Tootfinder

Dynamic Transmission Line Switching Amidst Wildfire-Prone Weather Under Decision-Dependent Uncertainty
Juan-Alberto Estrada-Garcia, Ruiwei Jiang, Alexandre Moreira
https://arxiv.org/abs/2507.13611

Dynamic Transmission Line Switching Amidst Wildfire-Prone Weather Under Decision-Dependent Uncertainty
During dry and windy seasons, environmental conditions significantly increase the risk of wildfires, exposing power grids to disruptions caused by transmission line failures. Wildfire propagation exacerbates grid vulnerability, potentially leading to prolonged power outages. To address this challenge, we propose a multi-stage optimization model that dynamically adjusts transmission grid topology in response to wildfire propagation, aiming to develop an optimal response policy. By accounting for…

CCL-XCoT: An Efficient Cross-Lingual Knowledge Transfer Method for Mitigating Hallucination Generation
Weihua Zheng, Roy Ka-Wei Lee, Zhengyuan Liu, Kui Wu, AiTi Aw, Bowei Zou
https://arxiv.org/abs/2507.14239

CCL-XCoT: An Efficient Cross-Lingual Knowledge Transfer Method for Mitigating Hallucination Generation
Multilingual Large Language Models(MLLMs) demonstrate strong generalization across languages, yet they remain prone to hallucinations, especially in low-resource languages, due to training data imbalances. These hallucinations, which include inaccurate or fabricated outputs, are particularly problematic in domain-specific generation tasks (Chataigner et al., 2024). To address this challenge, we propose CCL-XCoT(Curriculum-based Contrastive Learning-based Cross-lingual Chain-of-Thought), a two-s…

ERR@HRI 2.0 Challenge: Multimodal Detection of Errors and Failures in Human-Robot Conversations
Shiye Cao, Maia Stiber, Amama Mahmood, Maria Teresa Parreira, Wendy Ju, Micol Spitale, Hatice Gunes, Chien-Ming Huang
https://arxiv.org/abs/2507.13468

ERR@HRI 2.0 Challenge: Multimodal Detection of Errors and Failures in Human-Robot Conversations
The integration of large language models (LLMs) into conversational robots has made human-robot conversations more dynamic. Yet, LLM-powered conversational robots remain prone to errors, e.g., misunderstanding user intent, prematurely interrupting users, or failing to respond altogether. Detecting and addressing these failures is critical for preventing conversational breakdowns, avoiding task disruptions, and sustaining user trust. To tackle this problem, the ERR@HRI 2.0 Challenge provides a m…

OrthoInsight: Rib Fracture Diagnosis and Report Generation Based on Multi-Modal Large Models
Ningyong Wu, Jinzhi Wang, Wenhong Zhao, Chenzhan Yu, Zhigang Xiu, Duwei Dai
https://arxiv.org/abs/2507.13993

OrthoInsight: Rib Fracture Diagnosis and Report Generation Based on Multi-Modal Large Models
The growing volume of medical imaging data has increased the need for automated diagnostic tools, especially for musculoskeletal injuries like rib fractures, commonly detected via CT scans. Manual interpretation is time-consuming and error-prone. We propose OrthoInsight, a multi-modal deep learning framework for rib fracture diagnosis and report generation. It integrates a YOLOv9 model for fracture detection, a medical knowledge graph for retrieving clinical context, and a fine-tuned LLaVA lang…

Smart fault detection in satellite electrical power system
Niloofar Nobahari, Alireza Rezaee
https://arxiv.org/abs/2507.14004 https://

Smart fault detection in satellite electrical power system
This paper presents an new approach for detecting in the electrical power system of satellites operating in Low Earth Orbit (LEO) without an Attitude Determination and Control Subsystem (ADCS). Components of these systems are prone to faults, such as line-to-line faults in the photovoltaic subsystem, open circuits, and short circuits in the DC-to-DC converter, as well as ground faults in batteries. In the previous research has largely focused on detecting faults in each components, such as phot…

Eternal inflation near inflection points: a challenge to primordial black hole models
Eemeli Tomberg, Konstantinos Dimopoulos
https://arxiv.org/abs/2507.15522

Eternal inflation near inflection points: a challenge to primordial black hole models
Inflation with an inflection point potential is a popular model for producing primordial black holes. The potential near the inflection point is approximately flat, with a local maximum next to a local minimum, prone to eternal inflation. We show that a sufficient condition for eternal inflation is $λ_1 \leq 3$, where $λ_1$ is the index of the `exponential tail,' the lowest eigenvalue of the Fokker--Planck equation over a bounded region. We write $λ_1$ in terms of the model parameters for li…

We'll kick off this year's edition of #bbuzz with our keynote speaker @…!
Read our blogpost to learn more and join us this June in Berlin or online!

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images
Jiaqi Lv, Yijie Zhu, Carmen Guadalupe Colin Tenorio, Brinder Singh Chohan, Mark Eastwood, Shan E Ahmed Raza
https://arxiv.org/abs/2507.13974

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images
Melanoma is an aggressive form of skin cancer with rapid progression and high metastatic potential. Accurate characterisation of tissue morphology in melanoma is crucial for prognosis and treatment planning. However, manual segmentation of tissue regions from haematoxylin and eosin (H&E) stained whole-slide images (WSIs) is labour-intensive and prone to inter-observer variability, this motivates the need for reliable automated tissue segmentation methods. In this study, we propose a novel deep …

Unifying Listener Scoring Scales: Comparison Learning Framework for Speech Quality Assessment and Continuous Speech Emotion Recognition
Cheng-Hung Hu, Yusuke Yasud, Akifumi Yoshimoto, Tomoki Toda
https://arxiv.org/abs/2507.13626

Unifying Listener Scoring Scales: Comparison Learning Framework for Speech Quality Assessment and Continuous Speech Emotion Recognition
Speech Quality Assessment (SQA) and Continuous Speech Emotion Recognition (CSER) are two key tasks in speech technology, both relying on listener ratings. However, these ratings are inherently biased due to individual listener factors. Previous approaches have introduced a mean listener scoring scale and modeled all listener scoring scales in the training set. However, the mean listener approach is prone to distortion from averaging ordinal data, leading to potential biases. Moreover, learning …

Dual-Stage Value-Guided Inference with Margin-Based Reward Adjustment for Fast and Faithful VLM Captioning
Ankan Deria, Adinath Madhavrao Dukre, Feilong Tang, Sara Atito, Sudipta Roy, Muhammad Awais, Muhammad Haris Khan, Imran Razzak
https://arxiv.org/abs/2506.15649

Dual-Stage Value-Guided Inference with Margin-Based Reward Adjustment for Fast and Faithful VLM Captioning
Despite significant advances in inference-time search for vision-language models (VLMs), existing approaches remain both computationally expensive and prone to unpenalized, low-confidence generations which often lead to persistent hallucinations. We introduce \textbf{Value-guided Inference with Margin-based Reward (ViMaR)}, a two-stage inference framework that improves both efficiency and output fidelity by combining a temporal-difference value model with a margin-aware reward adjustment. In th…

RT: @PsyPost (X)
A new study suggests that people who view artificial intelligence positively may be more likely to overuse social media. The findings highlight a potential link between attitudes toward AI and problematic online behavior, especially among male users.
https://www.

Positive attitudes toward AI linked to more prone to problematic social media use
A new study suggests that people who view artificial intelligence positively may be more likely to overuse social media. The findings highlight a potential link between attitudes toward AI and problematic online behavior, especially among male users.

Trotz zahlreicher #Brände in bestehenden #Holzpelletanlagen sollen zwei neue Werke in #Kalifornien nahe

Wood Pellet Mills Are Prone to Catching Fire. Why Build Them in California?
Facilities that make wood pellets have a track record of catching alight. Yet there are plans to build several near Yosemite’s tinderbox forests.

