Tootfinder

Latest power struggle at Ukraine’s state grid operator ends as CEO dismissal reversed: https://benborges.xyz/2025/10/02/latest-power-struggle-at-ukraines.html

Amid systemic corruption, Amazon countries struggle to fight environmental crime https://news.mongabay.com/2025/10/amid-systemic-corruption-amazon-countries-struggle-to-fight-environmental-crime/

Amid systemic corruption, Amazon countries struggle to fight environmental crime
The most powerful individuals in courtrooms are the judges. Their leadership is essential for bringing any type of reform to fruition, be it an anti-corruption campaign or an ‘all-of-government’ drive to fight environmental crime. In the Andean Republics, their involvement in crimes of commission via bribery and extortion is a major source of judicial corruption. […]

Day 9: Eniko Fox
Edit: added a store link for Kitsune Tails.
We're back to videogames, and with another author who's on the fediverse: @…
Fox has developed a few games, but the one that I've played and love is Kitsune Tails. It's a sapphic romance take on Super Mario Bros. 3, and (critically for a platformer) it's got very crisp controls and runs smoothly. I think one thing a lot of indie platforms devs struggle with is getting those fundamentals right, because on the technical side they require very challenging things like optimization of your code and extremely careful input handling that go beyond the basic skills necessary to put together a game. From following her on Twitter and now the Fediverse, it's clear that Fox is a deeply competent programmer, and her games reflect that. Beyond the fundamentals, Kitsune Tails has a very sweet plot with a very cool twist in the middle, and without spoilers, that twist made both the levels and gameplay very difficult to design, but Fox rose to that challenge and put together a wonderful game. Particularly past the plot twist (but in subtle ways before it) Fox is able to build beyond SMB3 mechanics in ways that gracefully complement the original, and the movement in the game ends up being difficult but extremely satisfying, with an excellent skill/speed response allowing for both slower, easier approaches that work for a range of players and high-skill extremely-fast options for those who want to push themselves.
There have been plenty of people I follow with indie game projects that are kinda meh in the end, and I'll still boost them without much comment if they're decent. Fox' work is actually amazing, which is why if you've followed me for a while you'll know I tend to mention it periodically, and which is why she makes this list of authors I respect.
You can buy Kitsune Tails here: #20AuthorsNoMen

Why Do MLLMs Struggle with Spatial Understanding? A Systematic Analysis from Data to Architecture
Wanyue Zhang, Yibin Huang, Yangbin Xu, JingJing Huang, Helu Zhi, Shuo Ren, Wang Xu, Jiajun Zhang
https://arxiv.org/abs/2509.02359

Why Do MLLMs Struggle with Spatial Understanding? A Systematic Analysis from Data to Architecture
Spatial understanding is essential for Multimodal Large Language Models (MLLMs) to support perception, reasoning, and planning in embodied environments. Despite recent progress, existing studies reveal that MLLMs still struggle with spatial understanding. However, existing research lacks a comprehensive and systematic evaluation of these limitations, often restricted to isolated scenarios, such as single-view or video. In this work, we present a systematic analysis of spatial understanding from…

Enhancing Software Testing Education: Understanding Where Students Struggle
Shiza Andleeb, Teo Mendoza, Lucas Cordova, Gursimran Walia, Jeffrey C. Carver
https://arxiv.org/abs/2510.00957

Enhancing Software Testing Education: Understanding Where Students Struggle
Effective software testing is critical for producing reliable and secure software, yet many computer science students struggle to master the foundational concepts required to construct comprehensive test suites. While automated feedback tools are widely used to support student learning, it remains unclear which testing concepts are most frequently misunderstood and how these misunderstandings are reflected in students' test suite revisions. This study examines the specific testing concepts that…

💉 The ancient origins of the addiction-prone mind—and what it means for us today
#addiction

The ancient origins of the addiction-prone mind—and what it means for us today
Why do so many people struggle to stop drinking, quit smoking, or resist endless scrolling on their phones? Addiction touches millions of lives, yet it's often misunderstood as a personal weakness or moral failing. In reality, addiction is rooted in the ancient architecture of the human brain.

Social World Models
Xuhui Zhou, Jiarui Liu, Akhila Yerukola, Hyunwoo Kim, Maarten Sap
https://arxiv.org/abs/2509.00559 https://arxiv.org/pdf/2509.00559

Social World Models
Humans intuitively navigate social interactions by simulating unspoken dynamics and reasoning about others' perspectives, even with limited information. In contrast, AI systems struggle to automatically structure and reason about these implicit social contexts. In this paper, we introduce a novel structured social world representation formalism (S3AP), designed to help AI systems reason more effectively about social dynamics. Following a POMDP-driven design, S3AP represents social interactions …

Guardian:
"Greens struggle for coverage at the best of times, and these aren’t the best of times. The news today will be dominated by the repurcussions from the Manchester synagogue attack."
https://www.theguardian.com/politic…

StructCoh: Structured Contrastive Learning for Context-Aware Text Semantic Matching
Chao Xue, Ziyuan Gao
https://arxiv.org/abs/2509.02033 https://arxiv.org…

StructCoh: Structured Contrastive Learning for Context-Aware Text Semantic Matching
Text semantic matching requires nuanced understanding of both structural relationships and fine-grained semantic distinctions. While pre-trained language models excel at capturing token-level interactions, they often overlook hierarchical structural patterns and struggle with subtle semantic discrimination. In this paper, we proposed StructCoh, a graph-enhanced contrastive learning framework that synergistically combines structural reasoning with representation space optimization. Our approach …

QSearchNet: A Quantum Walk Search Framework for Link Prediction
Priyank Dubey
https://arxiv.org/abs/2510.00325 https://arxiv.org/pdf/2510.00325

QSearchNet: A Quantum Walk Search Framework for Link Prediction
Link prediction is one of the fundamental problems in graph theory, critical for understanding and forecasting the evolution of complex systems like social and biological networks. While classical heuristics capture certain aspects of graph topology, they often struggle to optimally integrate local and global structural information or adapt to complex dependencies. Quantum computing offers a powerful alternative by leveraging superposition for simultaneous multi-path exploration and interferenc…

Want to understand the geography of Gaza? Read this thread by Mohammed.
(And then try and help his fundraiser, if you can, as he’s currently being forced to endure genocide and famine.)
👇
#Gaza #geography

Mohammed Shobair from Gaza 🍉 (@mohshbair@mastodon.social)
Attached: 1 image Several of my friends here asked me if I’m in a safe place, but I struggle to explain the geography of Gaza and people from overseas struggle to understand it. So I’m writing a thread. 🧶⬇️⬇️⬇️ #Gaza #FreeGaza #Palestine #FreePalestine #StopGenocide #GazaMutualAid 1/14

Breaking the Euclidean Barrier: Hyperboloid-Based Biological Sequence Analysis
Sarwan Ali, Haris Mansoor, Murray Patterson
https://arxiv.org/abs/2510.01118 https://

Breaking the Euclidean Barrier: Hyperboloid-Based Biological Sequence Analysis
Genomic sequence analysis plays a crucial role in various scientific and medical domains. Traditional machine-learning approaches often struggle to capture the complex relationships and hierarchical structures of sequence data when working in high-dimensional Euclidean spaces. This limitation hinders accurate sequence classification and similarity measurement. To address these challenges, this research proposes a method to transform the feature representation of biological sequences into the hy…

Semi-Supervised Bayesian GANs with Log-Signatures for Uncertainty-Aware Credit Card Fraud Detection
David Hirnschall
https://arxiv.org/abs/2509.00931 https://

Semi-Supervised Bayesian GANs with Log-Signatures for Uncertainty-Aware Credit Card Fraud Detection
We present a novel deep generative semi-supervised framework for credit card fraud detection, formulated as time series classification task. As financial transaction data streams grow in scale and complexity, traditional methods often require large labeled datasets, struggle with time series of irregular sampling frequencies and varying sequence lengths. To address these challenges, we extend conditional Generative Adversarial Networks (GANs) for targeted data augmentation, integrate Bayesian i…

PBS, NPR stations struggle with Trump-fueled government funding cuts (David Bauder/Associated Press)
https://apnews.com/article/npr-pbs-public-broadcasting-trump-cuts-73de8fadf32655aa74752690d22a3758
http://www.memeorandum.com/250831/p104#a250831p104

