Tootfinder

Zelensky doesn't see corruption as a problem, prosecuted activist says: https://benborges.xyz/2025/07/18/zelensky-doesnt-see-corruption-as.html

Beyond Solving Math Quiz: Evaluating the Ability of Large Reasoning Models to Ask for Information
Youcheng Huang, Bowen Qin, Chen Huang, Duanyu Feng, Xi Yang, Wenqiang Lei
https://arxiv.org/abs/2508.11252

Beyond Solving Math Quiz: Evaluating the Ability of Large Reasoning Models to Ask for Information
Large Reasoning Models (LRMs) have demonstrated remarkable problem-solving abilities in mathematics, as evaluated by existing benchmarks exclusively on well-defined problems. However, such evaluation setup constitutes a critical gap, since a genuine intelligent agent should not only solve problems (as a math quiz solver), but also be able~to ask for information when the problems lack sufficient information, enabling proactivity in responding users' requests. To bridge such gap, we proposes a ne…

Efficient Online Learning and Adaptive Planning for Robotic Information Gathering Based on Streaming Data
Sanjeev Ramkumar Sudha, Joel Jose, Erlend M. Coates
https://arxiv.org/abs/2507.13053

Efficient Online Learning and Adaptive Planning for Robotic Information Gathering Based on Streaming Data
Robotic information gathering (RIG) techniques refer to methods where mobile robots are used to acquire data about the physical environment with a suite of sensors. Informative planning is an important part of RIG where the goal is to find sequences of actions or paths that maximize efficiency or the quality of information collected. Many existing solutions solve this problem by assuming that the environment is known in advance. However, real environments could be unknown or time-varying, and a…

Information-Theoretic Fairness with A Bounded Statistical Parity Constraint
Amirreza Zamani, Abolfazl Changizi, Ragnar Thobaben, Mikael Skoglund
https://arxiv.org/abs/2508.12847

Information-Theoretic Fairness with A Bounded Statistical Parity Constraint
In this paper, we study an information-theoretic problem of designing a fair representation that attains bounded statistical (demographic) parity. More specifically, an agent uses some useful data $X$ to solve a task $T$. Since both $X$ and $T$ are correlated with some sensitive attribute or secret $S$, the agent designs a representation $Y$ that satisfies a bounded statistical parity and/or privacy leakage constraint, that is, such that $I(Y;S) \leq ε$. Here, we relax the perfect demographic …

SpecDiff: Accelerating Diffusion Model Inference with Self-Speculation
Jiayi Pan, Jiaming Xu, Yongkang Zhou, Guohao Dai
https://arxiv.org/abs/2509.13848 https://

SpecDiff: Accelerating Diffusion Model Inference with Self-Speculation
Feature caching has recently emerged as a promising method for diffusion model acceleration. It effectively alleviates the inefficiency problem caused by high computational requirements by caching similar features in the inference process of the diffusion model. In this paper, we analyze existing feature caching methods from the perspective of information utilization, and point out that relying solely on historical information will lead to constrained accuracy and speed performance. And we prop…

Atom-Searcher: Enhancing Agentic Deep Research via Fine-Grained Atomic Thought Reward
Yong Deng, Guoqing Wang, Zhenzhe Ying, Xiaofeng Wu, Jinzhen Lin, Wenwen Xiong, Yuqin Dai, Shuo Yang, Zhanwei Zhang, Qiwen Wang, Yang Qin, Changhua Meng
https://arxiv.org/abs/2508.12800

Atom-Searcher: Enhancing Agentic Deep Research via Fine-Grained Atomic Thought Reward
Large language models (LLMs) exhibit remarkable problem-solving abilities, but struggle with complex tasks due to static internal knowledge. Retrieval-Augmented Generation (RAG) enhances access to external information, yet remains limited in multi-hop reasoning and strategic search due to rigid workflows. Recent advancements in agentic deep research empower LLMs to autonomously reason, search, and synthesize information. However, current approaches relying on outcome-based reinforcement learnin…

Transient-Stability-Aware Frequency Provision in IBR-Rich Grids via Information Gap Decision Theory and Deep Learning
Amin Masoumi, Mert Korkali
https://arxiv.org/abs/2507.13265

Transient-Stability-Aware Frequency Provision in IBR-Rich Grids via Information Gap Decision Theory and Deep Learning
This paper introduces a framework to address the critical loss of transient stability caused by reduced inertia in grids with high inverter-based resource (IBR) penetration. The proposed method integrates a predictive deep learning (DL) model with information gap decision theory (IGDT) to create a risk-averse dispatch strategy. By reformulating the conventional virtual inertia scheduling (VIS) problem, the framework uses early predictions of post-fault dynamics to proactively redispatch resourc…

It's funny how people asking for someone's gender when the information they need is pronouns. Whoever came up with the term XY problem clearly chose a suitable name.

Projective Plane Subdivision Method For Initial Orbit Determination
Ruiqi Huang, Anton Leykin, Michela Mancini
https://arxiv.org/abs/2509.14397 https://arx…

Projective Plane Subdivision Method For Initial Orbit Determination
Initial Orbit Determination (IOD) is the classical problem of estimating the orbit of a body in space without any presumed information about the orbit. The geometric formulation of the ''angles-only'' IOD in three-dimensional space: find a conic curve with a given focal point meeting the given lines of sight (LOS). We provide an algebraic reformulation of this problem and confirm that five is the minimal number of lines necessary to have a finite number of solutions in a non-special case, and t…

Limit theory for mean-field control problems with common noise adapted controls
Bruno Bouchard, Xiaolu Tan
https://arxiv.org/abs/2509.14734 https://arxiv.o…

Limit theory for mean-field control problems with common noise adapted controls
We consider a mean-field control problem in which admissible controls are required to be adapted to the common noise filtration. The main objective is to show how the mean-field control problem can be approximates by time consistent centralized finite population problems in which the central planner has full information on all agents' states and gives an identical signal to all agents. We also aim at establishing the optimal convergence rate. In a first general path-dependent setting, we only p…

Who to Trust? Aggregating Client Knowledge in Logit-Based Federated Learning
Viktor Kovalchuk, Nikita Kotelevskii, Maxim Panov, Samuel Horv\'ath, Martin Tak\'a\v{c}
https://arxiv.org/abs/2509.15147

Who to Trust? Aggregating Client Knowledge in Logit-Based Federated Learning
Federated learning (FL) usually shares model weights or gradients, which is costly for large models. Logit-based FL reduces this cost by sharing only logits computed on a public proxy dataset. However, aggregating information from heterogeneous clients is still challenging. This paper studies this problem, introduces and compares three logit aggregation methods: simple averaging, uncertainty-weighted averaging, and a learned meta-aggregator. Evaluated on MNIST and CIFAR-10, these methods reduce…

Spatial-CLAP: Learning Spatially-Aware audio--text Embeddings for Multi-Source Conditions
Kentaro Seki, Yuki Okamoto, Kouei Yamaoka, Yuki Saito, Shinnosuke Takamichi, Hiroshi Saruwatari
https://arxiv.org/abs/2509.14785

Spatial-CLAP: Learning Spatially-Aware audio--text Embeddings for Multi-Source Conditions
Contrastive language--audio pretraining (CLAP) has achieved remarkable success as an audio--text embedding framework, but existing approaches are limited to monaural or single-source conditions and cannot fully capture spatial information. The central challenge in modeling spatial information lies in multi-source conditions, where the correct correspondence between each sound source and its location is required. To tackle this problem, we propose Spatial-CLAP, which introduces a content-aware s…

Reducing AoI and Improving Throughput for NOMA-assisted SGF Systems: A Hierarchical Learning Approach
Yuqin Liu, Mona Jaber, Yan Liu, Arumugam Nallanathan
https://arxiv.org/abs/2508.11473

Reducing AoI and Improving Throughput for NOMA-assisted SGF Systems: A Hierarchical Learning Approach
A non-orthogonal multiple access (NOMA) assisted semi-grant-free (SGF) framework is proposed to enable channel access for grant-free users (GFUs) by using residual resources from grant-based users. Under this framework, the problem of joint beamforming design and transmission scheduling is formulated to improve the system throughput and reduce the age-of-information of GFUs. The aforementioned problem is transferred into a Markov Decision Process to model the changing environment with the trans…