QUEST: Quality-aware Semi-supervised Table Extraction for Business Documents
Eliott Thomas, Mickael Coustaty, Aurelie Joseph, Gaspar Deloin, Elodie Carel, Vincent Poulain D'Andecy, Jean-Marc Ogier
https://arxiv.org/abs/2506.14568

QUEST: Quality-aware Semi-supervised Table Extraction for Business Documents
Automating table extraction (TE) from business documents is critical for industrial workflows but remains challenging due to sparse annotations and error-prone multi-stage pipelines. While semi-supervised learning (SSL) can leverage unlabeled data, existing methods rely on confidence scores that poorly reflect extraction quality. We propose QUEST, a Quality-aware Semi-supervised Table extraction framework designed for business documents. QUEST introduces a novel quality assessment model that ev…

Quality Assessment of Python Tests Generated by Large Language Models
Victor Alves, Carla Bezerra, Ivan Machado, Larissa Rocha, T\'assio Virg\'inio, Publio Silva
https://arxiv.org/abs/2506.14297

Quality Assessment of Python Tests Generated by Large Language Models
The manual generation of test scripts is a time-intensive, costly, and error-prone process, indicating the value of automated solutions. Large Language Models (LLMs) have shown great promise in this domain, leveraging their extensive knowledge to produce test code more efficiently. This study investigates the quality of Python test code generated by three LLMs: GPT-4o, Amazon Q, and LLama 3.3. We evaluate the structural reliability of test suites generated under two distinct prompt contexts: Te…

Since halving my #sertraline dose, down to 50mg / day, my brain feels like it’s halved in processing power. Words are more difficult to find, my speech is slower, my thought patterns and creativity are stifled. I’m generally more lethargic. On the plus side I’m less agitated and less prone to angry outbursts. If only I could have all of the good bits and none of the bad. That’s not really h…

On Immutable Memory Systems for Artificial Agents: A Blockchain-Indexed Automata-Theoretic Framework Using ECDH-Keyed Merkle Chains
Craig Steven Wright
https://arxiv.org/abs/2506.13246

On Immutable Memory Systems for Artificial Agents: A Blockchain-Indexed Automata-Theoretic Framework Using ECDH-Keyed Merkle Chains
This paper presents a formalised architecture for synthetic agents designed to retain immutable memory, verifiable reasoning, and constrained epistemic growth. Traditional AI systems rely on mutable, opaque statistical models prone to epistemic drift and historical revisionism. In contrast, we introduce the concept of the Merkle Automaton, a cryptographically anchored, deterministic computational framework that integrates formal automata theory with blockchain-based commitments. Each agent tran…

Vector-level Feedforward Control of LPBF Melt Pool Area Using a Physics-Based Thermal Model
Nicholas Kirschbaum, Nathaniel Wood, Chang-Eun Kim, Thejaswi U. Tumkur, Chinedum Okwudire
https://arxiv.org/abs/2507.12557

Vector-level Feedforward Control of LPBF Melt Pool Area Using a Physics-Based Thermal Model
Laser powder bed fusion (LPBF) is an additive manufacturing technique that has gained popularity thanks to its ability to produce geometrically complex, fully dense metal parts. However, these parts are prone to internal defects and geometric inaccuracies, stemming in part from variations in the melt pool. This paper proposes a novel vector-level feedforward control framework for regulating melt pool area in LPBF. By decoupling part-scale thermal behavior from small-scale melt pool physics, the…

Jenga-Krotov algorithm: Efficient compilation of multi-qubit gates for exchange-only qubits
Jiahao Wu, Guanjie He, Wenyuan Zhuo, Quan Fu, Xin Wang
https://arxiv.org/abs/2507.12448

Jenga-Krotov algorithm: Efficient compilation of multi-qubit gates for exchange-only qubits
Exchange-only (EO) qubits, implemented in triple-quantum-dot systems, offer a compelling platform for scalable semiconductor-based quantum computing by enabling universal control through purely exchange interactions. While high-fidelity single- and two-qubit gates have been demonstrated, the synthesis of efficient multi-qubit operations -- such as the Toffoli gate -- remains a key bottleneck. Conventional gate decompositions into elementary operations lead to prohibitively long and error-prone …

Quantifying Diagnostic Signal Decay in Dementia: A National Study of Medicare Hospitalization Data
Federica Spoto, Jiazi Tian, Jonas H\"ugel, Daniel T. Ortega, Christine S. Ritchie, Deborah Blacker, Francesca Dominici, Chirag J. Patel, Daniel Mork, Hossein Estiri
https://arxiv.org/abs/2506.14669

Quantifying Diagnostic Signal Decay in Dementia: A National Study of Medicare Hospitalization Data
Background: Artificial intelligence (AI) models in healthcare depend on the fidelity of diagnostic data, yet the quality of such data is often compromised by variability in clinical documentation practices. In dementia, a condition already prone to diagnostic ambiguity, this variability may introduce systematic distortion into claims-based research and AI model development. Methods: We analyzed Medicare Part A hospitalization data from 2016-2018 to examine patterns of dementia-related ICD-10 …

Builder introduces hurricane-resistant homes exceeding Category 5 standards, offering safety and sustainability for families in storm-prone areas. #climatechange #climatesolutions #climate

Builder unveils incredible homes that can withstand Category 5 hurricanes: 'Just one solid, continuous wall'
Onx Homes unveiled storm-resistant houses designed to withstand even the strongest hurricanes, providing a safe haven for families in at-risk regions.

Imbalance in Balance: Online Concept Balancing in Generation Models
Yukai Shi, Jiarong Ou, Rui Chen, Haotian Yang, Jiahao Wang, Xin Tao, Pengfei Wan, Di Zhang, Kun Gai
https://arxiv.org/abs/2507.13345

Imbalance in Balance: Online Concept Balancing in Generation Models
In visual generation tasks, the responses and combinations of complex concepts often lack stability and are error-prone, which remains an under-explored area. In this paper, we attempt to explore the causal factors for poor concept responses through elaborately designed experiments. We also design a concept-wise equalization loss function (IMBA loss) to address this issue. Our proposed method is online, eliminating the need for offline dataset processing, and requires minimal code changes. In o…

VA used a DOGE AI tool by Gumroad founder Sahil Lavingia that hallucinated contract sizes to cancel 24 deals; Lavingia says "mistakes were made" (ProPublica)
https://www.propublica.org/article/trump-doge-veterans-affairs-ai-contra…

DOGE Developed Error-Prone AI Tool to “Munch” Veterans Affairs Contracts
We obtained records showing how a Department of Government Efficiency staffer with no medical experience used artificial intelligence to identify which VA contracts to kill. “AI is absolutely the wrong tool for this,” one expert said.

To all #toronto #ontario etc #musicians whom I am prone to SUSPECT¹ pay no attention to other than their undoubtably vital Summer outdoor Festival schedules (and priming their art for such) please do note:
#Toronto is about to enter a 5-day heat wave.
¹ because those I told today's said OMG they had a no-shade gig next week and didn't know.

A Versatile Dataset of Mouse and Eye Movements on Search Engine Results Pages
Kayhan Latifzadeh, Jacek Gwizdka, Luis A. Leiva
https://arxiv.org/abs/2507.08003

A Versatile Dataset of Mouse and Eye Movements on Search Engine Results Pages
We contribute a comprehensive dataset to study user attention and purchasing behavior on Search Engine Result Pages (SERPs). Previous work has relied on mouse movements as a low-cost large-scale behavioral proxy but also has relied on self-reported ground-truth labels, collected at post-task, which can be inaccurate and prone to biases. To address this limitation, we use an eye tracker to construct an objective ground-truth of continuous visual attention. Our dataset comprises 2,776 transaction…

Uncertainty Prioritized Experience Replay
Rodrigo Carrasco-Davis, Sebastian Lee, Claudia Clopath, Will Dabney
https://arxiv.org/abs/2506.09270 https://

Uncertainty Prioritized Experience Replay
Prioritized experience replay, which improves sample efficiency by selecting relevant transitions to update parameter estimates, is a crucial component of contemporary value-based deep reinforcement learning models. Typically, transitions are prioritized based on their temporal difference error. However, this approach is prone to favoring noisy transitions, even when the value estimation closely approximates the target mean. This phenomenon resembles the noisy TV problem postulated in the explo…

🎰 Blinding lights: The hidden science behind gambling's glow
#gambling

Blinding lights: The hidden science behind gambling's glow
There's a reason casinos rarely have windows or clocks, they're engineered to make you lose track of time. But what if it's not just time you're losing? New research suggests that the lighting used in gambling environments could be quietly altering how we make decisions, making us more prone to take risks.