Median2Median: Zero-shot Suppression of Structured Noise in Images
Jianxu Wang, Ge Wang
https://arxiv.org/abs/2510.01666 https://arxiv.org/pdf/2510.01666…

Median2Median: Zero-shot Suppression of Structured Noise in Images
Image denoising is a fundamental problem in computer vision and medical imaging. However, real-world images are often degraded by structured noise with strong anisotropic correlations that existing methods struggle to remove. Most data-driven approaches rely on large datasets with high-quality labels and still suffer from limited generalizability, whereas existing zero-shot methods avoid this limitation but remain effective only for independent and identically distributed (i.i.d.) noise. To add…

Memory-Augmented Log Analysis with Phi-4-mini: Enhancing Threat Detection in Structured Security Logs
Anbi Guo, Mahfuza Farooque
https://arxiv.org/abs/2510.00529 https://…

Memory-Augmented Log Analysis with Phi-4-mini: Enhancing Threat Detection in Structured Security Logs
Structured security logs are critical for detecting advanced persistent threats (APTs). Large language models (LLMs) struggle in this domain due to limited context and domain mismatch. We propose \textbf{DM-RAG}, a dual-memory retrieval-augmented generation framework for structured log analysis. It integrates a short-term memory buffer for recent summaries and a long-term FAISS-indexed memory for historical patterns. An instruction-tuned Phi-4-mini processes the combined context and outputs str…

LangGrasp: Leveraging Fine-Tuned LLMs for Language Interactive Robot Grasping with Ambiguous Instructions
Yunhan Lin, Wenqi Wu, Zhijie Zhang, Huasong Min
https://arxiv.org/abs/2510.02104

LangGrasp: Leveraging Fine-Tuned LLMs for Language Interactive Robot Grasping with Ambiguous Instructions
The existing language-driven grasping methods struggle to fully handle ambiguous instructions containing implicit intents. To tackle this challenge, we propose LangGrasp, a novel language-interactive robotic grasping framework. The framework integrates fine-tuned large language models (LLMs) to leverage their robust commonsense understanding and environmental perception capabilities, thereby deducing implicit intents from linguistic instructions and clarifying task requirements along with targe…

Temporal-Aware Iterative Speech Model for Dementia Detection
Chukwuemeka Ugwu, Oluwafemi Oyeleke
https://arxiv.org/abs/2510.00030 https://arxiv.org/pdf/251…

Temporal-Aware Iterative Speech Model for Dementia Detection
Deep learning systems often struggle with processing long sequences, where computational complexity can become a bottleneck. Current methods for automated dementia detection using speech frequently rely on static, time-agnostic features or aggregated linguistic content, lacking the flexibility to model the subtle, progressive deterioration inherent in speech production. These approaches often miss the dynamic temporal patterns that are critical early indicators of cognitive decline. In this pap…

Agentic Reasoning and Refinement through Semantic Interaction
Xuxin Tang, Rehema Abulikemu, Eric Krokos, Kirsten Whitley, Xuan Wang, Chris North
https://arxiv.org/abs/2510.02157

Agentic Reasoning and Refinement through Semantic Interaction
Sensemaking report writing often requires multiple refinements in the iterative process. While Large Language Models (LLMs) have shown promise in generating initial reports based on human visual workspace representations, they struggle to precisely incorporate sequential semantic interactions during the refinement process. We introduce VIS-ReAct, a framework that reasons about newly-added semantic interactions in visual workspaces to steer the LLM for report refinement. VIS-ReAct is a two-age…

Joe Alt injury updates: Why Chargers' season may hinge on star left tackle's latest high-ankle sprain

https://www.cbssports.com/nfl/news/joe-alt-injury-updat…

Scaling Legal AI: Benchmarking Mamba and Transformers for Statutory Classification and Case Law Retrieval
Anuraj Maurya
https://arxiv.org/abs/2509.00141 https://

Scaling Legal AI: Benchmarking Mamba and Transformers for Statutory Classification and Case Law Retrieval
The rapid growth of statutory corpora and judicial decisions requires scalable legal AI systems capable of classification and retrieval over extremely long contexts. Transformer-based architectures (e.g., Longformer, DeBERTa) dominate current legal NLP benchmarks but struggle with quadratic attention costs, limiting efficiency and scalability. In this work, we present the first comprehensive benchmarking of Mamba, a state-space model (SSM) with linear-time selective mechanisms, against leading …

Milco: Learned Sparse Retrieval Across Languages via a Multilingual Connector
Thong Nguyen, Yibin Lei, Jia-Huei Ju, Eugene Yang, Andrew Yates
https://arxiv.org/abs/2510.00671 ht…

Milco: Learned Sparse Retrieval Across Languages via a Multilingual Connector
Learned Sparse Retrieval (LSR) combines the efficiency of bi-encoders with the transparency of lexical matching, but existing approaches struggle to scale beyond English. We introduce MILCO, an LSR architecture that maps queries and documents from different languages into a shared English lexical space via a multilingual connector. MILCO is trained with a specialized two-stage regime that combines Sparse Alignment Pretraining with contrastive training to provide representation transparency and …

#CFP: I AM HERE TO FIGHT! An Anthology on the Struggle for Public Education
https://ift.tt/zCTUufF
I Am Here to Fight!
via Input 4 RELCFP

#CFP: I AM HERE TO FIGHT! An Anthology on the Struggle for Public Education
I Am Here to Fight!

Cowboys’ Trevon Diggs Shares Honest Thoughts on Not Starting https://heavy.com/sports/nfl/dallas-cowboys/cowboys-trevon-diggs-shares-honest-thoughts-on-not-starting/?adt_ei=[email]

@… I think a lot of people, including me, struggle with remembering that people in positions of authority (successful people in your field, management, elderly people, your mother) are not their superiors.

Secure and reversible face anonymization with diffusion models
Pol Labarbarie, Vincent Itier, William Puech
https://arxiv.org/abs/2510.01031 https://arxiv.…

Secure and reversible face anonymization with diffusion models
Face images processed by computer vision algorithms contain sensitive personal information that malicious actors can capture without consent. These privacy and security risks highlight the need for effective face anonymization methods. Current methods struggle to propose a good trade-off between a secure scheme with high-quality image generation and reversibility for later person authentication. Diffusion-based approaches produce high-quality anonymized images but lack the secret key mechanism …

CERA: A Framework for Improved Generalization of Machine Learning Models to Changed Climates
Shuchang Liu, Paul A. O'Gorman
https://arxiv.org/abs/2509.00010 https://

CERA: A Framework for Improved Generalization of Machine Learning Models to Changed Climates
Robust generalization under climate change remains a major challenge for machine learning applications in climate science. Most existing approaches struggle to extrapolate beyond the climate they were trained on, leading to a strong dependence on training data from model simulations of warm climates. Use of climate-invariant inputs improves generalization but requires challenging manual feature engineering. Here, we present CERA (Climate-invariant Encoding through Representation Alignment), a m…

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Anna von Hausswolff:
🎵 Struggle With the Beast
#AnnavonHausswolff
https://annavonhausswolff.bandcamp.com/track/struggle-with-the-beast-2
https://open.spotify.com/track/798vsh2RF4vx5UG0ez9QBn

Enhanced Fingerprint-based Positioning With Practical Imperfections: Deep learning-based approaches
Shugong Xu, Jun Jiang, Wenjun Yu, Yilin Gao, Guangjin Pan, Shiyi Mu, Zhiqi Ai, Yuan Gao, Peigang Jiang, Cheng-Xiang Wang
https://arxiv.org/abs/2509.01197

Enhanced Fingerprint-based Positioning With Practical Imperfections: Deep learning-based approaches
High-precision positioning is vital for cellular networks to support innovative applications such as extended reality, unmanned aerial vehicles (UAVs), and industrial Internet of Things (IoT) systems. Existing positioning algorithms using deep learning techniques require vast amounts of labeled data, which are difficult to obtain in real-world cellular environments, and these models often struggle to generalize effectively. To advance cellular positioning techniques, the 2024 Wireless Communica…

Gated X-TFC: Soft Domain Decomposition for Forward and Inverse Problems in Sharp-Gradient PDEs
Vikas Dwivedi, Enrico Schiassi, Monica Sigovan, Bruno Sixou
https://arxiv.org/abs/2510.01039

