Complexity of Classical Acceleration for ℓ1-Regularized PageRank
Kimon Fountoulakis
University of Waterloo, Canada
kimon.fountoulakis@uwaterloo.ca
David Martínez-Rubio
IMDEA Software Institute, Madrid, Spain
david.martinezrubio@imdea.org
February 25, 2026
Abstract
We study the degree-weighted work required to compute ℓ1-regularized PageRank using the standard one-gradient-
per-iteration accelerated proximal-gradient method (FISTA). For non-accelerated local methods, the best known
worst-case w
Failed to generate LLM review.
生成研究方向失败。
规则：
- 翻译为自然的中文，而非逐字死板直译
- 保持论文标题为英文（如有帮助，可附带中文说明）
- 保持模型名称（GPT、Claude、Gemini 等）为英文
- 保持 URL 和链接不变
- 保留所有 Markdown 格式（标题、加粗、列表等）
- 仅输出翻译后的文本，不含解释说明
LUMEN：用于预后和诊断的长时程多模态放射学模型 (LONGITUDINAL MULTI-MODAL RADIOLOGY MODEL FOR PROGNOSIS AND
DIAGNOSIS)
Zhifan Jiang1
Dong Yang2
Vishwesh Nath2
Abhijeet Parida1,3
Nishad P. Kulkarni1
Ziyue Xu2
Daguang Xu2
Syed Muhammad Anwar1,4
Holger R. Roth2
Marius George Linguraru1,4
1 谢赫扎耶德儿童手术创新研究所，全美儿童医院，华盛顿特区，美国
2 Nvidia Corporation，圣克拉拉，加利福尼亚州，美国
3 马德里理工大学电信学院，马德里，西班牙
4 医学院与健康科学学院
生成 LLM 评审失败。
规则：
- 翻译应符合中文习惯，而非逐字直译
- 论文标题保留英文（如有必要，可附带中文说明）
- 模型名称（GPT、Claude、Gemini 等）保留英文
- 链接和 URL 保持原样
- 保留所有 Markdown 格式（标题、加粗、列表等）
- 仅输出翻译后的文本，无需解释
生成研究方向失败。
规则：
- 翻译应自然流畅，避免生硬的字面直译。
- 论文标题保留英文（如有必要，可附带中文说明）。
- 模型名称（GPT、Claude、Gemini 等）保留英文。
- 网址和链接保持原样。
- 保留所有 Markdown 格式（标题、加粗、列表等）。
- 仅输出翻译后的文本，不含任何解释。
SOM-VQ: Topology-Aware Tokenization for Interactive Generative Models
Alessandro Londei 1 Denise Lanzieri 1 Matteo Benati 1 2
Abstract
Vector-quantized representations enable power-
ful discrete generative models but lack semantic
structure in token space, limiting interpretable
human control. We introduce SOM-VQ, a to-
kenization method that combines vector quanti-
zation with Self-Organizing Maps to learn dis-
crete codebooks with explicit low-dimensional
topology. Unlike standard VQ-VAE, SOM-
Failed to generate LLM review.
Failed to generate research directions.
SparkMe: Adaptive Semi-Structured
Interviewing for Qualitative Insight Discovery
David Anugraha, Vishakh Padmakumar, Diyi Yang
Stanford University
{davidanu, vishakhp, diyiy}@stanford.edu
February 25, 2026
Abstract
Qualitative insights from user experiences are critical for informing product and policy decisions, but
collecting such data at scale is constrained by the time and availability of experts to conduct semi-structured
interviews. Recent work has explored using large language models (LLM
Failed to generate LLM review.
Failed to generate research directions.