Positive maps and extendibility hierarchies from copositive matrices
Aabhas Gulati, Ion Nechita, Sang-Jun Park
https://arxiv.org/abs/2509.15201 https://arx…

Positive maps and extendibility hierarchies from copositive matrices
The characterization of positive, non-CP linear maps is a central problem in operator algebras and quantum information theory, where such maps serve as entanglement witnesses. This work introduces and systematically studies a new convex cone of PCOP (pairwise copositive). We establish that this cone is dual to the cone of PCP (pairwise completely positive) and, critically, provides a complete characterization for the positivity of the broad class of covariant maps. We provide a way to lift matr…

Fluid Dynamics and Domain Reconstruction from Noisy Flow Images Using Physics-Informed Neural Networks and Quasi-Conformal Mapping
Han Zhang, Xue-Cheng Tai, Jean-Michel Morel, Raymond H. Chan
https://arxiv.org/abs/2508.11216

Fluid Dynamics and Domain Reconstruction from Noisy Flow Images Using Physics-Informed Neural Networks and Quasi-Conformal Mapping
Blood flow imaging provides important information for hemodynamic behavior within the vascular system and plays an essential role in medical diagnosis and treatment planning. However, obtaining high-quality flow images remains a significant challenge. In this work, we address the problem of denoising flow images that may suffer from artifacts due to short acquisition times or device-induced errors. We formulate this task as an optimization problem, where the objective is to minimize the discrep…

LKFMixer: Exploring Large Kernel Feature For Efficient Image Super-Resolution
Yinggan Tang, Quanwei Hu
https://arxiv.org/abs/2508.11391 https://arxiv.org/p…

LKFMixer: Exploring Large Kernel Feature For Efficient Image Super-Resolution
The success of self-attention (SA) in Transformer demonstrates the importance of non-local information to image super-resolution (SR), but the huge computing power required makes it difficult to implement lightweight models. To solve this problem, we propose a pure convolutional neural network (CNN) model, LKFMixer, which utilizes large convolutional kernel to simulate the ability of self-attention to capture non-local features. Specifically, we increase the kernel size to 31 to obtain the larg…

Is This News Still Interesting to You?: Lifetime-aware Interest Matching for News Recommendation
Seongeun Ryu, Yunyong Ko, Sang-Wook Kim
https://arxiv.org/abs/2508.13064 https:/…

Is This News Still Interesting to You?: Lifetime-aware Interest Matching for News Recommendation
Personalized news recommendation aims to deliver news articles aligned with users' interests, serving as a key solution to alleviate the problem of information overload on online news platforms. While prior work has improved interest matching through refined representations of news and users, the following time-related challenges remain underexplored: (C1) leveraging the age of clicked news to infer users' interest persistence, and (C2) modeling the varying lifetime of news across topics and us…

Dynamic Non-Bayesian Persuasion
Masanori Kobayashi
https://arxiv.org/abs/2508.12328 https://arxiv.org/pdf/2508.12328

Dynamic Non-Bayesian Persuasion
We study a multi-step persuasion problem involving a Bayesian sender and a non-Bayesian receiver. When the receiver deviates from Bayesian updating, the sender may benefit from -- or be harmed by -- gradually revealing information over time. We show that under divisible updating rules (Cripps, 2018), delaying information provision does not affect the sender's ex-ante value. Focusing on the $α$--$β$ rule of Grether (1980), we derive the necessary and sufficient conditions under which the sende…

An Efficient Network-aware Direct Search Method for Influence Maximization
Matteo Bergamaschi, Sara Venturini, Francesco Tudisco, Francesco Rinaldi
https://arxiv.org/abs/2508.12164

An Efficient Network-aware Direct Search Method for Influence Maximization
Influence Maximization (IM) is a pivotal concept in social network analysis, involving the identification of influential nodes within a network to maximize the number of influenced nodes, and has a wide variety of applications that range from viral marketing and information dissemination to public health campaigns. IM can be modeled as a combinatorial optimization problem with a black-box objective function, where the goal is to select $B$ seed nodes that maximize the expected influence spread.…

AROMA: Mixed-Initiative AI Assistance for Non-Visual Cooking by Grounding Multi-modal Information Between Reality and Videos
Zheng Ning, Leyang Li, Daniel Killough, JooYoung Seo, Patrick Carrington, Yapeng Tian, Yuhang Zhao, Franklin Mingzhe Li, Toby Jia-Jun Li
https://arxiv.org/abs/2507.10963

AROMA: Mixed-Initiative AI Assistance for Non-Visual Cooking by Grounding Multi-modal Information Between Reality and Videos
Videos offer rich audiovisual information that can support people in performing activities of daily living (ADLs), but they remain largely inaccessible to blind or low-vision (BLV) individuals. In cooking, BLV people often rely on non-visual cues, such as touch, taste, and smell, to navigate their environment, making it difficult to follow the predominantly audiovisual instructions found in video recipes. To address this problem, we introduce AROMA, an AI system that provides timely responses t…

The “Neural Data” Goldilocks Problem: Defining “Neural Data” in U.S. State Privacy Laws
https://fpf.org/blog/the-neural-data-goldilocks-problem-defining-neural-data-in-u-s-state-privacy-laws/
@…

In 2030, OpenAI projects $200B in revenue, with R&D spending hitting ~45% of that, or ~$90B; R&D costs of Alphabet and others are currently 10%-20% of revenue (The Information)
https://www.theinformation.com/articles/openais-350-billion-computing-co…

OpenAI’s $350 Billion Computing Cost Problem
The most popular consumer internet services usually figure out how to make money even after a decade or more of losses. Amazon, Uber, Netflix and Snap all started generating cash by raising prices or leveraging their large audiences to sell ads or other services. OpenAI’s growing audience would ...

An optimal experimental design approach to sensor placement in continuous stochastic filtering
Sahani Pathiraja, Claudia Schillings, Philipp Wacker
https://arxiv.org/abs/2508.12288

An optimal experimental design approach to sensor placement in continuous stochastic filtering
Sequential filtering and spatial inverse problems assimilate data points distributed either temporally (in the case of filtering) or spatially (in the case of spatial inverse problems). Sometimes it is possible to choose the position of these data points (which we call sensors here) in advance, with the goal of maximising the expected information gain (or a different metric of performance) from future data, and this leads to an Optimal Experimental Design (OED) problem. Here we revisit an inter…

Detecting $k$-nonstretchability via a class of informationally complete symmetric measurements
Yan Hong, Mengjia Zhang, Limin Gao, Huaqi Zhou, Limei Zhang
https://arxiv.org/abs/2508.12817

Detecting $k$-nonstretchability via a class of informationally complete symmetric measurements
Multipartite entanglement characterization is a core problem in quantum information theory, with several classification schemes available. Among these, the $k$-stretchability, recently introduced [Szalay, Quantum 3, 204 (2019)], can be used to describe multipartite entanglement structure. Recently, a class of symmetric measurements (informationally complete $(s,t)$-POVMs) has been proposed [Siudzi$\textrm{ń}$ska, Phys. Rev. A \textbf{105}, 042209 (2022)] , which includes GSIC-POVMs and MUMs. I…

Towards a category-theoretic foundation of Classical and Quantum Information Geometry
Florio M. Ciaglia, Fabio Di Cosmo, Laura Gonz\'alez-Bravo
https://arxiv.org/abs/2509.10262

Towards a category-theoretic foundation of Classical and Quantum Information Geometry
We introduce the category $\mathsf{NCP}$, whose objects are pairs of W$^\ast$-algebras and normal states and whose morphisms are state-preserving unital completely positive (CPU) maps, as a common stage for classical and quantum information geometry, and we formulate two results that will appear in forthcoming works. First, we recast the problem of classifying admissible Riemannian geometries on classical and quantum statistical models in terms of functors $\mathfrak{C}:\mathsf{NCP}\to\mathsf…

Developing Visual Augmented Q&A System using Scalable Vision Embedding Retrieval & Late Interaction Re-ranker
Rachna Saxena, Abhijeet Kumar, Suresh Shanmugam
https://arxiv.org/abs/2507.12378