Physical Constraint Preserving Higher Order Finite Volume Schemes for Divergence-Free Astrophysical MHD and RMHD
Dinshaw S. Balsara, Deepak Bhoriya, Chetan Singh, Harish Kumar, Roger K\"appeli, Federico Gatti
https://arxiv.org/abs/2506.11181

Physical Constraint Preserving Higher Order Finite Volume Schemes for Divergence-Free Astrophysical MHD and RMHD
Higher order finite volume schemes for magnetohydrodynamics (MHD) and relativistic magnetohydrodynamics (RMHD) are very valuable because they allow us to carry out astrophysical simulations with very high accuracy. However, astrophysical problems sometimes have unusually large Mach numbers, exceptionally high Lorentz factors and very strong magnetic fields. All these effects cause higher order codes to become brittle and prone to code crashes. In this paper we document physical constraint prese…

Welcome to Berlin Buzzwords 2025! 
Be sure not to miss our keynote speaker, @…, who will be giving a talk on „Unpacking Digital Sovereignty: How to avoid fueling the nationalist rise," which commences at 9:35 am at stage Kesselhaus.
 
Learn more:

Unpacking Digital Sovereignty: How to avoid fueling the nationalist rise
With the new ruling coalition of Trump, Musk and Big Tech, European digital sovereignty seems to be the widely accepted solution to break free from US companies that are used to threaten European governments. The EU is reviving industrial policy and is willing to invest a lot of money into digital sovereignty - but which problems exactly should it address, and what should be part of the solution? This talk shows that digital sovereignty is prone to open the door to a nationalist agenda which fa…

Texas parents frantically posted photos of their young daughters on social media with pleas for information as more than 20 campers from an all-girls summer camp were unaccounted for Friday after floods tore through the state’s south-central region.
At least 13 people were dead Friday and dozens missing after months worth of heavy rain fell in a matter of hours on Texas Hill Country, Kerr County Sheriff Larry Leitha said.
The flood-prone region is dotted with century-old summer …

Texas families plead for information on more than 20 girls missing from summer camp after floods
Texas parents frantically posted photos of their young daughters on social media with pleas for information as more than 20 campers from an all-girls summer camp were unaccounted for Friday after floo...

On Immutable Memory Systems for Artificial Agents: A Blockchain-Indexed Automata-Theoretic Framework Using ECDH-Keyed Merkle Chains
Craig Steven Wright
https://arxiv.org/abs/2506.13246

On Immutable Memory Systems for Artificial Agents: A Blockchain-Indexed Automata-Theoretic Framework Using ECDH-Keyed Merkle Chains
This paper presents a formalised architecture for synthetic agents designed to retain immutable memory, verifiable reasoning, and constrained epistemic growth. Traditional AI systems rely on mutable, opaque statistical models prone to epistemic drift and historical revisionism. In contrast, we introduce the concept of the Merkle Automaton, a cryptographically anchored, deterministic computational framework that integrates formal automata theory with blockchain-based commitments. Each agent tran…

From Static to Intelligent: Evolving SaaS Pricing with LLMs
Francisco Javier Cavero, Juan C. Alonso, Antonio Ruiz-Cort\'es
https://arxiv.org/abs/2507.12104

From Static to Intelligent: Evolving SaaS Pricing with LLMs
The SaaS paradigm has revolutionized software distribution by offering flexible pricing options to meet diverse customer needs. However, the rapid expansion of the SaaS market has introduced significant complexity for DevOps teams, who must manually manage and evolve pricing structures, an approach that is both time-consuming and prone to errors. The absence of automated tools for pricing analysis restricts the ability to efficiently evaluate, optimize, and scale these models. This paper propos…

Revisiting Reliability in the Reasoning-based Pose Estimation Benchmark
Junsu Kim, Naeun Kim, Jaeho Lee, Incheol Park, Dongyoon Han, Seungryul Baek
https://arxiv.org/abs/2507.13314

Revisiting Reliability in the Reasoning-based Pose Estimation Benchmark
The reasoning-based pose estimation (RPE) benchmark has emerged as a widely adopted evaluation standard for pose-aware multimodal large language models (MLLMs). Despite its significance, we identified critical reproducibility and benchmark-quality issues that hinder fair and consistent quantitative evaluations. Most notably, the benchmark utilizes different image indices from those of the original 3DPW dataset, forcing researchers into tedious and error-prone manual matching processes to obtain…

Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust Recommendation
Narges Nemati, Mostafa Haghir Chehreghani
https://arxiv.org/abs/2506.10658

Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust Recommendation
Matrix completion is a widely adopted framework in recommender systems, as predicting the missing entries in the user-item rating matrix enables a comprehensive understanding of user preferences. However, current graph neural network (GNN)-based approaches are highly sensitive to noisy or irrelevant edges--due to their inherent message-passing mechanisms--and are prone to overfitting, which limits their generalizability. To overcome these challenges, we propose a novel method called Matrix Comp…

TTrace: Lightweight Error Checking and Diagnosis for Distributed Training
Haitian Jiang, Shaowei Zhu, Zhen Zhang, Zhenyu Song, Xinwei Fu, Zhen Jia, Yida Wang, Jinyang Li
https://arxiv.org/abs/2506.09280

TTrace: Lightweight Error Checking and Diagnosis for Distributed Training
Distributed training is essential for scaling the training of large neural network models, such as large language models (LLMs), across thousands of GPUs. However, the complexity of distributed training programs makes them particularly prone to silent bugs, which do not produce explicit error signal but lead to incorrect training outcome. Effectively detecting and localizing such silent bugs in distributed training is challenging. Common debugging practice using metrics like training loss or gr…

Dual LiDAR-Based Traffic Movement Count Estimation at a Signalized Intersection: Deployment, Data Collection, and Preliminary Analysis
Saswat Priyadarshi Nayak, Guoyuan Wu, Kanok Boriboonsomsin, Matthew Barth
https://arxiv.org/abs/2507.13073

Dual LiDAR-Based Traffic Movement Count Estimation at a Signalized Intersection: Deployment, Data Collection, and Preliminary Analysis
Traffic Movement Count (TMC) at intersections is crucial for optimizing signal timings, assessing the performance of existing traffic control measures, and proposing efficient lane configurations to minimize delays, reduce congestion, and promote safety. Traditionally, methods such as manual counting, loop detectors, pneumatic road tubes, and camera-based recognition have been used for TMC estimation. Although generally reliable, camera-based TMC estimation is prone to inaccuracies under poor l…

Optimized Qubit Routing for Commuting Gates via Integer Programming
Moritz Stargalla, Friedrich Wagner
https://arxiv.org/abs/2507.12199 https://

Optimized Qubit Routing for Commuting Gates via Integer Programming
Quantum computers promise to outperform their classical counterparts at certain tasks. However, existing quantum devices are error-prone and restricted in size. Thus, effective compilation methods are crucial to exploit limited quantum resources. In this work, we address the problem of qubit routing for commuting gates, which arises, for example, during the compilation of the well-known Quantum Approximate Optimization Algorithm. We propose a two-step decomposition approach based on integer pro…

#Blakes7 Series C, Episode 06 - City at the Edge of the World
CALLY: That's what we think.
AVON: [Pointing gun at soldier] What do you think?
[In room]
SHERM: All the guards are in place captain, he can't have got away.
BAYBAN: He must have done.

