Tootfinder

Muon tracking in a LiquidO opaque scintillator detector
LiquidO Collaboration, J. Apilluelo, L. Asquith, E. F. Bannister, N. P. Barradas, C. L. Baylis, J. L. Beney, M. Berberan e Santos, X. de la Bernardie, T. J. C. Bezerra, M. Bongrand, C. Bourgeois, D. Breton, J. Busto, A. Cabrera, A. Cadiou, E. Calvo, M. de Carlos Generowicz, E. Chauveau, B. J. Cattermole, M. Chen, P. Chimenti, D. F. Cowen, S. Kr. Das, S. Dusini, A. Earle, M. Felizardo, C. Frigerio Martins, J. Gal\'an, J. A. Gar…

Muon tracking in a LiquidO opaque scintillator detector
LiquidO is an innovative radiation detector concept. The core idea is to exploit stochastic light confinement in a highly scattering medium to self-segment the detector volume. In this paper, we demonstrate event-by-event muon tracking in a LiquidO opaque scintillator detector prototype. The detector consists of a 30 mm cubic scintillator volume instrumented with 64 wavelength-shifting fibres arranged in an 8$\times$8 grid with a 3.2 mm pitch and read out by silicon photomultipliers. A wax-base…

On small, local language models: 'In a world increasingly dominated by massive models and opaque APIs, we believe there’s still room for small, transparent, controllable systems. Models you can fine-tune, understand and run on your own terms' https://www.turing.ac.uk…

Anisotropy enables advanced light sheet microscopy with opaque lenses
Mikis Mylonakis, Evangelos Marakis, George J. Tserevelakis, Athanasios Zacharopoulos, Maira Tampakaki, Iro Manne, Angela Pasparaki, Nektarios Tavernarakis, Dimitrios G. Papazoglou, Giannis Zacharakis
https://arxiv.org/abs/2509.12840

Anisotropy enables advanced light sheet microscopy with opaque lenses
Advances in fluorescence microscopy have reached a plateau in improving depth-to-resolution ratios for imaging scattering tissues, highlighting an urgent need for innovative techniques. We introduce Wavelens, a groundbreaking opaque lens that combines dynamic wavefront shaping with a passive, angularly anisotropic photonic scattering plate-designed to scatter light predominantly in one direction. This compact, robust optoelectronic device, comparable in size to a conventional microscope lens, d…

There’s a new #OpenStreetMap website feature related to #Wikidata!
When viewing objects in OSM that have a Wikidata tag, you can now click the Wikidata icon to turn the opaque QID values into human-readable info (plus a link to Wikipedia if any).

BaMANI: Bayesian Multi-Algorithm causal Network Inference
Habibolla Latifizadeh, Anika C. Pirkey, Alanna Gould, David J. Klinke II
https://arxiv.org/abs/2508.11741 https://

BaMANI: Bayesian Multi-Algorithm causal Network Inference
Improved computational power has enabled different disciplines to predict causal relationships among modeled variables using Bayesian network inference. While many alternative algorithms have been proposed to improve the efficiency and reliability of network prediction, the predicted causal networks reflect the generative process but also bear an opaque imprint of the specific computational algorithm used. Following a ``wisdom of the crowds" strategy, we developed an ensemble learning approach …

Asynchronous Gathering of Opaque Robots with Mobility Faults
Subhajit Pramanick, Saswata Jana, Partha Sarathi Mandal, Gokarna Sharma
https://arxiv.org/abs/2509.10711 https://

Asynchronous Gathering of Opaque Robots with Mobility Faults
We consider the fundamental benchmarking problem of gathering in an $(N,f)$-fault system consisting of $N$ robots, of which at most $f$ might fail at any execution, under asynchrony. Two seminal results established impossibility of a solution in the oblivious robot (OBLOT) model in a $(2,0)$-fault system under semi-synchrony and in a $(3,1)$-Byzantine fault system under asynchrony. Recently, a breakthrough result circumvented the first impossibility result by giving a deterministic algorithm in…

A Comprehensive Perspective on Explainable AI across the Machine Learning Workflow
George Paterakis, Andrea Castellani, George Papoutsoglou, Tobias Rodemann, Ioannis Tsamardinos
https://arxiv.org/abs/2508.11529

A Comprehensive Perspective on Explainable AI across the Machine Learning Workflow
Artificial intelligence is reshaping science and industry, yet many users still regard its models as opaque "black boxes". Conventional explainable artificial-intelligence methods clarify individual predictions but overlook the upstream decisions and downstream quality checks that determine whether insights can be trusted. In this work, we present Holistic Explainable Artificial Intelligence (HXAI), a user-centric framework that embeds explanation into every stage of the data-analysis workflow …

Agentic AI for Financial Crime Compliance
Henrik Axelsen, Valdemar Licht, Jan Damsgaard
https://arxiv.org/abs/2509.13137 https://arxiv.org/pdf/2509.13137…

Agentic AI for Financial Crime Compliance
The cost and complexity of financial crime compliance (FCC) continue to rise, often without measurable improvements in effectiveness. While AI offers potential, most solutions remain opaque and poorly aligned with regulatory expectations. This paper presents the design and deployment of an agentic AI system for FCC in digitally native financial platforms. Developed through an Action Design Research (ADR) process with a fintech firm and regulatory stakeholders, the system automates onboarding, m…

What a rubbish idea:
Large corporations already hide too much information from their shareholders and the public.
This would make that hiding easier and make company performance more opaque.
It doubles the time that corporate ill deeds and management failures can be hidden.
It is a dumb idea - but coming from one of the great scammers in our corporate world, we should be glad that he did not suggest yearly, or longer, reporting.
"Trump renews push to end co…

Trump renews push to end companies' quarterly reports
The US president urges regulators to adopt a semi-annual schedule for financial reports.