Cooperative-Competitive Team Play of Real-World Craft Robots
Rui Zhao1∗, Xihui Li1,2∗, Yizheng Zhang1∗, Yuzhen Liu1∗,
Zhong Zhang1, Yufeng Zhang1, Cheng Zhou1, Zhengyou Zhang1, Lei Han1
Abstract— Multi-agent deep Reinforcement Learning (RL)
has made significant progress in developing intelligent game-
playing agents in recent years. However, the efficient training
of collective robots using multi-agent RL and the transfer
of learned policies to real-world applications remain open
research questi
Failed to generate LLM review.
Failed to generate research directions.
As AI "agents" evolve from simple chatbots into autonomous coworkers that handle our emails, medical data, and software code, we are entering a dangerous era of Agent-Mediated Deception. This research reveals a startling "Expert’s Paradox" where the more we trust these systems to handle complex tasks, the less likely we are to notice when a hidden attack has turned our trusted AI assistant into a digital double agent. By testing over 300 participants on a high-fidelity simulation platform called HAT-Lab, the authors found that a staggering 91% of users failed to detect stealthy attacks, often because their professional expertise created a "cognitive tunnel" that blinded them to security risks. To combat this, the study move beyond simple disclaimers, proving that the best defense is "calibrated friction"—smart, interruptive warnings that break our autopilot and force us to regain a healthy, protective skepticism of the algorithms we rely on.
Failed to generate LLM review.
Failed to generate research directions.
High-stakes reasoning in AI typically requires models to "think" out loud through long chains of thought, which makes them accurate but painfully slow and expensive to run. To solve this, researchers developed Prompt-Level Distillation (PLD), a clever shortcut that moves the complex logic of a giant "Teacher" model directly into the system instructions of a smaller, faster "Student" model. This approach allows compact models like Gemma-3 to perform complex legal and logical reasoning at super-human speeds without any expensive retraining or fine-tuning. By turning a black-box reasoning process into a set of transparent, human-readable instructions, PLD enables smaller AI to match the performance of industry leaders while remaining fast enough for real-time use in law, finance, and mobile devices.
Failed to generate LLM review.
Failed to generate research directions.
Ever wonder if you should keep renting skis or just buy them? This paper tackles the classic "ski rental" dilemma—making a decision today without knowing how long you’ll need it—by using a sophisticated weather-like forecast: a probability distribution instead of a single guess. The authors introduce a clever algorithm that uses these distributional predictions to minimize costs, proving that it remains highly efficient even if the prediction turns out to be wrong. Their main breakthrough is a strategy that doesn’t just perform brilliantly when the forecast is accurate, but also provides a guaranteed safety net if the forecast is a total disaster, all without needing to know the quality of the data beforehand.
Failed to generate LLM review.
Failed to generate research directions.
This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which
this version may no longer be accessible.
Attention-Based SINR Estimation in User-Centric
Non-Terrestrial Networks
Bruno De Filippo∗, Alessandro Guidotti∗†, Alessandro Vanelli-Coralli∗
∗Department of Electrical, Electronic, and Information Engineering (DEI), Univ. of Bologna, Bologna, Italy
†National Inter-University Consortium for Telecommunications (CNIT), Bologna, Italy
Failed to generate LLM review.
Failed to generate research directions.
Standard decision trees often struggle with complex data because they can only split information along one variable at a time, like trying to cut a diamond using only horizontal and vertical strokes. This paper introduces an enhanced "Projection Pursuit" tree classifier that finds the best diagonal angles to separate data groups, offering much-needed flexibility for high-dimensional problems where classes are overlapping or unusually shaped. To prove these upgrades actually work, the researchers developed interactive visual tools and "tours" that allow users to see exactly how the algorithm carves through 2D and 3D space. By consistently outperforming traditional models on dozens of benchmark datasets, this new approach provides a more powerful and interpretable way to navigate the "blind spots" of modern machine learning.
Failed to generate LLM review.
Failed to generate research directions.
