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Existing $w$-event local differential privacy (LDP) mechanisms for streaming data impose a uniform privacy window and budget, forcing all users to adopt the most stringent privacy requirement. We study personalized continual frequency estimation under LDP, where each user specifies her own window size and privacy budget, and identify two challenges. First, a single sampling size cannot balance sampling and perturbation errors for privacy groups with heterogeneous requirements. Second, borrowing estimates from groups with larger budgets reduces variance but introduces bias when privacy preferences correlate with the data. We first propose Optimal Population Selection (OPS) and two OPS-guided mechanisms, PLPD and PLPA, which use one privacy-compatible sampling size for all users. Since one size cannot fit all groups and pooling their estimates is biased under privacy--data correlation, we further develop PLPD$^+$ and PLPA$^+$, which combine group-aware population scheduling with bias-aware cross-group fusion while preserving personalized $w$-event LDP. Experiments on real-world and synthetic streams show that, compared with the direct personalized extension of LDP-IDS, PLPA$^+$ reduces the average mean squared error (AMSE) by 52.3\% and 40.8\% on average, respectively, and remains 4.3--5.1$\times$ more accurate than PLPA under strong privacy--data correlation.

新论文 · v1arXiv:2611.10329

更新于 2026-11-07

SIRIS: Can LLMs Tell When Simulation Errors Matter in AI-Driven Systems Research

Jinwoo Hwang, Sukmin Cho, Tao Zhang, Soheil Abbasloo, Jongse Park, Peng Cheng

原文摘要

LLM agents increasingly drive systems research and rely on simulators to evaluate candidate designs at scale. Simulators are imperfect by design: their modeling limitations can distort predictions yet leave the recommended design correct, or change which design should be selected. We introduce Siris to test whether LLMs can tell these cases apart, judging from their systems understanding when a limitation invalidates the simulator's recommendation. By perturbing a simulator, Siris constructs workload pairs with the same candidates, limitation, and simulator recommendation. The perturbation changes predictions on both workloads, yet a reference simulator supports the recommendation on only one. Evaluating thirteen frontier LLMs on LLM serving design problems, we find that identifying a limitation does not ensure a correct choice, and even the strongest models sometimes abandon valid recommendations. Self-correction through a review turn helps these models restore valid choices but introduces new errors in several others. These findings motivate evaluating systems understanding through design decisions under imperfect simulators.

cs.AI

版本更新 · v3arXiv:2607.00466

更新于 2026-11-06

ELDR: Expert-Locality-Aware Decode Routing for PD-Disaggregated MoE Serving

Sangjin Choi, Sukmin Cho, Yifan Xiong, Ziyue Yang, Youngjin Kwon, Peng Cheng

原文摘要

Equal load does not mean equal decode latency in prefill-decode (PD) disaggregated mixture-of-experts (MoE) serving. In bandwidth-bound decode, more distinct experts mean more weight loads and higher latency. We present ELDR, a lossless, expert-locality-aware decode router. Prefill signatures predict decode expert usage. Balanced $K$-means fits per-decoder centroids with background refitting. Locality-band routing selects the least-loaded worker among close matches. A block-granular cache keeps signatures exact under prefix caching. We evaluate ELDR in vLLM on up to 40 GPUs. Across three MoE models on 12 public task benchmarks and multilingual WildChat, it reduces median time-per-output-token (TPOT) by 5.4-14.5% on average across request rates and up to 17.8% over the best of four load-balancing baselines, preserving model outputs and token-to-expert assignments.

cs.DC

版本更新 · v2arXiv:2610.11316

更新于 2026-11-06

MetaEncoder: Exploring the Limit of Bi-Encoders for Multimodal System One Decision Making with Natural Language Interface

Jianpeng Cheng, Guangyu Sun, Aashu Singh, Benyu Zhang, Haixing Dai, Hossein Mansour, Jiangfan Zhang, Shlok Kumar Mishra 等 14 位作者