Towards Trustworthy Agentic IoEV: AI Agents for Explainable Cyberthreat Mitigation and State Analytics
Meryem Malak Dif, Mouhamed Amine Bouchiha, Abdelaziz Amara Korba, Yacine Ghamri-Doudane
https://arxiv.org/abs/2509.12233

Towards Trustworthy Agentic IoEV: AI Agents for Explainable Cyberthreat Mitigation and State Analytics
The Internet of Electric Vehicles (IoEV) envisions a tightly coupled ecosystem of electric vehicles (EVs), charging infrastructure, and grid services, yet it remains vulnerable to cyberattacks, unreliable battery-state predictions, and opaque decision processes that erode trust and performance. To address these challenges, we introduce a novel Agentic Artificial Intelligence (AAI) framework tailored for IoEV, where specialized agents collaborate to deliver autonomous threat mitigation, robust a…

Participatory AI: A Scandinavian Approach to Human-Centered AI
Niklas Elmqvist, Eve Hoggan, Hans-J\"org Schulz, Marianne Graves Petersen, Peter Dalsgaard, Ira Assent, Olav W. Bertelsen, Akhil Arora, Kaj Gr{\o}nb{\ae}k, Susanne B{\o}dker, Clemens Nylandsted Klokmose, Rachel Charlotte Smith, Sebastian Hubenschmid, Christoph A. Johns, Gabriela Molina Le\'on, Anton Wolter, Johannes Ellemose, Vaishali Dhanoa, Simon Aagaard Enni, Mille Skovhus Lunding, Karl-Emil Kj{\ae}r Bilstrup, J…

Participatory AI: A Scandinavian Approach to Human-Centered AI
AI's transformative impact on work, education, and everyday life makes it as much a political artifact as a technological one. Current AI models are opaque, centralized, and overly generic. The algorithmic automation they provide threatens human agency and democratic values in both workplaces and daily life. To confront such challenges, we turn to Scandinavian Participatory Design (PD), which was devised in the 1970s to face a similar threat from mechanical automation. In the PD tradition, tech…

Explicit lower bounds for opaque sets of unit square and unit disc
Markus Kiderlen, Florian Pausinger
https://arxiv.org/abs/2509.08842 https://arxiv.org/pd…

Explicit lower bounds for opaque sets of unit square and unit disc
Explicit lower bounds for the length of the shortest opaque set for the unit disc and the unit square in the Euclidean plane are derived. The results are based on an explicit application of the general method of Kawamura, Moriyama, Otachi and Pach. While the bound for the unit square only slightly improves the existing bound, the bound for the unit disc is new and based on the idea that the free parameters in the general method can be optimized due to the strong symmetry properties of the disc.…

V-ZOR: Enabling Verifiable Cross-Blockchain Communication via Quantum-Driven ZKP Oracle Relays
M. Z. Haider, Tayyaba Noreen, M. Salman, M. Dias de Assuncao, Kaiwen Zhang
https://arxiv.org/abs/2509.10996

V-ZOR: Enabling Verifiable Cross-Blockchain Communication via Quantum-Driven ZKP Oracle Relays
Cross-chain bridges and oracle DAOs represent some of the most vulnerable components of decentralized systems, with more than $2.8 billion lost due to trust failures, opaque validation behavior, and weak incentives. Current oracle designs are based on multisigs, optimistic assumptions, or centralized aggregation, exposing them to attacks and delays. Moreover, predictable committee selection enables manipulation, which threatens data integrity across chains. We propose V-ZOR, a verifiable oracle…

Ordinality of Visible-Thermal Image Intensities for Intrinsic Image Decomposition
Zeqing Leo Yuan, Mani Ramanagopal, Aswin C. Sankaranarayanan, Srinivasa G. Narasimhan
https://arxiv.org/abs/2509.10388 …

Ordinality of Visible-Thermal Image Intensities for Intrinsic Image Decomposition
Decomposing an image into its intrinsic photometric factors--shading and reflectance--is a long-standing challenge due to the lack of extensive ground-truth data for real-world scenes. Recent methods rely on synthetic data or sparse annotations for limited indoor and even fewer outdoor scenes. We introduce a novel training-free approach for intrinsic image decomposition using only a pair of visible and thermal images. We leverage the principle that light not reflected from an opaque surface is …

#Uber
https://www.theguardian.com/technology/2025/jun/25/second-study-finds-uber-us…

#Uber
https://www.theguardian.com/technology/2025/jun/25/second-study-finds-uber-us…

Relaxation and Its Effects on Electronic Structure in Twisted Systems: An Analytical Perspective
Junxi Yu, Bingbing Wang, Cheng-Cheng Liu
https://arxiv.org/abs/2509.13114 https:…

Relaxation and Its Effects on Electronic Structure in Twisted Systems: An Analytical Perspective
Lattice relaxation profoundly reshapes electronic structures in twisted materials. Prevailing treatments, however, typically rely on large-scale density functional theory (DFT), which is computationally costly and mechanistically opaque. Here, we develop a unified analytical framework to overcome these limitations. From continuum elastic theory, we derive closed-form solutions for both in-plane and out-of-plane relaxation fields. We further introduce an analytical phase factor expansion theory …

@… Preorder of Superdrag - Regretfully Yours is happening!
https://www.lovevinylrecords.com/products/

every good testcase comes with an opaque blob compressed with zlib and encoded with base85

The Anatomy of Alignment: Decomposing Preference Optimization by Steering Sparse Features
Jeremias Ferrao, Matthijs van der Lende, Ilija Lichkovski, Clement Neo
https://arxiv.org/abs/2509.12934

The Anatomy of Alignment: Decomposing Preference Optimization by Steering Sparse Features
Aligning large language models is critical for their usability and safety. However, the prevailing approach of Reinforcement Learning from Human Feedback (RLHF) induces diffuse, opaque parameter changes, making it difficult to discern what the model has internalized. Hence, we introduce Feature Steering with Reinforcement Learning (FSRL), a transparent alignment framework that trains a lightweight adapter to steer behavior by modulating interpretable features from a Sparse Autoencoder (SAE). Fi…

Moving Beyond Marginal Carbon Intensity: A Poor Metric for Both Carbon Accounting and Grid Flexibility
Philipp Wiesner, Odej Kao
https://arxiv.org/abs/2507.11377

Moving Beyond Marginal Carbon Intensity: A Poor Metric for Both Carbon Accounting and Grid Flexibility
Marginal Carbon Intensity (MCI) has been promoted as an effective metric for carbon-aware computing. Although it is already considered as impractical for carbon accounting purposes, many still view it as valuable when optimizing for grid flexibility by incentivizing electricity usage during curtailment periods. In this statement paper, we argue that MCI is neither reliable nor actionable for either purpose. We outline its fundamental limitations, including non-observability, reliance on opaque …

Transparent and Fair Profiling in Employment Services: Evidence from Switzerland
Tim R\"az
https://arxiv.org/abs/2509.11847 https://arxiv.org/pdf/2509…

Transparent and Fair Profiling in Employment Services: Evidence from Switzerland
Long-term unemployment (LTU) is a challenge for both jobseekers and public employment services. Statistical profiling tools are increasingly used to predict LTU risk. Some profiling tools are opaque, black-box machine learning models, which raise issues of transparency and fairness. This paper investigates whether interpretable models could serve as an alternative, using administrative data from Switzerland. Traditional statistical, interpretable, and black-box models are compared in terms of p…

Senior analytics leaders from Fox and ESPN criticize Nielsen's opaque and unaccredited audience measurement method for YouTube's Chiefs-Chargers livestream (Eric Fisher/Front Office Sports)
https://frontofficesports.com/fox-espn

Fox, ESPN Cry Foul Over Big Nielsen Number Expected for YouTube’s NFL Brazil Game
Even before YouTube streamed its NFL game Friday night in Brazil between the Chiefs and Chargers, other league rights holders cried foul.

