Tootfinder

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

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

XAgents: A Unified Framework for Multi-Agent Cooperation via IF-THEN Rules and Multipolar Task Processing Graph
Hailong Yang, Mingxian Gu, Jianqi Wang, Guanjin Wang, Zhaohong Deng
https://arxiv.org/abs/2509.10054

XAgents: A Unified Framework for Multi-Agent Cooperation via IF-THEN Rules and Multipolar Task Processing Graph
The rapid advancement of Large Language Models (LLMs) has significantly enhanced the capabilities of Multi-Agent Systems (MAS) in supporting humans with complex, real-world tasks. However, MAS still face challenges in effective task planning when handling highly complex tasks with uncertainty, often resulting in misleading or incorrect outputs that hinder task execution. To address this, we propose XAgents, a unified multi-agent cooperative framework built on a multipolar task processing graph …

A Task-Efficient Reinforcement Learning Task-Motion Planner for Safe Human-Robot Cooperation
Gaoyuan Liu, Joris de Winter, Kelly Merckaert, Denis Steckelmacher, Ann Nowe, Bram Vanderborght
https://arxiv.org/abs/2510.12477

A Task-Efficient Reinforcement Learning Task-Motion Planner for Safe Human-Robot Cooperation
In a Human-Robot Cooperation (HRC) environment, safety and efficiency are the two core properties to evaluate robot performance. However, safety mechanisms usually hinder task efficiency since human intervention will cause backup motions and goal failures of the robot. Frequent motion replanning will increase the computational load and the chance of failure. In this paper, we present a hybrid Reinforcement Learning (RL) planning framework which is comprised of an interactive motion planner and …

Task-Specific Dual-Model Framework for Comprehensive Traffic Safety Video Description and Analysis
Blessing Agyei Kyem, Neema Jakisa Owor, Andrews Danyo, Joshua Kofi Asamoah, Eugene Denteh, Tanner Muturi, Anthony Dontoh, Yaw Adu-Gyamfi, Armstrong Aboah
https://arxiv.org/abs/2510.11907

Task-Specific Dual-Model Framework for Comprehensive Traffic Safety Video Description and Analysis
Traffic safety analysis requires complex video understanding to capture fine-grained behavioral patterns and generate comprehensive descriptions for accident prevention. In this work, we present a unique dual-model framework that strategically utilizes the complementary strengths of VideoLLaMA and Qwen2.5-VL through task-specific optimization to address this issue. The core insight behind our approach is that separating training for captioning and visual question answering (VQA) tasks minimizes…

!MSA at BAREC Shared Task 2025: Ensembling Arabic Transformers for Readability Assessment
Mohamed Basem, Mohamed Younes, Seif Ahmed, Abdelrahman Moustafa
https://arxiv.org/abs/2509.10040

!MSA at BAREC Shared Task 2025: Ensembling Arabic Transformers for Readability Assessment
We present MSAs winning system for the BAREC 2025 Shared Task on fine-grained Arabic readability assessment, achieving first place in six of six tracks. Our approach is a confidence-weighted ensemble of four complementary transformer models (AraBERTv2, AraELECTRA, MARBERT, and CAMeLBERT) each fine-tuned with distinct loss functions to capture diverse readability signals. To tackle severe class imbalance and data scarcity, we applied weighted training, advanced preprocessing, SAMER corpus relabe…

Task-Agnostic Learnable Weighted-Knowledge Base Scheme for Robust Semantic Communications
Shiyao Jiang, Jian Jiao, Xingjian Zhang, Ye Wang, Dusit Niyato, Qinyu Zhang
https://arxiv.org/abs/2509.11636

Task-Agnostic Learnable Weighted-Knowledge Base Scheme for Robust Semantic Communications
With the emergence of diverse and massive data in the upcoming sixth-generation (6G) networks, the task-agnostic semantic communication system is regarded to provide robust intelligent services. In this paper, we propose a task-agnostic learnable weighted-knowledge base semantic communication (TALSC) framework for robust image transmission to address the real-world heterogeneous data bias in KB, including label flipping noise and class imbalance. The TALSC framework incorporates a sample confid…

Energy-Efficient Joint Offloading and Resource Allocation for Deadline-Constrained Tasks in Multi-Access Edge Computing
Chuanchao Gao, Arvind Easwaran
https://arxiv.org/abs/2509.11162

Energy-Efficient Joint Offloading and Resource Allocation for Deadline-Constrained Tasks in Multi-Access Edge Computing
This paper addresses the deadline-constrained task offloading and resource allocation problem in multi-access edge computing. We aim to determine where each task is offloaded and processed, as well as corresponding communication and computation resource allocations, to maximize the total saved energy for IoT devices, while considering task deadline and system resource constraints. Especially, our system allows each task to be offloaded to one of its accessible access points (APs) and processed …

Task-Aware Reduction for Scalable LLM-Database Systems
Marcus Emmanuel Barnes, Taher A. Ghaleb, Safwat Hassan
https://arxiv.org/abs/2510.11813 https://arxi…

Task-Aware Reduction for Scalable LLM-Database Systems
Large Language Models (LLMs) are increasingly applied to data-intensive workflows, from database querying to developer observability. Yet the effectiveness of these systems is constrained by the volume, verbosity, and noise of real-world text-rich data such as logs, telemetry, and monitoring streams. Feeding such data directly into LLMs is costly, environmentally unsustainable, and often misaligned with task objectives. Parallel efforts in LLM efficiency have focused on model- or architecture-l…

TASC: Task-Aware Shared Control for Teleoperated Manipulation
Ze Fu, Pinhao Song, Yutong Hu, Renaud Detry
https://arxiv.org/abs/2509.10416 https://arxiv.or…

TASC: Task-Aware Shared Control for Teleoperated Manipulation
We present TASC, a Task-Aware Shared Control framework for teleoperated manipulation that infers task-level user intent and provides assistance throughout the task. To support everyday tasks without predefined knowledge, TASC constructs an open-vocabulary interaction graph from visual input to represent functional object relationships, and infers user intent accordingly. A shared control policy then provides rotation assistance during both grasping and object interaction, guided by spatial cons…

Google announces Gemma 3 270M, a compact model designed for task-specific fine-tuning with strong capabilities in instruction following and text structuring (Google Developers Blog)
https://developers.googleblog.com/en/introducing-gemma-3-270m/

Introducing Gemma 3 270M: The compact model for hyper-efficient AI- Google Developers Blog
Explore Gemma 3 270M, a compact, energy-efficient AI model for task-specific fine-tuning, offering strong instruction-following and production-ready quantization.

MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control
Yuchen Zhu, Wei Guo, Jaemoo Choi, Guan-Horng Liu, Yongxin Chen, Molei Tao
https://arxiv.org/abs/2508.10684 h…

MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control
We study the problem of learning a neural sampler to generate samples from discrete state spaces where the target probability mass function $π\propto\mathrm{e}^{-U}$ is known up to a normalizing constant, which is an important task in fields such as statistical physics, machine learning, combinatorial optimization, etc. To better address this challenging task when the state space has a large cardinality and the distribution is multi-modal, we propose $\textbf{M}$asked $\textbf{D}$iffusion $\te…

M3ST-DTI: A multi-task learning model for drug-target interactions based on multi-modal features and multi-stage alignment
Xiangyu Li, Ran Su, Liangliang Liu
https://arxiv.org/abs/2510.12445

M3ST-DTI: A multi-task learning model for drug-target interactions based on multi-modal features and multi-stage alignment
Accurate prediction of drug-target interactions (DTI) is pivotal in drug discovery. However, existing approaches often fail to capture deep intra-modal feature interactions or achieve effective cross-modal alignment, limiting predictive performance and generalization. To address these challenges, we propose M3ST-DTI, a multi-task learning model that enables multi-stage integration and alignment of multi modal features for DTI prediction. M3ST-DTI incorporates three types of features-textual, st…

I should stop working quite so much but my latest task has engaged all the 'Puzzle! Must solve!' in me.

Using Medical Algorithms for Task-Oriented Dialogue in LLM-Based Medical Interviews
Rui Reis, Pedro Rangel Henriques, Jo\~ao Ferreira-Coimbra, Eva Oliveira, Nuno F. Rodrigues
https://arxiv.org/abs/2510.12490

Using Medical Algorithms for Task-Oriented Dialogue in LLM-Based Medical Interviews
We developed a task-oriented dialogue framework structured as a Directed Acyclic Graph (DAG) of medical questions. The system integrates: (1) a systematic pipeline for transforming medical algorithms and guidelines into a clinical question corpus; (2) a cold-start mechanism based on hierarchical clustering to generate efficient initial questioning without prior patient information; (3) an expand-and-prune mechanism enabling adaptive branching and backtracking based on patient responses; (4) a t…

UltraUPConvNet: A UPerNet- and ConvNeXt-Based Multi-Task Network for Ultrasound Tissue Segmentation and Disease Prediction
Zhi Chen
https://arxiv.org/abs/2509.11108 https://

UltraUPConvNet: A UPerNet- and ConvNeXt-Based Multi-Task Network for Ultrasound Tissue Segmentation and Disease Prediction
Ultrasound imaging is widely used in clinical practice due to its cost-effectiveness, mobility, and safety. However, current AI research often treats disease prediction and tissue segmentation as two separate tasks and their model requires substantial computational overhead. In such a situation, we introduce UltraUPConvNet, a computationally efficient universal framework designed for both ultrasound image classification and segmentation. Trained on a large-scale dataset containing more than 9,7…

Large Language Models Show Signs of Alignment with Human Neurocognition During Abstract Reasoning
Christopher Pinier, Sonia Acu\~na Vargas, Mariia Steeghs-Turchina, Dora Matzke, Claire E. Stevenson, Michael D. Nunez
https://arxiv.org/abs/2508.10057

Large Language Models Show Signs of Alignment with Human Neurocognition During Abstract Reasoning
This study investigates whether large language models (LLMs) mirror human neurocognition during abstract reasoning. We compared the performance and neural representations of human participants with those of eight open-source LLMs on an abstract-pattern-completion task. We leveraged pattern type differences in task performance and in fixation-related potentials (FRPs) as recorded by electroencephalography (EEG) during the task. Our findings indicate that only the largest tested LLMs (~70 billion…

Momentum-integrated Multi-task Stock Recommendation with Converge-based Optimization
Hao Wang, Jingshu Peng, Yanyan Shen, Xujia Li, Lei Chen
https://arxiv.org/abs/2509.10461 htt…

Momentum-integrated Multi-task Stock Recommendation with Converge-based Optimization
Stock recommendation is critical in Fintech applications, which use price series and alternative information to estimate future stock performance. Although deep learning models are prevalent in stock recommendation systems, traditional time-series forecasting training often fails to capture stock trends and rankings simultaneously, which are essential consideration factors for investors. To tackle this issue, we introduce a Multi-Task Learning (MTL) framework for stock recommendation, \textbf{M…

A Hierarchical Quantized Tokenization Framework for Task-Adaptive Graph Representation Learning
Yang Xiang, Li Fan, Chenke Yin, Chengtao Ji
https://arxiv.org/abs/2510.12369 http…

A Hierarchical Quantized Tokenization Framework for Task-Adaptive Graph Representation Learning
Recent progress in language and vision foundation models demonstrates the importance of discrete token interfaces that transform complex inputs into compact sequences for large-scale modeling. Extending this paradigm to graphs requires a tokenization scheme that handles non-Euclidean structures and multi-scale dependencies efficiently. Existing approaches to graph tokenization, linearized, continuous, and quantized, remain limited in adaptability and efficiency. In particular, most current quan…

A Deep Multi-Task Learning Approach to Impulsive Noise Parameter Estimation
Abdullahi Mohammad, Bdah Eya, Bassant Selim
https://arxiv.org/abs/2510.12179 https://

A Deep Multi-Task Learning Approach to Impulsive Noise Parameter Estimation
Impulsive noise poses a significant challenge to the reliability of wireless communication systems, necessitating accurate estimation of its statistical parameters for effective mitigation. This paper introduces a multitask learning (MTL) framework based on a CNN-LSTM architecture enhanced with an attention mechanism for the joint estimation of impulsive noise parameters. The proposed model leverages a unified weighted-loss function to enable simultaneous learning of multiple parameters within …

"Industrial society has collapsed. All semiconductor fabrication has ceased, society-wide electrification is no longer guaranteed. There is no longer any internet. Computing as it was once known in the early 21st century is impossible. You need a computer for a task. What do you do?"
#permacomputing

In case you missed it: SF is planning to axe its Bike Advisory Committee.
The task force also proposes removing confirmation for mayoral appointments (which is all appointments) to the transportation board. This would remove one of the few checks on a bike-hostile mayor, which... we currently have!