Gated X-TFC: Soft Domain Decomposition for Forward and Inverse Problems in Sharp-Gradient PDEs
Physics-informed neural networks (PINNs) and related methods struggle to resolve sharp gradients in singularly perturbed boundary value problems without resorting to some form of domain decomposition, which often introduce complex interface penalties. While the Extreme Theory of Functional Connections (X-TFC) avoids multi-objective optimization by employing exact boundary condition enforcement, it remains computationally inefficient for boundary layers and incompatible with decomposition. We pr…

Exploring Network-Knowledge Graph Duality: A Case Study in Agentic Supply Chain Risk Analysis
Evan Heus, Rick Bookstaber, Dhruv Sharma
https://arxiv.org/abs/2510.01115 https://

Exploring Network-Knowledge Graph Duality: A Case Study in Agentic Supply Chain Risk Analysis
Large Language Models (LLMs) struggle with the complex, multi-modal, and network-native data underlying financial risk. Standard Retrieval-Augmented Generation (RAG) oversimplifies relationships, while specialist models are costly and static. We address this gap with an LLM-centric agent framework for supply chain risk analysis. Our core contribution is to exploit the inherent duality between networks and knowledge graphs (KG). We treat the supply chain network as a KG, allowing us to use struc…

Earthquake Source Depth Determination using Single Station Waveforms and Deep Learning
Wenda Li, Miao Zhang
https://arxiv.org/abs/2509.02346 https://arxiv.…

Earthquake Source Depth Determination using Single Station Waveforms and Deep Learning
In areas with limited station coverage, earthquake depth constraints are much less accurate than their latitude and longitude. Traditional travel-time-based location methods struggle to constrain depths due to imperfect station distribution and the strong trade-off between source depth and origin time. Identifying depth phases at regional distances is usually hindered by strong wave scattering, which is particularly challenging for low-magnitude events. Deep learning algorithms, capable of extr…

FActBench: A Benchmark for Fine-grained Automatic Evaluation of LLM-Generated Text in the Medical Domain
Anum Afzal, Juraj Vladika, Florian Matthes
https://arxiv.org/abs/2509.02198

FActBench: A Benchmark for Fine-grained Automatic Evaluation of LLM-Generated Text in the Medical Domain
Large Language Models tend to struggle when dealing with specialized domains. While all aspects of evaluation hold importance, factuality is the most critical one. Similarly, reliable fact-checking tools and data sources are essential for hallucination mitigation. We address these issues by providing a comprehensive Fact-checking Benchmark FActBench covering four generation tasks and six state-of-the-art Large Language Models (LLMs) for the Medical domain. We use two state-of-the-art Fact-check…

To Remember, To Adapt, To Preempt: A Stable Continual Test-Time Adaptation Framework for Remote Physiological Measurement in Dynamic Domain Shifts
Shuyang Chu, Jingang Shi, Xu Cheng, Haoyu Chen, Xin Liu, Jian Xu, Guoying Zhao
https://arxiv.org/abs/2510.01282

To Remember, To Adapt, To Preempt: A Stable Continual Test-Time Adaptation Framework for Remote Physiological Measurement in Dynamic Domain Shifts
Remote photoplethysmography (rPPG) aims to extract non-contact physiological signals from facial videos and has shown great potential. However, existing rPPG approaches struggle to bridge the gap between source and target domains. Recent test-time adaptation (TTA) solutions typically optimize rPPG model for the incoming test videos using self-training loss under an unrealistic assumption that the target domain remains stationary. However, time-varying factors like weather and lighting in dynami…

Federated Foundation Models in Harsh Wireless Environments: Prospects, Challenges, and Future Directions
Evan Chen, Seyyedali Hosseinalipour, Christopher G. Brinton, David J. Love
https://arxiv.org/abs/2509.01957

Federated Foundation Models in Harsh Wireless Environments: Prospects, Challenges, and Future Directions
Foundation models (FMs) have shown remarkable capabilities in generalized intelligence, multimodal understanding, and adaptive learning across a wide range of domains. However, their deployment in harsh or austere environments -- characterized by intermittent connectivity, limited computation, noisy data, and dynamically changing network topologies -- remains an open challenge. Existing distributed learning methods such as federated learning (FL) struggle to adapt in such settings due to their …

Opal: A Modular Framework for Optimizing Performance using Analytics and LLMs
Mohammad Zaeed, Tanzima Z. Islam, Vladimir In{\dj}i\'c
https://arxiv.org/abs/2510.00932 https:/…

Opal: A Modular Framework for Optimizing Performance using Analytics and LLMs
Large Language Models (LLMs) show promise for automated code optimization but struggle without performance context. This work introduces Opal, a modular framework that connects performance analytics insights with the vast body of published by guiding LLMs to generate informed, trustworthy optimizations. Unlike traditional performance tools that identify bottlenecks but stop short of actionable suggestions, Opal bridges this long-standing gap by linking dynamic insights from hardware counters an…

'Struggle meals' and Hamburger Helper are trending because food is so expensive
https://flip.it/Pm0.q5

Object-AVEdit: An Object-level Audio-Visual Editing Model
Youquan Fu, Ruiyang Si, Hongfa Wang, Dongzhan Zhou, Jiacheng Sun, Ping Luo, Di Hu, Hongyuan Zhang, Xuelong Li
https://arxiv.org/abs/2510.00050 …

Object-AVEdit: An Object-level Audio-Visual Editing Model
There is a high demand for audio-visual editing in video post-production and the film making field. While numerous models have explored audio and video editing, they struggle with object-level audio-visual operations. Specifically, object-level audio-visual editing requires the ability to perform object addition, replacement, and removal across both audio and visual modalities, while preserving the structural information of the source instances during the editing process. In this paper, we pres…

FGO-SLAM: Enhancing Gaussian SLAM with Globally Consistent Opacity Radiance Field
Fan Zhu, Yifan Zhao, Ziyu Chen, Biao Yu, Hui Zhu
https://arxiv.org/abs/2509.01547 https://

FGO-SLAM: Enhancing Gaussian SLAM with Globally Consistent Opacity Radiance Field
Visual SLAM has regained attention due to its ability to provide perceptual capabilities and simulation test data for Embodied AI. However, traditional SLAM methods struggle to meet the demands of high-quality scene reconstruction, and Gaussian SLAM systems, despite their rapid rendering and high-quality mapping capabilities, lack effective pose optimization methods and face challenges in geometric reconstruction. To address these issues, we introduce FGO-SLAM, a Gaussian SLAM system that emplo…

TShape: Rescuing Machine Learning Models from Complex Shapelet Anomalies
Hang Cui, Jingjing Li, Haotian Si, Quan Zhou, Changhua Pei, Gaogang Xie, Dan Pei
https://arxiv.org/abs/2510.00680

TShape: Rescuing Machine Learning Models from Complex Shapelet Anomalies
Time series anomaly detection (TSAD) is critical for maintaining the reliability of modern IT infrastructures, where complex anomalies frequently arise in highly dynamic environments. In this paper, we present TShape, a novel framework designed to address the challenges in industrial time series anomaly detection. Existing methods often struggle to detect shapelet anomalies that manifest as complex shape deviations, which appear obvious to human experts but prove challenging for machine learnin…

Dereverberation Using Binary Residual Masking with Time-Domain Consistency
Daniel G. Williams
https://arxiv.org/abs/2510.00356 https://arxiv.org/pdf/2510.0…

Dereverberation Using Binary Residual Masking with Time-Domain Consistency
Vocal dereverberation remains a challenging task in audio processing, particularly for real-time applications where both accuracy and efficiency are crucial. Traditional deep learning approaches often struggle to suppress reverberation without degrading vocal clarity, while recent methods that jointly predict magnitude and phase have significant computational cost. We propose a real-time dereverberation framework based on residual mask prediction in the short-time Fourier transform (STFT) domai…

LLM-based Multi-Agent Blackboard System for Information Discovery in Data Science
Alireza Salemi, Mihir Parmar, Palash Goyal, Yiwen Song, Jinsung Yoon, Hamed Zamani, Hamid Palangi, Tomas Pfister
https://arxiv.org/abs/2510.01285

LLM-based Multi-Agent Blackboard System for Information Discovery in Data Science
The rapid advancement of Large Language Models (LLMs) has opened new opportunities in data science, yet their practical deployment is often constrained by the challenge of discovering relevant data within large heterogeneous data lakes. Existing methods struggle with this: single-agent systems are quickly overwhelmed by large, heterogeneous files in the large data lakes, while multi-agent systems designed based on a master-slave paradigm depend on a rigid central controller for task allocation …