Woohoo #OPAQUE now is an #IETF #RFC 9807!
https://www.rfc-edi…

I love watching tutorials for games where they literally can't talk about the game without the lingo. totally opaque to new players.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation
Zahra Atf, Peter R Lewis
https://arxiv.org/abs/2509.07190 https://arx…

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation
Large language models (LLMs) are increasingly used in high-stakes settings, where explaining uncertainty is both technical and ethical. Probabilistic methods are often opaque and misaligned with expectations of transparency. We propose a framework based on rule-based moral principles for handling uncertainty in LLM-generated text. Using insights from moral psychology and virtue ethics, we define rules such as precaution, deference, and responsibility to guide responses under epistemic or aleato…

Position: Intelligent Coding Systems Should Write Programs with Justifications
Xiangzhe Xu, Shiwei Feng, Zian Su, Chengpeng Wang, Xiangyu Zhang
https://arxiv.org/abs/2508.06017 …

Position: Intelligent Coding Systems Should Write Programs with Justifications
Intelligent coding systems are transforming software development by enabling users to specify code behavior in natural language. However, the opaque decision-making of AI-driven coders raises trust and usability concerns, particularly for non-expert users who cannot inspect low-level implementations. We argue that these systems should not only generate code but also produce clear, consistent justifications that bridge model reasoning and user understanding. To this end, we identify two critical…

Maisa AI, which offers an enterprise agentic automation service that uses a proprietary system to limit hallucinations, raised $25M seed led by Creandum (Anna Heim/TechCrunch)
https://techcrunch.com/2025/08/27/maisa-ai-gets-25m-to-fix-enterprise-ais…

Maisa AI gets $25M to fix enterprise AI's 95% failure rate | TechCrunch
Maisa AI is built on the premise that enterprise automation requires accountable AI agents, not opaque black boxes.

Metadata Management for AI-Augmented Data Workflows
Jinjin Zhao, Sanjay Krishnan
https://arxiv.org/abs/2508.06814 https://arxiv.org/pdf/2508.06814

Metadata Management for AI-Augmented Data Workflows
AI-augmented data workflows introduce complex governance challenges, as both human and model-driven processes generate, transform, and consume data artifacts. These workflows blend heterogeneous tools, dynamic execution patterns, and opaque model decisions, making comprehensive metadata capture difficult. In this work, we present TableVault, a metadata governance framework designed for human-AI collaborative data creation. TableVault records ingestion events, traces operation status, links exec…

I have just figured out the weird opaque process to add alt text to images in Teams and, as a result, discovered it only allows 240 characters.
Well done, fuckaroos.

A Novel Study on Intelligent Methods and Explainable AI for Dynamic Malware Analysis
Richa Dasila, Vatsala Upadhyay, Samo Bobek, Abhishek Vaish
https://arxiv.org/abs/2508.10652 …

A Novel Study on Intelligent Methods and Explainable AI for Dynamic Malware Analysis
Deep learning models are one of the security strategies, trained on extensive datasets, and play a critical role in detecting and responding to these threats by recognizing complex patterns in malicious code. However, the opaque nature of these models-often described as "black boxes"-makes their decision-making processes difficult to understand, even for their creators. This research addresses these challenges by integrating Explainable AI (XAI) techniques to enhance the interpretability and tr…

Nearly optimal algorithms to learn sparse quantum Hamiltonians in physically motivated distances
Amira Abbas, Nunzia Cerrato, Francisco Escudero Guti\'errez, Dmitry Grinko, Francesco Anna Mele, Pulkit Sinha
https://arxiv.org/abs/2509.09813

Nearly optimal algorithms to learn sparse quantum Hamiltonians in physically motivated distances
We study the problem of learning Hamiltonians $H$ that are $s$-sparse in the Pauli basis, given access to their time evolution. Although Hamiltonian learning has been extensively investigated, two issues recur in much of the existing literature: the absence of matching lower bounds and the use of mathematically convenient but physically opaque error measures. We address both challenges by introducing two physically motivated distances between Hamiltonians and designing a nearly optimal algori…

React-tRace: A Semantics for Understanding React Hooks
Jay Lee, Joongwon Ahn, Kwangkeun Yi
https://arxiv.org/abs/2507.05234 https://a…

React-tRace: A Semantics for Understanding React Hooks
React has become the most widely used web front-end framework, enabling the creation of user interfaces in a declarative and compositional manner. Hooks are a set of APIs that manage side effects in functional components in React. However, their semantics are often seen as opaque to developers, leading to UI bugs. In this paper, we formalize the semantics of the essence of React Hooks we name React-tRace, providing a framework that clarifies their behavior. We demonstrate that our model capture…

Replaced article(s) found for nucl-ex. https://arxiv.org/list/nucl-ex/new
[1/1]:
- Combining Hybrid and Opaque Scintillator Techniques in the Search for Double Beta Plus Decays
Collaboration, et al.