Streamlining Task Force Wants to Kill S.F.'s Bike Advisory Committee - Streetsblog San Francisco
It also recommends increasing the mayor's influence over the SFMTA Board by allowing him to fire directors

A Longitudinal Study on Different Annotator Feedback Loops in Complex RAG Tasks
Sara Rosenthal, Maeda Hanafi, Yannis Katsis, Lucian Popa, Marina Danilevsky
https://arxiv.org/abs/2510.11897

A Longitudinal Study on Different Annotator Feedback Loops in Complex RAG Tasks
Grounding conversations in existing passages, known as Retrieval-Augmented Generation (RAG), is an important aspect of Chat-Based Assistants powered by Large Language Models (LLMs) to ensure they are faithful and don't provide misinformation. Several benchmarks have been created to measure the performance of LLMs on this task. We present a longitudinal study comparing the feedback loop of an internal and external human annotator group for the complex annotation task of creating multi-turn RAG c…

MeTA-LoRA: Data-Efficient Multi-Task Fine-Tuning for Large Language Models
Bo Cheng, Xu Wang, Jinda Liu, Yi Chang, Yuan Wu
https://arxiv.org/abs/2510.11598 https://

MeTA-LoRA: Data-Efficient Multi-Task Fine-Tuning for Large Language Models
Low-Rank Adaptation (LoRA) has emerged as one of the most widely used parameter-efficient fine-tuning (PEFT) methods for adapting large language models (LLMs) to downstream tasks. While highly effective in single-task settings, it struggles to efficiently leverage inter-task knowledge in complex multi-task learning scenarios, often requiring substantial task-specific data to achieve optimal performance. To address this limitation, we introduce MeTA-LoRA, a two-stage optimization framework that …

“By 2040, the Locational scenario could reduce the total system cost by 23 – 59 Bn. EUR annually. These savings are in the order of magnitude of those associated with the integration of European electricity markets.”
https://publications.jrc.ec.europa.eu/repository/handle/J…

Locational Price Signals in Europe
As Europe strives to become the first climate neutral continent, grid congestion is becoming an increasingly urgent issue, with the potential to lead to large-scale curtailment of renewables and undermine the system’s security. Accordingly, the Mission Letter to Commissioner for Energy and Affordable Housing Dan Jørgensen lists the task to improve locational price signals. These aim to ensure that investments occur where they are most beneficial to the system, while incentivizing an efficien…

Reflection-Based Task Adaptation for Self-Improving VLA
Baicheng Li, Dong Wu, Zike Yan, Xinchen Liu, Zecui Zeng, Lusong Li, Hongbin Zha
https://arxiv.org/abs/2510.12710 https://…

Reflection-Based Task Adaptation for Self-Improving VLA
Pre-trained Vision-Language-Action (VLA) models represent a major leap towards general-purpose robots, yet efficiently adapting them to novel, specific tasks in-situ remains a significant hurdle. While reinforcement learning (RL) is a promising avenue for such adaptation, the process often suffers from low efficiency, hindering rapid task mastery. We introduce Reflective Self-Adaptation, a framework for rapid, autonomous task adaptation without human intervention. Our framework establishes a se…

🧑‍🤝‍🧑 Modular robots assemble into bridges, shelters and tools for outdoor tasks
#robots

Modular robots assemble into bridges, shelters and tools for outdoor tasks
Modular robots built by Dartmouth researchers are finding their feet outdoors. Engineered to assemble into structures that best suit the task at hand, the robots are pieced together from cube-shaped robotic blocks that combine rigid rods and soft, stretchy strings whose tension can be adjusted to deform the blocks and control their shape.

It's a life of ease
When every task is complete
Which never happens.
#dailyhaikuprompt - ease
#haiku
#poem

No Community Detection Method to Rule Them All!
Shrabani Ghosh, Erik Saule
https://arxiv.org/abs/2509.11490 https://arxiv.org/pdf/2509.11490

No Community Detection Method to Rule Them All!
Community detection is a core tool for analyzing large realworld graphs. It is often used to derive additional local features of vertices and edges that will be used to perform a downstream task, yet the impact of community detection on downstream tasks is poorly understood. Prior work largely evaluates community detection algorithms by their intrinsic objectives (e.g., modularity). Or they evaluate the impact of using community detection onto on the downstream task. But the impact of particula…

GAMMA: Generalizable Alignment via Multi-task and Manipulation-Augmented Training for AI-Generated Image Detection
Haozhen Yan, Yan Hong, Suning Lang, Jiahui Zhan, Yikun Ji, Yujie Gao, Jun Lan, Huijia Zhu, Weiqiang Wang, Jianfu Zhang
https://arxiv.org/abs/2509.10250

GAMMA: Generalizable Alignment via Multi-task and Manipulation-Augmented Training for AI-Generated Image Detection
With generative models becoming increasingly sophisticated and diverse, detecting AI-generated images has become increasingly challenging. While existing AI-genereted Image detectors achieve promising performance on in-distribution generated images, their generalization to unseen generative models remains limited. This limitation is largely attributed to their reliance on generation-specific artifacts, such as stylistic priors and compression patterns. To address these limitations, we propose G…

I think my biggest #adhd problem is task initiation. Not because tasks aren't urgent or uninteresting to me, but because of ✌🏻too many barriers✌🏻 in the way.
If I have to deal with an unclear work environment my momentum dies immediately.
Setting things up is a whole separate daily activity I have to account for in my life. If that's done and I have time I can bang out al…

Bears' D.J. Moore ready for 'daunting' task of working out of the backfield, Chicago WR sees role evolve

https://www.cbssports.com/nfl/news/bears-d…

Task Decoding based on Eye Movements using Synthetic Data Augmentation
Shanmuka Sadhu, Arca Baran, Preeti Pandey, Ayush Kumar
https://arxiv.org/abs/2509.11547 https://