原文摘要

System One models prioritize fast, low-latency decision-making and calibrated probability distributions over token generation. While prevailing systems rely on structured schemas and typed outputs, candidate-scoring trade-offs remain poorly understood. We systematically investigate the scaling dynamics, accuracy, and inference efficiency of three scoring architectures adapted from a pre-trained decoder: bi-encoders, cross-encoders, and constrained decoders. Experiments show clear performance scaling with model size across all three architectures. Cross-encoders consistently achieve the highest accuracy across model sizes, while bi-encoders and constrained decoders perform similarly as training tokens scale. Efficiency-wise, bi-encoders maintain near-constant inference latency as candidate pools grow because candidate embeddings can be cached offline, whereas cross-encoder and constrained-decoder costs scale quadratically with candidate size. This efficiency advantage makes bi-encoders uniquely suited to bridge small closed-set decisions and massive open-set retrieval. For closed-set tasks, including all candidate options in the request prompt is critical for bi-encoder competitiveness. Guided by these insights, we introduce MetaEncoder, a multimodal encoder fine-tuned from a Muse-Glimmer decoder via unidirectional contrastive learning to match requests and candidates. We present packed contrastive training to maximize efficiency for dynamically allocated negative candidates, enabling memory-efficient full fine-tuning of a 30B decoder into an encoder. Evaluated across 12 benchmark suites spanning closed-set decision-making, multimodal understanding, and open-set retrieval, MetaEncoder outperforms state-of-the-art multimodal embedding baselines on decision tasks and MMEB-v3 image/video retrieval, while scaling to open candidate spaces far too large for current decision models.

cs.CL · cs.AI

新论文 · v1arXiv:2611.02628

更新于 2026-11-02

ReplayFactor: Replay-Aware Dual-Attribute Learning for Practical Speech Deepfake Detection

Miao He, Jiayi Zhou, Yu Zheng, Zhongjie Ba, Peng Cheng, Yuxi Zhou, Junjie Zhu, Changhao Zhang 等 9 位作者

原文摘要

Advances in synthesis techniques have made deepfake speech increasingly realistic, amplifying voice security threats, including unauthorized voice cloning and impersonation. Although existing deepfake detectors perform well on digitally generated speech, their performance often degrades on physically replayed deepfakes, which constitute a low-cost and practical threat to real-world systems. Existing research focuses primarily on dataset construction and evaluation, while available dataset diversity and replay-aware detector design remain limited. We propose ReplayFactor, a dual-attribute framework that enhances replay evidence by fusing self-supervised learning (SSL) representations with an acoustic prior, while leveraging multi-task learning and supervised contrastive learning to promote robust, generalizable representations. We also construct ReplayFake-CN, a large-scale, multi-device Chinese dataset comprising approximately 185k replayed synthetic utterances to complement public benchmarks. Our framework improves macro-F1 from 60.99% to 76.44% and reduces EER from 11.86% to 3.05% on the EchoFake open set over the baseline, demonstrating strong potential for practical replayed deepfake detection.

cs.SD · eess.AS

版本更新 · v2arXiv:2610.05264

更新于 2026-11-02

Task-Aware Joint Pruning and Distillation for Efficient Audio Deepfake Detection

Miao He, Peng Cheng, Zhongjie Ba, Qing Wen, Li Lu, Xin Yang, Kui Ren

原文摘要

Advances in speech synthesis have made deepfake speeches increasingly convincing, posing growing threats to security. While self-supervised learning (SSL) based detectors achieve state-of-the-art performance, their computational demands (typically 300M+ parameters) prevent deployment on resource-constrained devices. Existing compression methods, designed mainly for content-centric tasks, struggle to maintain competitive performance when directly adapted to deepfake detection. We propose a Task-Aware Joint Pruning and Distillation framework that combines cross-domain knowledge distillation with movement-guided structured pruning to transfer forgery-discriminative knowledge and preserve critical structures under aggressive compression. Our framework reduces the model to 31.9M parameters with 6.3$\times$ FLOPs reduction, with an average performance drop of only 1.30\% across multiple datasets compared to the uncompressed baseline, demonstrating strong potential for on-device deployment.