Avoiding Over-Personalization with Rule-Guided Knowledge Graph Adaptation for LLM Recommendations
Fernando Spadea, Oshani Seneviratne
https://arxiv.org/abs/2509.07133 https://…

Avoiding Over-Personalization with Rule-Guided Knowledge Graph Adaptation for LLM Recommendations
We present a lightweight neuro-symbolic framework to mitigate over-personalization in LLM-based recommender systems by adapting user-side Knowledge Graphs (KGs) at inference time. Instead of retraining models or relying on opaque heuristics, our method restructures a user's Personalized Knowledge Graph (PKG) to suppress feature co-occurrence patterns that reinforce Personalized Information Environments (PIEs), i.e., algorithmically induced filter bubbles that constrain content diversity. These …

Rethinking Human Preference Evaluation of LLM Rationales
Ziang Li, Manasi Ganti, Zixian Ma, Helena Vasconcelos, Qijia He, Ranjay Krishna
https://arxiv.org/abs/2509.11026 https:/…

Rethinking Human Preference Evaluation of LLM Rationales
Large language models (LLMs) often generate natural language rationales -- free-form explanations that help improve performance on complex reasoning tasks and enhance interpretability for human users. However, evaluating these rationales remains challenging. While recent work has relied on binary preference judgments from humans or LLM judges, such evaluations are often opaque and coarse-grained, offering limited insight into what makes one rationale better than another. In this work, we rethin…

Wild Refitting for Model-Free Excess Risk Evaluation of Opaque ML/AI Models under Bregman Loss
Haichen Hu, David Simchi-Levi
https://arxiv.org/abs/2509.02476 https://

Wild Refitting for Model-Free Excess Risk Evaluation of Opaque ML/AI Models under Bregman Loss
We study the problem of evaluating the excess risk of classical penalized empirical risk minimization (ERM) with Bregman losses. We show that by leveraging the recently proposed wild refitting procedure (Wainwright, 2025), one can efficiently upper bound the excess risk through the so-called "wild optimism," without relying on the global structure of the underlying function class. This property makes our approach inherently model-free. Unlike conventional analyses, our framework operates with j…

opaque and firm

RAGTrace: Understanding and Refining Retrieval-Generation Dynamics in Retrieval-Augmented Generation
Sizhe Cheng, Jiaping Li, Huanchen Wang, Yuxin Ma
https://arxiv.org/abs/2508.06056

RAGTrace: Understanding and Refining Retrieval-Generation Dynamics in Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) systems have emerged as a promising solution to enhance large language models (LLMs) by integrating external knowledge retrieval with generative capabilities. While significant advancements have been made in improving retrieval accuracy and response quality, a critical challenge remains that the internal knowledge integration and retrieval-generation interactions in RAG workflows are largely opaque. This paper introduces RAGTrace, an interactive evaluation s…

Replaced article(s) found for physics.ins-det. https://arxiv.org/list/physics.ins-det/new
[1/1]:
- Combining Hybrid and Opaque Scintillator Techniques in the Search for Double Beta Plus Decays
Collaboration, et al.

A Keyframe-Based Approach for Auditing Bias in YouTube Shorts Recommendations
Mert Can Cakmak, Nitin Agarwal
https://arxiv.org/abs/2509.02543 https://arxiv…

A Keyframe-Based Approach for Auditing Bias in YouTube Shorts Recommendations
YouTube Shorts and other short-form video platforms now influence how billions engage with content, yet their recommendation systems remain largely opaque. Small shifts in promoted content can significantly impact user exposure, especially for politically sensitive topics. In this work, we propose a keyframe-based method to audit bias and drift in short-form video recommendations. Rather than analyzing full videos or relying on metadata, we extract perceptually salient keyframes, generate capti…

OpenS2S: Advancing Open-Source End-to-End Empathetic Large Speech Language Model
Chen Wang, Tianyu Peng, Wen Yang, Yinan Bai, Guangfu Wang, Jun Lin, Lanpeng Jia, Lingxiang Wu, Jinqiao Wang, Chengqing Zong, Jiajun Zhang
https://arxiv.org/abs/2507.05177

OpenS2S: Advancing Open-Source End-to-End Empathetic Large Speech Language Model
Empathetic interaction is a cornerstone of human-machine communication, due to the need for understanding speech enriched with paralinguistic cues and generating emotional and expressive responses. However, the most powerful empathetic LSLMs are increasingly closed off, leaving the crucial details about the architecture, data and development opaque to researchers. Given the critical need for transparent research into the LSLMs and empathetic behavior, we present OpenS2S, a fully open-source, tr…

A surge in AI company valuations is driving the popularity of special purpose vehicles, but some involve high fees, opaque structures, and layers of middlemen (Business Insider)
https://africa.businessinsider.com/mar

Investors warn AI FOMO could be fueling a risky bubble in companies like OpenAI and Anthropic
As AI valuations soar into the hundreds of billions, a parallel market of temporary financial shells is proliferating to meet investor FOMO, and many investors are starting to sound the alarm bells that it portends a bubble in the AI gold rush

From Legal Text to Tech Specs: Generative AI's Interpretation of Consent in Privacy Law
Aniket Kesari, Travis Breaux, Tom Norton, Sarah Santos, Anmol Singhal
https://arxiv.org/abs/2507.04185

From Legal Text to Tech Specs: Generative AI's Interpretation of Consent in Privacy Law
Privacy law and regulation have turned to "consent" as the legitimate basis for collecting and processing individuals' data. As governments have rushed to enshrine consent requirements in their privacy laws, such as the California Consumer Privacy Act (CCPA), significant challenges remain in understanding how these legal mandates are operationalized in software. The opaque nature of software development processes further complicates this translation. To address this, we explore the use of Large…

Learning Neural Vocoder from Range-Null Space Decomposition
Andong Li, Tong Lei, Zhihang Sun, Rilin Chen, Erwei Yin, Xiaodong Li, Chengshi Zheng
https://arxiv.org/abs/2507.20731

Learning Neural Vocoder from Range-Null Space Decomposition
Despite the rapid development of neural vocoders in recent years, they usually suffer from some intrinsic challenges like opaque modeling, and parameter-performance trade-off. In this study, we propose an innovative time-frequency (T-F) domain-based neural vocoder to resolve the above-mentioned challenges. To be specific, we bridge the connection between the classical signal range-null decomposition (RND) theory and vocoder task, and the reconstruction of target spectrogram can be decomposed in…

Demystifying NCCL: An In-depth Analysis of GPU Communication Protocols and Algorithms
Zhiyi Hu, Siyuan Shen, Tommaso Bonato, Sylvain Jeaugey, Cedell Alexander, Eric Spada, Jeff Hammond, Torsten Hoefler
https://arxiv.org/abs/2507.04786