Task Decoding based on Eye Movements using Synthetic Data Augmentation
Machine learning has been extensively used in various applications related to eye-tracking research. Understanding eye movement is one of the most significant subsets of eye-tracking research that reveals the scanning pattern of an individual. Researchers have thoroughly analyzed eye movement data to understand various eye-tracking applications, such as attention mechanisms, navigational behavior, task understanding, etc. The outcome of traditional machine learning algorithms used for decoding …

Does anyone else use a #Todo app to schedule things you'd like to research further?
I use #Todoist quite extensively for this. Whether it's a toot on Mastodon, an RSS item, or an article or web page I want to spend a little time with. I create a quick recurring task in Todoist and I set it to rec…

Learning-To-Measure: In-context Active Feature Acquisition
Yuta Kobayashi, Zilin Jing, Jiayu Yao, Hongseok Namkoong, Shalmali Joshi
https://arxiv.org/abs/2510.12624 https://

Learning-To-Measure: In-context Active Feature Acquisition
Active feature acquisition (AFA) is a sequential decision-making problem where the goal is to improve model performance for test instances by adaptively selecting which features to acquire. In practice, AFA methods often learn from retrospective data with systematic missingness in the features and limited task-specific labels. Most prior work addresses acquisition for a single predetermined task, limiting scalability. To address this limitation, we formalize the meta-AFA problem, where the goal…

OpenAI debuts GPT‑5-Codex, a version of GPT‑5 optimized for agentic coding in Codex and says it spends its "thinking" time more dynamically than previous models (Maxwell Zeff/TechCrunch)
https://techcrunch.com/2025/09/15/openai-upgrades-codex-wi…

OpenAI upgrades Codex with a new version of GPT-5 | TechCrunch
OpenAI's AI coding agent, Codex, can now spend anywhere from a few seconds to several hours on a task, thanks to a new, customized version of GPT-5.

PromptLocate: Localizing Prompt Injection Attacks
Yuqi Jia, Yupei Liu, Zedian Shao, Jinyuan Jia, Neil Gong
https://arxiv.org/abs/2510.12252 https://arxiv.o…

PromptLocate: Localizing Prompt Injection Attacks
Prompt injection attacks deceive a large language model into completing an attacker-specified task instead of its intended task by contaminating its input data with an injected prompt, which consists of injected instruction(s) and data. Localizing the injected prompt within contaminated data is crucial for post-attack forensic analysis and data recovery. Despite its growing importance, prompt injection localization remains largely unexplored. In this work, we bridge this gap by proposing Prompt…

AI Agents as Universal Task Solvers
Alessandro Achille, Stefano Soatto
https://arxiv.org/abs/2510.12066 https://arxiv.org/pdf/2510.12066

AI Agents as Universal Task Solvers
AI reasoning agents are already able to solve a variety of tasks by deploying tools, simulating outcomes of multiple hypotheses and reflecting on them. In doing so, they perform computation, although not in the classical sense -- there is no program being executed. Still, if they perform computation, can AI agents be universal? Can chain-of-thought reasoning solve any computable task? How does an AI Agent learn to reason? Is it a matter of model size? Or training dataset size? In this work, w…

GPU-Accelerated Algorithms for Process Mapping
Petr Samoldekin, Christian Schulz, Henning Woydt
https://arxiv.org/abs/2510.12196 https://arxiv.org/pdf/2510…

GPU-Accelerated Algorithms for Process Mapping
Process mapping asks to assign vertices of a task graph to processing elements of a supercomputer such that the computational workload is balanced while the communication cost is minimized. Motivated by the recent success of GPU-based graph partitioners, we propose two GPU-accelerated algorithms for this optimization problem. The first algorithm employs hierarchical multisection, which partitions the task graph alongside the hierarchy of the supercomputer. The method utilizes GPU-based graph pa…

MLM: Learning Multi-task Loco-Manipulation Whole-Body Control for Quadruped Robot with Arm
Xin Liu, Bida Ma, Chenkun Qi, Yan Ding, Zhaxizhuoma, Guorong Zhang, Pengan Chen, Kehui Liu, Zhongjie Jia, Chuyue Guan, Yule Mo, Jiaqi Liu, Feng Gao, Jiangwei Zhong, Bin Zhao, Xuelong Li
https://arxiv.org/abs/2508.10538…

MLM: Learning Multi-task Loco-Manipulation Whole-Body Control for Quadruped Robot with Arm
Whole-body loco-manipulation for quadruped robots with arm remains a challenging problem, particularly in achieving multi-task control. To address this, we propose MLM, a reinforcement learning framework driven by both real-world and simulation data. It enables a six-DoF robotic arm--equipped quadruped robot to perform whole-body loco-manipulation for multiple tasks autonomously or under human teleoperation. To address the problem of balancing multiple tasks during the learning of loco-manipula…

Learning Human Motion with Temporally Conditional Mamba
Quang Nguyen, Tri Le, Baoru Huang, Minh Nhat Vu, Ngan Le, Thieu Vo, Anh Nguyen
https://arxiv.org/abs/2510.12573 https://

Learning Human Motion with Temporally Conditional Mamba
Learning human motion based on a time-dependent input signal presents a challenging yet impactful task with various applications. The goal of this task is to generate or estimate human movement that consistently reflects the temporal patterns of conditioning inputs. Existing methods typically rely on cross-attention mechanisms to fuse the condition with motion. However, this approach primarily captures global interactions and struggles to maintain step-by-step temporal alignment. To address thi…

PledgeTracker: A System for Monitoring the Fulfilment of Pledges
Yulong Chen, Michael Sejr Schlichtkrull, Zhenyun Deng, David Corney, Nasim Asl, Joshua Salisbury, Andrew Dudfield, Andreas Vlachos
https://arxiv.org/abs/2509.11804

PledgeTracker: A System for Monitoring the Fulfilment of Pledges
Political pledges reflect candidates' policy commitments, but tracking their fulfilment requires reasoning over incremental evidence distributed across multiple, dynamically updated sources. Existing methods simplify this task into a document classification task, overlooking its dynamic, temporal and multi-document nature. To address this issue, we introduce \textsc{PledgeTracker}, a system that reformulates pledge verification into structured event timeline construction. PledgeTracker consists…