Improving Parametric Knowledge Access
in Reasoning Language Models
Melody Ma and John Hewitt
Columbia University
{ym3065, jh5020}@columbia.edu
Abstract
We study reasoning for accessing world knowl-
edge stored in a language model’s parame-
ters. For example, recalling that Canberra is
Australia’s capital may benefit from thinking
through major cities and the concept of purpose-
built capitals. While reasoning language mod-
els are trained via reinforcement learning to
produce reasoning traces on
Failed to generate LLM review.
Failed to generate research directions.
SumTablets
:
A Transliteration Dataset of Sumerian Tablets
Cole Simmons
Stanford University
coles@stanford.edu
Richard Diehl Martinez
University of Cambridge
rd654@cam.ac.uk
Dan Jurafsky
Stanford University
jurafsky@stanford.edu
Abstract
Sumerian transliteration is a conventional
system for representing a scholar’s inter-
pretation of a tablet in the Latin script.
Thanks to visionary digital Assyriology
projects such as ETCSL, CDLI, and Oracc,
a large number of Sumerian transliter-
ations have b
Failed to generate LLM review.
Failed to generate research directions.
Recovered in Translation: Efficient Pipeline for Automated Translation of
Benchmarks and Datasets
Hanna Yukhymenko1†, 2, Anton Alexandrov1, Martin Vechev1,2
1INSAIT, Sofia University "St. Kliment Ohridski", 2ETH Zurich
Correspondence: hanna.yukhymenko@insait.ai
§ Code: insait-institute/ritranslation
Benchmarks: insait-institute/multilingual-benchmarks
Abstract
The reliability of multilingual Large Language
Model (LLM) evaluation is currently compro-
mised by the inconsistent quality of translate
Failed to generate LLM review.
Failed to generate research directions.
GUI-Libra: Training Native GUI Agents to Reason and Act
with Action-aware Supervision and Partially Verifiable RL
Rui Yang1†, Qianhui Wu2∗, Zhaoyang Wang3†, Hanyang Chen1, Ke Yang1†, Hao Cheng2
Huaxiu Yao3, Baolin Peng2, Huan Zhang1, Jianfeng Gao2, Tong Zhang1
1UIUC,
2Microsoft,
3UNC-Chapel Hill
https://gui-libra.github.io
Abstract
Open-source native GUI agents have made rapid progress in visual grounding and low-level action
execution, yet they still lag behind closed-source systems on long-h
Failed to generate LLM review.
Failed to generate research directions.
Surrogate models for Rock–Fluid Interaction: A Grid-Size-Invariant
Approach
Nathalie C. Pinheiroa,∗, Donghu Guoa, Hannah P. Menkeb, Aniket C. Joshia,c, Claire E.
Heaneya,d,∗, Ahmed H. ElSheikhb, Christopher C. Paina,d,e
aApplied Modelling and Computation Group, Department of Earth Science and Engineering, Imperial College
London, London, SW7 2AZ UK
bInstitute of GeoEnergy Engineering, Heriot-Watt University, Edinburgh, EH14 1AS UK
cDepartment of Civil and Environmental Engineering, Imperial Coll
Failed to generate LLM review.
Failed to generate research directions.
DYSCO: Dynamic Attention-Scaling Decoding for Long-Context LMs
Xi Ye * 1 Wuwei Zhang * 1 Fangcong Yin 2 Howard Yen 1 Danqi Chen 1
Abstract
Understanding and reasoning over long contexts
is a crucial capability for language models (LMs).
Although recent models support increasingly long
context windows, their accuracy often deterio-
rates as input length grows. In practice, models
often struggle to keep attention aligned with the
most relevant context throughout decoding. In
this work, we propose
Failed to generate LLM review.
Failed to generate research directions.
As generative AI continues to grow, many creators have turned to "invisible shields"—imperceptible digital perturbations designed to protect images from being stolen, mimicked, or turned into deepfakes. However, this research reveals a startling vulnerability: common, off-the-shelf AI tools like ChatGPT (GPT-4o) and Stable Diffusion can be easily repurposed as "universal denoisers" to strip away these protections with a simple text prompt. By testing eight different case studies, the authors prove that these widely used generative models actually outperform specialized hacking tools at breaking defenses, often restoring the original image's quality while rendering the security measures useless. This study serves as a wake-up call for the cybersecurity community, demonstrating that current image protection schemes offer a false sense of security and must be reinvented to survive the power of modern AI.