cs.SD · cs.AI

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.120ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

Breaking the Generator Barrier: Disentangled Representation for Generalizable AI-Text Detection

Xiao Pu Chongqing University of Post and Telecommunications, Zepeng Cheng Chongqing University of Post and Telecommunications, Lin Yuan Chongqing University of Post and Telecommunications, Yu Wu Chongqing University of Post and Telecommunications, Xiuli Bi Chongqing University of Post and Telecommunications

原文摘要

As large language models (LLMs) generate text that increasingly resembles human writing, the subtle cues that distinguish AI-generated content from human-written content become increasingly challenging to capture. Reliance on generator-specific artifacts is inherently unstable, since new models emerge rapidly and reduce the robustness of such shortcuts. This generalizes unseen generators as a central and challenging problem for AI-text detection. To tackle this challenge, we propose a progressively structured framework that disentangles AI-detection semantics from generator-aware artifacts. This is achieved through a compact latent encoding that encourages semantic minimality, followed by perturbation-based regularization to reduce residual entanglement, and finally a discriminative adaptation stage that aligns representations with task objectives. Experiments on MAGE benchmark, covering 20 representative LLMs across 7 categories, demonstrate consistent improvements over state-of-the-art methods, achieving up to 24.2% accuracy gain and 26.2% F_1 improvement. Notably, performance continues to improve as the diversity of training generators increases, confirming strong scalability and generalization in open-set scenarios. Our source code will be publicly available at https://github.com/PuXiao06/DRGD.

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.1388ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

Reusable Experiences: Latent Routing and Modular Composition in LLMs

Shuai Ling Harbin Institute of Technology, Lizi Liao Singapore Management University, Dongmei Jiang Peng Cheng Laboratory, Weili Guan

原文摘要

Large language models (LLMs) have remarkable capabilities, but adapting them to specialized domains poses a fundamental question: how should accumulated experience be represented and leveraged? Existing approaches represent experience either as explicit textual artifacts in prompts (e.g., retrieved documents or dialogues) or implicitly within model weights via fine-tuning (e.g., LoRA adapters). However, textual methods are limited by context windows and cannot internalize knowledge, while parametric fine-tuning yields one adapter per task with minimal cross-task skill reuse. We propose ReX (Reusable eXperience), an experience-centric adaptation framework that treats latent experiences — recurring reasoning patterns and skills — as fundamental units for LLM specialization. Our method learns a shared Experience Bank of foundational skill vectors and uses a VAE-based encoder to map each input to a low-dimensional experience code. An Experience Router then dynamically composes the relevant skill vectors from this bank into a lightweight adapter for that input. By reusing skills across inputs, ReX enables implicit knowledge sharing across tasks without any explicit task identifiers. Experiments on multi-task NLP benchmarks show that this approach outperforms standard task-specific fine-tuning, yielding improved generalization through flexible skill reuse. Code is available at https://github.com/iLearn-Lab/ACL26-ReX.

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.1776ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

Evolving Beyond Snapshots: Harmonizing Structure and Sequence via Entity State Tuning for Temporal Knowledge Graph Forecasting

Siyuan Li Peng Cheng Laboratory and Harbin Institute of Technology, Shenzhen, Yunjia Wu, Yiyong Xiao Fudan University, Pingyang Huang, Peize Li, Ruitong Liu, Yan Wen, Te Sun

原文摘要

Temporal knowledge graph (TKG) forecasting requires predicting future facts by jointly modeling structural dependencies within each snapshot and temporal evolution across snapshots. However, most existing methods are stateless: they recompute entity representations at each timestamp from a limited query window, leading to episodic amnesia and rapid decay of long-term dependencies. To address this limitation, we propose Entity State Tuning (EST), an encoder-agnostic framework that endows TKG forecasters with persistent and continuously evolving entity states. EST maintains a global state buffer and progressively aligns structural evidence with sequential signals via a closed-loop design. Specifically, a topology-aware state perceiver first injects entity-state priors into structural encoding. Then, a unified temporal context module aggregates the state-enhanced events with a pluggable sequence backbone. Subsequently, a dual-track evolution mechanism writes the updated context back to the global entity state memory, balancing plasticity against stability. Experiments on multiple benchmarks show that EST consistently improves diverse backbones and achieves state-of-the-art performance, highlighting the importance of state persistence for long-horizon TKG forecasting.

