Tootfinder

Kickstarted a credit-card-sized dice replacement.
For IRL D&D it was fine, but dials spin forever. Made dice tray pointless, while less satisfying. Even though I choose yellow text on black, hard for me to see in mood-lit basement on table
For Shadowrun, 6D6 is embarrassingly insufficient, given I regularly roll 14D6. The VTT roller is far better (since it does the math on pools and successes).
But they’re sturdy and hefty! Which means my mini metal dice are lighter an…

Inside job
When an account gets hacked, social media giant Meta offers little support, spawning a shadowy network of brokers and Meta employees who profit from helping users get back online
https://www.theglobeandmail.com/canada/art

Good film... but OMG my son lost his everlovin' mind watching 'A #Minecraft Movie', screaming at not just the 'Chicken Jockey' but also the 'music', 'mods', YouTubers, 'Alex' & more.
✅ ‘A Minecraft Movie’ Ends Its Theatrical Run. How Much Did It Make?

‘A Minecraft Movie’ Ends Its Theatrical Run. How Much Did It Make?
Jack Black and Jason Momoa’s “A Minecraft Movie” has officially wrapped up its run in theaters. How much money did it make at the box office?

Tree-Structured Parzen Estimator Can Solve Black-Box Combinatorial Optimization More Efficiently
Kenshin Abe, Yunzhuo Wang, Shuhei Watanabe
https://arxiv.org/abs/2507.08053 https://arxiv.org/pdf/2507.08053 https://arxiv.org/html/2507.08053
arXiv:2507.08053v1 Announce Type: new
Abstract: Tree-structured Parzen estimator (TPE) is a versatile hyperparameter optimization (HPO) method supported by popular HPO tools. Since these HPO tools have been developed in line with the trend of deep learning (DL), the problem setups often used in the DL domain have been discussed for TPE such as multi-objective optimization and multi-fidelity optimization. However, the practical applications of HPO are not limited to DL, and black-box combinatorial optimization is actively utilized in some domains, e.g., chemistry and biology. As combinatorial optimization has been an untouched, yet very important, topic in TPE, we propose an efficient combinatorial optimization algorithm for TPE. In this paper, we first generalize the categorical kernel with the numerical kernel in TPE, enabling us to introduce a distance structure to the categorical kernel. Then we discuss modifications for the newly developed kernel to handle a large combinatorial search space. These modifications reduce the time complexity of the kernel calculation with respect to the size of a combinatorial search space. In the experiments using synthetic problems, we verified that our proposed method identifies better solutions with fewer evaluations than the original TPE. Our algorithm is available in Optuna, an open-source framework for HPO.
toXiv_bot_toot

AutoODD: Agentic Audits via Bayesian Red Teaming in Black-Box Models
Rebecca Martin, Jay Patrikar, Sebastian Scherer
https://arxiv.org/abs/2509.08638 https://

AutoODD: Agentic Audits via Bayesian Red Teaming in Black-Box Models
Specialized machine learning models, regardless of architecture and training, are susceptible to failures in deployment. With their increasing use in high risk situations, the ability to audit these models by determining their operational design domain (ODD) is crucial in ensuring safety and compliance. However, given the high-dimensional input spaces, this process often requires significant human resources and domain expertise. To alleviate this, we introduce \coolname, an LLM-Agent centric fr…

A Neurosymbolic Framework for Interpretable Cognitive Attack Detection in Augmented Reality
Rongqian Chen, Allison Andreyev, Yanming Xiu, Mahdi Imani, Bin Li, Maria Gorlatova, Gang Tan, Tian Lan
https://arxiv.org/abs/2508.09185

A Neurosymbolic Framework for Interpretable Cognitive Attack Detection in Augmented Reality
Augmented Reality (AR) enriches perception by overlaying virtual elements on the physical world. Due to its growing popularity, cognitive attacks that alter AR content to manipulate users' semantic perception have received increasing attention. Existing detection methods often focus on visual changes, which are restricted to pixel- or image-level processing and lack semantic reasoning capabilities, or they rely on pre-trained vision-language models (VLMs), which function as black-box approaches…

Side view of a decorated electric box inspired by the anime となりのトトロ (My Neighbor Totoro)
#photo #photography #ithaca #art

Subsampling Factorization Machine Annealing
Yusuke Hama, Tadashi Kadowaki
https://arxiv.org/abs/2508.08778 https://arxiv.org/pdf/2508.08778

Subsampling Factorization Machine Annealing
Quantum computing and machine learning are state-of-the-art technologies which have been investigated intensively in both academia and industry. The hybrid technology of these two ingredients is expected to be a powerful tool to solve complex problems in many branches of science and engineering such as combinatorial optimization problems and accelerate the creation of next-generation technologies. In this work, we develop an algorithm to solve a black-box optimization problem by improving Facto…

Deep opacity and AI: A threat to XAI and to privacy protection mechanisms
Vincent C. M\"uller
https://arxiv.org/abs/2509.08835 https://arxiv.org/pdf/2…

Deep opacity and AI: A threat to XAI and to privacy protection mechanisms
It is known that big data analytics and AI pose a threat to privacy, and that some of this is due to some kind of "black box problem" in AI. I explain how this becomes a problem in the context of justification for judgments and actions. Furthermore, I suggest distinguishing three kinds of opacity: 1) the subjects do not know what the system does ("shallow opacity"), 2) the analysts do not know what the system does ("standard black box opacity"), or 3) the analysts cannot possibly know what the …

SLIP: Soft Label Mechanism and Key-Extraction-Guided CoT-based Defense Against Instruction Backdoor in APIs
Zhengxian Wu, Juan Wen, Wanli Peng, Haowei Chang, Yinghan Zhou, Yiming Xue
https://arxiv.org/abs/2508.06153

SLIP: Soft Label Mechanism and Key-Extraction-Guided CoT-based Defense Against Instruction Backdoor in APIs
With the development of customized large language model (LLM) agents, a new threat of black-box backdoor attacks has emerged, where malicious instructions are injected into hidden system prompts. These attacks easily bypass existing defenses that rely on white-box access, posing a serious security challenge. To address this, we propose SLIP, a Soft Label mechanism and key-extraction-guided CoT-based defense against Instruction backdoors in APIs. SLIP is designed based on two key insights. First…

To buy or not to buy: deterministic rent-or-buy problems on node-weighted graphs
Sander Borst, Moritz Venzin
https://arxiv.org/abs/2507.08698 https://

To buy or not to buy: deterministic rent-or-buy problems on node-weighted graphs
We study the rent-or-buy variant of the online Steiner forest problem on node- and edge-weighted graphs. For $n$-node graphs with at most $\bar{n}$ non-zero node-weights, and at most $\tilde{k}$ different arriving terminal pairs, we obtain a deterministic, $O(\log n \log \bar{n})$-competitive algorithm. This improves on the previous best, $O(\log^4 n)$-competitive algorithm obtained by the black-box reduction from (Bartal et al. 2021) combined with the previously best deterministic algorithms f…

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…

Identifying nonequilibrium degrees of freedom in high-dimensional stochastic systems
Catherine Ji, Ravin Raj, Benjamin Eysenbach, Gautam Reddy
https://arxiv.org/abs/2508.08247 h…

Identifying nonequilibrium degrees of freedom in high-dimensional stochastic systems
Any coarse-grained description of a nonequilibrium system should faithfully represent its latent irreversible degrees of freedom. However, standard dimensionality reduction methods typically prioritize accurate reconstruction over physical relevance. Here, we introduce a model-free approach to identify irreversible degrees of freedom in stochastic systems that are in a nonequilibrium steady state. Our method leverages the insight that a black-box classifier, trained to differentiate between for…

Urban Solitude IV 🈳
城市孤独 IV 🈳
📷 Pentax MX
🎞️Fujifilm Neopan SS, expired 1995
buy me ☕️ ?/请我喝杯☕️？
#filmphotography

Yiding Cao Geld mit PayPal.Me senden
Folgen Sie dem Link PayPal.Me/dingsterdingster und geben Sie den Betrag ein. Es ist bequem und sicher. Sie haben noch kein PayPal-Konto? Kein Problem.