Developing Visual Augmented Q&A System using Scalable Vision Embedding Retrieval & Late Interaction Re-ranker
Traditional information extraction systems face challenges with text only language models as it does not consider infographics (visual elements of information) such as tables, charts, images etc. often used to convey complex information to readers. Multimodal LLM (MLLM) face challenges of finding needle in the haystack problem i.e., either longer context length or substantial number of documents as search space. Late interaction mechanism over visual language models has shown state of the art p…

Quickest Change Detection with Cost-Constrained Experiment Design
Patrick Vincent N. Lubenia, Taposh Banerjee
https://arxiv.org/abs/2509.14186 https://arxi…

Quickest Change Detection with Cost-Constrained Experiment Design
In the classical quickest change detection problem, an observer performs only one experiment to monitor a stochastic process. This paper considers the case where, at each observation time, the decision-maker needs to choose between multiple experiments with different information qualities and costs. The goal is to minimize the worst-case average detection delay subject to false alarm and cost constraints. An algorithm called the 2E-CUSUM Algorithm has been developed to achieve this goal for the…

A Nonparallel Support Tensor Machine for Binary Classification based Large Margin Distribution and Iterative Optimization
Zhuolin Du, Yisheng Song
https://arxiv.org/abs/2507.13012

A Nonparallel Support Tensor Machine for Binary Classification based Large Margin Distribution and Iterative Optimization
Based on the tensor-based large margin distribution and the nonparallel support tensor machine, we establish a novel classifier for binary classification problem in this paper, termed the Large Margin Distribution based NonParallel Support Tensor Machine (LDM-NPSTM). The proposed classifier has the following advantages: First, it utilizes tensor data as training samples, which helps to comprehensively preserve the inherent structural information of high-dimensional data, thereby improving class…

Information Field Theory based Event Reconstruction for Cosmic Ray Radio Detectors
Simon Str\"ahnz, Tim Huege, Torsten En{\ss}lin, Karen Terveer, Anna Nelles
https://arxiv.org/abs/2507.10738

Information Field Theory based Event Reconstruction for Cosmic Ray Radio Detectors
Detection of extensive air showers with radio antennas is an appealing technique in cosmic ray physics. However, because of the high level of measurement noise, current reconstruction methods still leave room for improvement. Furthermore, reconstruction efforts typically focus only on a single aspect of the signal, such as the energy fluence or arrival time. Bayesian inference is then a natural choice for a holistic approach to reconstruction, yet, this problem would be ill-posed, since the ele…

High-dimensional maximum-entropy phase space tomography
Austin Hoover
https://arxiv.org/abs/2508.11227 https://arxiv.org/pdf/2508.11227

High-dimensional maximum-entropy phase space tomography
Reconstructing 4D or 6D phase space distributions from 1D or 2D measurements is a challenging inverse problem encountered in particle accelerators. Entropy maximization is an established method to incorporate prior information in the reconstruction, but it is typically infeasible in high-dimensional spaces. In this paper, I review two recent approaches to high-dimensional entropy maximization. The first approach utilizes differentiable simulations and a class of generative models known as \text…

Sampled-Based Guided Quantum Walk: Non-variational quantum algorithm for combinatorial optimization
Ugo Nzongani, Dylan Laplace Mermoud, Giuseppe Di Molfetta, Andrea Simonetto
https://arxiv.org/abs/2509.15138

Sampled-Based Guided Quantum Walk: Non-variational quantum algorithm for combinatorial optimization
We introduce SamBa-GQW, a novel quantum algorithm for solving binary combinatorial optimization problems of arbitrary degree with no use of any classical optimizer. The algorithm is based on a continuous-time quantum walk on the solution space represented as a graph. The walker explores the solution space to find its way to vertices that minimize the cost function of the optimization problem. The key novelty of our algorithm is an offline classical sampling protocol that gives information about…

Happens a lot. There was a bug in the MedAdvisor Android app. It wouldn't accept photos of scripts for several weeks. I sent them an email explaining the problem we had encountered. I included the information that it didn't work on either my phone model or the husband's different model both running android version etc. Their first response was to ask what phone I was using. 🤦‍♀️
#TechHelp

SmokeSVD: Smoke Reconstruction from A Single View via Progressive Novel View Synthesis and Refinement with Diffusion Models
Chen Li, Shanshan Dong, Sheng Qiu, Jianmin Han, Zan Gao, Kemeng Huang, Taku Komura
https://arxiv.org/abs/2507.12156

SmokeSVD: Smoke Reconstruction from A Single View via Progressive Novel View Synthesis and Refinement with Diffusion Models
Reconstructing dynamic fluids from sparse views is a long-standing and challenging problem, due to the severe lack of 3D information from insufficient view coverage. While several pioneering approaches have attempted to address this issue using differentiable rendering or novel view synthesis, they are often limited by time-consuming optimization and refinement processes under ill-posed conditions. To tackle above challenges, we propose SmokeSVD, an efficient and effective framework to progress…

Data Synchronization at High Frequencies
Xinbing Kong, Cheng Liu, Bin Wu
https://arxiv.org/abs/2507.12220 https://arxiv.org/pdf/2507.…

Data Synchronization at High Frequencies
Asynchronous trading in high-frequency financial markets introduces significant biases into econometric analysis, distorting risk estimates and leading to suboptimal portfolio decisions. Existing synchronization methods, such as the previous-tick approach, suffer from information loss and create artificial price staleness. We introduce a novel framework that recasts the data synchronization challenge as a constrained matrix completion problem. Our approach recovers the potential matrix of high-…

Friend or Foe: Delegating to an AI Whose Alignment is Unknown
Drew Fudenberg, Annie Liang
https://arxiv.org/abs/2509.14396 https://arxiv.org/pdf/2509.14396…

Friend or Foe: Delegating to an AI Whose Alignment is Unknown
AI systems have the potential to improve decision-making, but decision makers face the risk that the AI may be misaligned with their objectives. We study this problem in the context of a treatment decision, where a designer decides which patient attributes to reveal to an AI before receiving a prediction of the patient's need for treatment. Providing the AI with more information increases the benefits of an aligned AI but also amplifies the harm from a misaligned one. We characterize how the de…

The Pandora's Box Problem with Sequential Inspections
Ali Aouad, Jingwei Ji, Yaron Shaposhnik
https://arxiv.org/abs/2507.07508 https://

The Pandora's Box Problem with Sequential Inspections
The Pandora's box problem (Weitzman 1979) is a core model in economic theory that captures an agent's (Pandora's) search for the best alternative (box). We study an important generalization of the problem where the agent can either fully open boxes for a certain fee to reveal their exact values or partially open them at a reduced cost. This introduces a new tradeoff between information acquisition and cost efficiency. We establish a hardness result and employ an array of techniques in stochasti…

GMail:
"Be careful with this message.
This message appears to be sent from your account but Gmail couldn't verify this. Someone might be impersonating your account. If you're not sure the message is from you, use caution when clicking links, downloading attachments, or replying with personal information."
I sent it while logged in to GMail using multiple authentication steps from my home network using a secure laptop. What are they talking about? Serious question: how could they not verify it? It's either an idiotic engineering problem on their end or I'm terrified that security is meaningless. Probably both.