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.427ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-training

Kailai Yang University of Manchester, Xiao Liu Microsoft Research Asia, Lei Ji, Hao Li, Xiao Liang, Zhiwei Liu, Yeyun Gong, Peng Cheng Microsoft Research 等 9 位作者

原文摘要

Continual pre-training on small-scale task-specific data is an effective method for improving large language models in new target fields, yet it risks catastrophic forgetting of their original capabilities. A common solution is to re-weight training data mixtures from source and target fields on a domain space to achieve balanced performance. Previous domain reweighting strategies rely on manual designation with certain heuristics based on human intuition or empirical results. In this work, we prove that more general heuristics can be parameterized by proposing Data Mixing Agent, the first model-based, end-to-end framework that learns to re-weight domains. The agent learns generalizable heuristics through reinforcement learning on large quantities of data mixing trajectories with corresponding feedback from an evaluation environment. Experiments in continual pre-training on math reasoning show that Data Mixing Agent outperforms strong baselines in achieving balanced performance across source and target field benchmarks. Furthermore, it generalizes well across unseen source fields, target models, and domain spaces without retraining. Direct application to the code generation field also indicates its adaptability across target domains. Further analysis showcases the agents’ well-aligned heuristics with human intuitions and their efficiency in achieving superior model performance with less source-field data.

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.602ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

RoboFailRing: Retrieval-Augmented and Language Grounding Failure Detection for VLM-enabled Robotic Manipulation

Chenduo Ying, Linkang Du Xi'an Jiaotong University, Yuanchao Shu Zhejiang University, Peng Cheng Zhejiang University

原文摘要

Reliable failure detection and causal reasoning are critical in robotic manipulation, as their absence risks robot damage and endangers human safety.Although recent Vision–Language Models (VLMs) are employed to attempt failure detection and causality reasoning, they typically make retrospective assessment only after task completion, and their reasoning accuracy is often limited.To address these issues, we introduce RoboFailRing, which enables timely failure detection during task execution and enhances the reasoning accuracy of VLMs.It achieves rapid failure detection by retrieving a pre-constructed failure memory and returning a similarity-based decision.In addition, by providing grounded failure report to VLMs, it improves the accuracy of their reasoning about the failure causes and repair strategies.We evaluate RoboFailRing on two large-scale simulated datasets comprising over 6,000 failure trajectories and covering 81 distinct manipulation tasks.The results show that the average success rate of out-of-distribution failure detection reaches 80%, while the mean detection time is cut to roughly 50% of the baseline.Moreover, evaluations on real-world systems show an average 35% gain in VLM failure-reasoning accuracy.We make our code publicly available at: https://github.com/DynamicPoet/RoboFailRing.

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.860ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

REG: Retrieval via Emotion Similarity for Guiding Empathetic Dialogue Generation

Xu Wang, Bo Wang, Yang Xiang Peng Cheng Laboratory, Yihong Tang, Dongming Zhao, Yuzifei, Yuexian Hou

原文摘要

Empathy relies on the cognitive capacity to relate to similar past experiences. Consequently, retrieval-based approaches utilize analogous exemplars to guide empathetic dialogue generation. However, existing methods prioritize semantic similarity over emotion characteristics, often leading to unempathetic responses. To address this, we propose REG, a framework that integrates four Emotion Attributes into the retrieval process to ensure explicit emotional alignment. Furthermore, to mitigate the noise and limited diversity caused by coarse-grained sentence-level attributes, we incorporate Token-level Retrieval for finer granularity and a Retrieval Candidate Augmentation strategy to enhance diversity. Empirical results on the EmpatheticDialogues dataset demonstrate that REG significantly outperforms baselines, offering a robust solution for empathetic generation.

cs.CL

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