Rethinking the Privacy of Text Embeddings: A Reproducibility Study of "Text Embeddings Reveal (Almost) As Much As Text"
Dominykas Seputis, Yongkang Li, Karsten Langerak, Serghei Mihailov
https://arxiv.org/abs/2507.07700

Rethinking the Privacy of Text Embeddings: A Reproducibility Study of "Text Embeddings Reveal (Almost) As Much As Text"
Text embeddings are fundamental to many natural language processing (NLP) tasks, extensively applied in domains such as recommendation systems and information retrieval (IR). Traditionally, transmitting embeddings instead of raw text has been seen as privacy-preserving. However, recent methods such as Vec2Text challenge this assumption by demonstrating that controlled decoding can successfully reconstruct original texts from black-box embeddings. The unexpectedly strong results reported by Vec2…

Risk Map As Middleware: Towards Interpretable Cooperative End-to-end Autonomous Driving for Risk-Aware Planning
Mingyue Lei, Zewei Zhou, Hongchen Li, Jiaqi Ma, Jia Hu
https://arxiv.org/abs/2508.07686

Risk Map As Middleware: Towards Interpretable Cooperative End-to-end Autonomous Driving for Risk-Aware Planning
End-to-end paradigm has emerged as a promising approach to autonomous driving. However, existing single-agent end-to-end pipelines are often constrained by occlusion and limited perception range, resulting in hazardous driving. Furthermore, their black-box nature prevents the interpretability of the driving behavior, leading to an untrustworthiness system. To address these limitations, we introduce Risk Map as Middleware (RiskMM) and propose an interpretable cooperative end-to-end driving frame…

Spectral Masking and Interpolation Attack (SMIA): A Black-box Adversarial Attack against Voice Authentication and Anti-Spoofing Systems
Kamel Kamel, Hridoy Sankar Dutta, Keshav Sood, Sunil Aryal
https://arxiv.org/abs/2509.07677

Spectral Masking and Interpolation Attack (SMIA): A Black-box Adversarial Attack against Voice Authentication and Anti-Spoofing Systems
Voice Authentication Systems (VAS) use unique vocal characteristics for verification. They are increasingly integrated into high-security sectors such as banking and healthcare. Despite their improvements using deep learning, they face severe vulnerabilities from sophisticated threats like deepfakes and adversarial attacks. The emergence of realistic voice cloning complicates detection, as systems struggle to distinguish authentic from synthetic audio. While anti-spoofing countermeasures (CMs) …

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Black Box:
🎵 Everybody Everybody
#BlackBox
https://edit-mix.bandcamp.com/track/black-box-everybody-everybody-hot-mix-118-bpm
https://open.spotify.com/track/6wBMTv8yTiNh50fqTkT0JC

Efficient optimization of expensive black-box simulators via marginal means, with application to neutrino detector design
Hwanwoo Kim, Simon Mak, Ann-Kathrin Schuetz, Alan Poon
https://arxiv.org/abs/2508.01834

Efficient optimization of expensive black-box simulators via marginal means, with application to neutrino detector design
With advances in scientific computing, computer experiments are increasingly used for optimizing complex systems. However, for modern applications, e.g., the optimization of nuclear physics detectors, each experiment run can require hundreds of CPU hours, making the optimization of its black-box simulator over a high-dimensional space a challenging task. Given limited runs at inputs $\mathbf{x}_1, \cdots, \mathbf{x}_n$, the best solution from these evaluated inputs can be far from optimal, part…

Transferable Direct Prompt Injection via Activation-Guided MCMC Sampling
Minghui Li, Hao Zhang, Yechao Zhang, Wei Wan, Shengshan Hu, pei Xiaobing, Jing Wang
https://arxiv.org/abs/2509.07617

Transferable Direct Prompt Injection via Activation-Guided MCMC Sampling
Direct Prompt Injection (DPI) attacks pose a critical security threat to Large Language Models (LLMs) due to their low barrier of execution and high potential damage. To address the impracticality of existing white-box/gray-box methods and the poor transferability of black-box methods, we propose an activations-guided prompt injection attack framework. We first construct an Energy-based Model (EBM) using activations from a surrogate model to evaluate the quality of adversarial prompts. Guided b…

On the Convergence of Large Language Model Optimizer for Black-Box Network Management
Hoon Lee, Wentao Zhou, Merouane Debbah, Inkyu Lee
https://arxiv.org/abs/2507.02689

On the Convergence of Large Language Model Optimizer for Black-Box Network Management
Future wireless networks are expected to incorporate diverse services that often lack general mathematical models. To address such black-box network management tasks, the large language model (LLM) optimizer framework, which leverages pretrained LLMs as optimization agents, has recently been promoted as a promising solution. This framework utilizes natural language prompts describing the given optimization problems along with past solutions generated by LLMs themselves. As a result, LLMs can ob…

Algorithmic Complexity Attacks on All Learned Cardinality Estimators: A Data-centric Approach
Yingze Li, Xianglong Liu, Dong Wang, Zixuan Wang, Hongzhi Wang, Kaixing Zhang, Yiming Guan
https://arxiv.org/abs/2507.07438

Algorithmic Complexity Attacks on All Learned Cardinality Estimators: A Data-centric Approach
Learned cardinality estimators show promise in query cardinality prediction, yet they universally exhibit fragility to training data drifts, posing risks for real-world deployment. This work is the first to theoretical investigate how minimal data-level drifts can maximally degrade the accuracy of learned estimators. We propose data-centric algorithmic complexity attacks against learned estimators in a black-box setting, proving that finding the optimal attack strategy is NP-Hard. To address th…

Asymptotic Consistency and Generalization in Hybrid Models of Regularized Selection and Nonlinear Learning
Luciano Ribeiro Galv\~ao, Rafael de Andrade Mora
https://arxiv.org/abs/2508.07754

Asymptotic Consistency and Generalization in Hybrid Models of Regularized Selection and Nonlinear Learning
This study explores how different types of supervised models perform in the task of predicting and selecting relevant variables in high-dimensional contexts, especially when the data is very noisy. We analyzed three approaches: regularized models (such as Lasso, Ridge, and Elastic Net), black-box models (such as Random Forest, XGBoost, LightGBM, CatBoost, and H2O GBM), and hybrid models that combine both approaches: regularization with nonlinear algorithms. Based on simulations inspired by the …

Observations from the Seattle Seahawks Press Box After 23-23 Raiders Tie https://www.si.com/nfl/raiders/las-vegas-seattle-seahawks-podcast-pete-carroll-chip-kelly-gen0-smith-cam-miller

Componentwise Automata Learning for System Integration (Extended Version)
Hiroya Fujinami, Masaki Waga, Jie An, Kohei Suenaga, Nayuta Yanagisawa, Hiroki Iseri, Ichiro Hasuo
https://arxiv.org/abs/2508.04458

Componentwise Automata Learning for System Integration (Extended Version)
Compositional automata learning is attracting attention as an analysis technique for complex black-box systems. It exploits a target system's internal compositional structure to reduce complexity. In this paper, we identify system integration -- the process of building a new system as a composite of potentially third-party and black-box components -- as a new application domain of compositional automata learning. Accordingly, we propose a new problem setting, where the learner has direct access…

Black-Box Bug-Amplification for Multithreaded Software
Yeshayahu Weiss, Gal Amram, Achiya Elyasaf, Eitan Farchi, Oded Margalit, Gera Weiss
https://arxiv.org/abs/2507.21318 https…