Personalized Product Search Ranking: A Multi-Task Learning Approach with Tabular and Non-Tabular Data
Lalitesh Morishetti, Abhay Kumar, Jonathan Scott, Kaushiki Nag, Gunjan Sharma, Shanu Vashishtha, Rahul Sridhar, Rohit Chatter, Kannan Achan
https://arxiv.org/abs/2508.09636

Personalized Product Search Ranking: A Multi-Task Learning Approach with Tabular and Non-Tabular Data
In this paper, we present a novel model architecture for optimizing personalized product search ranking using a multi-task learning (MTL) framework. Our approach uniquely integrates tabular and non-tabular data, leveraging a pre-trained TinyBERT model for semantic embeddings and a novel sampling technique to capture diverse customer behaviors. We evaluate our model against several baselines, including XGBoost, TabNet, FT-Transformer, DCN-V2, and MMoE, focusing on their ability to handle mixed d…

HiLWS: A Human-in-the-Loop Weak Supervision Framework for Curating Clinical and Home Video Data for Neurological Assessment
Atefeh Irani, Maryam S. Mirian, Alex Lassooij, Reshad Hosseini, Hadi Moradi, Martin J. McKeown
https://arxiv.org/abs/2509.10557

HiLWS: A Human-in-the-Loop Weak Supervision Framework for Curating Clinical and Home Video Data for Neurological Assessment
Video-based assessment of motor symptoms in conditions such as Parkinson's disease (PD) offers a scalable alternative to in-clinic evaluations, but home-recorded videos introduce significant challenges, including visual degradation, inconsistent task execution, annotation noise, and domain shifts. We present HiLWS, a cascaded human-in-the-loop weak supervision framework for curating and annotating hand motor task videos from both clinical and home settings. Unlike conventional single-stage weak…

Adapting and Evaluating Multimodal Large Language Models for Adolescent Idiopathic Scoliosis Self-Management: A Divide and Conquer Framework
Zhaolong Wu, Pu Luo, Jason Pui Yin Cheung, Teng Zhang
https://arxiv.org/abs/2509.11645

Adapting and Evaluating Multimodal Large Language Models for Adolescent Idiopathic Scoliosis Self-Management: A Divide and Conquer Framework
This study presents the first comprehensive evaluation of Multimodal Large Language Models (MLLMs) for Adolescent Idiopathic Scoliosis (AIS) self-management. We constructed a database of approximately 3,000 anteroposterior X-rays with diagnostic texts and evaluated five MLLMs through a `Divide and Conquer' framework consisting of a visual question-answering task, a domain knowledge assessment task, and a patient education counseling assessment task. Our investigation revealed limitations of MLL…

Query-Focused Extractive Summarization for Sentiment Explanation
Ahmed Moubtahij, Sylvie Ratt\'e, Yazid Attabi, Maxime Dumas
https://arxiv.org/abs/2509.11989 https://…

Query-Focused Extractive Summarization for Sentiment Explanation
Constructive analysis of feedback from clients often requires determining the cause of their sentiment from a substantial amount of text documents. To assist and improve the productivity of such endeavors, we leverage the task of Query-Focused Summarization (QFS). Models of this task are often impeded by the linguistic dissonance between the query and the source documents. We propose and substantiate a multi-bias framework to help bridge this gap at a domain-agnostic, generic level; we then for…

Learning Task Execution Hierarchies for Redundant Robots
Alessandro Adami, Aris Synodinos, Matteo Iovino, Ruggero Carli, Pietro Falco
https://arxiv.org/abs/2508.10780 https://…

Learning Task Execution Hierarchies for Redundant Robots
Modern robotic systems, such as mobile manipulators, humanoids, and aerial robots with arms, often possess high redundancy, enabling them to perform multiple tasks simultaneously. Managing this redundancy is key to achieving reliable and flexible behavior. A widely used approach is the Stack of Tasks (SoT), which organizes control objectives by priority within a unified framework. However, traditional SoTs are manually designed by experts, limiting their adaptability and accessibility. This pap…

BIBERT-Pipe on Biomedical Nested Named Entity Linking at BioASQ 2025
Chunyu Li, Xindi Zheng, Siqi Liu
https://arxiv.org/abs/2509.09725 https://arxiv.org/pd…

BIBERT-Pipe on Biomedical Nested Named Entity Linking at BioASQ 2025
Entity linking (EL) for biomedical text is typically benchmarked on English-only corpora with flat mentions, leaving the more realistic scenario of nested and multilingual mentions largely unexplored. We present our system for the BioNNE 2025 Multilingual Biomedical Nested Named Entity Linking shared task (English & Russian), closing this gap with a lightweight pipeline that keeps the original EL model intact and modifies only three task-aligned components: Two-stage retrieval-ranking. We lever…

Energy-Based Models for Predicting Mutational Effects on Proteins
Patrick Soga, Zhenyu Lei, Yinhan He, Camille Bilodeau, Jundong Li
https://arxiv.org/abs/2508.10629 https://

Energy-Based Models for Predicting Mutational Effects on Proteins
Predicting changes in binding free energy ($ΔΔG$) is a vital task in protein engineering and protein-protein interaction (PPI) engineering for drug discovery. Previous works have observed a high correlation between $ΔΔG$ and entropy, using probabilities of biologically important objects such as side chain angles and residue identities to estimate $ΔΔG$. However, estimating the full conformational distribution of a protein complex is generally considered intractable. In this work, we propo…

Maximal Adaptation, Minimal Guidance: Permissive Reactive Robot Task Planning with Humans in the Loop
Oz Gitelson, Satya Prakash Nayak, Ritam Raha, Anne-Kathrin Schmuck
https://arxiv.org/abs/2510.12662

Maximal Adaptation, Minimal Guidance: Permissive Reactive Robot Task Planning with Humans in the Loop
We present a novel framework for human-robot \emph{logical} interaction that enables robots to reliably satisfy (infinite horizon) temporal logic tasks while effectively collaborating with humans who pursue independent and unknown tasks. The framework combines two key capabilities: (i) \emph{maximal adaptation} enables the robot to adjust its strategy \emph{online} to exploit human behavior for cooperation whenever possible, and (ii) \emph{minimal tunable feedback} enables the robot to request …