CSnake: Detecting Self-Sustaining Cascading Failure via Causal Stitching of Fault Propagations
Shangshu Qian, Lin Tan, Yongle Zhang
https://arxiv.org/abs/2509.26529 https://

CSnake: Detecting Self-Sustaining Cascading Failure via Causal Stitching of Fault Propagations
Recent studies have revealed that self-sustaining cascading failures in distributed systems frequently lead to widespread outages, which are challenging to contain and recover from. Existing failure detection techniques struggle to expose such failures prior to deployment, as they typically require a complex combination of specific conditions to be triggered. This challenge stems from the inherent nature of cascading failures, as they typically involve a sequence of fault propagations, each act…

An Efficient Quality Metric for Video Frame Interpolation Based on Motion-Field Divergence
Conall Daly, Darren Ramsook, Anil Kokaram
https://arxiv.org/abs/2510.01361 https://

An Efficient Quality Metric for Video Frame Interpolation Based on Motion-Field Divergence
Video frame interpolation is a fundamental tool for temporal video enhancement, but existing quality metrics struggle to evaluate the perceptual impact of interpolation artefacts effectively. Metrics like PSNR, SSIM and LPIPS ignore temporal coherence. State-of-the-art quality metrics tailored towards video frame interpolation, like FloLPIPS, have been developed but suffer from computational inefficiency that limits their practical application. We present $\text{PSNR}_{\text{DIV}}$, a novel ful…

Several US states have passed bills to ban or restrict AI mental health treatment, as experts say state laws lag behind the fast-moving AI therapy landscape (Devi Shastri/Associated Press)
https://apnews.com/article/ai-therapy-ban-illinois-therabo…

States struggle with AI therapy app rules amid mental health needs
As mental health chatbots driven by artificial intelligence proliferate, a small number of states are trying to regulate them. Laws in Illinois, Nevada and Utah are among the first in the nation to put limits on or ban therapy chatbots. But app creators, policymakers and mental health advocates say it is just a start and federal regulation would be ideal. App makers worry the patchwork of laws could stifle innovation needed due to a nationwide shortage of human therapists. They also note that m…

MolErr2Fix:Benchmarking LLM Trustworthiness in Chemistry via Modular Error Detection, Localization, Explanation, and Revision
Yuyang Wu, Jinhui Ye, Shuhao Zhang, Lu Dai, Yonatan Bisk, Olexandr Isayev
https://arxiv.org/abs/2509.00063

MolErr2Fix:Benchmarking LLM Trustworthiness in Chemistry via Modular Error Detection, Localization, Explanation, and Revision
Large Language Models (LLMs) have shown growing potential in molecular sciences, but they often produce chemically inaccurate descriptions and struggle to recognize or justify potential errors. This raises important concerns about their robustness and reliability in scientific applications. To support more rigorous evaluation of LLMs in chemical reasoning, we present the MolErr2Fix benchmark, designed to assess LLMs on error detection and correction in molecular descriptions. Unlike existing be…

Enabling Transparent Cyber Threat Intelligence Combining Large Language Models and Domain Ontologies
Luca Cotti, Anisa Rula, Devis Bianchini, Federico Cerutti
https://arxiv.org/abs/2509.00081

Enabling Transparent Cyber Threat Intelligence Combining Large Language Models and Domain Ontologies
Effective Cyber Threat Intelligence (CTI) relies upon accurately structured and semantically enriched information extracted from cybersecurity system logs. However, current methodologies often struggle to identify and interpret malicious events reliably and transparently, particularly in cases involving unstructured or ambiguous log entries. In this work, we propose a novel methodology that combines ontology-driven structured outputs with Large Language Models (LLMs), to build an Artificial Int…

Hybrid Quantum-Classical Walks for Graph Representation Learning in Community Detection
Adri\'an Mar{\i}n, Mauricio Soto-Gomez, Giorgio Valentini, Elena Casiraghi, Carlos Cano, Daniel Manzano
https://arxiv.org/abs/2510.01918

Hybrid Quantum-Classical Walks for Graph Representation Learning in Community Detection
Graph Representation Learning (GRL) has emerged as a cornerstone technique for analysing complex, networked data across diverse domains, including biological systems, social networks, and data analysis. Traditional GRL methods often struggle to capture intricate relationships within complex graphs, particularly those exhibiting non-trivial structural properties such as power-law distributions or hierarchical structures. This paper introduces a novel quantum-inspired algorithm for GRL, utilizing…

Optimal Control Meets Flow Matching: A Principled Route to Multi-Subject Fidelity
Eric Tillmann Bill, Enis Simsar, Thomas Hofmann
https://arxiv.org/abs/2510.02315 https://

Optimal Control Meets Flow Matching: A Principled Route to Multi-Subject Fidelity
Text-to-image (T2I) models excel on single-entity prompts but struggle with multi-subject descriptions, often showing attribute leakage, identity entanglement, and subject omissions. We introduce the first theoretical framework with a principled, optimizable objective for steering sampling dynamics toward multi-subject fidelity. Viewing flow matching (FM) through stochastic optimal control (SOC), we formulate subject disentanglement as control over a trained FM sampler. This yields two architec…

Exploring Network-Knowledge Graph Duality: A Case Study in Agentic Supply Chain Risk Analysis
Evan Heus, Rick Bookstaber, Dhruv Sharma
https://arxiv.org/abs/2510.01115 https://

Exploring Network-Knowledge Graph Duality: A Case Study in Agentic Supply Chain Risk Analysis
Large Language Models (LLMs) struggle with the complex, multi-modal, and network-native data underlying financial risk. Standard Retrieval-Augmented Generation (RAG) oversimplifies relationships, while specialist models are costly and static. We address this gap with an LLM-centric agent framework for supply chain risk analysis. Our core contribution is to exploit the inherent duality between networks and knowledge graphs (KG). We treat the supply chain network as a KG, allowing us to use struc…

PromptPilot: Improving Human-AI Collaboration Through LLM-Enhanced Prompt Engineering
Niklas Gutheil, Valentin Mayer, Leopold M\"uller, J\"org Rommelt, Niklas K\"uhl
https://arxiv.org/abs/2510.00555

PromptPilot: Improving Human-AI Collaboration Through LLM-Enhanced Prompt Engineering
Effective prompt engineering is critical to realizing the promised productivity gains of large language models (LLMs) in knowledge-intensive tasks. Yet, many users struggle to craft prompts that yield high-quality outputs, limiting the practical benefits of LLMs. Existing approaches, such as prompt handbooks or automated optimization pipelines, either require substantial effort, expert knowledge, or lack interactive guidance. To address this gap, we design and evaluate PromptPilot, an interacti…

5 free agents edge rushers Cowboys can sign to replace Micah Parsons include 4x Pro Bowler https://cowboyswire.usatoday.com/story/sports/nfl/cowboys/2025/09/01/5-free-agents-edge-players-cowboys-replace-mica…

CMRAG: Co-modality-based document retrieval and visual question answering
Wang Chen, Guanqiang Qi, Weikang Li, Yang Li
https://arxiv.org/abs/2509.02123 https://

CMRAG: Co-modality-based document retrieval and visual question answering
Retrieval-Augmented Generation (RAG) has become a core paradigm in document question answering tasks. However, existing methods have limitations when dealing with multimodal documents: one category of methods relies on layout analysis and text extraction, which can only utilize explicit text information and struggle to capture images or unstructured content; the other category treats document segmentation as visual input and directly passes it to visual language models (VLMs) for processing, ye…

Generalizing Unsupervised Lidar Odometry Model from Normal to Snowy Weather Conditions
Beibei Zhou, Zhiyuan Zhang, Zhenbo Song, Jianhui Guo, Hui Kong
https://arxiv.org/abs/2509.02011

Generalizing Unsupervised Lidar Odometry Model from Normal to Snowy Weather Conditions
Deep learning-based LiDAR odometry is crucial for autonomous driving and robotic navigation, yet its performance under adverse weather, especially snowfall, remains challenging. Existing models struggle to generalize across conditions due to sensitivity to snow-induced noise, limiting real-world use. In this work, we present an unsupervised LiDAR odometry model to close the gap between clear and snowy weather conditions. Our approach focuses on effective denoising to mitigate the impact of snow…