TL;DR: what if instead of denying the harms of fascism, we denied its suppressive threats of punishment
Many of us have really sharpened our denial skills since the advent of the ongoing pandemic (perhaps you even hesitated at the word "ongoing" there and thought "maybe I won't read this one, it seems like it'll be tiresome"). I don't say this as a preface to a fiery condemnation or a plea to "sanity" or a bunch of evidence of how bad things are, because I too have honed my denial skills in these recent years, and I feel like talking about that development.
Denial comes in many forms, including strategic information avoidance ("I don't have time to look that up right now", "I keep forgetting to look into that", "well this author made a tiny mistake, so I'll click away and read something else", "I'm so tired of hearing about this, let me scroll farther", etc.) strategic dismissal ("look, there's a bit of uncertainty here, I should ignore this", "this doesn't line up perfectly with my anecdotal experience, it must be completely wrong", etc.) and strategic forgetting ("I don't remember what that one study said exactly; it was painful to think about", "I forgot exactly what my friend was saying when we got into that argument", etc.). It's in fact a kind of skill that you can get better at, along with the complementary skill of compartmentalization. It can of course be incredibly harmful, and a huge genre of fables exists precisely to highlight its harms, but it also has some short-term psychological benefits, chiefly in the form of muting anxiety. This is not an endorsement of denial (the harms can be catastrophic), but I want to acknowledge that there *are* short-term benefits. Via compartmentalization, it's even possible to be honest with ourselves about some of our own denials without giving them up immediately.
But as I said earlier, I'm not here to talk you out of your denials. Instead, given that we are so good at denial now, I'm here to ask you to be strategic about it. In particular, we live in a world awash with propaganda/advertising that serves both political and commercial ends. Why not use some of our denial skills to counteract that?
For example, I know quite a few people in complete denial of our current political situation, but those who aren't (including myself) often express consternation about just how many people in the country are supporting literal fascism. Of course, logically that appearance of widespread support is going to be partly a lie, given how much our public media is beholden to the fascists or outright in their side. Finding better facts on the true level of support is hard, but in the meantime, why not be in denial about the "fact" that Trump has widespread popular support?
To give another example: advertisers constantly barrage us with messages about our bodies and weight, trying to keep us insecure (and thus in the mood to spend money to "fix" the problem). For sure cutting through that bullshit by reading about body positivity etc. is a better solution, but in the meantime, why not be in denial about there being anything wrong with your body?
This kind of intentional denial certainly has its own risks (our bodies do actually need regular maintenance, for example, so complete denial on that front is risky) but there's definitely a whole lot of misinformation out there that it would be better to ignore. To the extent such denial expands to a more general denial of underlying problems, this idea of intentional denial is probably just bad. But I sure wish that in a world where people (including myself) routinely deny significant widespread dangers like COVID-19's long-term risks or the ongoing harms of escalating fascism, they'd at least also deny some of the propaganda keeping them unhappy and passive. Instead of being in denial about US-run concentration camps, why not be in denial that the state will be able to punish you for resisting them?

Searching for Privacy Risks in LLM Agents via Simulation
Yanzhe Zhang, Diyi Yang
https://arxiv.org/abs/2508.10880 https://arxiv.org/pdf/2508.10880

Searching for Privacy Risks in LLM Agents via Simulation
The widespread deployment of LLM-based agents is likely to introduce a critical privacy threat: malicious agents that proactively engage others in multi-turn interactions to extract sensitive information. These dynamic dialogues enable adaptive attack strategies that can cause severe privacy violations, yet their evolving nature makes it difficult to anticipate and discover sophisticated vulnerabilities manually. To tackle this problem, we present a search-based framework that alternates betwee…

Multi-Satellite Cooperative MIMO Transmission: Statistical CSI-Aware RSMA Precoding Design
Sangwon Jo, Seok-Hwan Park
https://arxiv.org/abs/2508.11132 https://

Multi-Satellite Cooperative MIMO Transmission: Statistical CSI-Aware RSMA Precoding Design
We investigate inter-satellite cooperative transmission in a multiple low-Earth orbit (LEO) satellite communication system to enhance spectral efficiency. Specifically, we design multiple-input multipleoutput (MIMO) precoding at LEO satellites for cooperative rate-splitting multiple access (RSMA). Given the difficulty of acquiring instantaneous channel state information (iCSI) due to long delays and Doppler effects, we formulate an ergodic max-min fairness rate (MMFR) maximization problem based…

From Distrust to Retribution: America’s Escalating Spiral: https://blog.rmendes.net/2025/09/11/from-distrust-to-retribution-americas.html

Aligned Query Expansion: Efficient Query Expansion for Information Retrieval through LLM Alignment
Adam Yang, Gustavo Penha, Enrico Palumbo, Hugues Bouchard
https://arxiv.org/abs/2507.11042

Aligned Query Expansion: Efficient Query Expansion for Information Retrieval through LLM Alignment
With the breakthroughs in large language models (LLMs), query generation techniques that expand documents and queries with related terms are becoming increasingly popular in the information retrieval field. Such techniques have been shown to improve the effectiveness of traditional lexical retrieval methods by dealing with the vocabulary mismatch problem. Recent work has found that generating queries with a greedy decoding strategy can produce sub-optimal queries, including hallucinations, and …

Towards a Global Scale Quantum Information Network: A Study Applied to Satellite-Enabled Distributed Quantum Computing
Laurent de Forges de Parny, Luca Paccard, Mathieu Bertrand, Luca Lazzarini, Valentin Leloup, Raphael Aymeric, Agathe Blaise, St\'ephanie Molin, Pierre Besancenot, Cyrille Laborde, Mathias van den Bossche
https://arxiv.…

Towards a Global Scale Quantum Information Network: A Study Applied to Satellite-Enabled Distributed Quantum Computing
Recent developments have reported on the feasibility of interconnecting small quantum registers in a quantum information network of a few meter-scale for distributed quantum computing purposes. This multiple small-scale quantum processors communicating and cooperating to execute computational tasks is considered as a promising solution to the scalability problem of reaching more than thousands of noise-free qubits. Here, we propose and assess a satellite-enabled distributed quantum computing sy…

Automatic reproducing kernel and regularization for learning convolution kernels
Haibo Li, Fei Lu
https://arxiv.org/abs/2507.11944 https://

Automatic reproducing kernel and regularization for learning convolution kernels
Learning convolution kernels in operators from data arises in numerous applications and represents an ill-posed inverse problem of broad interest. With scant prior information, kernel methods offer a natural nonparametric approach with regularization. However, a major challenge is to select a proper reproducing kernel, especially as operators and data vary. We show that the input data and convolution operator themselves induce an automatic, data-adaptive RKHS (DA-RKHS), obviating manual kernel …

Optimal Simultaneous Byzantine Agreement, Common Knowledge and Limited Information Exchange
Ron van der Meyden
https://arxiv.org/abs/2508.03418 https://arx…

Optimal Simultaneous Byzantine Agreement, Common Knowledge and Limited Information Exchange
In order to develop solutions that perform actions as early as possible, analysis of distributed algorithms using epistemic logic has generally concentrated on ``full information protocols'', which may be inefficient with respect to space and computation time. The paper reconsiders the epistemic analysis of the problem of Simultaneous Byzantine Agreement with respect to weaker, but more practical, exchanges of information. The paper first clarifies some issues concerning both the specification …

Evaluation of a deliberate-practice informed supplemental intervention in graduate Quantum Mechanics
Michael E. Robbins, Guillaume M. Laurent, Eric W. Burkholder
https://arxiv.org/abs/2508.09917

Evaluation of a deliberate-practice informed supplemental intervention in graduate Quantum Mechanics
Despite the prevalence of physics education research literature related to problem solving, recent studies have illustrated that opportunities for ``authentic'' problem solving -- conceptualized as making decisions with limited information using one's physics knowledge -- are limited at both the graduate and undergraduate levels in physics curricula. Building on one of these studies, we designed a supplemental intervention for a graduate-level quantum mechanics course which scaffolded the pract…

Efficient Reward Identification In Max Entropy Reinforcement Learning with Sparsity and Rank Priors
Mohamad Louai Shehab, Alperen Tercan, Necmiye Ozay
https://arxiv.org/abs/2508.07400

Efficient Reward Identification In Max Entropy Reinforcement Learning with Sparsity and Rank Priors
In this paper, we consider the problem of recovering time-varying reward functions from either optimal policies or demonstrations coming from a max entropy reinforcement learning problem. This problem is highly ill-posed without additional assumptions on the underlying rewards. However, in many applications, the rewards are indeed parsimonious, and some prior information is available. We consider two such priors on the rewards: 1) rewards are mostly constant and they change infrequently, 2) rew…

A malicious Jira ticket can cause Cursor to exfiltrate secrets from the repository or local file system. But this is not just a problem with Cursor: GitHub MCP connections can also be exploited to expose private repository data, and a vulnerability in GitLab Duo allowed private information to be exposed through automatically rendered HTML code.

AgentFlayer: When a Jira Ticket Can Steal Your Secrets
TL;DR: A 0click attack through a malicious Jira ticket can cause Cursor to exfiltrate secrets from the repository or local file system.