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

Interfacially ordered phase states enable high-strength ductile eutectic Al alloys
Hemant Kumar, Praveen Kumar, Dierk Raabe, Baptiste Gault, Surendra Kumar Makineni
https://arxiv.org/abs/2507.08327

Interfacially ordered phase states enable high-strength ductile eutectic Al alloys
Lightweight, high-strength structural materials are component enablers in transportation and aerospace, improving carbon footprint and fuel efficiency. Aluminium (Al)-based eutectics have property combinations that qualify them for such applications. However, they are prone to catastrophic failure because of insufficient load transfer across the interfaces between the brittle eutectic phase and the ductile matrix. Here we present a general solution to this problem by engineering these interface…

If we value ourselves as we are, we are less prone to buying stuff we don't need. https://www.overcomecompulsivehoarding.co.uk/podcast-ep-193-laziness-is-a-myth/

Force-Based Viscosity and Elasticity Measurements for Material Biomechanical Characterisation with a Collaborative Robotic Arm
Luca Beber, Edoardo Lamon, Giacomo Moretti, Matteo Saveriano, Luca Fambri, Luigi Palopoli, Daniele Fontanelli
https://arxiv.org/abs/2507.11133

Force-Based Viscosity and Elasticity Measurements for Material Biomechanical Characterisation with a Collaborative Robotic Arm
Diagnostic activities, such as ultrasound scans and palpation, are relatively low-cost. They play a crucial role in the early detection of health problems and in assessing their progression. However, they are also error-prone activities, which require highly skilled medical staff. The use of robotic solutions can be key to decreasing the inherent subjectivity of the results and reducing the waiting list. For a robot to perform palpation or ultrasound scans, it must effectively manage physical i…

The Curious Case of Factuality Finetuning: Models' Internal Beliefs Can Improve Factuality
Benjamin Newman, Abhilasha Ravichander, Jaehun Jung, Rui Xin, Hamish Ivison, Yegor Kuznetsov, Pang Wei Koh, Yejin Choi
https://arxiv.org/abs/2507.08371

The Curious Case of Factuality Finetuning: Models' Internal Beliefs Can Improve Factuality
Language models are prone to hallucination - generating text that is factually incorrect. Finetuning models on high-quality factual information can potentially reduce hallucination, but concerns remain; obtaining factual gold data can be expensive and training on correct but unfamiliar data may potentially lead to even more downstream hallucination. What data should practitioners finetune on to mitigate hallucinations in language models? In this work, we study the relationship between the factu…

Discontinuity-aware KAN-based physics-informed neural networks
Guoqiang Lei, D. Exposito, Xuerui Mao
https://arxiv.org/abs/2507.08338 https://

Discontinuity-aware KAN-based physics-informed neural networks
Physics-informed neural networks (PINNs) have proved to be a promising method for the rapid solving of partial differential equations (PDEs) in both forward and inverse problems. However, due to the smoothness assumption of functions approximated by general neural networks, PINNs are prone to spectral bias and numerical instability and suffer from reduced accuracy when solving PDEs with sharp spatial transitions or fast temporal evolution. To address this limitation, a discontinuity-aware physi…

Effective Viscosity in the Intracluster Medium During Magnetic Field Amplification via Turbulent Dynamo
S. Adduci Faria, R. Santos-Lima, E. M. de Gouveia Dal Pino
https://arxiv.org/abs/2506.12221

Effective Viscosity in the Intracluster Medium During Magnetic Field Amplification via Turbulent Dynamo
Galaxy clusters host a hot, diffuse plasma with poorly understood viscosity and magnetic field amplification. Astrophysical plasmas are often modeled with magnetohydrodynamics (MHD), but low collision rates in environments like the intracluster medium (ICM) hinder thermodynamic equilibrium, causing pressure anisotropies and high viscosity. High-$β$ plasmas, dominated by thermal pressure, are prone to instabilities (e.g., firehose, mirror) that limit anisotropy, reduce viscosity, and enable sma…

This https://arxiv.org/abs/2412.05561 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Can the Rookies Cut the Tough Cookie? Exploring the Use of LLMs for SQL Equivalence Checking
Equivalence checking of SQL queries is an intractable problem often encountered in settings ranging from grading SQL submissions to debugging query optimizers. Despite recent work toward developing practical solutions, only simple queries written using a small subset of SQL are supported, leaving the equivalence checking of sophisticated SQL queries at the mercy of intensive, potentially error-prone, manual analysis. In this paper, we explore how LLMs can be used to reason with SQL queries to a…

Model Discovery and Graph Simulation: A Lightweight Alternative to Chaos Engineering
Anatoly A. Krasnovsky, Alexander Zorkin
https://arxiv.org/abs/2506.11176

Model Discovery and Graph Simulation: A Lightweight Alternative to Chaos Engineering
Microservice applications are prone to cascading failures because of dense inter-service dependencies. Ensuring resilience usually demands fault-injection experiments in production-like setups. We propose \textit{model discovery} -- an automated CI/CD step that extracts a live dependency graph from trace data -- and show that this lightweight representation is sufficient for accurate resilience prediction. Using the DeathStarBench Social Network, we build the graph, simulate failures via Monte-…

Efficient Estimation of Causal Effects Under Two-Phase Sampling with Error-Prone Outcome and Treatment Measurements
Keith Barnatchez, Kevin P. Josey, Nima S. Hejazi, Bryan E. Shepherd, Giovanni Parmigiani, Rachel Nethery
https://arxiv.org/abs/2506.21777

Efficient Estimation of Causal Effects Under Two-Phase Sampling with Error-Prone Outcome and Treatment Measurements
Measurement error is a common challenge for causal inference studies using electronic health record (EHR) data, where clinical outcomes and treatments are frequently mismeasured. Researchers often address measurement error by conducting manual chart reviews to validate measurements in a subset of the full EHR data -- a form of two-phase sampling. To improve efficiency, phase-two samples are often collected in a biased manner dependent on the patients' initial, error-prone measurements. In this …

Repeated ancilla reuse for logical computation on a neutral atom quantum computer
J. A. Muniz, D. Crow, H. Kim, J. M. Kindem, W. B. Cairncross, A. Ryou, T. C. Bohdanowicz, C. -A. Chen, Y. Ji, A. M. W. Jones, E. Megidish, C. Nishiguchi, M. Urbanek, L. Wadleigh, T. Wilkason, D. Aasen, K. Barnes, J. M. Bello-Rivas, I. Bloomfield, G. Booth, A. Brown, M. O. Brown, K. Cassella, G. Cowan, J. Epstein, M. Feldkamp, C. Griger, Y. Hassan, A. Heinz, E. Halperin, T. Hofler, F. Hummel, M. Jaffe, E. …

Repeated ancilla reuse for logical computation on a neutral atom quantum computer
Quantum processors based on neutral atoms trapped in arrays of optical tweezers have appealing properties, including relatively easy qubit number scaling and the ability to engineer arbitrary gate connectivity with atom movement. However, these platforms are inherently prone to atom loss, and the ability to replace lost atoms during a quantum computation is an important but previously elusive capability. Here, we demonstrate the ability to measure and re-initialize, and if necessary replace, a …

Entropy stable high-order discontinuous Galerkin spectral-element methods on curvilinear, hybrid meshes
Jens Keim, Anna Schwarz, Patrick Kopper, Marcel Blind, Christian Rohde, Andrea Beck
https://arxiv.org/abs/2507.04334

Entropy stable high-order discontinuous Galerkin spectral-element methods on curvilinear, hybrid meshes
Hyperbolic-parabolic partial differential equations are widely used for the modeling of complex, multiscale problems. High-order methods such as the discontinuous Galerkin (DG) scheme are attractive candidates for their numerical approximation. However, high-order methods are prone to instabilities in the presence of underresolved flow features. A popular counter measure to stabilize DG methods is the use of entropy-stable formulations based on summation-by-parts (SBP) operators. The present pa…

This https://arxiv.org/abs/2505.23868 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Noise-Robustness Through Noise: Asymmetric LoRA Adaption with Poisoning Expert
Current parameter-efficient fine-tuning methods for adapting pre-trained language models to downstream tasks are susceptible to interference from noisy data. Conventional noise-handling approaches either rely on laborious data pre-processing or employ model architecture modifications prone to error accumulation. In contrast to existing noise-process paradigms, we propose a noise-robust adaptation method via asymmetric LoRA poisoning experts (LoPE), a novel framework that enhances model robustne…

SurGSplat: Progressive Geometry-Constrained Gaussian Splatting for Surgical Scene Reconstruction
Yuchao Zheng, Jianing Zhang, Guochen Ning, Hongen Liao
https://arxiv.org/abs/2506.05935