Demystifying NCCL: An In-depth Analysis of GPU Communication Protocols and Algorithms
The NVIDIA Collective Communication Library (NCCL) is a critical software layer enabling high-performance collectives on large-scale GPU clusters. Despite being open source with a documented API, its internal design remains largely opaque. The orchestration of communication channels, selection of protocols, and handling of memory movement across devices and nodes are not well understood, making it difficult to analyze performance or identify bottlenecks. This paper presents a comprehensive anal…

huh, i just found this. it is clearly llm generated, and thus riddled with inaccuracies and hallucinations. but on first glance, i am very much impressed by the stuff that doesn't immediately jump at me as wrong: https://deepwiki.com/stef/libopaque and

From Traces to Program Incorrectness: A Type-Theoretic Approach
Yongwei Yuan, Zhe Zhou, Julia Belyakova, Benjamin Delaware, Suresh Jagannathan
https://arxiv.org/abs/2509.02428 h…

From Traces to Program Incorrectness: A Type-Theoretic Approach
We present a type-theoretic framework for reasoning about incorrectness in functional programs that interact with effectful, opaque library APIs. Our approach centers on traces -- temporally-ordered sequences of library API invocations -- which naturally characterize both the preconditions of individual APIs and their composite behavior. We represent these traces using symbolic regular expressions (SREs), enabling formal specification of incorrect abstract data type (ADT) behaviors across funct…

The Quantified Body: Identity, Empowerment, and Control in Smart Wearables
Maijunxian Wang
https://arxiv.org/abs/2506.15991 https://a…

The Quantified Body: Identity, Empowerment, and Control in Smart Wearables
In an era where bodies are increasingly rendered as streams of biometric data, smart wearables have emerged not only as tools of self-optimization but as infrastructures of predictive governance. This paper critically examines how devices such as the Apple Watch, Fitbit, and Oura Ring reconfigure bodily autonomy through feedback-driven self-surveillance, while embedding users within opaque systems of data extraction and algorithmic control. Drawing on Deleuze's concept of the control society, Z…

Dataset Ownership in the Era of Large Language Models
Kun Li, Cheng Wang, Minghui Xu, Yue Zhang, Xiuzhen Cheng
https://arxiv.org/abs/2509.05921 https://arx…

Dataset Ownership in the Era of Large Language Models
As datasets become critical assets in modern machine learning systems, ensuring robust copyright protection has emerged as an urgent challenge. Traditional legal mechanisms often fail to address the technical complexities of digital data replication and unauthorized use, particularly in opaque or decentralized environments. This survey provides a comprehensive review of technical approaches for dataset copyright protection, systematically categorizing them into three main classes: non-intrusive…

Interpreting CFD Surrogates through Sparse Autoencoders
Yeping Hu, Shusen Liu
https://arxiv.org/abs/2507.16069 https://arxiv.org/pdf/2507.16069

Interpreting CFD Surrogates through Sparse Autoencoders
Learning-based surrogate models have become a practical alternative to high-fidelity CFD solvers, but their latent representations remain opaque and hinder adoption in safety-critical or regulation-bound settings. This work introduces a posthoc interpretability framework for graph-based surrogate models used in computational fluid dynamics (CFD) by leveraging sparse autoencoders (SAEs). By obtaining an overcomplete basis in the node embedding space of a pretrained surrogate, the method extracts…

Dim cores of radio-bright AGN jets: VLBI and Gaia astrometry pinpoint different parsec-scale features
A. V. Popkov (MIPT, LPI), Y. Y. Kovalev (MPIfR), A. V. Plavin (BHI Harvard), L. Y. Petrov (NASA GSFC), I. N. Pashchenko (LPI)
https://arxiv.org/abs/2506.17769

Dim cores of radio-bright AGN jets: VLBI and Gaia astrometry pinpoint different parsec-scale features
Astrometry with the very long baseline radio interferometry (VLBI) allows us to determine the position of a point close to the brightest compact detail of the source at milliarcsecond scales. For most active galactic nuclei (AGNs), this compact detail is the opaque core of the radio jet. Rare cases of sources whose brightest detail is not the core but a prominent jet feature parsecs away from the core have been reported, but such sources remained elusive. In this work, we use a novel method for…

Exploiting nonlinear incoherent image formation through linear volume metaoptics for inference
Nan Zhang, Arvin Keshvari, Ata Shakeri, Zin Lin
https://arxiv.org/abs/2508.19436 h…

Exploiting nonlinear incoherent image formation through linear volume metaoptics for inference
We showed that a 2D depth map representing an incoherent 3D opaque scene is directly encoded in the response function of an imaging optics. As a result, the optics creates an image that depends nonlinearly on the depth map. Furthermore, strong spatio-spectral dispersions in volume metaoptics can be engineered to create a complex image in response to a depth map. We hypothesize that this complexity will allow the linear volume metaoptics to nonlinearly sense and process 3D opaque scenes.

Improving reproducibility of cheminformatics workflows with chembl-downloader
Charles Tapley Hoyt
https://arxiv.org/abs/2507.17783 https://arxiv.org/pdf/25…

Improving reproducibility of cheminformatics workflows with chembl-downloader
Many modern cheminformatics workflows derive datasets from ChEMBL, but few of these datasets are published with accompanying code for their generation. Consequently, their methodologies (e.g., selection, filtering, aggregation) are opaque, reproduction is difficult, and interpretation of results therefore lacks important context. Further, such static datasets quickly become out-of-date. For example, the current version of ChEMBL is v35 (as of December 2024), but ExCAPE-DB uses v20, Deep Confide…

Time-resolved 3D imaging opportunities with XMPI at ForMAX
Julia Katharina Rogalinski, Zisheng Yao, Yuhe Zhang, Zhe Hu, Korneliya Gordeyeva, Tomas Ros\'en, Daniel S\"oderberg, Andrea Mazzolari, Jackson da Silva, Vahid Haghighat, Samuel A. McDonald, Kim Nyg{\aa}rd, Eleni Myrto Asimakopoulou, Pablo Villanueva-Perez
https://arxiv.org…

Time-resolved 3D imaging opportunities with XMPI at ForMAX
X-rays are commonly used in imaging experiments due to their penetration power, which enables non-destructive resolution of internal structures in samples that are opaque to visible light. Time-resolved X-ray tomography is the state-of-the-art method for obtaining volumetric 4D (3D + time) information by rotating the sample and acquiring projections from different angular viewpoints over time. This method enables studies to address a plethora of research questions across various scientific disc…