Beer Path Problems in Temporal Graphs
Andrea D'Ascenzo, Giuseppe F. Italiano, Sotiris Kanellopoulos, Anna Mpanti, Aris Pagourtzis, Christos Pergaminelis
https://arxiv.org/abs/2507.08685

Beer Path Problems in Temporal Graphs
Computing paths in graph structures is a fundamental operation in a wide range of applications, from transportation networks to data analysis. The beer path problem, which captures the option of visiting points of interest, such as gas stations or convenience stops, prior to reaching the final destination, has been recently introduced and extensively studied in static graphs. However, existing approaches do not account for temporal information, which is often crucial in real-world scenarios. Fo…

Incorporating Fixed Pole Information in the Data-Driven Least Squares Realization Problem
Christof Vermeersch, Sibren Lagauw, Bart De Moor
https://arxiv.org/abs/2509.09394 https…

Incorporating Fixed Pole Information in the Data-Driven Least Squares Realization Problem
In practical least squares realization problems, partial information about the poles of the dynamical model may be known a priori. Existing techniques for incorporating prior knowledge, such as prefiltering the given data, are typically heuristic and lack theoretical guarantees. We extend our previously developed globally optimal approach for the least squares realization problem to accommodate fixed poles. In particular, we reformulate the problem as a (rectangular) multiparameter eigenvalue p…

ESSENTIAL: Episodic and Semantic Memory Integration for Video Class-Incremental Learning
Jongseo Lee, Kyungho Bae, Kyle Min, Gyeong-Moon Park, Jinwoo Choi
https://arxiv.org/abs/2508.10896

ESSENTIAL: Episodic and Semantic Memory Integration for Video Class-Incremental Learning
In this work, we tackle the problem of video classincremental learning (VCIL). Many existing VCIL methods mitigate catastrophic forgetting by rehearsal training with a few temporally dense samples stored in episodic memory, which is memory-inefficient. Alternatively, some methods store temporally sparse samples, sacrificing essential temporal information and thereby resulting in inferior performance. To address this trade-off between memory-efficiency and performance, we propose EpiSodic and SE…

Anthropic adds a memory feature for Claude to reference information from past chats, available now for Max, Team, and Enterprise plans, and soon for other plans (Zac Hall/9to5Mac)
https://9to5mac.com/2025/08/11/claude-memory-feature/

Claude just learned a useful ChatGPT trick - 9to5Mac
Anthropic has introduced a helpful new feature for Claude that solves a problem similar to one ChatGPT already addressed. As...

Scaling Up without Fading Out: Goal-Aware Sparse GNN for RL-based Generalized Planning
Sangwoo Jeon, Juchul Shin, Gyeong-Tae Kim, YeonJe Cho, Seongwoo Kim
https://arxiv.org/abs/2508.10747

Scaling Up without Fading Out: Goal-Aware Sparse GNN for RL-based Generalized Planning
Generalized planning using deep reinforcement learning (RL) combined with graph neural networks (GNNs) has shown promising results in various symbolic planning domains described by PDDL. However, existing approaches typically represent planning states as fully connected graphs, leading to a combinatorial explosion in edge information and substantial sparsity as problem scales grow, especially evident in large grid-based environments. This dense representation results in diluted node-level infor…

Debiased machine learning for combining probability and non-probability survey data
Shaun Seaman
https://arxiv.org/abs/2508.08948 https://arxiv.org/pdf/250…

Debiased machine learning for combining probability and non-probability survey data
We consider the problem of estimating the finite population mean $\bar{Y}$ of an outcome variable $Y$ using data from a nonprobability sample and auxiliary information from a probability sample. Existing double robust (DR) estimators of this mean $\bar{Y}$ require the estimation of two nuisance functions: the conditional probability of selection into the nonprobability sample given covariates $X$ that are observed in both samples, and the conditional expectation of $Y$ given $X$. These nuisance…

Energy Efficiency Maximization for Movable Antenna-Enhanced MIMO Downlink System Based on S-CSI
Xintai Chen, Biqian Feng, Yongpeng Wu, Xiang-Gen Xia, Chengshan Xiao
https://arxiv.org/abs/2509.12036

Energy Efficiency Maximization for Movable Antenna-Enhanced MIMO Downlink System Based on S-CSI
This paper presents an innovative movable antenna (MA)-enhanced multi-user multiple-input multiple-output (MIMO) downlink system. We aim to maximize the energy efficiency (EE) under statistical channel state information (S-CSI) through a joint optimization of the precoding matrix and the antenna position vectors (APVs). To solve the resulting stochastic problem, we first resort to deterministic equivalent (DE) tecnology to formulate the deterministic minorizing function of the system EE and the…

An iterated $I$-projection procedure for solving the generalized minimum information checkerboard copula problem
Ivan Kojadinovic, Tommaso Martini
https://arxiv.org/abs/2509.02829

An iterated $I$-projection procedure for solving the generalized minimum information checkerboard copula problem
The minimum information copula principle initially suggested in \cite{MeeBed97} is a maximum entropy-like approach for finding the least informative copula, if it exists, that satisfies a certain number of expectation constraints specified either from domain knowledge or the available data. We first propose a generalization of this principle allowing the inclusion of additional constraints fixing certain higher-order margins of the copula. We next show that the associated optimization problem h…

Retrodicting Chaotic Systems: An Algorithmic Information Theory Approach
Kamal Dingle, Boumediene Hamzi, Marcus Hutter, Houman Owhadi
https://arxiv.org/abs/2507.04780

Retrodicting Chaotic Systems: An Algorithmic Information Theory Approach
Making accurate inferences about data is a key task in science and mathematics. Here we study the problem of \emph{retrodiction}, inferring past values of a series, in the context of chaotic dynamical systems. Specifically, we are interested in inferring the starting value $x_0$ in the series $x_0,x_1,x_2,\dots,x_n$ given the value of $x_n$, and the associated function $f$ which determines the series as $f(x_i)=x_{i+1}$. Even in the deterministic case this is a challenging problem, due to mixin…

Distributed Finite-Horizon Optimal Control for Consensus with Differential Privacy Guarantees
Yuwen Ma, Yongqiang Wang, Sarah K. Spurgeon, Boli Chen
https://arxiv.org/abs/2509.11917

Distributed Finite-Horizon Optimal Control for Consensus with Differential Privacy Guarantees
This paper addresses the problem of privacy-preserving consensus control for multi-agent systems (MAS) using differential privacy. We propose a novel distributed finite-horizon linear quadratic regulator (LQR) framework, in which agents share individual state information while preserving the confidentiality of their local pairwise weight matrices, which are considered sensitive data in MAS. Protecting these matrices effectively safeguards each agent's private cost function and control preferenc…

City Sampling for Citizens' Assemblies
Paul G\"olz, Jan Maly, Ulrike Schmidt-Kraepelin, Markus Utke, Philipp C. Verpoort
https://arxiv.org/abs/2509.07557 https://

City Sampling for Citizens' Assemblies
In citizens' assemblies, a group of constituents is randomly selected to weigh in on policy issues. We study a two-stage sampling problem faced by practitioners in countries such as Germany, in which constituents' contact information is stored at a municipal level. As a result, practitioners can only select constituents from a bounded number of cities ex post, while ensuring equal selection probability for constituents ex ante. We develop several algorithms for this problem. Although minimizi…

Wigner's friend's black hole adventure: an argument for complementarity?
Laurens Walleghem
https://arxiv.org/abs/2507.05369 https://

Wigner's friend's black hole adventure: an argument for complementarity?
At the heart of both black hole physics and Wigner's friend scenarios lies the question of unitarity. In Wigner's friend setups, sealed-lab measurements are modeled unitarily, probing the measurement problem. In black hole physics, the unitarity problem concerns information preservation in evaporation. We extend a recent analogy between these two puzzles exposed by Hausmann and Renner [arXiv:2504.03835v1] by constructing new paradoxes that merge black hole physics with nonlocal extensions of Wi…

Monotonicity in half-spaces for singular quasilinear elliptic problems involving the gradient
Phuong Le
https://arxiv.org/abs/2508.08859 https://arxiv.org/…

Monotonicity in half-spaces for singular quasilinear elliptic problems involving the gradient
We study positive solutions to the problem $-Δ_p u + \vartheta |\nabla u|^q = \frac{1}{u^γ} + f(u)$ in $\mathbb{R}^N_+$ with the zero Dirichlet boundary condition, where $p>1$, $γ>0$, $0

Cascaded Information Disclosure for Generalized Evaluation of Problem Solving Capabilities
Yunxiang Yan, Tomohiro Sawada, Kartik Goyal
https://arxiv.org/abs/2507.23776 https://

Cascaded Information Disclosure for Generalized Evaluation of Problem Solving Capabilities
While question-answering~(QA) benchmark performance is an automatic and scalable method to compare LLMs, it is an indirect method of evaluating their underlying problem-solving capabilities. Therefore, we propose a holistic and generalizable framework based on \emph{cascaded question disclosure} that provides a more accurate estimate of the models' problem-solving capabilities while maintaining the scalability and automation. This approach collects model responses in a stagewise manner with eac…

The Cosine Schedule is Fisher-Rao-Optimal for Masked Discrete Diffusion Models
Leo Zhang
https://arxiv.org/abs/2508.04884 https://arxiv.org/pdf/2508.04884

The Cosine Schedule is Fisher-Rao-Optimal for Masked Discrete Diffusion Models
In this work, we study the problem of choosing the discretisation schedule for sampling from masked discrete diffusion models in terms of the information geometry of the induced probability path. Specifically, we show that the optimal schedule under the Fisher-Rao geometry recovers the popularly-used cosine schedule.