SurGSplat: Progressive Geometry-Constrained Gaussian Splatting for Surgical Scene Reconstruction
Intraoperative navigation relies heavily on precise 3D reconstruction to ensure accuracy and safety during surgical procedures. However, endoscopic scenarios present unique challenges, including sparse features and inconsistent lighting, which render many existing Structure-from-Motion (SfM)-based methods inadequate and prone to reconstruction failure. To mitigate these constraints, we propose SurGSplat, a novel paradigm designed to progressively refine 3D Gaussian Splatting (3DGS) through the …

Reducing Disorder-Induced Backscattering in Photonic Crystal Waveguides through Inverse Design
Dominic Thompson, Antonia Neill, Nir Rotenberg, Stephen Hughes
https://arxiv.org/abs/2507.05429

Reducing Disorder-Induced Backscattering in Photonic Crystal Waveguides through Inverse Design
Photonic crystal waveguides (PCWs) allow for the engineering of photonic modes and band structures to control the flow of light and light-matter interactions within the waveguide. They have shown potential for enhancing optical nonlinearities, quantum dot single photon emissions, as well as optical buffers due to their ability to confine fields on-chip and produce slow-light modes. While these features are promising for applications in nanophotonics, PCWs are prone to high scattering losses due…

This https://arxiv.org/abs/2409.12874 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Privacy-Preserving Framework for Cell-Free MIMO ISAC Systems
Integrated Sensing and Communication (ISAC) systems are prone to privacy violations, once they aim at handling sensitive identifiable information in several applications. This paper raises the necessity of implementing privacy-preservation measures on the design of cell-free massive multiple-input multiple-output ISAC systems. To that purpose, given an adversary model, we propose an iterative framework of two blocks, precoder design and access point selection. The precoder design aims at maximi…

Anti-interrupted sampling repeater jamming via linear canonical Wigner distribution lightweight LFM detection
Jia-Mian Li, Bing-Zhao Li
https://arxiv.org/abs/2506.06302

Anti-interrupted sampling repeater jamming via linear canonical Wigner distribution lightweight LFM detection
Interrupted sampling repeater jamming (ISRJ) poses a serious threat to radar target detection. Traditional time-frequency (TF) domain anti-jamming methods are prone to TF aliasing in multi-component signal scenarios, and cannot effectively suppress ISRJ with energy close to the real target under low signal-to-noise ratio (SNR) conditions. To address these challenges, this paper proposes an anti-jamming method based on generalized linear canonical Wigner distribution (GLWD) line detection. By se…

Sonnet 141 - CXLI
In faith I do not love thee with mine eyes,
For they in thee a thousand errors note;
But 'tis my heart that loves what they despise,
Who, in despite of view, is pleased to dote.
Nor are mine ears with thy tongue's tune delighted;
Nor tender feeling, to base touches prone,
Nor taste, nor smell, desire to be invited
To any sensual feast with thee alone:
But my five wits nor my five senses can
Dissuade one foo…

ReTern: Exploiting Natural Redundancy and Sign Transformations for Enhanced Fault Tolerance in Compute-in-Memory based Ternary LLMs
Akul Malhotra, Sumeet Kumar Gupta
https://arxiv.org/abs/2506.01140

ReTern: Exploiting Natural Redundancy and Sign Transformations for Enhanced Fault Tolerance in Compute-in-Memory based Ternary LLMs
Ternary large language models (LLMs), which utilize ternary precision weights and 8-bit activations, have demonstrated competitive performance while significantly reducing the high computational and memory requirements of full-precision LLMs. The energy efficiency and performance of Ternary LLMs can be further improved by deploying them on ternary computing-in-memory (TCiM) accelerators, thereby alleviating the von-Neumann bottleneck. However, TCiM accelerators are prone to memory stuck-at faul…

This https://arxiv.org/abs/2407.07114 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Alternatives to the statistical mass confusion of testing for no-effect
In cell biology, statistical analysis means testing the hypothesis that there was no effect. This weak form of hypothesis testing neglects effect size, is universally misinterpreted, and is disastrously prone to error when combined with high-throughput cell biology. The solution is for analysis of measurements to start and end with an interpretation of effect size.

Correcting for Position Bias in Learning to Rank: A Control Function Approach
Md Aminul Islam, Kathryn Vasilaky, Elena Zheleva
https://arxiv.org/abs/2506.06989

Correcting for Position Bias in Learning to Rank: A Control Function Approach
Implicit feedback data, such as user clicks, is commonly used in learning-to-rank (LTR) systems because it is easy to collect and it often reflects user preferences. However, this data is prone to various biases, and training an LTR system directly on biased data can result in suboptimal ranking performance. One of the most prominent and well-studied biases in implicit feedback data is position bias, which occurs because users are more likely to interact with higher-ranked documents regardless …

MmBack: Clock-free Multi-Sensor Backscatter with Synchronous Acquisition and Multiplexing
Yijie Li, Weichong Ling, Taiting Lu, Yi-Chao Chen, Vaishnavi Ranganathan, Lili Qiu, Jingxian Wang
https://arxiv.org/abs/2507.01360

MmBack: Clock-free Multi-Sensor Backscatter with Synchronous Acquisition and Multiplexing
Backscatter tags provide a low-power solution for sensor applications, yet many real-world scenarios require multiple sensors-often of different types-for complex sensing tasks. However, existing designs support only a single sensor per tag, increasing spatial overhead. State-of-the-art approaches to multiplexing multiple sensor streams on a single tag rely on onboard clocks or multiple modulation chains, which add cost, enlarge form factor, and remain prone to timing drift-disrupting synchroni…

This https://arxiv.org/abs/2506.01056 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

MCP-Zero: Proactive Toolchain Construction for LLM Agents from Scratch
Function-calling has enabled large language models (LLMs) to act as tool-using agents, but injecting thousands of tool schemas into the prompt is costly and error-prone. We introduce MCP-Zero, a proactive agent framework that lets the LLM itself decide when and which external tools to retrieve, thereby assembling a task-specific toolchain from scratch. The framework is built upon three components: (1) Proactive Tool Request, where the model emits a structured $\left<\operatorname{tool\_assistan…

Automating MD simulations for Proteins using Large language Models: NAMD-Agent
Achuth Chandrasekhar, Amir Barati Farimani
https://arxiv.org/abs/2507.07887 …

Automating MD simulations for Proteins using Large language Models: NAMD-Agent
Molecular dynamics simulations are an essential tool in understanding protein structure, dynamics, and function at the atomic level. However, preparing high quality input files for MD simulations can be a time consuming and error prone process. In this work, we introduce an automated pipeline that leverages Large Language Models (LLMs), specifically Gemini 2.0 Flash, in conjunction with python scripting and Selenium based web automation to streamline the generation of MD input files. The pipeli…

Generating Proto-Personas through Prompt Engineering: A Case Study on Efficiency, Effectiveness and Empathy
Fernando Ayach, Vitor Lameir\~ao, Raul Le\~ao, Jerfferson Felizardo, Rafael Sobrinho, Vanessa Borges, Patr\'icia Matsubara, Awdren Font\~ao
https://arxiv.org/abs/2507.08594

Generating Proto-Personas through Prompt Engineering: A Case Study on Efficiency, Effectiveness and Empathy
Proto-personas are commonly used during early-stage Product Discovery, such as Lean Inception, to guide product definition and stakeholder alignment. However, the manual creation of proto-personas is often time-consuming, cognitively demanding, and prone to bias. In this paper, we propose and empirically investigate a prompt engineering-based approach to generate proto-personas with the support of Generative AI (GenAI). Our goal is to evaluate the approach in terms of efficiency, effectiveness,…

This https://arxiv.org/abs/2505.24195 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

WikiGap: Promoting Epistemic Equity by Surfacing Knowledge Gaps Between English Wikipedia and other Language Editions
With more than 11 times as many pageviews as the next, English Wikipedia dominates global knowledge access relative to other language editions. Readers are prone to assuming English Wikipedia as a superset of all language editions, leading many to prefer it even when their primary language is not English. Other language editions, however, comprise complementary facts rooted in their respective cultures and media environments, which are marginalized in English Wikipedia. While Wikipedia's user i…