Black-Box Bug-Amplification for Multithreaded Software
Bugs, especially those in concurrent systems, are often hard to reproduce because they manifest only under rare conditions. Testers frequently encounter failures that occur only under specific inputs, even when occurring with low probability. We propose an approach to systematically amplify the occurrence of such elusive bugs. We treat the system under test as a black-box and use repeated trial executions to train a predictive model that estimates the probability of a given input configuration …

Trust-region Filter Algorithms utilising Hessian Information for Grey-Box Optimisation
Gul Hameed, Tao Chen, Antonio del Rio Chanona, Lorenz T. Biegler, Michael Short
https://arxiv.org/abs/2509.01651

Trust-region Filter Algorithms utilising Hessian Information for Grey-Box Optimisation
Optimising industrial processes is essential for enhancing profitability, efficiency, and sustainability. Conventional optimisation relies on equation-oriented (EO) glass-box models with fully specified equations and accessible derivatives. However, many real-world systems contain black-box components (such as high-fidelity computational fluid dynamics simulators or proprietary unit models) that lack explicit formulations or analytic gradients. This limits the applicability of derivative-based …

Feature Space Analysis by Guided Diffusion Model
Kimiaki Shirahama, Miki Yanobu, Kaduki Yamashita, Miho Ohsaki
https://arxiv.org/abs/2509.07936 https://arx…

Feature Space Analysis by Guided Diffusion Model
One of the key issues in Deep Neural Networks (DNNs) is the black-box nature of their internal feature extraction process. Targeting vision-related domains, this paper focuses on analysing the feature space of a DNN by proposing a decoder that can generate images whose features are guaranteed to closely match a user-specified feature. Owing to this guarantee that is missed in past studies, our decoder allows us to evidence which of various attributes in an image are encoded into a feature by th…

BDFirewall: Towards Effective and Expeditiously Black-Box Backdoor Defense in MLaaS
Ye Li, Chengcheng Zhu, Yanchao Zhao, Jiale Zhang
https://arxiv.org/abs/2508.03307 https://

BDFirewall: Towards Effective and Expeditiously Black-Box Backdoor Defense in MLaaS
In this paper, we endeavor to address the challenges of backdoor attacks countermeasures in black-box scenarios, thereby fortifying the security of inference under MLaaS. We first categorize backdoor triggers from a new perspective, i.e., their impact on the patched area, and divide them into: high-visibility triggers (HVT), semi-visibility triggers (SVT), and low-visibility triggers (LVT). Based on this classification, we propose a progressive defense framework, BDFirewall, that removes these …

Graph-based Integrated Gradients for Explaining Graph Neural Networks
Lachlan Simpson, Kyle Millar, Adriel Cheng, Cheng-Chew Lim, Hong Gunn Chew
https://arxiv.org/abs/2509.07648

Graph-based Integrated Gradients for Explaining Graph Neural Networks
Integrated Gradients (IG) is a common explainability technique to address the black-box problem of neural networks. Integrated gradients assumes continuous data. Graphs are discrete structures making IG ill-suited to graphs. In this work, we introduce graph-based integrated gradients (GB-IG); an extension of IG to graphs. We demonstrate on four synthetic datasets that GB-IG accurately identifies crucial structural components of the graph used in classification tasks. We further demonstrate on t…

from my link log —
NaN-propagation: a novel method for sparsity detection in black-box computational functions.
https://arxiv.org/abs/2507.23186
saved 2025-08-14

NaN-Propagation: A Novel Method for Sparsity Detection in Black-Box Computational Functions
When numerically evaluating a function's gradient, sparsity detection can enable substantial computational speedups through Jacobian coloring and compression. However, sparsity detection techniques for black-box functions are limited, and existing finite-difference-based methods suffer from false negatives due to coincidental zero gradients. These false negatives can silently corrupt gradient calculations, leading to difficult-to-diagnose errors. We introduce NaN-propagation, which exploits the…

The current bane of my life. Those who know what this is, know my pain.

🇺🇦 #NowPlaying on KEXP's #AfternoonShow
Open Mike Eagle:
🎵 my co-worker clark kent's secret black box
#OpenMikeEagle
https://openmikeeagle.bandcamp.com/track/my-co-worker-clark-kents-secret-black-box
https://open.spotify.com/track/4l8t27aV3hDR55Dv7Nx7UX

Sampling as Bandits: Evaluation-Efficient Design for Black-Box Densities
Takuo Matsubara, Andrew Duncan, Simon Cotter, Konstantinos Zygalakis
https://arxiv.org/abs/2509.01437 ht…

Sampling as Bandits: Evaluation-Efficient Design for Black-Box Densities
We introduce bandit importance sampling (BIS), a new class of importance sampling methods designed for settings where the target density is expensive to evaluate. In contrast to adaptive importance sampling, which optimises a proposal distribution, BIS directly designs the samples through a sequential strategy that combines space-filling designs with multi-armed bandits. Our method leverages Gaussian process surrogates to guide sample selection, enabling efficient exploration of the parameter s…

Stabilizing RED using the Koopman Operator
Shraddha Chavan, Kunal N. Chaudhury
https://arxiv.org/abs/2509.05736 https://arxiv.org/pdf/2509.05736

Stabilizing RED using the Koopman Operator
The widely used RED (Regularization-by-Denoising) framework uses pretrained denoisers as implicit regularizers for model-based reconstruction. Although RED generally yields high-fidelity reconstructions, the use of black-box denoisers can sometimes lead to instability. In this letter, we propose a data-driven mechanism to stabilize RED using the Koopman operator, a classical tool for analyzing dynamical systems. Specifically, we use the operator to capture the local dynamics of RED in a low-dim…

An Interpretable AI Framework to Disentangle Self-Interacting and Cold Dark Matter in Galaxy Clusters: The CKAN Approach
Zhenyang Huang, Haihao Shi, Zhiyong Liu, Na Wang
https://arxiv.org/abs/2509.06788

An Interpretable AI Framework to Disentangle Self-Interacting and Cold Dark Matter in Galaxy Clusters: The CKAN Approach
Convolutional neural networks have shown their ability to differentiate between self-interacting dark matter (SIDM) and cold dark matter (CDM) on galaxy cluster scales. However, their large parameter counts and ''black-box'' nature make it difficult to assess whether their decisions adhere to physical principles. To address this issue, we have built a Convolutional Kolmogorov-Arnold Network (CKAN) that reduces parameter count and enhances interpretability, and propose a novel analytical framewo…

👊 Building my own paper tape punch
#hardware #technology

If you need me, I'll be in my office.

aivi & surasshu make my fav background music when reading
#music

Behaviour Space Analysis of LLM-driven Meta-heuristic Discovery
Niki van Stein, Haoran Yin, Anna V. Kononova, Thomas B\"ack, Gabriela Ochoa
https://arxiv.org/abs/2507.03605

Behaviour Space Analysis of LLM-driven Meta-heuristic Discovery
We investigate the behaviour space of meta-heuristic optimisation algorithms automatically generated by Large Language Model driven algorithm discovery methods. Using the Large Language Evolutionary Algorithm (LLaMEA) framework with a GPT o4-mini LLM, we iteratively evolve black-box optimisation heuristics, evaluated on 10 functions from the BBOB benchmark suite. Six LLaMEA variants, featuring different mutation prompt strategies, are compared and analysed. We log dynamic behavioural metrics in…

Instance-level Performance Prediction for Long-form Generation Tasks
Chi-Yang Hsu, Alexander Braylan, Yiheng Su, Omar Alonso, Matthew Lease
https://arxiv.org/abs/2509.07309 http…