Approaching Maximal Information Extraction in Low-Signal Regimes via Multiple Instance Learning
Atakan Azakli, Bernd Stelzer
https://arxiv.org/abs/2508.07114 https://

Approaching Maximal Information Extraction in Low-Signal Regimes via Multiple Instance Learning
In this work, we propose a new machine learning (ML) methodology to obtain more precise predictions for some parameters of interest in a given hypotheses testing problem. Our proposed method also allows ML models to have more discriminative power in cases where it is extremely challenging for state-of-the-art classifiers to have any level of accurate predictions. This method can also allow us to systematically decrease the error from ML models in their predictions. In this paper, we provide a m…

Joint Power Allocation and Reflecting-Element Activation for Energy Efficiency Maximization in IRS-Aided Communications Under CSI Uncertainty
Christos N. Efrem, Ioannis Krikidis
https://arxiv.org/abs/2507.11413

Joint Power Allocation and Reflecting-Element Activation for Energy Efficiency Maximization in IRS-Aided Communications Under CSI Uncertainty
We study the joint power allocation and reflecting element (RE) activation to maximize the energy efficiency (EE) in communication systems assisted by an intelligent reflecting surface (IRS), taking into account imperfections in channel state information (CSI). The robust optimization problem is mixed integer, i.e., the optimization variables are continuous (transmit power) and discrete (binary states of REs). In order to solve this challenging problem we develop two algorithms. The first one i…

Koopman-von Neumann Field Theory
James Stokes
https://arxiv.org/abs/2507.11541 https://arxiv.org/pdf/2507.11541

Koopman-von Neumann Field Theory
The classical many-body problem is reformulated as a bosonic quantum field theory. Quantum field operators evolve unitarily in the Heisenberg picture so that a quantum Vlasov equation is satisfied as an operator identity. The formalism enables the direct transfer of techniques from quantum information and quantum many-body field theory to classical nonequilibrium statistical mechanics. Implications for quantum algorithms are discussed.

Recursive Bound-Constrained AdaGrad with Applications to Multilevel and Domain Decomposition Minimization
Serge Gratton, Alena Kopani\v{c}\'akov\'a, Philippe Toint
https://arxiv.org/abs/2507.11513

Recursive Bound-Constrained AdaGrad with Applications to Multilevel and Domain Decomposition Minimization
Two OFFO (Objective-Function Free Optimization) noise tolerant algorithms are presented that handle bound constraints, inexact gradients and use second-order information when available.The first is a multi-level method exploiting a hierarchical description of the problem and the second is a domain-decomposition method covering the standard addditive Schwarz decompositions. Both are generalizations of the first-order AdaGrad algorithm for unconstrained optimization. Because these algorithms shar…

Accelerated and Optimized Search of Imperceptible Color Vibration for Embedding Information into LCD images
Shingo Hattori, Takefumi Hiraki
https://arxiv.org/abs/2507.22901 http…

Accelerated and Optimized Search of Imperceptible Color Vibration for Embedding Information into LCD images
Large, high-resolution displays are installed throughout the city as public displays. By superimposing invisible information on the images of these displays, large numbers of devices with cameras and sensors can communicate with the displays without prior pairing. Several applications have been proposed, such as operating robots or communicating information to users by displaying 2D codes on images. However, the display of 2D codes has the problem of compromising the appearance of displayed con…

Connected k-Median with Disjoint and Non-disjoint Clusters
Jan Eube, Kelin Luo, Dorian Reineccius, Heiko R\"oglin, Melanie Schmidt
https://arxiv.org/abs/2507.02774

Connected k-Median with Disjoint and Non-disjoint Clusters
The connected $k$-median problem is a constrained clustering problem that combines distance-based $k$-clustering with connectivity information. The problem allows to input a metric space and an unweighted undirected connectivity graph that is completely unrelated to the metric space. The goal is to compute $k$ centers and corresponding clusters such that each cluster forms a connected subgraph of $G$, and such that the $k$-median cost is minimized. The problem has applications in very differe…

MambaITD: An Efficient Cross-Modal Mamba Network for Insider Threat Detection
Kaichuan Kong, Dongjie Liu, Xiaobo Jin, Zhiying Li, Guanggang Geng, Jian Weng
https://arxiv.org/abs/2508.05695

MambaITD: An Efficient Cross-Modal Mamba Network for Insider Threat Detection
Enterprises are facing increasing risks of insider threats, while existing detection methods are unable to effectively address these challenges due to reasons such as insufficient temporal dynamic feature modeling, computational efficiency and real-time bottlenecks and cross-modal information island problem. This paper proposes a new insider threat detection framework MambaITD based on the Mamba state space model and cross-modal adaptive fusion. First, the multi-source log preprocessing module …

Rollout-Based Approximate Dynamic Programming for MDPs with Information-Theoretic Constraints
Zixuan He, Charalambos D. Charalambous, Photios A. Stavrou
https://arxiv.org/abs/2509.02812

Rollout-Based Approximate Dynamic Programming for MDPs with Information-Theoretic Constraints
This paper studies a finite-horizon Markov decision problem with information-theoretic constraints, where the goal is to minimize directed information from the controlled source process to the control process, subject to stage-wise cost constraints, aiming for an optimal control policy. We propose a new way of approximating a solution for this problem, which is known to be formulated as an unconstrained MDP with a continuous information-state using Q-factors. To avoid the computational complexi…

FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
Gwanhyeong Koo, Sunjae Yoon, Younghwan Lee, Ji Woo Hong, Chang D. Yoo
https://arxiv.org/abs/2507.08285

FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
Drag-based editing allows precise object manipulation through point-based control, offering user convenience. However, current methods often suffer from a geometric inconsistency problem by focusing exclusively on matching user-defined points, neglecting the broader geometry and leading to artifacts or unstable edits. We propose FlowDrag, which leverages geometric information for more accurate and coherent transformations. Our approach constructs a 3D mesh from the image, using an energy functi…

$d 1$ Measurement Bases are Sufficient for Determining $d$-Dimensional Quantum States: Theory and Experiment
Tianqi Xiao, Yaxin Wang, Ying Xia, Zhihao Li, Xiaoqi Zhou
https://arxiv.org/abs/2507.11204

$d+1$ Measurement Bases are Sufficient for Determining $d$-Dimensional Quantum States: Theory and Experiment
A long-standing problem in quantum physics is to determine the minimal number of measurement bases required for the complete characterization of unknown quantum states, a question of particular relevance to high-dimensional quantum information processing. Here, we propose a quantum state tomography scheme that requires only $d+1$ projective measurement bases to fully reconstruct an arbitrary $d$-dimensional quantum state. As a proof-of-principle, we experimentally verified this scheme on a sili…

Want to invest in Ukrainian startups from the US? This platform promises to be the bridge: https://benborges.xyz/2025/07/01/want-to-invest-in-ukrainian.html

Is External Information Useful for Data Fusion? An Evaluation before Acquisition
Guorong Dai, Lingxuan Shao, Jinbo Chen
https://arxiv.org/abs/2507.22351 https://