The Carbon Footprint Wizard: A Knowledge-Augmented AI Interface for Streamlining Food Carbon Footprint Analysis
Mustafa Kaan Aslan, Reinout Heijungs, Filip Ilievski
https://arxiv.org/abs/2509.07733

The Carbon Footprint Wizard: A Knowledge-Augmented AI Interface for Streamlining Food Carbon Footprint Analysis
Environmental sustainability, particularly in relation to climate change, is a key concern for consumers, producers, and policymakers. The carbon footprint, based on greenhouse gas emissions, is a standard metric for quantifying the contribution to climate change of activities and is often assessed using life cycle assessment (LCA). However, conducting LCA is complex due to opaque and global supply chains, as well as fragmented data. This paper presents a methodology that combines advances in L…

Murakkab: Resource-Efficient Agentic Workflow Orchestration in Cloud Platforms
Gohar Irfan Chaudhry, Esha Choukse, Haoran Qiu, \'I\~nigo Goiri, Rodrigo Fonseca, Adam Belay, Ricardo Bianchini
https://arxiv.org/abs/2508.18298

Murakkab: Resource-Efficient Agentic Workflow Orchestration in Cloud Platforms
Agentic workflows commonly coordinate multiple models and tools with complex control logic. They are quickly becoming the dominant paradigm for AI applications. However, serving them remains inefficient with today's frameworks. The key problem is that they expose workflows as opaque sequences of model and tool calls that tightly couple agent logic with model and hardware choices. Often, these workflow components are fragmented across different entities, preventing systems from reasoning about t…

Towards Trustworthy Multimodal Moderation via Policy-Aligned Reasoning and Hierarchical Labeling
Anqi Li, Wenwei Jin, Jintao Tong, Pengda Qin, Weijia Li, Guo Lu
https://arxiv.org/abs/2508.03296

Towards Trustworthy Multimodal Moderation via Policy-Aligned Reasoning and Hierarchical Labeling
Social platforms have revolutionized information sharing, but also accelerated the dissemination of harmful and policy-violating content. To ensure safety and compliance at scale, moderation systems must go beyond efficiency and offer accuracy and interpretability. However, current approaches largely rely on noisy, label-driven learning, lacking alignment with moderation rules and producing opaque decisions that hinder human review. Therefore, we propose Hierarchical Guard (Hi-Guard), a multimo…

Designing a Lightweight GenAI Interface for Visual Data Analysis
Ratanond Koonchanok, Alex Kale, Khairi Reda
https://arxiv.org/abs/2509.02878 https://arxiv…

Designing a Lightweight GenAI Interface for Visual Data Analysis
Recent advances in Generative AI have transformed how users interact with data analysis through natural language interfaces. However, many systems rely too heavily on LLMs, creating risks of hallucination, opaque reasoning, and reduced user control. We present a hybrid visual analysis system that integrates GenAI in a constrained, high-level role to support statistical modeling while preserving transparency and user agency. GenAI translates natural language intent into formal statistical formul…

Optimal Calibrated Signaling in Digital Auctions
Zhicheng Du, Wei Tang, Zihe Wang, Shuo Zhang
https://arxiv.org/abs/2507.17187 https://arxiv.org/pdf/2507.1…

Optimal Calibrated Signaling in Digital Auctions
In digital advertising, online platforms allocate ad impressions through real-time auctions, where advertisers typically rely on autobidding agents to optimize bids on their behalf. Unlike traditional auctions for physical goods, the value of an ad impression is uncertain and depends on the unknown click-through rate (CTR). While platforms can estimate CTRs more accurately using proprietary machine learning algorithms, these estimates/algorithms remain opaque to advertisers. This information as…

Is Lindblad for me?
Martino Stefanini, Aleksandra A. Ziolkowska, Dmitry Budker, Ulrich Poschinger, Ferdinand Schmidt-Kaler, Antoine Browaeys, Atac Imamoglu, Darrick Chang, Jamir Marino
https://arxiv.org/abs/2506.22436

Is Lindblad for me?
The Lindblad master equation is a foundational tool for modeling the dynamics of open quantum systems. As its use has extended far beyond its original domain, the boundaries of its validity have grown opaque. In particular, the rise of new research areas including open quantum many-body systems, non-equilibrium condensed matter, and the possibility to test its limits in driven-open quantum simulators, call for a critical revision of its regimes of applicability. In this pedagogical review, we r…

Improving Biomedical Knowledge Graph Quality: A Community Approach
Katherina G Cortes, Shilpa Sundar, Sarah Gehrke, Keenan Manpearl, Junxia Lin, Daniel Robert Korn, Harry Caufield, Kevin Schaper, Justin Reese, Kushal Koirala, Lawrence E Hunter, E. Kathleen Carter, Marcello DeLuca, Arjun Krishnan, Chris Mungall, Melissa Haendel
https://arxi…

Improving Biomedical Knowledge Graph Quality: A Community Approach
Biomedical knowledge graphs (KGs) are widely used across research and translational settings, yet their design decisions and implementation are often opaque. Unlike ontologies that more frequently adhere to established creation principles, biomedical KGs lack consistent practices for construction, documentation, and dissemination. To address this gap, we introduce a set of evaluation criteria grounded in widely accepted data standards and principles from related fields. We apply these criteria …

On Identifying Why and When Foundation Models Perform Well on Time-Series Forecasting Using Automated Explanations and Rating
Michael Widener, Kausik Lakkaraju, John Aydin, Biplav Srivastava
https://arxiv.org/abs/2508.20437

On Identifying Why and When Foundation Models Perform Well on Time-Series Forecasting Using Automated Explanations and Rating
Time-series forecasting models (TSFM) have evolved from classical statistical methods to sophisticated foundation models, yet understanding why and when these models succeed or fail remains challenging. Despite this known limitation, time series forecasting models are increasingly used to generate information that informs real-world actions with equally real consequences. Understanding the complexity, performance variability, and opaque nature of these models then becomes a valuable endeavor to…

A Concept-based approach to Voice Disorder Detection
Davide Ghia, Gabriele Ciravegna, Alkis Koudounas, Marco Fantini, Erika Crosetti, Giovanni Succo, Tania Cerquitelli
https://arxiv.org/abs/2507.17799 …

A Concept-based approach to Voice Disorder Detection
Voice disorders affect a significant portion of the population, and the ability to diagnose them using automated, non-invasive techniques would represent a substantial advancement in healthcare, improving the quality of life of patients. Recent studies have demonstrated that artificial intelligence models, particularly Deep Neural Networks (DNNs), can effectively address this task. However, due to their complexity, the decision-making process of such models often remain opaque, limiting their t…