Failed to generate LLM review.
这是一次非常精彩的分析请求。这篇论文提出了一个引人注目且令人担忧的发现：艺术家和创作者所恐惧的生成式模型，本身也是拆解他们所采用的防御手段的强力工具。这种“收敛威胁”（convergent threat）是未来研究的一个极佳切入点。
以下是针对未来研究方向和领域的建议，按要求进行了分类。
这些是基于论文的方法论和发现而直接展开的逻辑后续步骤。
扩大攻击范围：
表征攻击面：
这些是更具野心的项目，旨在创建能够抵御本文所识别的攻击矢量的下一代防御措施。核心挑战在于设计去噪器要么将其作为信号保留，要么不破坏图像就无法移除的扰动。
语义与风格空间扰动：
本文的攻击之所以有效，是因为它将扰动视为高频噪声。下一个前沿是设计并非噪声、而是具有意义的语义信息的扰动。
针对去噪器的对抗性攻击：
论文中的攻击破坏了防御者的效用。一种新颖的防御可以旨在破坏攻击者的效用。
作为去噪固定点的扰动：
论文显示，旨在使扰动具备“去噪器感知”能力的简单对抗训练失败了。这指向了一个更基本的优化问题。
D 的近似固定点（fixed points）的保护性扰动 P。目标是求解 P，使得 D(Image + P) ≈ Image + P。换句话说，去噪器认为受保护的图像已经是“干净”的，因此只做极小的改动，从而保留了保护性能。这是一个极具挑战性但潜在非常鲁棒的防御方向。鲁棒的低频水印：
论文强调了 VINE 的低频方法“很有前景”，但其实现存在“缺陷”（由于边缘伪影而易受裁剪影响）。
这些是论文暴露出的空白或关键问题。
黑盒背后的“为什么”： 最强的攻击者 GPT-4o 是闭源模型。目前尚不清楚为什么它的架构或训练使其如此有效。是因为自回归特性、庞大的训练规模、多模态预训练，还是其他原因？需要开展安全可解释性研究，旨在探测和理解基础模型中使其能有效“去噪”的具体机制，从而构建更好的防御。
生成式洗白的取证： 这种攻击可以被视为“洗白”受保护图像以移除其保护措施的过程。一个未被探索的问题是检测这种洗白过程。 经这些去噪器处理后的图像是否具有独特的、可检测的“指纹”？研究可以集中于构建一个分类器，区分原始干净图像、受保护图像以及通过了 img2img 去噪器的“洗白”图像。这将是一个至关重要的取证工具。
生成式攻击下的效用-安全前沿： 论文实际上使先前关于保护强度与图像质量权衡的假设失效了。未探索的问题是正式描绘新的帕累托前沿（Pareto frontier）。 对于针对尖端 img2img 攻击者（如 FLUX 或 GPT-4o）的给定鲁棒性水平，可实现的最大图像效用（PSNR, SSIM, BRISQUE）是多少？这为所有未来的保护方案创建了一个新的、难度大得多的基准。
论文的发现虽然是在安全背景下提出的，但具有更广泛的意义。
“通用去噪”的积极应用： 攻击本身就是一种高效的盲图像修复技术。
基础模型的新基准： 论文的方法可以被重新利用为评估指标。
AI 生态系统的“免疫系统”：
LiCQA : A Lightweight Complex Question Answering System
Sourav Saha
Indian Statistical Institute
Kolkata, India
sourav.saha_r@isical.ac.in
Dwaipayan Roy
Indian Institute of Science Education
and Research
Kolkata, India
dwaipayan.roy@iiserkol.ac.in
Mandar Mitra
Indian Statistical Institute
Kolkata, India
mandar@isical.ac.in
Abstract
Over the last twenty years, significant progress has been made in
designing and implementing Question Answering (QA) systems.
