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新论文 · v1arXiv:2611.15097

GuardianAgentFlow: Multi-Agent Coordination for Web Privacy Protection

Ruiyi Yang, Gayathri Lihinikaduarachchi, Rahat Masood, Flora D. Salim, Salil S. Kanhere

原文摘要

Web agents need to use private context without unnecessarily disclosing it. Effective protection must distinguish between requests that may be sent after sanitization and those that must be withheld, while preserving the information needed for authorized tasks. We present GuardianAgentFlow, a multi-agent framework that mediates outgoing Web requests through an explicit release contract. A dedicated restriction check grounds permission in the user's instruction; a shared protection scope aligns rewriting with disclosure checking; and candidate-grounded feedback guides revision before transmission. Experiments on Web requests with human-reviewed intent labels show improved request-contract success, while controlled comparisons and role ablations identify the contributions of restriction handling, policy evidence, and candidate checking. Evaluations of action proposals from PrivacyLens and AgentDAM demonstrate complementary protection when mediation is composed with privacy-aware agents. Experiments with smaller mediation models show that the coordination benefit persists with reduced model capacity. Dedicated-server measurements show lower mediation latency than full-policy reasoning, while real-site browser tests quantify integration overhead.

cs.AI · cs.MA

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2607.25875

EvoST: Adapting Traffic Forecasters to Evolving Sensor Graphs

Du Yin, Xiachong Lin, Yue Tan, Ruiyi Yang, Jinliang Deng, Estrid He, Hao Xue, Flora D. Salim

原文摘要

Web-based navigation and traffic information services depend on accurate forecasts as sensor networks evolve. On checkpoints reused years later, updating only shared weights or adding a low-rank node prompt leaves substantial degradation, whereas, in the evaluated architectures, refreshing node identities recovers most of it while the shared spatio-temporal weights are reused. We propose EvoST, a framework that maintains traffic forecasters by refreshing node identities while keeping shared weights fixed. EvoST updates existing identities using recently released labels and initializes new sensors with identities borrowed from behaviorally similar established sensors. Once enough delayed labels arrive, it jointly refits all identities, and a per-sensor recursive least-squares calibrator corrects forecasts as labels become available. Across twelve PeMS region-year settings, identity refresh reduces the relative one-hour MAE gap between five-year-old STAEFormer checkpoints and target-year models from 17.7% to 2.5%, with all forecasters calibrated, and its MAE is 0.7% above that of full-parameter fine-tuning on the same data. In Graph WaveNet, identity refresh recovers 88% of the five-year MAE gap to calibrated target-year models. Similar recovery holds for STAEFormer checkpoints up to fifteen years old, while refreshing STID recovers 67% of its five-year gap. On an annual benchmark covering nine EvoXXLTraffic districts and Transport for NSW, EvoST has the lowest average MAE, RMSE, and MAPE among the reported non-foundation methods on all ten networks, and calibration accounts for most of its Avg-MAE reduction from the matched frozen checkpoints. Code and the extended version: https://github.com/lixus7/EvoST

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09472

ENGRAM: From Agent Experience to Weights through Verified Consolidation

Ruiyi Yang, Zechen Li, Lihuan Li, Du Yin, Hao Xue, Flora D. Salim

原文摘要

Language-model agents use experience through external retrieval and parameter learning. Consolidating stored experience into model weights requires deciding both which experience to train on and when its external records can leave retrieval. Admission based solely on student mastery can exclude useful teacher demonstrations, while removal immediately after training can discard guidance that the model still needs. We introduce ENGRAM, a framework that separates training admission from memory release. A demonstration-aware gate admits skills based on student mastery or sufficient successful teacher demonstrations. The student learns from selected action traces with context from an Engram Trace Graph (ETG). A verification gate then compares the updated model without episodic retrieval against the base model with retrieval on reserved probes. Release requires valid training participation and scores meeting both relative and absolute criteria; records for skills that fail verification remain retrievable. Experiments across interactive and reasoning benchmarks show consistent gains over frozen-weight ETG controls, with ablations supporting demonstration-aware admission and selective adapter activation. A controlled held-out comparison shows that verified release substantially reduces the retrievable footprint while remaining close to retain-all performance and outperforming immediate and budget-matched random release. ENGRAM also improves task performance while reducing input-token usage and model calls. Skill-level verification further reduces retrievable experience through selective archival, retaining external guidance for skills that do not meet the release criteria. Code is available in anonymous.4open.science/r/engram-anonymous.

cs.AI · cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02550

Re$^3$Rank: Listwise Reranking with Reasoning-Conditioned Retrieval Heads

Ruiyi Yan, Zhuoyuan Mao, Yugo Murawaki, Yiwen Guo

原文摘要

Attention-based rerankers score each candidate by the attention mass that retrieval heads route to it from the question, but the question, serving as a fixed query, carries limited information for locating relevant evidence. We propose Re$^3$Rank, a listwise reranker built on reasoning-conditioned retrieval heads (RRHeads). A generator first writes a reasoning trace over the question and the candidates, and a scorer then uses the trace tokens as the attention query of a few selected RRHeads, reading relevance as continuous scores. We present a straight-through estimator to propagate the ranking loss through the discrete trace, so that the generator and the scorer can be trained jointly for ranking. Averaged over 9 benchmarks, Re$^3$Rank outperforms 11 baseline rerankers: our 4B variant exceeds the strongest baseline by 3.11 NDCG@10 points on average, and even our 0.6B variant surpasses rerankers with up to 32B parameters.

cs.CL

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.39ACL Anthology · ACL 2026

Efficient Provably Secure Linguistic Steganography via Range Coding

Ruiyi Yan Kyoto University, Yugo Murawaki Kyoto University

原文摘要

Linguistic steganography involves embedding secret messages within seemingly innocuous texts to enable covert communication. Provable security, which is a long-standing goal and key motivation, has been extended to language-model-based steganography. Previous provably secure approaches have achieved perfect imperceptibility, measured by zero Kullback-Leibler (KL) divergence, but at the expense of embedding capacity. In this paper, we attempt to directly use a classic entropy coding method (range coding) to achieve secure steganography, and then propose an efficient and provably secure linguistic steganographic method with a rotation mechanism. Experiments across various language models show that our method achieves around 100% entropy utilization (embedding efficiency) for embedding capacity, outperforming the existing baseline methods. Moreover, it achieves high embedding speeds (up to 1554.66 bits/s on GPT-2). The code is available at github.com/ryehr/RRC_steganography.

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.44ACL Anthology · ACL 2026

Anchored Sliding Window: Toward Robust and Imperceptible Linguistic Steganography

Ruiyi Yan Kyoto University, Shiao Meng Tsinghua University, Yugo Murawaki Kyoto University

原文摘要

Linguistic steganography based on language models typically assumes that steganographic texts are transmitted without alteration, making them fragile to even minor modifications. While previous work mitigates this fragility by limiting the context window, it significantly compromises text quality. In this paper, we propose the anchored sliding window (ASW) framework to improve imperceptibility and robustness. In addition to the latest tokens, the prompt and a bridge context are anchored within the context window, encouraging the model to compensate for the excluded tokens. We formulate the optimization of the bridge context as a variant of prompt distillation, which we further extend using self-distillation strategies. Experiments show that our ASW significantly and consistently outperforms the baseline method in text quality, imperceptibility, and robustness across diverse settings. The code is available at github.com/ryehr/ASW_steganography.

cs.CL

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