Instance-level Performance Prediction for Long-form Generation Tasks
We motivate and share a new benchmark for instance-level performance prediction of long-form generation tasks having multi-faceted, fine-grained quality metrics. Our task-, model- and metric-agnostic formulation predicts continuous evaluation metric scores given only black-box model inputs and outputs. Beyond predicting point estimates of metric scores, the benchmark also requires inferring prediction intervals to quantify uncertainty around point estimates. Evaluation spans 11 long-form datase…

Today has been mainly about stripping down my favourite mountain bike for repainting and renovation.
#BikeTooter

So arguably the Pac-Man ghost logic is the exact opposite of “AI”.
It’s extremely well defined behavior based on very basic deterministic algorithms that are so simple a 8-year old player can understand them and anticipate them correctly for those high-scores.
The game actually is famous for the simple enemy logic and studied by games designers for it.
Pac-Man enemy behavior:
deterministic, well-defined algorithm that can easily be executed on the most primitive* computer hardware
“AI”:
black-box algorithm with indeterministic behavior that requires a metric shitload of compute
—
*By “primitive computer hardware” I mean that the Pac-Man arcade machine runs on a Zilog Z80 CPU with 8,500 transistors.The CPU in the phone you are reading this on has (latest iPhone as example) 15 billion transistors or so (almost 2 million times more) and runs tens of thousands of times faster.

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/5]:
- Wild refitting for black box prediction
Martin J. Wainwright
…

Urban Snaps 📸
城市抓拍 📸
📷 Pentax MX
🎞️Fujifilm Neopan SS, expired 1995
buy me ☕️ ?/请我喝杯☕️？
#filmphotography

Yiding Cao Geld mit PayPal.Me senden
Folgen Sie dem Link PayPal.Me/dingsterdingster und geben Sie den Betrag ein. Es ist bequem und sicher. Sie haben noch kein PayPal-Konto? Kein Problem.

Tight Bound for Quantum Unitary Time-Reversal
Kean Chen, Nengkun Yu, Zhicheng Zhang
https://arxiv.org/abs/2507.05736 https://arxiv.or…

Tight Bound for Quantum Unitary Time-Reversal
Time-reversal of unitary evolution is fundamental in quantum information processing. Many scenarios, particularly those in quantum learning and metrology, assume free access to the time-reverse of an unknown unitary. In this paper, we settle the query complexity of the unitary time-reversal task: approximately implementing $U^{-1}$ given only black-box access to an unknown $d$-dimensional unitary $U$. We provide a tight query lower bound $Ω((1-ε)d^2)$ for the unitary time-reversal to within d…

Quantifying Cross-Attention Interaction in Transformers for Interpreting TCR-pMHC Binding
Jiarui Li, Zixiang Yin, Haley Smith, Zhengming Ding, Samuel J. Landry, Ramgopal R. Mettu
https://arxiv.org/abs/2507.03197

Quantifying Cross-Attention Interaction in Transformers for Interpreting TCR-pMHC Binding
CD8+ "killer" T cells and CD4+ "helper" T cells play a central role in the adaptive immune system by recognizing antigens presented by Major Histocompatibility Complex (pMHC) molecules via T Cell Receptors (TCRs). Modeling binding between T cells and the pMHC complex is fundamental to understanding basic mechanisms of human immune response as well as in developing therapies. While transformer-based models such as TULIP have achieved impressive performance in this domain, their black-box nature …

Correct Black-Box Monitors for Distributed Deadlock Detection: Formalisation and Implementation (Technical Report)
Rados{\l}aw Jan Rowicki, Adrian Francalanza, Alceste Scalas
https://arxiv.org/abs/2508.14851

Correct Black-Box Monitors for Distributed Deadlock Detection: Formalisation and Implementation (Technical Report)
Many software applications rely on concurrent and distributed (micro)services that interact via message-passing and various forms of remote procedure calls (RPC). As these systems organically evolve and grow in scale and complexity, the risk of introducing deadlocks increases and their impact may worsen: even if only a few services deadlock, many other services may block while awaiting responses from the deadlocked ones. As a result, the "core" of the deadlock can be obfuscated by its consequen…

Fast Feeder Reconfiguration via Mesh Adaptive Direct Search in Black-Box Distribution System Environments
Junyuan Zheng, Wenlong Shi, Zhaoyu Wang
https://arxiv.org/abs/2507.16027 …

Fast Feeder Reconfiguration via Mesh Adaptive Direct Search in Black-Box Distribution System Environments
Feeder reconfiguration is a critical operational strategy in power distribution systems. However, existing optimization approaches typically rely on explicit mathematical formulations and analytical models, which are often infeasible in practical utility environments characterized by heterogeneous, proprietary, and black-box simulation modules. To address this challenge, this paper proposes a fast feeder reconfiguration framework based on Mesh Adaptive Direct Search (MADS). The proposed approac…

Cooperation as Black Box: Conceptual Fluctuation and Diagnostic Tools for Misalignment in MAS
Shayak Nandi, Fernanda M. Eliott
https://arxiv.org/abs/2506.22876

Cooperation as Black Box: Conceptual Fluctuation and Diagnostic Tools for Misalignment in MAS
Misalignment in multi-agent systems (MAS) is often treated as a technical failure; yet many such failures originate upstream, during the conceptual design phase, where semantic ambiguity and normative projection take place. This paper identifies a foundational source of interpretive misalignment in MAS: the systemic conflation of cooperation and coordination, and the moral overreading that follows. Using the Rabbit-Duck illusion, we illustrate how perspective-dependent readings of agent behavio…

ProvX: Generating Counterfactual-Driven Attack Explanations for Provenance-Based Detection
Weiheng Wu, Wei Qiao, Teng Li, Yebo Feng, Zhuo Ma, Jianfeng Ma, Yang Liu
https://arxiv.org/abs/2508.06073

ProvX: Generating Counterfactual-Driven Attack Explanations for Provenance-Based Detection
Provenance graph-based intrusion detection systems are deployed on hosts to defend against increasingly severe Advanced Persistent Threat. Using Graph Neural Networks to detect these threats has become a research focus and has demonstrated exceptional performance. However, the widespread adoption of GNN-based security models is limited by their inherent black-box nature, as they fail to provide security analysts with any verifiable explanations for model predictions or any evidence regarding th…

Demystifying Sequential Recommendations: Counterfactual Explanations via Genetic Algorithms
Domiziano Scarcelli, Filippo Betello, Giuseppe Perelli, Fabrizio Silvestri, Gabriele Tolomei
https://arxiv.org/abs/2508.03606

Demystifying Sequential Recommendations: Counterfactual Explanations via Genetic Algorithms
Sequential Recommender Systems (SRSs) have demonstrated remarkable effectiveness in capturing users' evolving preferences. However, their inherent complexity as "black box" models poses significant challenges for explainability. This work presents the first counterfactual explanation technique specifically developed for SRSs, introducing a novel approach in this space, addressing the key question: What minimal changes in a user's interaction history would lead to different recommendations? To a…

Does magnetic field promote or suppress fragmentation in AGN disks? Results from local shearing box simulations with simple cooling
Tsun Hin Navin Tsung, Mitchell C. Begelman, Philip J. Armitage, Yan-Fei Jiang, Hannalore J. Gerling-Dunsmore
https://arxiv.org/abs/2507.21991

Does magnetic field promote or suppress fragmentation in AGN disks? Results from local shearing box simulations with simple cooling
Accretion disks in Active Galactic Nuclei (AGN) are predicted to become gravitationally unstable substantially interior to the black hole's sphere of influence, at radii where the disk is simultaneously unstable to the magnetorotational instability (MRI). Using local shearing box simulations with net vertical flux and a simple cooling prescription, we investigate the effect of magnetic fields on fragmentation in the limit of ideal magnetohydrodyamics. Different levels of in-disk magnetic field …