Location Matters: Leveraging Multi-Resolution Geo-Embeddings for Housing Search
Ivo Silva (QuintoAndar), Pedro Nogueira (QuintoAndar), Guilherme Bonaldo (QuintoAndar)
https://arxiv.org/abs/2510.01196

Location Matters: Leveraging Multi-Resolution Geo-Embeddings for Housing Search
QuintoAndar Group is Latin America's largest housing platform, revolutionizing property rentals and sales. Headquartered in Brazil, it simplifies the housing process by eliminating paperwork and enhancing accessibility for tenants, buyers, and landlords. With thousands of houses available for each city, users struggle to find the ideal home. In this context, location plays a pivotal role, as it significantly influences property value, access to amenities, and life quality. A great location can …

Look Less, Reason More: Rollout-Guided Adaptive Pixel-Space Reasoning
Xuchen Li, Xuzhao Li, Jiahui Gao, Renjie Pi, Shiyu Hu, Wentao Zhang
https://arxiv.org/abs/2510.01681 https:…

Look Less, Reason More: Rollout-Guided Adaptive Pixel-Space Reasoning
Vision-Language Models (VLMs) excel at many multimodal tasks, yet they frequently struggle with tasks requiring precise understanding and handling of fine-grained visual elements. This is mainly due to information loss during image encoding or insufficient attention to critical regions. Recent work has shown promise by incorporating pixel-level visual information into the reasoning process, enabling VLMs to access high-resolution visual details during their thought process. However, this pixel-…

Quantum-inspired probability metrics define a complete, universal space for statistical learning
Logan S. McCarty
https://arxiv.org/abs/2508.21086 https://…

Quantum-inspired probability metrics define a complete, universal space for statistical learning
Comparing probability distributions is a core challenge across the natural, social, and computational sciences. Existing methods, such as Maximum Mean Discrepancy (MMD), struggle in high-dimensional and non-compact domains. Here we introduce quantum probability metrics (QPMs), derived by embedding probability measures in the space of quantum states: positive, unit-trace operators on a Hilbert space. This construction extends kernel-based methods and overcomes the incompleteness of MMD on non-co…

DRetNet: A Novel Deep Learning Framework for Diabetic Retinopathy Diagnosis
Idowu Paul Okuwobi, Jingyuan Liu, Jifeng Wan, Jiaojiao Jiang
https://arxiv.org/abs/2509.01072 https:/…

DRetNet: A Novel Deep Learning Framework for Diabetic Retinopathy Diagnosis
Diabetic retinopathy (DR) is a leading cause of blindness worldwide, necessitating early detection to prevent vision loss. Current automated DR detection systems often struggle with poor-quality images, lack interpretability, and insufficient integration of domain-specific knowledge. To address these challenges, we introduce a novel framework that integrates three innovative contributions: (1) Adaptive Retinal Image Enhancement Using Physics-Informed Neural Networks (PINNs): this technique dyna…

Statistics-Friendly Confidentiality Protection for Establishment Data, with Applications to the QCEW
Kaitlyn Webb, Prottay Protivash, John Durrell, Daniell Toth, Aleksandra Slavkovi\'c, Daniel Kifer
https://arxiv.org/abs/2509.01597

Statistics-Friendly Confidentiality Protection for Establishment Data, with Applications to the QCEW
Confidentiality for business data is an understudied area of disclosure avoidance, where legacy methods struggle to provide acceptable results. Modern formal privacy techniques designed for person-level data do not provide suitable confidentiality/utility trade-offs due to the highly skewed nature of business data and because extreme outlier records are often important contributors to query answers. In this paper, inspired by Gaussian Differential Privacy, we propose a novel confidentiality fra…

Reference-free automatic speech severity evaluation using acoustic unit language modelling
Bence Mark Halpern, Tomoki Toda
https://arxiv.org/abs/2510.00639 https://

Reference-free automatic speech severity evaluation using acoustic unit language modelling
Speech severity evaluation is becoming increasingly important as the economic burden of speech disorders grows. Current speech severity models often struggle with generalization, learning dataset-specific acoustic cues rather than meaningful correlates of speech severity. Furthermore, many models require reference speech or a transcript, limiting their applicability in ecologically valid scenarios, such as spontaneous speech evaluation. Previous research indicated that automatic speech naturaln…

Adaptive Heavy-Tailed Stochastic Gradient Descent
Bodu Gong, Gustavo Enrique Batista, Pierre Lafaye de Micheaux
https://arxiv.org/abs/2508.21353 https://ar…

Adaptive Heavy-Tailed Stochastic Gradient Descent
In the era of large-scale neural network models, optimization algorithms often struggle with generalization due to an overreliance on training loss. One key insight widely accepted in the machine learning community is the idea that wide basins (regions around a local minimum where the loss increases gradually) promote better generalization by offering greater stability to small changes in input data or model parameters. In contrast, sharp minima are typically more sensitive and less stable. Mot…

MetaLogic: Robustness Evaluation of Text-to-Image Models via Logically Equivalent Prompts
Yifan Shen, Yangyang Shu, Hye-young Paik, Yulei Sui
https://arxiv.org/abs/2510.00796 ht…

MetaLogic: Robustness Evaluation of Text-to-Image Models via Logically Equivalent Prompts
Recent advances in text-to-image (T2I) models, especially diffusion-based architectures, have significantly improved the visual quality of generated images. However, these models continue to struggle with a critical limitation: maintaining semantic consistency when input prompts undergo minor linguistic variations. Despite being logically equivalent, such prompt pairs often yield misaligned or semantically inconsistent images, exposing a lack of robustness in reasoning and generalisation. To ad…

Top-H Decoding: Adapting the Creativity and Coherence with Bounded Entropy in Text Generation
Erfan Baghaei Potraghloo, Seyedarmin Azizi, Souvik Kundu, Massoud Pedram
https://arxiv.org/abs/2509.02510

Top-H Decoding: Adapting the Creativity and Coherence with Bounded Entropy in Text Generation
Large language models (LLMs), despite their impressive performance across a wide range of tasks, often struggle to balance two competing objectives in open-ended text generation: fostering diversity and creativity while preserving logical coherence. Existing truncated sampling techniques, including temperature scaling, top-\$p\$ (nucleus) sampling, and min-\$p\$ sampling, aim to manage this trade-off. However, they exhibit limitations, particularly in the effective incorporation of the confiden…

#CFP: I AM HERE TO FIGHT! An Anthology on the Struggle for Public Education
https://ift.tt/fMG21ln
I Am Here to Fight!
via Input 4 RELCFP

#CFP: I AM HERE TO FIGHT! An Anthology on the Struggle for Public Education
I Am Here to Fight!

Online Hierarchical Policy Learning using Physics Priors for Robot Navigation in Unknown Environments
Wei Han Chen, Yuchen Liu, Alexiy Buynitsky, Ahmed H. Qureshi
https://arxiv.org/abs/2510.01519

Online Hierarchical Policy Learning using Physics Priors for Robot Navigation in Unknown Environments
Robot navigation in large, complex, and unknown indoor environments is a challenging problem. The existing approaches, such as traditional sampling-based methods, struggle with resolution control and scalability, while imitation learning-based methods require a large amount of demonstration data. Active Neural Time Fields (ANTFields) have recently emerged as a promising solution by using local observations to learn cost-to-go functions without relying on demonstrations. Despite their potential,…

Learning to Generate Object Interactions with Physics-Guided Video Diffusion
David Romero, Ariana Bermudez, Hao Li, Fabio Pizzati, Ivan Laptev
https://arxiv.org/abs/2510.02284 h…

Learning to Generate Object Interactions with Physics-Guided Video Diffusion
Recent models for video generation have achieved remarkable progress and are now deployed in film, social media production, and advertising. Beyond their creative potential, such models also hold promise as world simulators for robotics and embodied decision making. Despite strong advances, however, current approaches still struggle to generate physically plausible object interactions and lack physics-grounded control mechanisms. To address this limitation, we introduce KineMask, an approach fo…

Resting-state fMRI Analysis using Quantum Time-series Transformer
Junghoon Justin Park, Jungwoo Seo, Sangyoon Bae, Samuel Yen-Chi Chen, Huan-Hsin Tseng, Jiook Cha, Shinjae Yoo
https://arxiv.org/abs/2509.00711