Automated Attack Testflow Extraction from Cyber Threat Report using BERT for Contextual Analysis
Faissal Ahmadou, Sepehr Ghaffarzadegan, Boubakr Nour, Makan Pourzandi, Mourad Debbabi, Chadi Assi
https://arxiv.org/abs/2507.07244

Automated Attack Testflow Extraction from Cyber Threat Report using BERT for Contextual Analysis
In the ever-evolving landscape of cybersecurity, the rapid identification and mitigation of Advanced Persistent Threats (APTs) is crucial. Security practitioners rely on detailed threat reports to understand the tactics, techniques, and procedures (TTPs) employed by attackers. However, manually extracting attack testflows from these reports requires elusive knowledge and is time-consuming and prone to errors. This paper proposes FLOWGUARDIAN, a novel solution leveraging language models (i.e., B…

Crowd-SFT: Crowdsourcing for LLM Alignment
Alex Sotiropoulos, Sulyab Thottungal Valapu, Linus Lei, Jared Coleman, Bhaskar Krishnamachari
https://arxiv.org/abs/2506.04063

Crowd-SFT: Crowdsourcing for LLM Alignment
Large Language Models (LLMs) increasingly rely on Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) to align model responses with human preferences. While RLHF employs a reinforcement learning approach with a separate reward model, SFT uses human-curated datasets for supervised learning. Both approaches traditionally depend on small, vetted groups of annotators, making them costly, prone to bias, and limited in scalability. We propose an open, crowd-sourced fine…

Effect of alloying additions on the lattice ordering of Ti$_2$AlNb intermetallic
Adilakshmi Chirumamilla, Gopalakrishnan Sai Gautam
https://arxiv.org/abs/2507.03618

Effect of alloying additions on the lattice ordering of Ti$_2$AlNb intermetallic
Alloys based on the orthorhombic-Ti$_2$AlNb intermetallic phase (O-phase) are promising materials for high-temperature applications in jet engines, given that they can potentially replace Ni-based superalloys in some operating regions of the engines. However, the O-phase is prone to lattice disordering at high temperatures, primarily via anti-site defect formation across the Ti and Nb sites, which can reduce the material's creep resistance and high-temperature tensile properties, necessitating …

This https://arxiv.org/abs/2506.06091 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

MIRIAD: Augmenting LLMs with millions of medical query-response pairs
LLMs are bound to transform healthcare with advanced decision support and flexible chat assistants. However, LLMs are prone to generate inaccurate medical content. To ground LLMs in high-quality medical knowledge, LLMs have been equipped with external knowledge via RAG, where unstructured medical knowledge is split into small text chunks that can be selectively retrieved and integrated into the LLMs context. Yet, existing RAG pipelines rely on raw, unstructured medical text, which can be noisy,…

Robust Unsupervised Adaptation of a Speech Recogniser Using Entropy Minimisation and Speaker Codes
Rogier C. van Dalen, Shucong Zhang, Titouan Parcollet, Sourav Bhattacharya
https://arxiv.org/abs/2506.10653

Robust Unsupervised Adaptation of a Speech Recogniser Using Entropy Minimisation and Speaker Codes
Speech recognisers usually perform optimally only in a specific environment and need to be adapted to work well in another. For adaptation to a new speaker, there is often too little data for fine-tuning to be robust, and that data is usually unlabelled. This paper proposes a combination of approaches to make adaptation to a single minute of data robust. First, instead of estimating the adaptation parameters with cross-entropy on a single error-prone hypothesis or "pseudo-label", this paper pro…

This https://arxiv.org/abs/2502.11475 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Focused-DPO: Enhancing Code Generation Through Focused Preference Optimization on Error-Prone Points
Code generation models have shown significant potential for automating programming tasks. However, the challenge of generating accurate and reliable code persists due to the highly complex and long-reasoning nature of the task. Even state-of-the-art models often fail in code generation due to small errors, which can drastically affect the overall functionality of code. Our study identifies that current models tend to produce errors concentrated at specific error-prone points, which significantl…

WGLE:Backdoor-free and Multi-bit Black-box Watermarking for Graph Neural Networks
Tingzhi Li, Xuefeng Liu
https://arxiv.org/abs/2506.08602 https://

WGLE:Backdoor-free and Multi-bit Black-box Watermarking for Graph Neural Networks
Graph Neural Networks (GNNs) are increasingly deployed in graph-related applications, making ownership verification critical to protect their intellectual property against model theft. Fingerprinting and black-box watermarking are two main methods. However, the former relies on determining model similarity, which is computationally expensive and prone to ownership collisions after model post-processing such as model pruning or fine-tuning. The latter embeds backdoors, exposing watermarked model…

OntoRAG: Enhancing Question-Answering through Automated Ontology Derivation from Unstructured Knowledge Bases
Yash Tiwari, Owais Ahmad Lone, Mayukha Pal
https://arxiv.org/abs/2506.00664

OntoRAG: Enhancing Question-Answering through Automated Ontology Derivation from Unstructured Knowledge Bases
Ontologies are pivotal for structuring knowledge bases to enhance question answering (QA) systems powered by Large Language Models (LLMs). However, traditional ontology creation relies on manual efforts by domain experts, a process that is time intensive, error prone, and impractical for large, dynamic knowledge domains. This paper introduces OntoRAG, an automated pipeline designed to derive ontologies from unstructured knowledge bases, with a focus on electrical relay documents. OntoRAG integr…

Searching Clinical Data Using Generative AI
Karan Hanswadkar, Anika Kanchi, Shivani Tripathi, Shi Qiao, Rony Chatterjee, Alekh Jindal
https://arxiv.org/abs/2505.24090

Searching Clinical Data Using Generative AI
Artificial Intelligence (AI) is making a major impact on healthcare, particularly through its application in natural language processing (NLP) and predictive analytics. The healthcare sector has increasingly adopted AI for tasks such as clinical data analysis and medical code assignment. However, searching for clinical information in large and often unorganized datasets remains a manual and error-prone process. Assisting this process with automations can help physicians improve their operationa…

Oobleck: Low-Compromise Design for Fault Tolerant Accelerators
Guy Wilks, Brian Li, Jonathan Balkind
https://arxiv.org/abs/2506.22654 https://

Oobleck: Low-Compromise Design for Fault Tolerant Accelerators
Data center hardware refresh cycles are lengthening. However, increasing processor complexity is raising the potential for faults. To achieve longevity in the face of increasingly fault-prone datapaths, fault tolerance is needed, especially in on-chip accelerator datapaths. Previously researched methods for adding fault tolerance to accelerator designs require high area, lowering chip utilisation. We propose a novel architecture for accelerator fault tolerance, Oobleck, which leverages modular …

This https://arxiv.org/abs/2506.05675 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Zero-Shot Event Causality Identification via Multi-source Evidence Fuzzy Aggregation with Large Language Models
Event Causality Identification (ECI) aims to detect causal relationships between events in textual contexts. Existing ECI models predominantly rely on supervised methodologies, suffering from dependence on large-scale annotated data. Although Large Language Models (LLMs) enable zero-shot ECI, they are prone to causal hallucination-erroneously establishing spurious causal links. To address these challenges, we propose MEFA, a novel zero-shot framework based on Multi-source Evidence Fuzzy Aggrega…

PenTest2.0: Towards Autonomous Privilege Escalation Using GenAI
Haitham S. Al-Sinani, Chris J. Mitchell
https://arxiv.org/abs/2507.06742 https://

PenTest2.0: Towards Autonomous Privilege Escalation Using GenAI
Ethical hacking today relies on highly skilled practitioners executing complex sequences of commands, which is inherently time-consuming, difficult to scale, and prone to human error. To help mitigate these limitations, we previously introduced 'PenTest++', an AI-augmented system combining automation with generative AI supporting ethical hacking workflows. However, a key limitation of PenTest++ was its lack of support for privilege escalation, a crucial element of ethical hacking. In this paper…

Optimal-PhiBE: A PDE-based Model-free framework for Continuous-time Reinforcement Learning
Yuhua Zhu, Yuming Zhang, Haoyu Zhang
https://arxiv.org/abs/2506.05208