However, addressing complex questions, t
Failed to generate LLM review.
Excellent analysis of the research paper "LiCQA: A Lightweight Complex Question Answering System". Based on its contributions, methodology, and limitations, here are several potential research directions and areas for future work, focusing on actionable and innovative ideas.
These are ideas that build directly on the LiCQA pipeline, improving its individual components or refining its core logic.
max-score aggregation (using only the single best-matching sentence) worked best. This suggests that for many complex questions, a single, highly relevant sentence is sufficient. An extension would be to develop an adaptive aggregation strategy. The system could first check the max-score. If it's above a certain confidence threshold, it's used. If not, the system could fall back to a more sophisticated aggregation model (like avg-maxscore or a weighted average) that synthesizes evidence from weaker, distributed signals. This would combine the precision of max-score with the recall of other methods.comb-score*). This is an unsupervised heuristic. A direct extension is to replace this with a lightweight, learnable ranking model (e.g., a simple linear model, or LambdaMART). One could create a small, domain-specific dataset of (question, candidate answer, relevance) tuples to train this model, turning LiCQA into a "weakly supervised" system that learns how to best combine different evidence features (e.g., df, max-score, average score, entity prominence) without needing a large, end-to-end training corpus.These are more transformative ideas that take LiCQA's core philosophy—lightweight, corpus-based, unsupervised—and apply it to new problems or architectures.
+"Brad Pitt" +"Troy", +"Brad Pitt" +"Seven").This work, by succeeding in some areas, implicitly shines a light on problems that remain unsolved.
max-score model works when a single sentence contains most of the required context. What if the evidence is spread across a paragraph? E.g., "The film starred Actor X. ... It was directed by Director Y. ... The movie went on to win an Oscar for best picture." Answering "Which Oscar-winning film starred Actor X and was directed by Director Y?" is impossible for LiCQA if no single sentence contains all three elements. The unexplored problem is entity-centric context aggregation, where a system builds a "profile" for an entity by merging information from multiple sentences within a document before scoring, using techniques like co-reference resolution.The "lightweight, fast, and unsupervised" nature of LiCQA makes it uniquely suited for specific domains where other methods fail.
Learning and Naming Subgroups with Exceptional Survival Characteristics
Mhd Jawad Al Rahwanji 1 Sascha Xu 1 Nils Philipp Walter 1 Jilles Vreeken 1
Abstract
In many applications, it is important to iden-
tify subpopulations that survive longer or shorter
than the rest of the population. In medicine, for
example, it allows determining which patients
benefit from treatment, and in predictive main-
tenance, which components are more likely to
fail. Existing methods for discovering subgroups
with exc
Failed to generate LLM review.
Failed to generate research directions.
DYNAMIC PERSONALITY ADAPTATION IN
LARGE LANGUAGE MODELS VIA STATE MACHINES
PREPRINT
Leon Pielage1,2,
Ole Hätscher3,
Prof. Dr. Mitja Back
3,
Prof. Dr. med. Bernhard Marschall4, and
Prof. Dr. Benjamin Risse*1,2
1Institute for Geoinformatics, University of Münster, 48149 Münster, Germany
2Faculty of Mathematics and Computer Science, University of Münster, 48149 Münster, Germany
3Department of Psychology, University of Münster, 48149 Münster, Germany
4Institute of Medical Education and Student Affai
Failed to generate LLM review.
生成研究方向失败。
规则：
- 翻译为自然的中文，而非逐字死译
- 论文标题保留英文（如有必要，可附加中文说明）
- 模型名称（GPT、Claude、Gemini 等）保留英文
- 网址和链接保持不变
- 保留所有 Markdown 格式（标题、加粗、列表等）
- 仅输出翻译后的文本，不含解释说明