Resting-state fMRI Analysis using Quantum Time-series Transformer
Resting-state functional magnetic resonance imaging (fMRI) has emerged as a pivotal tool for revealing intrinsic brain network connectivity and identifying neural biomarkers of neuropsychiatric conditions. However, classical self-attention transformer models--despite their formidable representational power--struggle with quadratic complexity, large parameter counts, and substantial data requirements. To address these barriers, we introduce a Quantum Time-series Transformer, a novel quantum-enha…

Breaking the Cold-Start Barrier: Reinforcement Learning with Double and Dueling DQNs
Minda Zhao
https://arxiv.org/abs/2508.21259 https://arxiv.org/pdf/2508…

Breaking the Cold-Start Barrier: Reinforcement Learning with Double and Dueling DQNs
Recommender systems struggle to provide accurate suggestions to new users with limited interaction history, a challenge known as the cold-user problem. This paper proposes a reinforcement learning approach using Double and Dueling Deep Q-Networks (DQN) to dynamically learn user preferences from sparse feedback, enhancing recommendation accuracy without relying on sensitive demographic data. By integrating these advanced DQN variants with a matrix factorization model, we achieve superior perform…

Black-box Context-free Grammar Inference for Readable & Natural Grammars
Mohammad Rifat Arefin, Shanto Rahman, Christoph Csallner
https://arxiv.org/abs/2509.26616 https://…

Black-box Context-free Grammar Inference for Readable & Natural Grammars
Black-box context-free grammar inference is crucial for program analysis, reverse engineering, and security, yet existing tools such as Arvada, TreeVada, and Kedavra struggle with scalability, readability, and accuracy on large, complex languages. We present NatGI, a novel LLM-guided grammar inference framework that extends TreeVada's parse tree recovery with three key innovations: bracket-guided bubble exploration, LLM- driven bubble generation and non-terminal labeling, and hierarchical delta…

Towards High-Fidelity, Identity-Preserving Real-Time Makeup Transfer: Decoupling Style Generation
Lydia Kin Ching Chau, Zhi Yu, Ruo Wei Jiang
https://arxiv.org/abs/2509.02445 ht…

Towards High-Fidelity, Identity-Preserving Real-Time Makeup Transfer: Decoupling Style Generation
We present a novel framework for real-time virtual makeup try-on that achieves high-fidelity, identity-preserving cosmetic transfer with robust temporal consistency. In live makeup transfer applications, it is critical to synthesize temporally coherent results that accurately replicate fine-grained makeup and preserve user's identity. However, existing methods often struggle to disentangle semitransparent cosmetics from skin tones and other identify features, causing identity shifts and raising…

MoTo: A Zero-shot Plug-in Interaction-aware Navigation for General Mobile Manipulation
Zhenyu Wu, Angyuan Ma, Xiuwei Xu, Hang Yin, Yinan Liang, Ziwei Wang, Jiwen Lu, Haibin Yan
https://arxiv.org/abs/2509.01658

MoTo: A Zero-shot Plug-in Interaction-aware Navigation for General Mobile Manipulation
Mobile manipulation stands as a core challenge in robotics, enabling robots to assist humans across varied tasks and dynamic daily environments. Conventional mobile manipulation approaches often struggle to generalize across different tasks and environments due to the lack of large-scale training. However, recent advances in manipulation foundation models demonstrate impressive generalization capability on a wide range of fixed-base manipulation tasks, which are still limited to a fixed setting…

LLM-Assisted News Discovery in High-Volume Information Streams: A Case Study
Nick Hagar, Ethan Silver, Clare Spencer, Nicholas Diakopoulos
https://arxiv.org/abs/2509.25491 https…

LLM-Assisted News Discovery in High-Volume Information Streams: A Case Study
Journalists face mounting challenges in monitoring ever-expanding digital information streams to identify newsworthy content. While traditional automation tools gather information at scale, they struggle with the editorial judgment needed to assess newsworthiness. This paper investigates whether large language models (LLMs) can serve as effective first-pass filters for journalistic monitoring. We develop a prompt-based approach encoding journalistic news values - timeliness, impact, controversy…

Linking Process to Outcome: Conditional Reward Modeling for LLM Reasoning
Zheng Zhang, Ziwei Shan, Kaitao Song, Yexin Li, Kan Ren
https://arxiv.org/abs/2509.26578 https://

Linking Process to Outcome: Conditional Reward Modeling for LLM Reasoning
Process Reward Models (PRMs) have emerged as a promising approach to enhance the reasoning capabilities of large language models (LLMs) by guiding their step-by-step reasoning toward a final answer. However, existing PRMs either treat each reasoning step in isolation, failing to capture inter-step dependencies, or struggle to align process rewards with the final outcome. Consequently, the reward signal fails to respect temporal causality in sequential reasoning and faces ambiguous credit assign…

HilbertA: Hilbert Attention for Image Generation with Diffusion Models
Shaoyi Zheng, Wenbo Lu, Yuxuan Xia, Haomin Liu, Shengjie Wang
https://arxiv.org/abs/2509.26538 https://

HilbertA: Hilbert Attention for Image Generation with Diffusion Models
Designing sparse attention for diffusion transformers requires reconciling two-dimensional spatial locality with GPU efficiency, a trade-off that current methods struggle to achieve. Existing approaches enforce two-dimensional spatial locality but often incur uncoalesced memory access. We present HilbertA, a 2D-aware and GPU-efficient sparse attention mechanism. HilbertA reorders image tokens along Hilbert curves to achieve a contiguous memory layout while preserving spatial neighborhoods, and …

DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole-Slide Image Survival Prediction
Yucheng Xing, Ling Huang, Jingying Ma, Ruping Hong, Jiangdong Qiu, Pei Liu, Kai He, Huazhu Fu, Mengling Feng
https://arxiv.org/abs/2510.00053

DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole-Slide Image Survival Prediction
Pathology whole-slide images (WSIs) are widely used for cancer survival analysis because of their comprehensive histopathological information at both cellular and tissue levels, enabling quantitative, large-scale, and prognostically rich tumor feature analysis. However, most existing methods in WSI survival analysis struggle with limited interpretability and often overlook predictive uncertainty in heterogeneous slide images. In this paper, we propose DPsurv, a dual-prototype whole-slide image …

QUAV: Quantum-Assisted Path Planning and Optimization for UAV Navigation with Obstacle Avoidance
Nouhaila Innan, Muhammad Kashif, Alberto Marchisio, Yung-Sze Gan, Frederic Barbaresco, Muhammad Shafique
https://arxiv.org/abs/2508.21361

QUAV: Quantum-Assisted Path Planning and Optimization for UAV Navigation with Obstacle Avoidance
The growing demand for drone navigation in urban and restricted airspaces requires real-time path planning that is both safe and scalable. Classical methods often struggle with the computational load of high-dimensional optimization under dynamic constraints like obstacle avoidance and no-fly zones. This work introduces QUAV, a quantum-assisted UAV path planning framework based on the Quantum Approximate Optimization Algorithm (QAOA), to the best of our knowledge, this is one of the first appli…

Real-time Multi-Plane Segmentation Based on GPU Accelerated High-Resolution 3D Voxel Mapping for Legged Robot Locomotion
Shun Niijima, Ryoichi Tsuzaki, Noriaki Takasugi, Masaya Kinoshita
https://arxiv.org/abs/2510.01592

Real-time Multi-Plane Segmentation Based on GPU Accelerated High-Resolution 3D Voxel Mapping for Legged Robot Locomotion
This paper proposes a real-time multi-plane segmentation method based on GPU-accelerated high-resolution 3D voxel mapping for legged robot locomotion. Existing online planar mapping approaches struggle to balance accuracy and computational efficiency: direct depth image segmentation from specific sensors suffers from poor temporal integration, height map-based methods cannot represent complex 3D structures like overhangs, and voxel-based plane segmentation remains unexplored for real-time appli…

VideoNSA: Native Sparse Attention Scales Video Understanding
Enxin Song, Wenhao Chai, Shusheng Yang, Ethan Armand, Xiaojun Shan, Haiyang Xu, Jianwen Xie, Zhuowen Tu
https://arxiv.org/abs/2510.02295

VideoNSA: Native Sparse Attention Scales Video Understanding
Video understanding in multimodal language models remains limited by context length: models often miss key transition frames and struggle to maintain coherence across long time scales. To address this, we adapt Native Sparse Attention (NSA) to video-language models. Our method, VideoNSA, adapts Qwen2.5-VL through end-to-end training on a 216K video instruction dataset. We employ a hardware-aware hybrid approach to attention, preserving dense attention for text, while employing NSA for video. Co…