Is External Information Useful for Data Fusion? An Evaluation before Acquisition
We consider a general statistical estimation problem involving a finite-dimensional target parameter vector. Beyond an internal data set drawn from the population distribution, external information, such as additional individual data or summary statistics, can potentially improve the estimation when incorporated via appropriate data fusion techniques. However, since acquiring external information often incurs costs, it is desirable to assess its utility beforehand using only the internal data. …

Addressing the Cold-Start Problem for Personalized Combination Drug Screening
Antoine de Mathelin, Christopher Tosh, Wesley Tansey
https://arxiv.org/abs/2509.07850 https://

Addressing the Cold-Start Problem for Personalized Combination Drug Screening
Personalizing combination therapies in oncology requires navigating an immense space of possible drug and dose combinations, a task that remains largely infeasible through exhaustive experimentation. Recent developments in patient-derived models have enabled high-throughput ex vivo screening, but the number of feasible experiments is limited. Further, a tight therapeutic window makes gathering molecular profiling information (e.g. RNA-seq) impractical as a means of guiding drug response predict…

Mutual Information Optimal Control of Discrete-Time Linear Systems
Shoju Enami, Kenji Kashima
https://arxiv.org/abs/2507.04712 https://

Mutual Information Optimal Control of Discrete-Time Linear Systems
In this paper, we formulate a mutual information optimal control problem (MIOCP) for discrete-time linear systems. This problem can be regarded as an extension of a maximum entropy optimal control problem (MEOCP). Differently from the MEOCP where the prior is fixed to the uniform distribution, the MIOCP optimizes the policy and prior simultaneously. As analytical results, under the policy and prior classes consisting of Gaussian distributions, we derive the optimal policy and prior of the MIOCP…

DMF2Mel: A Dynamic Multiscale Fusion Network for EEG-Driven Mel Spectrogram Reconstruction
Cunhang Fan, Sheng Zhang, Jingjing Zhang, Enrui Liu, Xinhui Li, Minggang Zhao, Zhao Lv
https://arxiv.org/abs/2507.07526

DMF2Mel: A Dynamic Multiscale Fusion Network for EEG-Driven Mel Spectrogram Reconstruction
Decoding speech from brain signals is a challenging research problem. Although existing technologies have made progress in reconstructing the mel spectrograms of auditory stimuli at the word or letter level, there remain core challenges in the precise reconstruction of minute-level continuous imagined speech: traditional models struggle to balance the efficiency of temporal dependency modeling and information retention in long-sequence decoding. To address this issue, this paper proposes the Dy…

Neural Estimation of the Information Bottleneck Based on a Mapping Approach
Lingyi Chen, Shitong Wu, Sicheng Xu, Huihui Wu, Wenyi Zhang
https://arxiv.org/abs/2507.19832 https://…

Neural Estimation of the Information Bottleneck Based on a Mapping Approach
The information bottleneck (IB) method is a technique designed to extract meaningful information related to one random variable from another random variable, and has found extensive applications in machine learning problems. In this paper, neural network based estimation of the IB problem solution is studied, through the lens of a novel formulation of the IB problem. Via exploiting the inherent structure of the IB functional and leveraging the mapping approach, the proposed formulation of the I…

Getting In Contract with Large Language Models -- An Agency Theory Perspective On Large Language Model Alignment
Sascha Kaltenpoth, Oliver M\"uller
https://arxiv.org/abs/2509.07642

Getting In Contract with Large Language Models -- An Agency Theory Perspective On Large Language Model Alignment
Adopting Large language models (LLMs) in organizations potentially revolutionizes our lives and work. However, they can generate off-topic, discriminating, or harmful content. This AI alignment problem often stems from misspecifications during the LLM adoption, unnoticed by the principal due to the LLM's black-box nature. While various research disciplines investigated AI alignment, they neither address the information asymmetries between organizational adopters and black-box LLM agents nor con…

Fair Domain Generalization: An Information-Theoretic View
Tangzheng Lian, Guanyu Hu, Dimitrios Kollias, Xinyu Yang, Oya Celiktutan
https://arxiv.org/abs/2507.05823

Fair Domain Generalization: An Information-Theoretic View
Domain generalization (DG) and algorithmic fairness are two critical challenges in machine learning. However, most DG methods focus only on minimizing expected risk in the unseen target domain without considering algorithmic fairness. Conversely, fairness methods typically do not account for domain shifts, so the fairness achieved during training may not generalize to unseen test domains. In this work, we bridge these gaps by studying the problem of Fair Domain Generalization (FairDG), which ai…

Integrated Learning and Optimization to Control Load Demand and Wind Generation for Minimizing Ramping Cost in Real-Time Electricity Market
Imran Pervez, Omar Knio
https://arxiv.org/abs/2508.09774

Integrated Learning and Optimization to Control Load Demand and Wind Generation for Minimizing Ramping Cost in Real-Time Electricity Market
We developed a new integrated learning and optimization (ILO) methodology to predict context-aware unknown parameters in economic dispatch (ED), a crucial problem in power systems solved to generate optimal power dispatching decisions to serve consumer load. The ED formulation in the current study consists of load and renewable generation as unknown parameters in its constraints predicted using contextual information (e.g., prior load, temperature). The ILO framework train a neural network (NN)…

Asymmetric Modulation Design for Fluid-Antenna SWIPT Systems
Ahsan Mehmood, Ioannis Krikidis, Ghassan M. Kraidy
https://arxiv.org/abs/2509.07610 https://ar…

Asymmetric Modulation Design for Fluid-Antenna SWIPT Systems
In this work, we propose the design of modulation schemes that improve the rate-energy region of fluid antenna-assisted simultaneous wireless information and power transfer (SWIPT) systems. By considering the nonlinear characteristics of practical energy harvesting circuits, we formulate a dual-objective rate-energy (RE) region optimization problem to jointly maximize the discrete-input mutual information (DIMI) and harvested current. The problem is solved using the epsilon-constraint method an…

Commitment Gap via Correlation Gap
Shuchi Chawla, Dimitris Christou, Trung Dang
https://arxiv.org/abs/2508.20246 https://arxiv.org/pdf/2508.20246

Commitment Gap via Correlation Gap
Selection problems with costly information, dating back to Weitzman's Pandora's Box problem, have received much attention recently. We study the general model of Costly Information Combinatorial Selection (CICS) that was recently introduced by Chawla et al. [2024] and Bowers et al. [2025]. In this problem, a decision maker needs to select a feasible subset of stochastic variables, and can only learn information about their values through a series of costly steps, modeled by a Markov decision pr…

Unambiguous discrimination of the change point for quantum channels
Kenji Nakahira
https://arxiv.org/abs/2508.06785 https://arxiv.org/pdf/2508.06785…

Unambiguous discrimination of the change point for quantum channels
Identifying the precise moment when a quantum channel undergoes a change is a fundamental problem in quantum information theory. We study how accurately one can determine the time at which a channel transitions to another. We investigate the quantum limit of the average success probability in unambiguous discrimination, in which errors are completely avoided by allowing inconclusive results with a certain probability. This problem can be viewed as a quantum process discrimination task, where th…

Blackbox Dataset Inference for LLM
Ruikai Zhou, Kang Yang, Xun Chen, Wendy Hui Wang, Guanhong Tao, Jun Xu
https://arxiv.org/abs/2507.03619 https://

Blackbox Dataset Inference for LLM
Today, the training of large language models (LLMs) can involve personally identifiable information and copyrighted material, incurring dataset misuse. To mitigate the problem of dataset misuse, this paper explores \textit{dataset inference}, which aims to detect if a suspect model $\mathcal{M}$ used a victim dataset $\mathcal{D}$ in training. Previous research tackles dataset inference by aggregating results of membership inference attacks (MIAs) -- methods to determine whether individual samp…

NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation
Jinfeng Xu, Zheyu Chen, Shuo Yang, Jinze Li, Hewei Wang, Wei Wang, Xiping Hu, Edith Ngai
https://arxiv.org/abs/2507.07522

NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation
Graph Neural Networks (GNNs) are widely used in collaborative filtering to capture high-order user-item relationships. To address the data sparsity problem in recommendation systems, Graph Contrastive Learning (GCL) has emerged as a promising paradigm that maximizes mutual information between contrastive views. However, existing GCL methods rely on augmentation techniques that introduce semantically irrelevant noise and incur significant computational and storage costs, limiting effectiveness a…