A Variance Decomposition Approach to Inconclusives in Forensic Black Box Studies
Amanda Luby, Joseph B. Kadane
https://arxiv.org/abs/2507.02240 https://

A Variance Decomposition Approach to Inconclusives in Forensic Black Box Studies
In the US, `black box' studies are increasingly being used to estimate the error rate of forensic disciplines. A sample of forensic examiner participants are asked to evaluate a set of items whose source is known to the researchers but not to the participants. Participants are asked to make a source determination (typically an identification, exclusion, or some kind of inconclusive). We study inconclusives in two black box studies, one on fingerprints and one on bullets. Rather than treating al…

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks
Yuxuan Liu, Peihong Zhang, Rui Sang, Zhixin Li, Shengchen Li
https://arxiv.org/abs/2509.04980 https://

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks
Music adversarial attacks have garnered significant interest in the field of Music Information Retrieval (MIR). In this paper, we present Music Adversarial Inpainting Attack (MAIA), a novel adversarial attack framework that supports both white-box and black-box attack scenarios. MAIA begins with an importance analysis to identify critical audio segments, which are then targeted for modification. Utilizing generative inpainting models, these segments are reconstructed with guidance from the outp…

A linesearch-based derivative-free method for noisy black-box problems
Alberto De Santis, Giampaolo Liuzzi, Stefano Lucidi
https://arxiv.org/abs/2508.00495 https://

A linesearch-based derivative-free method for noisy black-box problems
In this work we consider unconstrained optimization problems. The objective function is known through a zeroth order stochastic oracle that gives an estimate of the true objective function. To solve these problems, we propose a derivative-free algorithm based on extrapolation techniques. Under reasonable assumptions we are able to prove convergence properties for the proposed algorithms. Furthermore, we also give a worst-case complexity result stating that the total number of iterations where t…

Wild refitting for black box prediction
Martin J. Wainwright
https://arxiv.org/abs/2506.21460 https://arxiv.org/pdf/2506.21460…

Wild refitting for black box prediction
We describe and analyze a computionally efficient refitting procedure for computing high-probability upper bounds on the instance-wise mean-squared prediction error of penalized nonparametric estimates based on least-squares minimization. Requiring only a single dataset and black box access to the prediction method, it consists of three steps: computing suitable residuals, symmetrizing and scaling them with a pre-factor $ρ$, and using them to define and solve a modified prediction problem rece…

Investigating Advanced Reasoning of Large Language Models via Black-Box Interaction
Congchi Yin, Tianyi Wu, Yankai Shu, Alex Gu, Yunhan Wang, Jun Shao, Xun Jiang, Piji Li
https://arxiv.org/abs/2508.19035

Investigating Advanced Reasoning of Large Language Models via Black-Box Interaction
Existing tasks fall short in evaluating reasoning ability of Large Language Models (LLMs) in an interactive, unknown environment. This deficiency leads to the isolated assessment of deductive, inductive, and abductive reasoning, neglecting the integrated reasoning process that is indispensable for humans discovery of real world. We introduce a novel evaluation paradigm, \textit{black-box interaction}, to tackle this challenge. A black-box is defined by a hidden function that maps a specific set…

Save me your fake nostalgia, people. There is only one true #BSOD, and it never was blue. 💣

My project that I completed yesterday. An old patio umbrella base, a fence post from a big box store, two old wall brackets for flower baskets, a couple of bird feeders that were on sale at CanTire, and a repurposed squirrel baffle. About $100 in total cost, not including the black oil seed. It's positioned right outside our sunroom for bifd watching, by us and our cat Casper.
#DIY #Birdwatching #Gardening

Learning-Augmented Online Covering Problems
Afrouz Jabal Ameli, Laura Sanita, Moritz Venzin
https://arxiv.org/abs/2507.06032 https://…

Learning-Augmented Online Covering Problems
We give a very general and simple framework to incorporate predictions on requests for online covering problems in a rigorous and black-box manner. Our framework turns any online algorithm with competitive ratio $ρ(k, \cdot)$ depending on $k$, the number of arriving requests, into an algorithm with competitive ratio of $ρ(η, \cdot)$, where $η$ is the prediction error. With accurate enough prediction, the resulting competitive ratio breaks through the corresponding worst-case online lower bo…

Beyond the Black Box: Integrating Lexical and Semantic Methods in Quantitative Discourse Analysis with BERTopic
Thomas Compton
https://arxiv.org/abs/2508.19099 https://

Beyond the Black Box: Integrating Lexical and Semantic Methods in Quantitative Discourse Analysis with BERTopic
Quantitative Discourse Analysis has seen growing adoption with the rise of Large Language Models and computational tools. However, reliance on black box software such as MAXQDA and NVivo risks undermining methodological transparency and alignment with research goals. This paper presents a hybrid, transparent framework for QDA that combines lexical and semantic methods to enable triangulation, reproducibility, and interpretability. Drawing from a case study in historical political discourse, we …

Towards Interpretable PolSAR Image Classification: Polarimetric Scattering Mechanism Informed Concept Bottleneck and Kolmogorov-Arnold Network
Jinqi Zhang, Fangzhou Han, Di Zhuang, Lamei Zhang, Bin Zou, Li Yuan
https://arxiv.org/abs/2507.03315

Towards Interpretable PolSAR Image Classification: Polarimetric Scattering Mechanism Informed Concept Bottleneck and Kolmogorov-Arnold Network
In recent years, Deep Learning (DL) based methods have received extensive and sufficient attention in the field of PolSAR image classification, which show excellent performance. However, due to the ``black-box" nature of DL methods, the interpretation of the high-dimensional features extracted and the backtracking of the decision-making process based on the features are still unresolved problems. In this study, we first highlight this issue and attempt to achieve the interpretability analysis o…

Sequentially Auditing Differential Privacy
Tom\'as Gonz\'alez, Mateo Dulce-Rubio, Aaditya Ramdas, M\'onica Ribero
https://arxiv.org/abs/2509.07055 https://

Sequentially Auditing Differential Privacy
We propose a practical sequential test for auditing differential privacy guarantees of black-box mechanisms. The test processes streams of mechanisms' outputs providing anytime-valid inference while controlling Type I error, overcoming the fixed sample size limitation of previous batch auditing methods. Experiments show this test detects violations with sample sizes that are orders of magnitude smaller than existing methods, reducing this number from 50K to a few hundred examples, across divers…

Solving Markov Chains with Analog Quantum Computing: The Fine Print
Ward van der Schoot, Niels M. P. Neumann
https://arxiv.org/abs/2508.02199 https://arxiv…

Solving Markov Chains with Analog Quantum Computing: The Fine Print
With a growing interest in quantum computing, the number of proposed quantum algorithms grows as well. The practical applicability of these algorithms differs: Some can be applied out-of-the-box, while others require black box oracles, which can not always be easily implemented. One of the first works to explicitly discuss these practical applicability aspects is by Aaronson discussing the \textit{fine print} of the HHL quantum algorithm that solves linear systems of equations. We extend this l…

Black-box optimization using factorization and Ising machines
Ryo Tamura, Yuya Seki, Yuki Minamoto, Koki Kitai, Yoshiki Matsuda, Shu Tanaka, Koji Tsuda
https://arxiv.org/abs/2507.18003

Black-box optimization using factorization and Ising machines
Black-box optimization (BBO) is used in materials design, drug discovery, and hyperparameter tuning in machine learning. The world is experiencing several of these problems. In this review, a factorization machine with quantum annealing or with quadratic-optimization annealing (FMQA) algorithm to realize fast computations of BBO using Ising machines (IMs) is discussed. The FMQA algorithm uses a factorization machine (FM) as a surrogate model for BBO. The FM model can be directly transformed int…