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks
Xuan Hou, Shuhan Liu, Zhaohui Peng, Yaohui Chu, Yue Zhang, Yining Wang
https://arxiv.org/abs/2508.21340

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks
Time series synthesis is an effective approach to ensuring the secure circulation of time series data. Existing time series synthesis methods typically perform temporal modeling based on random sequences to generate target sequences, which often struggle to ensure the temporal dependencies in the generated time series. Additionally, directly modeling temporal features on random sequences makes it challenging to accurately capture the feature information of the original time series. To address t…

DisCo-Layout: Disentangling and Coordinating Semantic and Physical Refinement in a Multi-Agent Framework for 3D Indoor Layout Synthesis
Jialin Gao, Donghao Zhou, Mingjian Liang, Lihao Liu, Chi-Wing Fu, Xiaowei Hu, Pheng-Ann Heng
https://arxiv.org/abs/2510.02178

DisCo-Layout: Disentangling and Coordinating Semantic and Physical Refinement in a Multi-Agent Framework for 3D Indoor Layout Synthesis
3D indoor layout synthesis is crucial for creating virtual environments. Traditional methods struggle with generalization due to fixed datasets. While recent LLM and VLM-based approaches offer improved semantic richness, they often lack robust and flexible refinement, resulting in suboptimal layouts. We develop DisCo-Layout, a novel framework that disentangles and coordinates physical and semantic refinement. For independent refinement, our Semantic Refinement Tool (SRT) corrects abstract objec…

Decoding Memories: An Efficient Pipeline for Self-Consistency Hallucination Detection
Weizhi Gao, Xiaorui Liu, Feiyi Wang, Dan Lu, Junqi Yin
https://arxiv.org/abs/2508.21228 htt…

Decoding Memories: An Efficient Pipeline for Self-Consistency Hallucination Detection
Large language models (LLMs) have demonstrated impressive performance in both research and real-world applications, but they still struggle with hallucination. Existing hallucination detection methods often perform poorly on sentence-level generation or rely heavily on domain-specific knowledge. While self-consistency approaches help address these limitations, they incur high computational costs due to repeated generation. In this paper, we conduct the first study on identifying redundancy in s…

Category-Aware 3D Object Composition with Disentangled Texture and Shape Multi-view Diffusion
Zeren Xiong, Zikun Chen, Zedong Zhang, Xiang Li, Ying Tai, Jian Yang, Jun Li
https://arxiv.org/abs/2509.02357

Category-Aware 3D Object Composition with Disentangled Texture and Shape Multi-view Diffusion
In this paper, we tackle a new task of 3D object synthesis, where a 3D model is composited with another object category to create a novel 3D model. However, most existing text/image/3D-to-3D methods struggle to effectively integrate multiple content sources, often resulting in inconsistent textures and inaccurate shapes. To overcome these challenges, we propose a straightforward yet powerful approach, category+3D-to-3D (C33D), for generating novel and structurally coherent 3D models. Our method…

Reinforcement Learning-Guided Chain-of-Draft for Token-Efficient Code Generation
Xunzhu Tang, Iyiola Emmanuel Olatunji, Tiezhu Sun, Jacques Klein, Tegawende F. Bissyande
https://arxiv.org/abs/2509.25243

Reinforcement Learning-Guided Chain-of-Draft for Token-Efficient Code Generation
LLMs demonstrate surface-level fluency in code generation but struggle with structured reasoning tasks requiring correctness and semantic alignment. While Chain-of-Thought (CoT) prompting enhances reasoning through intermediate steps, it suffers from verbosity and inefficiency. Chain-of-Draft (CoD) prompting offers more concise reasoning, but the stochastic nature of LLMs produces varying solution quality, making optimal selection challenging. We propose \multicod, a reinforcement learning fram…

RewardMap: Tackling Sparse Rewards in Fine-grained Visual Reasoning via Multi-Stage Reinforcement Learning
Sicheng Feng, Kaiwen Tuo, Song Wang, Lingdong Kong, Jianke Zhu, Huan Wang
https://arxiv.org/abs/2510.02240

RewardMap: Tackling Sparse Rewards in Fine-grained Visual Reasoning via Multi-Stage Reinforcement Learning
Fine-grained visual reasoning remains a core challenge for multimodal large language models (MLLMs). The recently introduced ReasonMap highlights this gap by showing that even advanced MLLMs struggle with spatial reasoning in structured and information-rich settings such as transit maps, a task of clear practical and scientific importance. However, standard reinforcement learning (RL) on such tasks is impeded by sparse rewards and unstable optimization. To address this, we first construct Reaso…

Think in Games: Learning to Reason in Games via Reinforcement Learning with Large Language Models
Yi Liao, Yu Gu, Yuan Sui, Zining Zhu, Yifan Lu, Guohua Tang, Zhongqian Sun, Wei Yang
https://arxiv.org/abs/2508.21365

Think in Games: Learning to Reason in Games via Reinforcement Learning with Large Language Models
Large language models (LLMs) excel at complex reasoning tasks such as mathematics and coding, yet they frequently struggle with simple interactive tasks that young children perform effortlessly. This discrepancy highlights a critical gap between declarative knowledge (knowing about something) and procedural knowledge (knowing how to do something). Although traditional reinforcement learning (RL) agents can acquire procedural knowledge through environmental interaction, they often operate as bla…

Feedback Forensics: A Toolkit to Measure AI Personality
Arduin Findeis, Timo Kaufmann, Eyke H\"ullermeier, Robert Mullins
https://arxiv.org/abs/2509.26305 https://

Feedback Forensics: A Toolkit to Measure AI Personality
Some traits making a "good" AI model are hard to describe upfront. For example, should responses be more polite or more casual? Such traits are sometimes summarized as model character or personality. Without a clear objective, conventional benchmarks based on automatic validation struggle to measure such traits. Evaluation methods using human feedback such as Chatbot Arena have emerged as a popular alternative. These methods infer "better" personality and other desirable traits implicitly by ra…

Decoupling Bidirectional Geometric Representations of 4D cost volume with 2D convolution
Xiaobao Wei, Changyong Shu, Zhaokun Yue, Chang Huang, Weiwei Liu, Shuai Yang, Lirong Yang, Peng Gao, Wenbin Zhang, Gaochao Zhu, Chengxiang Wang
https://arxiv.org/abs/2509.02415

Decoupling Bidirectional Geometric Representations of 4D cost volume with 2D convolution
High-performance real-time stereo matching methods invariably rely on 3D regularization of the cost volume, which is unfriendly to mobile devices. And 2D regularization based methods struggle in ill-posed regions. In this paper, we present a deployment-friendly 4D cost aggregation network DBStereo, which is based on pure 2D convolutions. Specifically, we first provide a thorough analysis of the decoupling characteristics of 4D cost volume. And design a lightweight bidirectional geometry aggrega…

Diversity-Incentivized Exploration for Versatile Reasoning
Zican Hu, Shilin Zhang, Yafu Li, Jianhao Yan, Xuyang Hu, Leyang Cui, Xiaoye Qu, Chunlin Chen, Yu Cheng, Zhi Wang
https://arxiv.org/abs/2509.26209

Diversity-Incentivized Exploration for Versatile Reasoning
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a crucial paradigm for incentivizing reasoning capabilities in Large Language Models (LLMs). Due to vast state-action spaces and reward sparsity in reasoning tasks, existing methods often struggle with deficient exploration and poor sample efficiency. In the paper, we propose \textbf{DIVER} (\textbf{D}iversity-\textbf{I}ncentivized Exploration for \textbf{V}ersatil\textbf{E} \textbf{R}easoning), an innovative framework that hi…

Accept or Deny? Evaluating LLM Fairness and Performance in Loan Approval across Table-to-Text Serialization Approaches
Israel Abebe Azime, Deborah D. Kanubala, Tejumade Afonja, Mario Fritz, Isabel Valera, Dietrich Klakow, Philipp Slusallek
https://arxiv.org/abs/2508.21512

Accept or Deny? Evaluating LLM Fairness and Performance in Loan Approval across Table-to-Text Serialization Approaches
Large Language Models (LLMs) are increasingly employed in high-stakes decision-making tasks, such as loan approvals. While their applications expand across domains, LLMs struggle to process tabular data, ensuring fairness and delivering reliable predictions. In this work, we assess the performance and fairness of LLMs on serialized loan approval datasets from three geographically distinct regions: Ghana, Germany, and the United States. Our evaluation focuses on the model's zero-shot and in-cont…