Multimodal SAM-adapter for Semantic Segmentation
Iacopo Curti, Pierluigi Zama Ramirez, Alioscia Petrelli, Luigi Di Stefano
https://arxiv.org/abs/2509.10408 https://

Multimodal SAM-adapter for Semantic Segmentation
Semantic segmentation, a key task in computer vision with broad applications in autonomous driving, medical imaging, and robotics, has advanced substantially with deep learning. Nevertheless, current approaches remain vulnerable to challenging conditions such as poor lighting, occlusions, and adverse weather. To address these limitations, multimodal methods that integrate auxiliary sensor data (e.g., LiDAR, infrared) have recently emerged, providing complementary information that enhances robus…

Quantum deep reinforcement learning for humanoid robot navigation task
Romerik Lokossou, Birhanu Shimelis Girma, Ozan K. Tonguz, Ahmed Biyabani
https://arxiv.org/abs/2509.11388 …

Quantum deep reinforcement learning for humanoid robot navigation task
Classical reinforcement learning (RL) methods often struggle in complex, high-dimensional environments because of their extensive parameter requirements and challenges posed by stochastic, non-deterministic settings. This study introduces quantum deep reinforcement learning (QDRL) to train humanoid agents efficiently. While previous quantum RL models focused on smaller environments, such as wheeled robots and robotic arms, our work pioneers the application of QDRL to humanoid robotics, specific…

Enhancing Fairness in Autoencoders for Node-Level Graph Anomaly Detection
Shouju Wang, Yuchen Song, Sheng'en Li, Dongmian Zou
https://arxiv.org/abs/2508.10785 https://

Enhancing Fairness in Autoencoders for Node-Level Graph Anomaly Detection
Graph anomaly detection (GAD) has become an increasingly important task across various domains. With the rapid development of graph neural networks (GNNs), GAD methods have achieved significant performance improvements. However, fairness considerations in GAD remain largely underexplored. Indeed, GNN-based GAD models can inherit and amplify biases present in training data, potentially leading to unfair outcomes. While existing efforts have focused on developing fair GNNs, most approaches target…

Achieving Meaningful Collaboration: Worker-centered Design of a Physical Human-Robot Collaborative Blending Task
Nicky Mol, Luka Peternel, Alessandro Ianniello, Denis Zatyagov, Auke Nachenius, Stephan Balvert, J. Micah Prendergast, Sara Muscolo, Olger Siebinga, Eva Verhoef, Deborah Forster, David A. Abbink
https://arxiv.org/abs/2510.12340

Achieving Meaningful Collaboration: Worker-centered Design of a Physical Human-Robot Collaborative Blending Task
The use of robots in industrial settings continues to grow, driven by the need to address complex societal challenges such as labor shortages, aging populations, and ever-increasing production demands. In this abstract, we advocate for (and demonstrate) a transdisciplinary approach when considering robotics in the workplace. Transdisciplinarity emphasizes the integration of academic research with pragmatic expertise and embodied experiential knowledge, that prioritize values such as worker well…

Mask Consistency Regularization in Object Removal
Hua Yuan, Jin Yuan, Yicheng Jiang, Yao Zhang, Xin Geng, Yong Rui
https://arxiv.org/abs/2509.10259 https://

Mask Consistency Regularization in Object Removal
Object removal, a challenging task within image inpainting, involves seamlessly filling the removed region with content that matches the surrounding context. Despite advancements in diffusion models, current methods still face two critical challenges. The first is mask hallucination, where the model generates irrelevant or spurious content inside the masked region, and the second is mask-shape bias, where the model fills the masked area with an object that mimics the mask's shape rather than su…

SoK: Data Minimization in Machine Learning
Robin Staab, Nikola Jovanovi\'c, Kimberly Mai, Prakhar Ganesh, Martin Vechev, Ferdinando Fioretto, Matthew Jagielski
https://arxiv.org/abs/2508.10836

SoK: Data Minimization in Machine Learning
Data minimization (DM) describes the principle of collecting only the data strictly necessary for a given task. It is a foundational principle across major data protection regulations like GDPR and CPRA. Violations of this principle have substantial real-world consequences, with regulatory actions resulting in fines reaching hundreds of millions of dollars. Notably, the relevance of data minimization is particularly pronounced in machine learning (ML) applications, which typically rely on large…

Improving Generative Behavior Cloning via Self-Guidance and Adaptive Chunking
Junhyuk So, Chiwoong Lee, Shinyoung Lee, Jungseul Ok, Eunhyeok Park
https://arxiv.org/abs/2510.12392

Improving Generative Behavior Cloning via Self-Guidance and Adaptive Chunking
Generative Behavior Cloning (GBC) is a simple yet effective framework for robot learning, particularly in multi-task settings. Recent GBC methods often employ diffusion policies with open-loop (OL) control, where actions are generated via a diffusion process and executed in multi-step chunks without replanning. While this approach has demonstrated strong success rates and generalization, its inherent stochasticity can result in erroneous action sampling, occasionally leading to unexpected task …

Leveraging Multi-View Weak Supervision for Occlusion-Aware Multi-Human Parsing
Laura Bragagnolo, Matteo Terreran, Leonardo Barcellona, Stefano Ghidoni
https://arxiv.org/abs/2509.10093

Leveraging Multi-View Weak Supervision for Occlusion-Aware Multi-Human Parsing
Multi-human parsing is the task of segmenting human body parts while associating each part to the person it belongs to, combining instance-level and part-level information for fine-grained human understanding. In this work, we demonstrate that, while state-of-the-art approaches achieved notable results on public datasets, they struggle considerably in segmenting people with overlapping bodies. From the intuition that overlapping people may appear separated from a different point of view, we pro…

LyS at SemEval 2025 Task 8: Zero-Shot Code Generation for Tabular QA
Adri\'an Gude, Roi Santos-R\'ios, Francisco Prado-Vali\~no, Ana Ezquerro, Jes\'us Vilares
https://arxiv.org/abs/2508.09012

LyS at SemEval 2025 Task 8: Zero-Shot Code Generation for Tabular QA
This paper describes our participation in SemEval 2025 Task 8, focused on Tabular Question Answering. We developed a zero-shot pipeline that leverages an Large Language Model to generate functional code capable of extracting the relevant information from tabular data based on an input question. Our approach consists of a modular pipeline where the main code generator module is supported by additional components that identify the most relevant columns and analyze their data types to improve extr…