Black-Box Test Code Fault Localization Driven by Large Language Models and Execution Estimation
Ahmadreza Saboor Yaraghi, Golnaz Gharachorlu, Sakina Fatima, Lionel C. Briand, Ruiyuan Wan, Ruifeng Gao
https://arxiv.org/abs/2506.19045

Black-Box Test Code Fault Localization Driven by Large Language Models and Execution Estimation
Fault localization (FL) is a critical step in debugging which typically relies on repeated executions to pinpoint faulty code regions. However, repeated executions can be impractical in the presence of non-deterministic failures or high execution costs. While recent efforts have leveraged Large Language Models (LLMs) to aid execution-free FL, these have primarily focused on identifying faults in the system under test (SUT) rather than in the often complex system test code. However, the latter i…

Lyapunov-Based Deep Learning Control for Robots with Unknown Jacobian
Koji Matsuno, Chien Chern Cheah
https://arxiv.org/abs/2509.04984 https://arxiv.org/pd…

Lyapunov-Based Deep Learning Control for Robots with Unknown Jacobian
Deep learning, with its exceptional learning capabilities and flexibility, has been widely applied in various applications. However, its black-box nature poses a significant challenge in real-time robotic applications, particularly in robot control, where trustworthiness and robustness are critical in ensuring safety. In robot motion control, it is essential to analyze and ensure system stability, necessitating the establishment of methodologies that address this need. This paper aims to develo…

What If, But Privately: Private Counterfactual Retrieval
Shreya Meel, Mohamed Nomeir, Pasan Dissanayake, Sanghamitra Dutta, Sennur Ulukus
https://arxiv.org/abs/2508.03681 https:…

What If, But Privately: Private Counterfactual Retrieval
Transparency and explainability are two important aspects to be considered when employing black-box machine learning models in high-stake applications. Providing counterfactual explanations is one way of catering this requirement. However, this also poses a threat to the privacy of the institution that is providing the explanation, as well as the user who is requesting it. In this work, we are primarily concerned with the user's privacy who wants to retrieve a counterfactual instance, without r…

Uneven Lives VII 👁️
不工整人生 VII 👁️
📷 Minolta Hi-Matic AF
🎞️ERA 100, expired 1994
#filmphotography #Photography #blackandwhite

Opening the Black Box: Interpretable Remedies for Popularity Bias in Recommender Systems
Parviz Ahmadov, Masoud Mansoury
https://arxiv.org/abs/2508.17297 https://

Opening the Black Box: Interpretable Remedies for Popularity Bias in Recommender Systems
Popularity bias is a well-known challenge in recommender systems, where a small number of popular items receive disproportionate attention, while the majority of less popular items are largely overlooked. This imbalance often results in reduced recommendation quality and unfair exposure of items. Although existing mitigation techniques address this bias to some extent, they typically lack transparency in how they operate. In this paper, we propose a post-hoc method using a Sparse Autoencoder (S…

A Zeroth-Order Extra-Gradient Method For Black-Box Constrained Optimization
Yuke Zhou, Ruiyang Jin, Siyang Gao, Jianxiao Wang, Jie Song
https://arxiv.org/abs/2506.20546

A Zeroth-Order Extra-Gradient Method For Black-Box Constrained Optimization
Non-analytical objectives and constraints often arise in control systems, particularly in problems with complex dynamics, which are challenging yet lack efficient solution methods. In this work, we consider general constrained optimization problems involving black-box objectives and constraints. To solve it, we reformulate it as a min-max problem and propose a zeroth-order extra gradient (ZOEG) algorithm that combines the extra gradient method with a feedback-based stochastic zeroth-order gradi…

Breaking the Black Box: Inherently Interpretable Physics-Informed Machine Learning for Imbalanced Seismic Data
Vemula Sreenath, Filippo Gatti, Pierre Jehel
https://arxiv.org/abs/2508.19031

Breaking the Black Box: Inherently Interpretable Physics-Informed Machine Learning for Imbalanced Seismic Data
Ground motion models (GMMs) predict how strongly the ground will shake during an earthquake. They are essential for structural analysis, seismic design, and seismic risk assessment studies. Traditional machine learning (ML) approaches are popular to develop GMMs, due to large earthquake databases worldwide. However, they operate as "black boxes," which are hard to interpret and trust, limiting their use in high-stake decisions. Additionally, these databases suffer from significant data imbalanc…

Clone What You Can't Steal: Black-Box LLM Replication via Logit Leakage and Distillation
Kanchon Gharami, Hansaka Aluvihare, Shafika Showkat Moni, Berker Pek\"oz
https://arxiv.org/abs/2509.00973

Clone What You Can't Steal: Black-Box LLM Replication via Logit Leakage and Distillation
Large Language Models (LLMs) are increasingly deployed in mission-critical systems, facilitating tasks such as satellite operations, command-and-control, military decision support, and cyber defense. Many of these systems are accessed through application programming interfaces (APIs). When such APIs lack robust access controls, they can expose full or top-k logits, creating a significant and often overlooked attack surface. Prior art has mainly focused on reconstructing the output projection la…

Computationally efficient Gauss-Newton reinforcement learning for model predictive control
Dean Brandner, Sebastien Gros, Sergio Lucia
https://arxiv.org/abs/2508.02441 https://

Computationally efficient Gauss-Newton reinforcement learning for model predictive control
Model predictive control (MPC) is widely used in process control due to its interpretability and ability to handle constraints. As a parametric policy in reinforcement learning (RL), MPC offers strong initial performance and low data requirements compared to black-box policies like neural networks. However, most RL methods rely on first-order updates, which scale well to large parameter spaces but converge at most linearly, making them inefficient when each policy update requires solving an opt…

Towards a 3D Transfer-based Black-box Attack via Critical Feature Guidance
Shuchao Pang, Zhenghan Chen, Shen Zhang, Liming Lu, Siyuan Liang, Anan Du, Yongbin Zhou
https://arxiv.org/abs/2508.15650

Towards a 3D Transfer-based Black-box Attack via Critical Feature Guidance
Deep neural networks for 3D point clouds have been demonstrated to be vulnerable to adversarial examples. Previous 3D adversarial attack methods often exploit certain information about the target models, such as model parameters or outputs, to generate adversarial point clouds. However, in realistic scenarios, it is challenging to obtain any information about the target models under conditions of absolute security. Therefore, we focus on transfer-based attacks, where generating adversarial poin…

A Black-Box Approach for Exogenous Replenishment in Online Resource Allocation
Suho Kang, Ziyang Liu, Rajan Udwani
https://arxiv.org/abs/2507.14812 https:/…

A Black-Box Approach for Exogenous Replenishment in Online Resource Allocation
In a typical online resource allocation problem, we start with a fixed inventory of resources and make online allocation decisions in response to resource requests that arrive sequentially over a finite horizon. We consider settings where the inventory is replenished over time according to an unknown exogenous process. We introduce black-box methods that extend any existing algorithm, originally designed without considering replenishment, into one that works with an arbitrary (adversarial or st…

It's Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation
Jikai Jin, Lester Mackey, Vasilis Syrgkanis
https://arxiv.org/abs/2507.02275

It's Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation
Structure-agnostic causal inference studies how well one can estimate a treatment effect given black-box machine learning estimates of nuisance functions (like the impact of confounders on treatment and outcomes). Here, we find that the answer depends in a surprising way on the distribution of the treatment noise. Focusing on the partially linear model of \citet{robinson1988root}, we first show that the widely adopted double machine learning (DML) estimator is minimax rate-optimal for Gaussian …

Black-Box Edge AI Model Selection with Conformal Latency and Accuracy Guarantees
Anders E. Kal{\o}r, Tomoaki Ohtsuki
https://arxiv.org/abs/2506.11391 https…