PyramidStyler: Transformer-Based Neural Style Transfer with Pyramidal Positional Encoding and Reinforcement Learning
Raahul Krishna Durairaju (California State University, Fullerton), K. Saruladha (Puducherry Technological University)
https://arxiv.org/abs/2510.01715

PyramidStyler: Transformer-Based Neural Style Transfer with Pyramidal Positional Encoding and Reinforcement Learning
Neural Style Transfer (NST) has evolved from Gatys et al.'s (2015) CNN-based algorithm, enabling AI-driven artistic image synthesis. However, existing CNN and transformer-based models struggle to scale efficiently to complex styles and high-resolution inputs. We introduce PyramidStyler, a transformer framework with Pyramidal Positional Encoding (PPE): a hierarchical, multi-scale encoding that captures both local details and global context while reducing computational load. We further incorporat…

Uncovering Overconfident Failures in CXR Models via Augmentation-Sensitivity Risk Scoring
Han-Jay Shu, Wei-Ning Chiu, Shun-Ting Chang, Meng-Ping Huang, Takeshi Tohyama, Ahram Han, Po-Chih Kuo
https://arxiv.org/abs/2510.01683

Uncovering Overconfident Failures in CXR Models via Augmentation-Sensitivity Risk Scoring
Deep learning models achieve strong performance in chest radiograph (CXR) interpretation, yet fairness and reliability concerns persist. Models often show uneven accuracy across patient subgroups, leading to hidden failures not reflected in aggregate metrics. Existing error detection approaches -- based on confidence calibration or out-of-distribution (OOD) detection -- struggle with subtle within-distribution errors, while image- and representation-level consistency-based methods remain undere…

GateMABSA: Aspect-Image Gated Fusion for Multimodal Aspect-based Sentiment Analysis
Adamu Lawan, Haruna Yunusa
https://arxiv.org/abs/2509.25037 https://arx…

GateMABSA: Aspect-Image Gated Fusion for Multimodal Aspect-based Sentiment Analysis
Aspect-based Sentiment Analysis (ABSA) has recently advanced into the multimodal domain, where user-generated content often combines text and images. However, existing multimodal ABSA (MABSA) models struggle to filter noisy visual signals, and effectively align aspects with opinion-bearing content across modalities. To address these challenges, we propose GateMABSA, a novel gated multimodal architecture that integrates syntactic, semantic, and fusion-aware mLSTM. Specifically, GateMABSA introdu…

Few-Shot Neuro-Symbolic Imitation Learning for Long-Horizon Planning and Acting
Pierrick Lorang, Hong Lu, Johannes Huemer, Patrik Zips, Matthias Scheutz
https://arxiv.org/abs/2508.21501

Few-Shot Neuro-Symbolic Imitation Learning for Long-Horizon Planning and Acting
Imitation learning enables intelligent systems to acquire complex behaviors with minimal supervision. However, existing methods often focus on short-horizon skills, require large datasets, and struggle to solve long-horizon tasks or generalize across task variations and distribution shifts. We propose a novel neuro-symbolic framework that jointly learns continuous control policies and symbolic domain abstractions from a few skill demonstrations. Our method abstracts high-level task structures i…

Igniting Creative Writing in Small Language Models: LLM-as-a-Judge versus Multi-Agent Refined Rewards
Xiaolong Wei, Bo Lu, Xingyu Zhang, Zhejun Zhao, Dongdong Shen, Long Xia, Dawei Yin
https://arxiv.org/abs/2508.21476

Igniting Creative Writing in Small Language Models: LLM-as-a-Judge versus Multi-Agent Refined Rewards
Large Language Models (LLMs) have demonstrated remarkable creative writing capabilities, yet their substantial computational demands hinder widespread use. Enhancing Small Language Models (SLMs) offers a promising alternative, but current methods like Supervised Fine-Tuning (SFT) struggle with novelty, and Reinforcement Learning from Human Feedback (RLHF) is costly. This paper explores two distinct AI-driven reward strategies within a Reinforcement Learning from AI Feedback (RLAIF) framework to…

OmniRetarget: Interaction-Preserving Data Generation for Humanoid Whole-Body Loco-Manipulation and Scene Interaction
Lujie Yang, Xiaoyu Huang, Zhen Wu, Angjoo Kanazawa, Pieter Abbeel, Carmelo Sferrazza, C. Karen Liu, Rocky Duan, Guanya Shi
https://arxiv.org/abs/2509.26633

OmniRetarget: Interaction-Preserving Data Generation for Humanoid Whole-Body Loco-Manipulation and Scene Interaction
A dominant paradigm for teaching humanoid robots complex skills is to retarget human motions as kinematic references to train reinforcement learning (RL) policies. However, existing retargeting pipelines often struggle with the significant embodiment gap between humans and robots, producing physically implausible artifacts like foot-skating and penetration. More importantly, common retargeting methods neglect the rich human-object and human-environment interactions essential for expressive loco…

PRISM: Progressive Rain removal with Integrated State-space Modeling
Pengze Xue, Shanwen Wang, Fei Zhou, Yan Cui, Xin Sun
https://arxiv.org/abs/2509.26413 https://

PRISM: Progressive Rain removal with Integrated State-space Modeling
Image deraining is an essential vision technique that removes rain streaks and water droplets, enhancing clarity for critical vision tasks like autonomous driving. However, current single-scale models struggle with fine-grained recovery and global consistency. To address this challenge, we propose Progressive Rain removal with Integrated State-space Modeling (PRISM), a progressive three-stage framework: Coarse Extraction Network (CENet), Frequency Fusion Network (SFNet), and Refine Network (RNe…

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation
Ronan Docherty, Antonis Vamvakeros, Samuel J. Cooper
https://arxiv.org/abs/2508.21529

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation
Feature foundation models - usually vision transformers - offer rich semantic descriptors of images, useful for downstream tasks such as (interactive) segmentation and object detection. For computational efficiency these descriptors are often patch-based, and so struggle to represent the fine features often present in micrographs; they also struggle with the large image sizes present in materials and biological image analysis. In this work, we train a convolutional neural network to upsample lo…

Mapping like a Skeptic: Probabilistic BEV Projection for Online HD Mapping
Fatih Erdo\u{g}an, Merve Rabia Bar{\i}n, Fatma G\"uney
https://arxiv.org/abs/2508.21689 https://

Mapping like a Skeptic: Probabilistic BEV Projection for Online HD Mapping
Constructing high-definition (HD) maps from sensory input requires accurately mapping the road elements in image space to the Bird's Eye View (BEV) space. The precision of this mapping directly impacts the quality of the final vectorized HD map. Existing HD mapping approaches outsource the projection to standard mapping techniques, such as attention-based ones. However, these methods struggle with accuracy due to generalization problems, often hallucinating non-existent road elements. Our key i…

Personalized Vision via Visual In-Context Learning
Yuxin Jiang, Yuchao Gu, Yiren Song, Ivor Tsang, Mike Zheng Shou
https://arxiv.org/abs/2509.25172 https://

Personalized Vision via Visual In-Context Learning
Modern vision models, trained on large-scale annotated datasets, excel at predefined tasks but struggle with personalized vision -- tasks defined at test time by users with customized objects or novel objectives. Existing personalization approaches rely on costly fine-tuning or synthetic data pipelines, which are inflexible and restricted to fixed task formats. Visual in-context learning (ICL) offers a promising alternative, yet prior methods confine to narrow, in-domain tasks and fail to gener…

CAD2DMD-SET: Synthetic Generation Tool of Digital Measurement Device CAD Model Datasets for fine-tuning Large Vision-Language Models
Jo\~ao Valente, Atabak Dehban, Rodrigo Ventura
https://arxiv.org/abs/2508.21732

CAD2DMD-SET: Synthetic Generation Tool of Digital Measurement Device CAD Model Datasets for fine-tuning Large Vision-Language Models
Recent advancements in Large Vision-Language Models (LVLMs) have demonstrated impressive capabilities across various multimodal tasks. They continue, however, to struggle with trivial scenarios such as reading values from Digital Measurement Devices (DMDs), particularly in real-world conditions involving clutter, occlusions, extreme viewpoints, and motion blur; common in head-mounted cameras and Augmented Reality (AR) applications. Motivated by these limitations, this work introduces CAD2DMD-SE…