Learning from the past in an irreversible investment problem
Topias Tolonen-Weckstr\"om
https://arxiv.org/abs/2508.21731 https://arxiv.org/pdf/2508.21…

Learning from the past in an irreversible investment problem
We consider an irreversible investment problem under incomplete information, where the investor is able to exercise multiple investment rights to a project. The investor does not observe the project value directly and instead only a noisy observation process is observed. Upon each investment, the investor acquires previously hidden information from the project's past (''learning from the past''), and so the learning rate of the problem is controlled by investing. The acquisition of additional i…

Bayesian Radio Map Estimation: Fundamentals and Implementation via Diffusion Models
Tien Ngoc Ha, Daniel Romero
https://arxiv.org/abs/2508.06037 https://ar…

Bayesian Radio Map Estimation: Fundamentals and Implementation via Diffusion Models
Radio map estimation (RME) is the problem of inferring the value of a certain metric (e.g. signal power) across an area of interest given a collection of measurements. While most works tackle this problem from a purely non-Bayesian perspective, some Bayesian estimators have been proposed. However, the latter focus on estimating the map itself, the Bayesian standpoint is adopted mainly to exploit prior information or to capture uncertainty. This paper pursues a more general formulation, where th…

I2I-STRADA -- Information to Insights via Structured Reasoning Agent for Data Analysis
SaiBarath Sundar, Pranav Satheesan, Udayaadithya Avadhanam
https://arxiv.org/abs/2507.17874

I2I-STRADA -- Information to Insights via Structured Reasoning Agent for Data Analysis
Recent advances in agentic systems for data analysis have emphasized automation of insight generation through multi-agent frameworks, and orchestration layers. While these systems effectively manage tasks like query translation, data transformation, and visualization, they often overlook the structured reasoning process underlying analytical thinking. Reasoning large language models (LLMs) used for multi-step problem solving are trained as general-purpose problem solvers. As a result, their rea…

Tackling Federated Unlearning as a Parameter Estimation Problem
Antonio Balordi, Lorenzo Manini, Fabio Stella, Alessio Merlo
https://arxiv.org/abs/2508.19065 https://

Tackling Federated Unlearning as a Parameter Estimation Problem
Privacy regulations require the erasure of data from deep learning models. This is a significant challenge that is amplified in Federated Learning, where data remains on clients, making full retraining or coordinated updates often infeasible. This work introduces an efficient Federated Unlearning framework based on information theory, modeling leakage as a parameter estimation problem. Our method uses second-order Hessian information to identify and selectively reset only the parameters most se…

Average Consensus with Dynamic Compression in Bandwidth-Limited Directed Networks
Evagoras Makridis, Gabriele Oliva, Apostolos I. Rikos, Themistoklis Charalambous
https://arxiv.org/abs/2508.06893

Average Consensus with Dynamic Compression in Bandwidth-Limited Directed Networks
In this paper, the average consensus problem has been considered for directed unbalanced networks under finite bit-rate communication. We propose the Push-Pull Average Consensus algorithm with Dynamic Compression (PP-ACDC) algorithm, a distributed consensus algorithm that deploys an adaptive quantization scheme and achieves convergence to the exact average without the need of global information. A preliminary numerical convergence analysis and simulation results corroborate the performance of P…

On Optimality of Private Information in Bayesian Routing Games
Alexia Ambrogio, Leonardo Cianfanelli, Giacomo Como
https://arxiv.org/abs/2509.03357 https://

On Optimality of Private Information in Bayesian Routing Games
We study an information design problem in transportation networks, in the presence of a random state that affects the travel times on the links. An omniscient system planner -- aiming at reducing congestion -- observes the network state realization and sends private messages to the users -- who share a common prior on the network state but do not observe it directly -- in order to nudge them towards a socially desirable behavior. The desired effect of these private signals is to correlate the u…

Using causal abstractions to accelerate decision-making in complex bandit problems
Joel Dyer, Nicholas Bishop, Anisoara Calinescu, Michael Wooldridge, Fabio Massimo Zennaro
https://arxiv.org/abs/2509.04296

Using causal abstractions to accelerate decision-making in complex bandit problems
Although real-world decision-making problems can often be encoded as causal multi-armed bandits (CMABs) at different levels of abstraction, a general methodology exploiting the information and computational advantages of each abstraction level is missing. In this paper, we propose AT-UCB, an algorithm which efficiently exploits shared information between CMAB problem instances defined at different levels of abstraction. More specifically, AT-UCB leverages causal abstraction (CA) theory to explo…

Tailored First-order and Interior-point methods and a new semidefinite programming hierarchy for entanglement detection
Javier Pena, Vikesh Siddhu, Sridhar Tayur
https://arxiv.org/abs/2508.05854

Tailored First-order and Interior-point methods and a new semidefinite programming hierarchy for entanglement detection
Quantum entanglement lies at the heart of quantum information science, yet its reliable detection in high-dimensional or noisy systems remains a fundamental computational challenge. Semidefinite programming (SDP) hierarchies, such as the Doherty-Parrilo-Spedalieri (DPS) and Extension (EXT) hierarchies, offer complete methods for entanglement detection, but their practical use is limited by exponential growth in problem size. In this paper, we introduce a new SDP hierarchy, PST, that is sandwich…

Decentralized Optimization via RC-ALADIN with Efficient Quantized Communication
Xu Du, Karl H. Johansson, Apostolos I. Rikos
https://arxiv.org/abs/2508.06197 https://

Decentralized Optimization via RC-ALADIN with Efficient Quantized Communication
In this paper, we investigate the problem of decentralized consensus optimization over directed graphs with limited communication bandwidth. We introduce a novel decentralized optimization algorithm that combines the Reduced Consensus Augmented Lagrangian Alternating Direction Inexact Newton (RC-ALADIN) method with a finite time quantized coordination protocol, enabling quantized information exchange among nodes. Assuming the nodes' local objective functions are $μ$-strongly convex and simply …

Quantum Shadows: The Dining Information Brokers
Theodore Andronikos, Constantinos Bitsakos, Konstantinos Nikas, Georgios I. Goumas, Nectarios Koziris
https://arxiv.org/abs/2507.13810

Quantum Shadows: The Dining Information Brokers
This article introduces the innovative Quantum Dining Information Brokers Problem, presenting a novel entanglement-based quantum protocol to address it. The scenario involves $n$ information brokers, all located in distinct geographical regions, engaging in a metaphorical virtual dinner. The objective is for each broker to share a unique piece of information with all others simultaneously. Unlike previous approaches, this protocol enables a fully parallel, single-step communication exchange amo…

Secrecy Energy Efficiency Maximization in RIS-Aided Networks: Active or Nearly-Passive RIS?
Robert Kuku Fotock, Agbotiname Lucky Imoize, Alessio Zappone, Marco Di Renzo, Roberto Garello
https://arxiv.org/abs/2507.07241

Secrecy Energy Efficiency Maximization in RIS-Aided Networks: Active or Nearly-Passive RIS?
This work addresses the problem of secrecy energy efficiency (SEE) maximization in RIS-aided wireless networks. The use of active and nearly-passive RISs are compared and their trade-off in terms of SEE is analyzed. Considering both perfect and statistical channel state information, two SEE maximization algorithms are developed to optimize the transmit powers of the mobile users, the RIS reflection coefficients, and the base station receive filters. Numerical results quantify the trade-off betw…

Treasure Hunt in Anonymous Graphs with Quantum Pebbles by Oblivious Agents
Gaurav Gaur, Barun Gorain, Rishi Ranjan Singh, Daya Gaur
https://arxiv.org/abs/2509.02909 https://

Treasure Hunt in Anonymous Graphs with Quantum Pebbles by Oblivious Agents
We investigate the problem of finding a static treasure in anonymous graphs using oblivious agents and introduce a novel approach that leverages quantum information. In anonymous graphs, vertices are unlabelled, indistinguishable, and edges are locally labelled with port numbers. Agents typically rely on stationary classical pebbles placed by an oracle to guide their search. However, this classical approach is constrained by limited information transmission and high traversal complexity. Classi…