Optimal-PhiBE: A PDE-based Model-free framework for Continuous-time Reinforcement Learning
This paper addresses continuous-time reinforcement learning (CTRL) where the system dynamics are governed by a stochastic differential equation but are unknown, and only discrete-time observations are available. Existing approaches face limitations: model-based PDE methods suffer from non-identifiability, while model-free methods based on the optimal Bellman equation (Optimal-BE) are prone to large discretization errors sensitive to both the dynamics and reward structure. To overcome these chal…

QTurbo: A Robust and Efficient Compiler for Analog Quantum Simulation
Junyu Zhou, Yuhao Liu, Shize Che, Anupam Mitra, Efekan K\"okc\"u, Ermal Rrapaj, Costin Iancu, Gushu Li
https://arxiv.org/abs/2506.22958

QTurbo: A Robust and Efficient Compiler for Analog Quantum Simulation
Analog quantum simulation leverages native hardware dynamics to emulate complex quantum systems with great efficiency by bypassing the quantum circuit abstraction. However, conventional compilation methods for analog simulators are typically labor-intensive, prone to errors, and computationally demanding. This paper introduces QTurbo, a powerful analog quantum simulation compiler designed to significantly enhance compilation efficiency and optimize hardware execution time. By generating precise…

Unsupervised Cardiac Video Translation Via Motion Feature Guided Diffusion Model
Swakshar Deb, Nian Wu, Frederick H. Epstein, Miaomiao Zhang
https://arxiv.org/abs/2507.02003

Unsupervised Cardiac Video Translation Via Motion Feature Guided Diffusion Model
This paper presents a novel motion feature guided diffusion model for unpaired video-to-video translation (MFD-V2V), designed to synthesize dynamic, high-contrast cine cardiac magnetic resonance (CMR) from lower-contrast, artifact-prone displacement encoding with stimulated echoes (DENSE) CMR sequences. To achieve this, we first introduce a Latent Temporal Multi-Attention (LTMA) registration network that effectively learns more accurate and consistent cardiac motions from cine CMR image videos.…

Moir\'e-assisted charge instability in ultrathin RuO$_2$
Philipp Ke{\ss}ler, Andreas Feuerpfeil, Armando Consiglio, Hendrik Hohmann, Ronny Thomale, Jonas Erhardt, Bing Liu, Vedran Jovic, Ralph Claessen, Patrick H\"artl, Matteo D\"urrnagel, Simon Moser
https://arxiv.org/abs/2507.05047

Moiré-assisted charge instability in ultrathin RuO$_2$
Ruthenium dioxide (RuO$_2$) has been in the focus of contemporary condensed matter research as a prototypical candidate material for altermagnetism. In the face of daunting evidence for bulk magnetic order despite promising theoretical predictions, it naturally suggests the focus on thin films where Coulomb interactions are dimensionally quenched and may yield a more strongly correlated environment prone to magnetic ordering. Here, we combine scanning tunneling microscopy (STM), density functio…

WikiGap: Promoting Epistemic Equity by Surfacing Knowledge Gaps Between English Wikipedia and other Language Editions
Zining Wang, Yuxuan Zhang, Dongwook Yoon, Nicholas Vincent, Farhan Samir, Vered Shwartz
https://arxiv.org/abs/2505.24195

WikiGap: Promoting Epistemic Equity by Surfacing Knowledge Gaps Between English Wikipedia and other Language Editions
With more than 11 times as many pageviews as the next, English Wikipedia dominates global knowledge access relative to other language editions. Readers are prone to assuming English Wikipedia as a superset of all language editions, leading many to prefer it even when their primary language is not English. Other language editions, however, comprise complementary facts rooted in their respective cultures and media environments, which are marginalized in English Wikipedia. While Wikipedia's user i…

A First Look at Bugs in LLM Inference Engines
Mugeng Liu, Siqi Zhong, Weichen Bi, Yixuan Zhang, Zhiyang Chen, Zhenpeng Chen, Xuanzhe Liu, Yun Ma
https://arxiv.org/abs/2506.09713

A First Look at Bugs in LLM Inference Engines
Large language model-specific inference engines (in short as \emph{LLM inference engines}) have become a fundamental component of modern AI infrastructure, enabling the deployment of LLM-powered applications (LLM apps) across cloud and local devices. Despite their critical role, LLM inference engines are prone to bugs due to the immense resource demands of LLMs and the complexities of cross-platform compatibility. However, a systematic understanding of these bugs remains lacking. To bridge this…

Knowledge-Aware Self-Correction in Language Models via Structured Memory Graphs
Swayamjit Saha
https://arxiv.org/abs/2507.04625 https://

Knowledge-Aware Self-Correction in Language Models via Structured Memory Graphs
Large Language Models (LLMs) are powerful yet prone to generating factual errors, commonly referred to as hallucinations. We present a lightweight, interpretable framework for knowledge-aware self-correction of LLM outputs using structured memory graphs based on RDF triples. Without retraining or fine-tuning, our method post-processes model outputs and corrects factual inconsistencies via external semantic memory. We demonstrate the approach using DistilGPT-2 and show promising results on simpl…

CoP: Agentic Red-teaming for Large Language Models using Composition of Principles
Chen Xiong, Pin-Yu Chen, Tsung-Yi Ho
https://arxiv.org/abs/2506.00781 ht…

CoP: Agentic Red-teaming for Large Language Models using Composition of Principles
Recent advances in Large Language Models (LLMs) have spurred transformative applications in various domains, ranging from open-source to proprietary LLMs. However, jailbreak attacks, which aim to break safety alignment and user compliance by tricking the target LLMs into answering harmful and risky responses, are becoming an urgent concern. The practice of red-teaming for LLMs is to proactively explore potential risks and error-prone instances before the release of frontier AI technology. This …

HI-SQL: Optimizing Text-to-SQL Systems through Dynamic Hint Integration
Ganesh Parab, Zishan Ahmad, Dagnachew Birru
https://arxiv.org/abs/2506.18916 https:…

HI-SQL: Optimizing Text-to-SQL Systems through Dynamic Hint Integration
Text-to-SQL generation bridges the gap between natural language and databases, enabling users to query data without requiring SQL expertise. While large language models (LLMs) have significantly advanced the field, challenges remain in handling complex queries that involve multi-table joins, nested conditions, and intricate operations. Existing methods often rely on multi-step pipelines that incur high computational costs, increase latency, and are prone to error propagation. To address these l…

Literature-Grounded Novelty Assessment of Scientific Ideas
Simra Shahid, Marissa Radensky, Raymond Fok, Pao Siangliulue, Daniel S. Weld, Tom Hope
https://arxiv.org/abs/2506.22026 …

Literature-Grounded Novelty Assessment of Scientific Ideas
Automated scientific idea generation systems have made remarkable progress, yet the automatic evaluation of idea novelty remains a critical and underexplored challenge. Manual evaluation of novelty through literature review is labor-intensive, prone to error due to subjectivity, and impractical at scale. To address these issues, we propose the Idea Novelty Checker, an LLM-based retrieval-augmented generation (RAG) framework that leverages a two-stage retrieve-then-rerank approach. The Idea Nove…

Logic Mining from Process Logs: Towards Automated Specification and Verification
Radoslaw Klimek, Julia Witek
https://arxiv.org/abs/2506.08628 https://

Logic Mining from Process Logs: Towards Automated Specification and Verification
Logical specifications play a key role in the formal analysis of behavioural models. Automating the derivation of such specifications is particularly valuable in complex systems, where manual construction is time-consuming and error-prone. This article presents an approach for generating logical specifications from process models discovered via workflow mining, combining pattern-based translation with automated reasoning techniques. In contrast to earlier work, we evaluate the method on both ge…

FrameShift: Learning to Resize Fuzzer Inputs Without Breaking Them
Harrison Green, Claire Le Goues, Fraser Brown
https://arxiv.org/abs/2507.05421 https://

FrameShift: Learning to Resize Fuzzer Inputs Without Breaking Them
Coverage-guided fuzzers are powerful automated bug-finding tools. They mutate program inputs, observe coverage, and save any input that hits an unexplored path for future mutation. Unfortunately, without knowledge of input formats--for example, the relationship between formats' data fields and sizes--fuzzers are prone to generate destructive frameshift mutations. These time-wasting mutations yield malformed inputs that are rejected by the target program. To avoid such breaking mutations, this p…

