prediction problem. Inspired by neural attention, PACE dynamically constructs context by adjusting historical memory granularity based on its predicted relevance for the next action. Comprehensive evaluation across diverse benchmarks and models demonstrates that PACE consistently improves task success rates, with larger gains on complex tasks and robust cross-lingual performance. Crucially, PACE enables agents to sustain effective reasoning for 4,897 interaction steps in ultra-long-horizon scenarios, achieving a 66.2 improvement over the full-context ReAct baseline and 5.1 over advanced folding baselines. This fundamentally advances the capability of LLM-based agents in previously intractable long-horizon scenarios. Our code and data are available at https://anonymous.4open.science/r/PACE-B000/.

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.1297ACL Anthology · ACL 2026

You Can Have a Second Chance: Unbiased and Multi-bit Watermarking for Diffusion Language Models with Regret-based Remasking

Ke Yang Institute of Information Engineering, Chinese Academy of Sciences, Dongyang Liang, Jing Yu Institute of Information Engineering, Chinese Academy of Sciences, Shuguang Yuan Institute of Information Engineering, Chinese Academy of Sciences, Chi Chen

原文摘要

The rapid development of Diffusion Language Models (DLMs) raises concerns about watermarking for DLM-generated detection. However, existing sequential LLM watermarking cannot be directly applied to DLMs, as DLMs’ generation order is arbitrary. While emerging studies adapt biased LLM watermarking to DLMs by temporarily predicting the watermark prefix, they suffer from degraded quality and unstable watermarking due to bias accumulation and prediction errors. Besides, they cannot carry multi-bit watermarks. In this paper, we propose unbiased multi-bit watermarking for DLMs. We introduce a stability-aware constraint that allows watermarking only in stable contexts and a bit-controlled, unbiased modulation to preserve the original DLM output distribution, achieving stable watermarking with minimal quality impact. To enhance detection robustness, we design a Regret-based Remasking, which grants a “second chance” for unwatermarked tokens to be regenerated. It can seamlessly integrate into DLM inference with no added diffusion steps and latency. Experiments across DLMs and various tasks show that our scheme is effective, achieving superior generation quality compared to baselines while maintaining high detection accuracy and multi-bit capacity. Our code is available here https://github.com/iieSKLCSDsg/UMR.

cs.CL

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