CAMNet: Leveraging Cooperative Awareness Messages for Vehicle Trajectory Prediction
Mattia Grasselli, Angelo Porrello, Carlo Augusto Grazia
https://arxiv.org/abs/2510.12703 http…

CAMNet: Leveraging Cooperative Awareness Messages for Vehicle Trajectory Prediction
Autonomous driving remains a challenging task, particularly due to safety concerns. Modern vehicles are typically equipped with expensive sensors such as LiDAR, cameras, and radars to reduce the risk of accidents. However, these sensors face inherent limitations: their field of view and line of sight can be obstructed by other vehicles, thereby reducing situational awareness. In this context, vehicle-to-vehicle communication plays a crucial role, as it enables cars to share information and rema…

Conditional Information Bottleneck for Multimodal Fusion: Overcoming Shortcut Learning in Sarcasm Detection
Yihua Wang, Qi Jia, Cong Xu, Feiyu Chen, Yuhan Liu, Haotian Zhang, Liang Jin, Lu Liu, Zhichun Wang
https://arxiv.org/abs/2508.10644

Conditional Information Bottleneck for Multimodal Fusion: Overcoming Shortcut Learning in Sarcasm Detection
Multimodal sarcasm detection is a complex task that requires distinguishing subtle complementary signals across modalities while filtering out irrelevant information. Many advanced methods rely on learning shortcuts from datasets rather than extracting intended sarcasm-related features. However, our experiments show that shortcut learning impairs the model's generalization in real-world scenarios. Furthermore, we reveal the weaknesses of current modality fusion strategies for multimodal sarcasm…

Inference-stage Adaptation-projection Strategy Adapts Diffusion Policy to Cross-manipulators Scenarios
Xiangtong Yao, Yirui Zhou, Yuan Meng, Yanwen Liu, Liangyu Dong, Zitao Zhang, Zhenshan Bing, Kai Huang, Fuchun Sun, Alois Knoll
https://arxiv.org/abs/2509.11621

Inference-stage Adaptation-projection Strategy Adapts Diffusion Policy to Cross-manipulators Scenarios
Diffusion policies are powerful visuomotor models for robotic manipulation, yet they often fail to generalize to manipulators or end-effectors unseen during training and struggle to accommodate new task requirements at inference time. Addressing this typically requires costly data recollection and policy retraining for each new hardware or task configuration. To overcome this, we introduce an adaptation-projection strategy that enables a diffusion policy to perform zero-shot adaptation to novel…

HoloGarment: 360{\deg} Novel View Synthesis of In-the-Wild Garments
Johanna Karras, Yingwei Li, Yasamin Jafarian, Ira Kemelmacher-Shlizerman
https://arxiv.org/abs/2509.12187 htt…

HoloGarment: 360° Novel View Synthesis of In-the-Wild Garments
Novel view synthesis (NVS) of in-the-wild garments is a challenging task due significant occlusions, complex human poses, and cloth deformations. Prior methods rely on synthetic 3D training data consisting of mostly unoccluded and static objects, leading to poor generalization on real-world clothing. In this paper, we propose HoloGarment (Hologram-Garment), a method that takes 1-3 images or a continuous video of a person wearing a garment and generates 360° novel views of the garment in a cano…

VideoAgent: Personalized Synthesis of Scientific Videos
Xiao Liang, Bangxin Li, Zixuan Chen, Hanyue Zheng, Zhi Ma, Di Wang, Cong Tian, Quan Wang
https://arxiv.org/abs/2509.11253

VideoAgent: Personalized Synthesis of Scientific Videos
Automating the generation of scientific videos is a crucial yet challenging task for effective knowledge dissemination. However, existing works on document automation primarily focus on static media such as posters and slides, lacking mechanisms for personalized dynamic orchestration and multimodal content synchronization. To address these challenges, we introduce VideoAgent, a novel multi-agent framework that synthesizes personalized scientific videos through a conversational interface. VideoA…

The Robustness of Differentiable Causal Discovery in Misspecified Scenarios
Huiyang Yi, Yanyan He, Duxin Chen, Mingyu Kang, He Wang, Wenwu Yu
https://arxiv.org/abs/2510.12503 ht…

The Robustness of Differentiable Causal Discovery in Misspecified Scenarios
Causal discovery aims to learn causal relationships between variables from targeted data, making it a fundamental task in machine learning. However, causal discovery algorithms often rely on unverifiable causal assumptions, which are usually difficult to satisfy in real-world data, thereby limiting the broad application of causal discovery in practical scenarios. Inspired by these considerations, this work extensively benchmarks the empirical performance of various mainstream causal discovery a…

A Semantic-Aware Framework for Safe and Intent-Integrative Assistance in Upper-Limb Exoskeletons
Yu Chen, Shu Miao, Chunyu Wu, Jingsong Mu, Bo OuYang, Xiang Li
https://arxiv.org/abs/2508.10378

A Semantic-Aware Framework for Safe and Intent-Integrative Assistance in Upper-Limb Exoskeletons
Upper-limb exoskeletons are primarily designed to provide assistive support by accurately interpreting and responding to human intentions. In home-care scenarios, exoskeletons are expected to adapt their assistive configurations based on the semantic information of the task, adjusting appropriately in accordance with the nature of the object being manipulated. However, existing solutions often lack the ability to understand task semantics or collaboratively plan actions with the user, limiting …

Large Language Models for Summarizing Czech Historical Documents and Beyond
V\'aclav Tran, Jakub \v{S}m\'id, Ji\v{r}\'i Mart\'inek, Ladislav Lenc, Pavel Kr\'al
https://arxiv.org/abs/2508.10368

Large Language Models for Summarizing Czech Historical Documents and Beyond
Text summarization is the task of shortening a larger body of text into a concise version while retaining its essential meaning and key information. While summarization has been significantly explored in English and other high-resource languages, Czech text summarization, particularly for historical documents, remains underexplored due to linguistic complexities and a scarcity of annotated datasets. Large language models such as Mistral and mT5 have demonstrated excellent results on many natura…

PanoTPS-Net: Panoramic Room Layout Estimation via Thin Plate Spline Transformation
Hatem Ibrahem, Ahmed Salem, Qinmin Vivian Hu, Guanghui Wang
https://arxiv.org/abs/2510.11992 h…