This https://arxiv.org/abs/2407.02722 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Pulse Design of Baseband Flux Control for Adiabatic Controlled-Phase Gates in Superconducting Circuits
Despite progress towards achieving low error rates with superconducting qubits, error-prone two-qubit gates remain a bottleneck for realizing large-scale quantum computers. Therefore, a systematic framework to design high-fidelity gates becomes imperative. One type of two-qubit gate in superconducting qubits is the controlled-phase (CPHASE) gate, which utilizes a conditional interaction between higher energy levels of the qubits controlled by a baseband flux pulse on one of the qubits or a tuna…

Autoadaptive Medical Segment Anything Model
Tyler Ward, Meredith K. Owen, O'Kira Coleman, Brian Noehren, Abdullah-Al-Zubaer Imran
https://arxiv.org/abs/2507.01828

Autoadaptive Medical Segment Anything Model
Medical image segmentation is a key task in the imaging workflow, influencing many image-based decisions. Traditional, fully-supervised segmentation models rely on large amounts of labeled training data, typically obtained through manual annotation, which can be an expensive, time-consuming, and error-prone process. This signals a need for accurate, automatic, and annotation-efficient methods of training these models. We propose ADA-SAM (automated, domain-specific, and adaptive segment anything…

This https://arxiv.org/abs/2408.02383 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

A Novel Stabilizer-based Entanglement Distillation Protocol for Qudits
Entanglement distillation, the process of converting weakly entangled states into maximally entangled ones using Local Operations and Classical Communication (LOCC), is pivotal for robust entanglement-assisted quantum information processing in error-prone environments. A construction based on stabilizer codes offers an effective method for designing such protocols. By analytically investigating the effective action of stabilizer protocols for systems of prime dimension $d$, we establish a stand…

Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know?
Zhiting Mei, Christina Zhang, Tenny Yin, Justin Lidard, Ola Shorinwa, Anirudha Majumdar
https://arxiv.org/abs/2506.18183

Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know?
Reasoning language models have set state-of-the-art (SOTA) records on many challenging benchmarks, enabled by multi-step reasoning induced using reinforcement learning. However, like previous language models, reasoning models are prone to generating confident, plausible responses that are incorrect (hallucinations). Knowing when and how much to trust these models is critical to the safe deployment of reasoning models in real-world applications. To this end, we explore uncertainty quantification…

DRAG: Distilling RAG for SLMs from LLMs to Transfer Knowledge and Mitigate Hallucination via Evidence and Graph-based Distillation
Jennifer Chen, Aidar Myrzakhan, Yaxin Luo, Hassaan Muhammad Khan, Sondos Mahmoud Bsharat, Zhiqiang Shen
https://arxiv.org/abs/2506.01954

DRAG: Distilling RAG for SLMs from LLMs to Transfer Knowledge and Mitigate Hallucination via Evidence and Graph-based Distillation
Retrieval-Augmented Generation (RAG) methods have proven highly effective for tasks requiring factual consistency and robust knowledge retrieval. However, large-scale RAG systems consume significant computational resources and are prone to generating hallucinated content from Humans. In this work, we introduce $\texttt{DRAG}$, a novel framework for distilling RAG knowledge from large-scale Language Models (LLMs) into small LMs (SLMs). Our approach leverages evidence- and knowledge graph-based d…

This https://arxiv.org/abs/2506.00377 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

A Systematic Review of Metaheuristics-Based and Machine Learning-Driven Intrusion Detection Systems in IoT
The widespread adoption of the Internet of Things (IoT) has raised a new challenge for developers since it is prone to known and unknown cyberattacks due to its heterogeneity, flexibility, and close connectivity. To defend against such security breaches, researchers have focused on building sophisticated intrusion detection systems (IDSs) using machine learning (ML) techniques. Although these algorithms notably improve detection performance, they require excessive computing power and resources,…

LLMREI: Automating Requirements Elicitation Interviews with LLMs
Alexander Korn, Samuel Gorsch, Andreas Vogelsang
https://arxiv.org/abs/2507.02564 https://…

LLMREI: Automating Requirements Elicitation Interviews with LLMs
Requirements elicitation interviews are crucial for gathering system requirements but heavily depend on skilled analysts, making them resource-intensive, susceptible to human biases, and prone to miscommunication. Recent advancements in Large Language Models present new opportunities for automating parts of this process. This study introduces LLMREI, a chat bot designed to conduct requirements elicitation interviews with minimal human intervention, aiming to reduce common interviewer errors and…

This https://arxiv.org/abs/2409.04459 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

WET: Overcoming Paraphrasing Vulnerabilities in Embeddings-as-a-Service with Linear Transformation Watermarks
Embeddings-as-a-Service (EaaS) is a service offered by large language model (LLM) developers to supply embeddings generated by LLMs. Previous research suggests that EaaS is prone to imitation attacks -- attacks that clone the underlying EaaS model by training another model on the queried embeddings. As a result, EaaS watermarks are introduced to protect the intellectual property of EaaS providers. In this paper, we first show that existing EaaS watermarks can be removed by paraphrasing when att…

Computational Detection of Intertextual Parallels in Biblical Hebrew: A Benchmark Study Using Transformer-Based Language Models
David M. Smiley
https://arxiv.org/abs/2506.24117

Computational Detection of Intertextual Parallels in Biblical Hebrew: A Benchmark Study Using Transformer-Based Language Models
Identifying parallel passages in biblical Hebrew is foundational in biblical scholarship for uncovering intertextual relationships. Traditional methods rely on manual comparison, which is labor-intensive and prone to human error. This study evaluates the potential of pre-trained transformer-based language models, including E5, AlephBERT, MPNet, and LaBSE, for detecting textual parallels in the Hebrew Bible. Focusing on known parallels between the books of Samuel/Kings and Chronicles, I assessed…

This https://arxiv.org/abs/2504.05424 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Speculative Automated Refactoring of Imperative Deep Learning Programs to Graph Execution
Efficiency is essential to support ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code -- supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approach…

Large Language Model-Driven Code Compliance Checking in Building Information Modeling
Soumya Madireddy, Lu Gao, Zia Din, Kinam Kim, Ahmed Senouci, Zhe Han, Yunpeng Zhang
https://arxiv.org/abs/2506.20551

Large Language Model-Driven Code Compliance Checking in Building Information Modeling
This research addresses the time-consuming and error-prone nature of manual code compliance checking in Building Information Modeling (BIM) by introducing a Large Language Model (LLM)-driven approach to semi-automate this critical process. The developed system integrates LLMs such as GPT, Claude, Gemini, and Llama, with Revit software to interpret building codes, generate Python scripts, and perform semi-automated compliance checks within the BIM environment. Case studies on a single-family res…

FORGE: An LLM-driven Framework for Large-Scale Smart Contract Vulnerability Dataset Construction
Jiachi Chen, Yiming Shen, Jiashuo Zhang, Zihao Li, John Grundy, Zhenzhe Shao, Yanlin Wang, Jiashui Wang, Ting Chen, Zibin Zheng
https://arxiv.org/abs/2506.18795

FORGE: An LLM-driven Framework for Large-Scale Smart Contract Vulnerability Dataset Construction
High-quality smart contract vulnerability datasets are critical for evaluating security tools and advancing smart contract security research. Two major limitations of current manual dataset construction are (1) labor-intensive and error-prone annotation processes limiting the scale, quality, and evolution of the dataset, and (2) absence of standardized classification rules results in inconsistent vulnerability categories and labeling results across different datasets. To address these limitatio…

Complex Model Transformations by Reinforcement Learning with Uncertain Human Guidance
Kyanna Dagenais, Istvan David
https://arxiv.org/abs/2506.20883 https:…

Complex Model Transformations by Reinforcement Learning with Uncertain Human Guidance
Model-driven engineering problems often require complex model transformations (MTs), i.e., MTs that are chained in extensive sequences. Pertinent examples of such problems include model synchronization, automated model repair, and design space exploration. Manually developing complex MTs is an error-prone and often infeasible process. Reinforcement learning (RL) is an apt way to alleviate these issues. In RL, an autonomous agent explores the state space through trial and error to identify benef…