Black-Box Edge AI Model Selection with Conformal Latency and Accuracy Guarantees
Edge artificial intelligence (AI) will be a central part of 6G, with powerful edge servers supporting devices in performing machine learning (ML) inference. However, it is challenging to deliver the latency and accuracy guarantees required by 6G applications, such as automated driving and robotics. This stems from the black-box nature of ML models, the complexities of the tasks, and the interplay between transmitted data quality, chosen inference model, and the random wireless channel. This pap…

Sample-Constrained Black Box Optimization for Audio Personalization
Rajalaxmi Rajagopalan, Yu-Lin Wei, Romit Roy Choudhury
https://arxiv.org/abs/2507.12773

Sample-Constrained Black Box Optimization for Audio Personalization
We consider the problem of personalizing audio to maximize user experience. Briefly, we aim to find a filter $h^*$, which applied to any music or speech, will maximize the user's satisfaction. This is a black-box optimization problem since the user's satisfaction function is unknown. Substantive work has been done on this topic where the key idea is to play audio samples to the user, each shaped by a different filter $h_i$, and query the user for their satisfaction scores $f(h_i)$. A family of …

A Foundation Model for Chest X-ray Interpretation with Grounded Reasoning via Online Reinforcement Learning
Qika Lin, Yifan Zhu, Bin Pu, Ling Huang, Haoran Luo, Jingying Ma, Zhen Peng, Tianzhe Zhao, Fangzhi Xu, Jian Zhang, Kai He, Zhonghong Ou, Swapnil Mishra, Mengling Feng
https://arxiv.org/abs/2509.03906

A Foundation Model for Chest X-ray Interpretation with Grounded Reasoning via Online Reinforcement Learning
Medical foundation models (FMs) have shown tremendous promise amid the rapid advancements in artificial intelligence (AI) technologies. However, current medical FMs typically generate answers in a black-box manner, lacking transparent reasoning processes and locally grounded interpretability, which hinders their practical clinical deployments. To this end, we introduce DeepMedix-R1, a holistic medical FM for chest X-ray (CXR) interpretation. It leverages a sequential training pipeline: initiall…

Hijacking JARVIS: Benchmarking Mobile GUI Agents against Unprivileged Third Parties
Guohong Liu, Jialei Ye, Jiacheng Liu, Yuanchun Li, Wei Liu, Pengzhi Gao, Jian Luan, Yunxin Liu
https://arxiv.org/abs/2507.04227

Hijacking JARVIS: Benchmarking Mobile GUI Agents against Unprivileged Third Parties
Mobile GUI agents are designed to autonomously execute diverse device-control tasks by interpreting and interacting with mobile screens. Despite notable advancements, their resilience in real-world scenarios where screen content may be partially manipulated by untrustworthy third parties remains largely unexplored. Owing to their black-box and autonomous nature, these agents are vulnerable to manipulations that could compromise user devices. In this work, we present the first systematic investi…

Viscosity Stabilized Plug-and-Play Reconstruction
Arghya Sinha, Trishit Mukherjee, Kunal N. Chaudhury
https://arxiv.org/abs/2508.01441 https://arxiv.org/pd…

Viscosity Stabilized Plug-and-Play Reconstruction
The plug-and-play (PnP) method uses a deep denoiser within a proximal algorithm for model-based image reconstruction (IR). Unlike end-to-end IR, PnP allows the same pretrained denoiser to be used across different imaging tasks, without the need for retraining. However, black-box networks can make the iterative process in PnP unstable. A common issue observed across architectures like CNNs, diffusion models, and transformers is that the visual quality and PSNR often improve initially but then de…

Generative Co-Design of Antibody Sequences and Structures via Black-Box Guidance in a Shared Latent Space
Yinghua Yao, Yuangang Pan, Xixian Chen
https://arxiv.org/abs/2508.11424

Generative Co-Design of Antibody Sequences and Structures via Black-Box Guidance in a Shared Latent Space
Advancements in deep generative models have enabled the joint modeling of antibody sequence and structure, given the antigen-antibody complex as context. However, existing approaches for optimizing complementarity-determining regions (CDRs) to improve developability properties operate in the raw data space, leading to excessively costly evaluations due to the inefficient search process. To address this, we propose LatEnt blAck-box Design (LEAD), a sequence-structure co-design framework that opt…

Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE
Zahra Zehtabi Sabeti Moghaddam, Zeinab Dehghani, Maneeha Rani, Koorosh Aslansefat, Bhupesh Kumar Mishra, Rameez Raja Kureshi, Dhavalkumar Thakker
https://arxiv.org/abs/2509.03626

Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE
Generative AI, such as Large Language Models (LLMs), has achieved impressive progress but still produces hallucinations and unverifiable claims, limiting reliability in sensitive domains. Retrieval-Augmented Generation (RAG) improves accuracy by grounding outputs in external knowledge, especially in domains like healthcare, where precision is vital. However, RAG remains opaque and essentially a black box, heavily dependent on data quality. We developed a method-agnostic, perturbation-based fram…

Towards Efficient Verification of Computation in Quantum Devices
Keren Li, Peng Yan, Hanru Jiang, Nengkun Yu
https://arxiv.org/abs/2508.00262 https://arxiv…

Towards Efficient Verification of Computation in Quantum Devices
Designing quantum processors is a complex task that demands advanced verification methods to ensure their correct functionality. However, traditional methods of comprehensively verifying quantum devices, such as quantum process tomography, face significant limitations because of the exponential growth in computational resources. These limitations arise from treating the system as a black box and ignoring its design structure. Consequently, new testing methods must be developed considering the d…

Probe before You Talk: Towards Black-box Defense against Backdoor Unalignment for Large Language Models
Biao Yi, Tiansheng Huang, Sishuo Chen, Tong Li, Zheli Liu, Zhixuan Chu, Yiming Li
https://arxiv.org/abs/2506.16447

Probe before You Talk: Towards Black-box Defense against Backdoor Unalignment for Large Language Models
Backdoor unalignment attacks against Large Language Models (LLMs) enable the stealthy compromise of safety alignment using a hidden trigger while evading normal safety auditing. These attacks pose significant threats to the applications of LLMs in the real-world Large Language Model as a Service (LLMaaS) setting, where the deployed model is a fully black-box system that can only interact through text. Furthermore, the sample-dependent nature of the attack target exacerbates the threat. Instead …

From Black Box to Transparency: Enhancing Automated Interpreting Assessment with Explainable AI in College Classrooms
Zhaokun Jiang, Ziyin Zhang
https://arxiv.org/abs/2508.10860

From Black Box to Transparency: Enhancing Automated Interpreting Assessment with Explainable AI in College Classrooms
Recent advancements in machine learning have spurred growing interests in automated interpreting quality assessment. Nevertheless, existing research suffers from insufficient examination of language use quality, unsatisfactory modeling effectiveness due to data scarcity and imbalance, and a lack of efforts to explain model predictions. To address these gaps, we propose a multi-dimensional modeling framework that integrates feature engineering, data augmentation, and explainable machine learning…

EigenBench: A Comparative Behavioral Measure of Value Alignment
Jonathn Chang, Leonard Piff, Suvadip Sana, Jasmine X. Li, Lionel Levine
https://arxiv.org/abs/2509.01938 https://…

EigenBench: A Comparative Behavioral Measure of Value Alignment
Aligning AI with human values is a pressing unsolved problem. To address the lack of quantitative metrics for value alignment, we propose EigenBench: a black-box method for comparatively benchmarking language models' values. Given an ensemble of models, a constitution describing a value system, and a dataset of scenarios, our method returns a vector of scores quantifying each model's alignment to the given constitution. To produce these scores, each model judges the outputs of other models acro…