SAFER: Probing Safety in Reward Models with Sparse Autoencoder
Sihang Li, Wei Shi, Ziyuan Xie, Tao Liang, Guojun Ma, Xiang Wang
https://arxiv.org/abs/2507.00665

SAFER: Probing Safety in Reward Models with Sparse Autoencoder
Reinforcement learning from human feedback (RLHF) is a key paradigm for aligning large language models (LLMs) with human values, yet the reward models at its core remain largely opaque. In this work, we present sparse Autoencoder For Enhanced Reward model (\textbf{SAFER}), a novel framework for interpreting and improving reward models through mechanistic analysis. Leveraging Sparse Autoencoders (SAEs), we uncover human-interpretable features in reward model activations, enabling insight into sa…

Emergent Hierarchical Reasoning in LLMs through Reinforcement Learning
Haozhe Wang, Qixin Xu, Che Liu, Junhong Wu, Fangzhen Lin, Wenhu Chen
https://arxiv.org/abs/2509.03646 http…

Emergent Hierarchical Reasoning in LLMs through Reinforcement Learning
Reinforcement Learning (RL) has proven highly effective at enhancing the complex reasoning abilities of Large Language Models (LLMs), yet underlying mechanisms driving this success remain largely opaque. Our analysis reveals that puzzling phenomena like ``aha moments", ``length-scaling'' and entropy dynamics are not disparate occurrences but hallmarks of an emergent reasoning hierarchy, akin to the separation of high-level strategic planning from low-level procedural execution in human cognitio…

A Fourier Explanation of AI-music Artifacts
Darius Afchar, Gabriel Meseguer-Brocal, Kamil Akesbi, Romain Hennequin
https://arxiv.org/abs/2506.19108 https:/…

A Fourier Explanation of AI-music Artifacts
The rapid rise of generative AI has transformed music creation, with millions of users engaging in AI-generated music. Despite its popularity, concerns regarding copyright infringement, job displacement, and ethical implications have led to growing scrutiny and legal challenges. In parallel, AI-detection services have emerged, yet these systems remain largely opaque and privately controlled, mirroring the very issues they aim to address. This paper explores the fundamental properties of synthet…

Biases in LLM-Generated Musical Taste Profiles for Recommendation
Bruno Sguerra, Elena V. Epure, Harin Lee, Manuel Moussallam
https://arxiv.org/abs/2507.16708

Biases in LLM-Generated Musical Taste Profiles for Recommendation
One particularly promising use case of Large Language Models (LLMs) for recommendation is the automatic generation of Natural Language (NL) user taste profiles from consumption data. These profiles offer interpretable and editable alternatives to opaque collaborative filtering representations, enabling greater transparency and user control. However, it remains unclear whether users consider these profiles to be an accurate representation of their taste, which is crucial for trust and usability.…

Dynamic compression of glassy GeO$2$ up to the TPa range and first observation of shock induced crystallization
R. Torchio, J. A. Hernandez, A. Cordone, S. Balugani, T. Vinci, A. Ravasio, C. Pepin, N. Sevelin-Radiguet, E. Guillam, F. Dorchies, A. Benuzzi
https://arxiv.org/abs/2508.20695

Dynamic compression of glassy GeO$2$ up to the TPa range and first observation of shock induced crystallization
In this work we present an extensive study of glassy GeO$_2$ under laser induced dynamic compression. New VISAR and SOP data provide the extension of Hugoniot EoS up to the TPa range for this material including temperature measurements. Reflectivity data at both 532 and 1064 nm wavelenght are also reported. In the low compression range we observe changes of the optical properties from transparent, to opaque, to metallic state. The second part of this work describes a further laser shock experim…

Measuring Fractal Dimension using Discrete Global Grid Systems
Pramit Ghosh
https://arxiv.org/abs/2506.18175 https://arxiv.org/pdf/25…

Measuring Fractal Dimension using Discrete Global Grid Systems
This study builds a bridge between two well-studied but distant topics: fractal dimension and Discrete Global Grid System (DGGS). DGGSs are used as covering sets for geospatial vector data to calculate the Minkowski-Bouligand dimension. Using the method on synthetic data yields results within 1% of their theoretical fractal dimensions. A case study on opaque cloud fields obtained from satellite images gives fractal dimension in agreement with that available in the literature. The proposed metho…

The Detection of Teraelectronvolt Radiation from a Flat Spectrum Radio Quasar
Vaidehi S. Paliya, Markus Bottcher, Kiran Wani, P. N. Naseef Mohammed, C. S. Stalin, S. Sahayanathan, D. J. Saikia, S. Muneer
https://arxiv.org/abs/2508.17659

The Detection of Teraelectronvolt Radiation from a Flat Spectrum Radio Quasar
The very high-energy (VHE; $>$100 GeV) radiation carries the signatures of the matter-energy interaction in some of the most extreme astrophysical environments. Considering broad emission line blazars, i.e., flat spectrum radio quasars (FSRQs), the dense photon fields surrounding the relativistic jet can prohibit the particle population from accelerating to very high energies and producing VHE radiation. They can also possibly make the environment opaque for the VHE $γ$ rays due to $γγ$ pair…

PAX-TS: Model-agnostic multi-granular explanations for time series forecasting via localized perturbations
Tim Kreuzer, Jelena Zdravkovic, Panagiotis Papapetrou
https://arxiv.org/abs/2508.18982

PAX-TS: Model-agnostic multi-granular explanations for time series forecasting via localized perturbations
Time series forecasting has seen considerable improvement during the last years, with transformer models and large language models driving advancements of the state of the art. Modern forecasting models are generally opaque and do not provide explanations for their forecasts, while well-known post-hoc explainability methods like LIME are not suitable for the forecasting context. We propose PAX-TS, a model-agnostic post-hoc algorithm to explain time series forecasting models and their forecasts.…

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…

Large Language Model-Based Framework for Explainable Cyberattack Detection in Automatic Generation Control Systems
Muhammad Sharshar, Ahmad Mohammad Saber, Davor Svetinovic, Amr M. Youssef, Deepa Kundur, Ehab F. El-Saadany
https://arxiv.org/abs/2507.22239