Hot-Swap MarkBoard: An Efficient Black-box Watermarking Approach for Large-scale Model Distribution
Zhicheng Zhang, Peizhuo Lv, Mengke Wan, Jiang Fang, Diandian Guo, Yezeng Chen, Yinlong Liu, Wei Ma, Jiyan Sun, Liru Geng
https://arxiv.org/abs/2507.20650

Hot-Swap MarkBoard: An Efficient Black-box Watermarking Approach for Large-scale Model Distribution
Recently, Deep Learning (DL) models have been increasingly deployed on end-user devices as On-Device AI, offering improved efficiency and privacy. However, this deployment trend poses more serious Intellectual Property (IP) risks, as models are distributed on numerous local devices, making them vulnerable to theft and redistribution. Most existing ownership protection solutions (e.g., backdoor-based watermarking) are designed for cloud-based AI-as-a-Service (AIaaS) and are not directly applicab…

Jailbreaking Commercial Black-Box LLMs with Explicitly Harmful Prompts
Chiyu Zhang, Lu Zhou, Xiaogang Xu, Jiafei Wu, Liming Fang, Zhe Liu
https://arxiv.org/abs/2508.10390 https:…

Jailbreaking Commercial Black-Box LLMs with Explicitly Harmful Prompts
Evaluating jailbreak attacks is challenging when prompts are not overtly harmful or fail to induce harmful outputs. Unfortunately, many existing red-teaming datasets contain such unsuitable prompts. To evaluate attacks accurately, these datasets need to be assessed and cleaned for maliciousness. However, existing malicious content detection methods rely on either manual annotation, which is labor-intensive, or large language models (LLMs), which have inconsistent accuracy in harmful types. To b…

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing
Ruicheng Xian, Yuxuan Wan, Han Zhao
https://arxiv.org/abs/2508.11258 https://

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing
Instruction fine-tuned large language models (LLMs) enable a simple zero-shot or few-shot prompting paradigm, also known as in-context learning, for building prediction models. This convenience, combined with continued advances in LLM capability, has the potential to drive their adoption across a broad range of domains, including high-stakes applications where group fairness -- preventing disparate impacts across demographic groups -- is essential. The majority of existing approaches to enforci…

Multi-task Adversarial Attacks against Black-box Model with Few-shot Queries
Wenqiang Wang, Yan Xiao, Hao Lin, Yangshijie Zhang, Xiaochun Cao
https://arxiv.org/abs/2508.10039 ht…

Multi-task Adversarial Attacks against Black-box Model with Few-shot Queries
Current multi-task adversarial text attacks rely on abundant access to shared internal features and numerous queries, often limited to a single task type. As a result, these attacks are less effective against practical scenarios involving black-box feedback APIs, limited queries, or multiple task types. To bridge this gap, we propose \textbf{C}luster and \textbf{E}nsemble \textbf{M}ulti-task Text Adversarial \textbf{A}ttack (\textbf{CEMA}), an effective black-box attack that exploits the transf…

PLA: Prompt Learning Attack against Text-to-Image Generative Models
Xinqi Lyu, Yihao Liu, Yanjie Li, Bin Xiao
https://arxiv.org/abs/2508.03696 https://arxi…

PLA: Prompt Learning Attack against Text-to-Image Generative Models
Text-to-Image (T2I) models have gained widespread adoption across various applications. Despite the success, the potential misuse of T2I models poses significant risks of generating Not-Safe-For-Work (NSFW) content. To investigate the vulnerability of T2I models, this paper delves into adversarial attacks to bypass the safety mechanisms under black-box settings. Most previous methods rely on word substitution to search adversarial prompts. Due to limited search space, this leads to suboptimal p…

Removing Box-Free Watermarks for Image-to-Image Models via Query-Based Reverse Engineering
Haonan An, Guang Hua, Hangcheng Cao, Zhengru Fang, Guowen Xu, Susanto Rahardja, Yuguang Fang
https://arxiv.org/abs/2507.18034

Removing Box-Free Watermarks for Image-to-Image Models via Query-Based Reverse Engineering
The intellectual property of deep generative networks (GNets) can be protected using a cascaded hiding network (HNet) which embeds watermarks (or marks) into GNet outputs, known as box-free watermarking. Although both GNet and HNet are encapsulated in a black box (called operation network, or ONet), with only the generated and marked outputs from HNet being released to end users and deemed secure, in this paper, we reveal an overlooked vulnerability in such systems. Specifically, we show that t…

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS
Sabrine Ennaji, Elhadj Benkhelifa, Luigi V. Mancini
https://arxiv.org/abs/2506.20576

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS
Adversarial attacks, wherein slight inputs are carefully crafted to mislead intelligent models, have attracted increasing attention. However, a critical gap persists between theoretical advancements and practical application, particularly in structured data like network traffic, where interdependent features complicate effective adversarial manipulations. Moreover, ambiguity in current approaches restricts reproducibility and limits progress in this field. Hence, existing defenses often fail to…

FPEdit: Robust LLM Fingerprinting through Localized Knowledge Editing
Shida Wang, Chaohu Liu, Yubo Wang, Linli Xu
https://arxiv.org/abs/2508.02092 https://…

FPEdit: Robust LLM Fingerprinting through Localized Knowledge Editing
Large language models represent significant investments in computation, data, and engineering expertise, making them extraordinarily valuable intellectual assets. Nevertheless, these AI assets remain vulnerable to unauthorized redistribution and commercial exploitation through fine-tuning or black-box deployment. Current fingerprinting approaches face a fundamental trade-off: intrinsic methods require full parameter access, while backdoor-based techniques employ statistically anomalous triggers…

Quantifying Classifier Utility under Local Differential Privacy
Ye Zheng, Yidan Hu
https://arxiv.org/abs/2507.02727 https://arxiv.org…

Quantifying Classifier Utility under Local Differential Privacy
Local differential privacy (LDP) provides a rigorous and quantifiable privacy guarantee for personal data by introducing perturbation at the data source. However, quantifying the impact of these perturbations on classifier utility remains a theoretical challenge, particularly for complex or black-box classifiers. This paper presents a framework for theoretically quantifying classifier utility under LDP mechanisms. The key insight is that LDP perturbation is concentrated around the original da…

PREE: Towards Harmless and Adaptive Fingerprint Editing in Large Language Models via Knowledge Prefix Enhancement
Xubin Yue, Zhenhua Xu, Wenpeng Xing, Jiahui Yu, Mohan Li, Meng Han
https://arxiv.org/abs/2509.00918

PREE: Towards Harmless and Adaptive Fingerprint Editing in Large Language Models via Knowledge Prefix Enhancement
Addressing the intellectual property protection challenges in commercial deployment of large language models (LLMs), existing black-box fingerprinting techniques face dual challenges from incremental fine-tuning erasure and feature-space defense due to their reliance on overfitting high-perplexity trigger patterns. Recent work has revealed that model editing in the fingerprinting domain offers distinct advantages, including significantly lower false positive rates, enhanced harmlessness, and su…

NWaaS: Nonintrusive Watermarking as a Service for X-to-Image DNN
Haonan An, Guang Hua, Yu Guo, Hangcheng Cao, Susanto Rahardja, Yuguang Fang
https://arxiv.org/abs/2507.18036 htt…

NWaaS: Nonintrusive Watermarking as a Service for X-to-Image DNN
The intellectual property of deep neural network (DNN) models can be protected with DNN watermarking, which embeds copyright watermarks into model parameters (white-box), model behavior (black-box), or model outputs (box-free), and the watermarks can be subsequently extracted to verify model ownership or detect model theft. Despite recent advances, these existing methods are inherently intrusive, as they either modify the model parameters or alter the structure. This natural intrusiveness raise…