Large Language Model-Based Framework for Explainable Cyberattack Detection in Automatic Generation Control Systems
The increasing digitization of smart grids has improved operational efficiency but also introduced new cybersecurity vulnerabilities, such as False Data Injection Attacks (FDIAs) targeting Automatic Generation Control (AGC) systems. While machine learning (ML) and deep learning (DL) models have shown promise in detecting such attacks, their opaque decision-making limits operator trust and real-world applicability. This paper proposes a hybrid framework that integrates lightweight ML-based attac…

Evaluation Guidelines for Empirical Studies in Software Engineering involving LLMs
Sebastian Baltes, Florian Angermeir, Chetan Arora, Marvin Mu\~noz Bar\'on, Chunyang Chen, Lukas B\"ohme, Fabio Calefato, Neil Ernst, Davide Falessi, Brian Fitzgerald, Davide Fucci, Marcos Kalinowski, Stefano Lambiase, Daniel Russo, Mircea Lungu, Lutz Prechelt, Paul Ralph, Christoph Treude, Stefan Wagner

Evaluation Guidelines for Empirical Studies in Software Engineering involving LLMs
Large language models (LLMs) are increasingly being integrated into software engineering (SE) research and practice, yet their non-determinism, opaque training data, and evolving architectures complicate the reproduction and replication of empirical studies. We present a community effort to scope this space, introducing a taxonomy of LLM-based study types together with eight guidelines for designing and reporting empirical studies involving LLMs. The guidelines present essential (must) criteria…

Attention is also needed for form design
B. Sankar, Dibakar Sen
https://arxiv.org/abs/2508.19708 https://arxiv.org/pdf/2508.19708

Attention is also needed for form design
Conventional product design is a cognitively demanding process, limited by its time-consuming nature, reliance on subjective expertise, and the opaque translation of inspiration into tangible concepts. This research introduces a novel, attention-aware framework that integrates two synergistic systems: EUPHORIA, an immersive Virtual Reality environment using eye-tracking to implicitly capture a designer's aesthetic preferences, and RETINA, an agentic AI pipeline that translates these implicit pr…

Explainable AI in Deep Learning-Based Prediction of Solar Storms
Adam O. Rawashdeh, Jason T. L. Wang, Katherine G. Herbert
https://arxiv.org/abs/2508.16543 https://

Explainable AI in Deep Learning-Based Prediction of Solar Storms
A deep learning model is often considered a black-box model, as its internal workings tend to be opaque to the user. Because of the lack of transparency, it is challenging to understand the reasoning behind the model's predictions. Here, we present an approach to making a deep learning-based solar storm prediction model interpretable, where solar storms include solar flares and coronal mass ejections (CMEs). This deep learning model, built based on a long short-term memory (LSTM) network with a…

Probing Local Branching Dynamics with Stern-Gerlach Interferometers and Dual Sensing
Xing M. Wang
https://arxiv.org/abs/2508.16019 https://arxiv.org/pdf/25…

Probing Local Branching Dynamics with Stern-Gerlach Interferometers and Dual Sensing
We propose a new experimental program to empirically distinguish the Branched Hilbert Subspace Interpretation (BHSI) from the Copenhagen Interpretation (CI) and Many-Worlds Interpretations (MWI) by examining the dynamics of local quantum branching. Our approach uses Stern-Gerlach interferometers (SGIs) equipped with a novel dual sensing technique, combining non-destructive transparent sensors (TSs) and projective opaque detectors (ODs), to test foundational principles in a closed system. The fi…

The Confidence Paradox: Can LLM Know When It's Wrong
Sahil Tripathi, Md Tabrez Nafis, Imran Hussain, Jiechao Gao
https://arxiv.org/abs/2506.23464 https…

The Confidence Paradox: Can LLM Know When It's Wrong
Document Visual Question Answering (DocVQA) systems are increasingly deployed in real world applications, yet they remain ethically opaque-often producing overconfident answers to ambiguous questions or failing to communicate uncertainty in a trustworthy manner. This misalignment between model confidence and actual knowledge poses significant risks, particularly in domains requiring ethical accountability. Existing approaches such as LayoutLMv3, UDOP, and DONUT have advanced SOTA performance by…

RadReason: Radiology Report Evaluation Metric with Reasons and Sub-Scores
Yingshu Li, Yunyi Liu, Lingqiao Liu, Lei Wang, Luping Zhou
https://arxiv.org/abs/2508.15464 https://

RadReason: Radiology Report Evaluation Metric with Reasons and Sub-Scores
Evaluating automatically generated radiology reports remains a fundamental challenge due to the lack of clinically grounded, interpretable, and fine-grained metrics. Existing methods either produce coarse overall scores or rely on opaque black-box models, limiting their usefulness in real-world clinical workflows. We introduce RadReason, a novel evaluation framework for radiology reports that not only outputs fine-grained sub-scores across six clinically defined error types, but also produces h…

Multi-Granular Discretization for Interpretable Generalization in Precise Cyberattack Identification
Wen-Cheng Chung, Shu-Ting Huang, Hao-Ting Pai
https://arxiv.org/abs/2507.14223

Multi-Granular Discretization for Interpretable Generalization in Precise Cyberattack Identification
Explainable intrusion detection systems (IDS) are now recognized as essential for mission-critical networks, yet most "XAI" pipelines still bolt an approximate explainer onto an opaque classifier, leaving analysts with partial and sometimes misleading insights. The Interpretable Generalization (IG) mechanism, published in IEEE Transactions on Information Forensics and Security, eliminates that bottleneck by learning coherent patterns - feature combinations unique to benign or malicious traffic …

ArgRAG: Explainable Retrieval Augmented Generation using Quantitative Bipolar Argumentation
Yuqicheng Zhu, Nico Potyka, Daniel Hern\'andez, Yuan He, Zifeng Ding, Bo Xiong, Dongzhuoran Zhou, Evgeny Kharlamov, Steffen Staab
https://arxiv.org/abs/2508.20131

ArgRAG: Explainable Retrieval Augmented Generation using Quantitative Bipolar Argumentation
Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, yet suffers from critical limitations in high-stakes domains -- namely, sensitivity to noisy or contradictory evidence and opaque, stochastic decision-making. We propose ArgRAG, an explainable, and contestable alternative that replaces black-box reasoning with structured inference using a Quantitative Bipolar Argumentation Framework (QBAF). ArgRAG constructs a QBAF from retrieved documents a…