PanoTPS-Net: Panoramic Room Layout Estimation via Thin Plate Spline Transformation
Accurately estimating the 3D layout of rooms is a crucial task in computer vision, with potential applications in robotics, augmented reality, and interior design. This paper proposes a novel model, PanoTPS-Net, to estimate room layout from a single panorama image. Leveraging a Convolutional Neural Network (CNN) and incorporating a Thin Plate Spline (TPS) spatial transformation, the architecture of PanoTPS-Net is divided into two stages: First, a convolutional neural network extracts the high-l…

HiRef: Leveraging Hierarchical Ontology and Network Refinement for Robust Medication Recommendation
Yan Ting Chok, Soyon Park, Seungheun Baek, Hajung Kim, Junhyun Lee, Jaewoo Kang
https://arxiv.org/abs/2508.10425

HiRef: Leveraging Hierarchical Ontology and Network Refinement for Robust Medication Recommendation
Medication recommendation is a crucial task for assisting physicians in making timely decisions from longitudinal patient medical records. However, real-world EHR data present significant challenges due to the presence of rarely observed medical entities and incomplete records that may not fully capture the clinical ground truth. While data-driven models trained on longitudinal Electronic Health Records often achieve strong empirical performance, they struggle to generalize under missing or nov…

Technical Report: Facilitating the Adoption of Causal Inference Methods Through LLM-Empowered Co-Pilot
Jeroen Berrevoets, Julianna Piskorz, Robert Davis, Harry Amad, Jim Weatherall, Mihaela van der Schaar
https://arxiv.org/abs/2508.10581

Technical Report: Facilitating the Adoption of Causal Inference Methods Through LLM-Empowered Co-Pilot
Estimating treatment effects (TE) from observational data is a critical yet complex task in many fields, from healthcare and economics to public policy. While recent advances in machine learning and causal inference have produced powerful estimation techniques, their adoption remains limited due to the need for deep expertise in causal assumptions, adjustment strategies, and model selection. In this paper, we introduce CATE-B, an open-source co-pilot system that uses large language models (LLMs…

CoachMe: Decoding Sport Elements with a Reference-Based Coaching Instruction Generation Model
Wei-Hsin Yeh, Yu-An Su, Chih-Ning Chen, Yi-Hsueh Lin, Calvin Ku, Wen-Hsin Chiu, Min-Chun Hu, Lun-Wei Ku
https://arxiv.org/abs/2509.11698

CoachMe: Decoding Sport Elements with a Reference-Based Coaching Instruction Generation Model
Motion instruction is a crucial task that helps athletes refine their technique by analyzing movements and providing corrective guidance. Although recent advances in multimodal models have improved motion understanding, generating precise and sport-specific instruction remains challenging due to the highly domain-specific nature of sports and the need for informative guidance. We propose CoachMe, a reference-based model that analyzes the differences between a learner's motion and a reference un…

Open-ended Hierarchical Streaming Video Understanding with Vision Language Models
Hyolim Kang, Yunsu Park, Youngbeom Yoo, Yeeun Choi, Seon Joo Kim
https://arxiv.org/abs/2509.12145

Open-ended Hierarchical Streaming Video Understanding with Vision Language Models
We introduce Hierarchical Streaming Video Understanding, a task that combines online temporal action localization with free-form description generation. Given the scarcity of datasets with hierarchical and fine-grained temporal annotations, we demonstrate that LLMs can effectively group atomic actions into higher-level events, enriching existing datasets. We then propose OpenHOUSE (Open-ended Hierarchical Online Understanding System for Events), which extends streaming action perception beyond …

Evaluation of Black-Box XAI Approaches for Predictors of Values of Boolean Formulae
Stav Armoni-Friedmann, Hana Chockler, David A. Kelly
https://arxiv.org/abs/2509.09982 https:/…

Evaluation of Black-Box XAI Approaches for Predictors of Values of Boolean Formulae
Evaluating explainable AI (XAI) approaches is a challenging task in general, due to the subjectivity of explanations. In this paper, we focus on tabular data and the specific use case of AI models predicting the values of Boolean functions. We extend the previous work in this domain by proposing a formal and precise measure of importance of variables based on actual causality, and we evaluate state-of-the-art XAI tools against this measure. We also present a novel XAI tool B-ReX, based on the e…

FlyLoRA: Boosting Task Decoupling and Parameter Efficiency via Implicit Rank-Wise Mixture-of-Experts
Heming Zou, Yunliang Zang, Wutong Xu, Yao Zhu, Xiangyang Ji
https://arxiv.org/abs/2510.08396

FlyLoRA: Boosting Task Decoupling and Parameter Efficiency via Implicit Rank-Wise Mixture-of-Experts
Low-Rank Adaptation (LoRA) is a widely used parameter-efficient fine-tuning method for foundation models, but it suffers from parameter interference, resulting in suboptimal performance. Although Mixture-of-Experts (MoE)-based LoRA variants show promise in mitigating intra-task correlations in single-task instruction tuning, they introduce additional router parameters and remain ineffective in multi-task model merging where inter-task interference arises. Inspired by the fly olfactory circuit, …

Dynamic Span Interaction and Graph-Aware Memory for Entity-Level Sentiment Classification
Md. Mithun Hossain, Sanjara, Md. Shakil Hossain, Sudipto Chaki
https://arxiv.org/abs/2509.11604

Dynamic Span Interaction and Graph-Aware Memory for Entity-Level Sentiment Classification
Entity-level sentiment classification involves identifying the sentiment polarity linked to specific entities within text. This task poses several challenges: effectively modeling the subtle and complex interactions between entities and their surrounding sentiment expressions; capturing dependencies that may span across sentences; and ensuring consistent sentiment predictions for multiple mentions of the same entity through coreference resolution. Additionally, linguistic phenomena such as nega…

Exploring Efficient Open-Vocabulary Segmentation in the Remote Sensing
Bingyu Li, Haocheng Dong, Da Zhang, Zhiyuan Zhao, Junyu Gao, Xuelong Li
https://arxiv.org/abs/2509.12040 h…

Exploring Efficient Open-Vocabulary Segmentation in the Remote Sensing
Open-Vocabulary Remote Sensing Image Segmentation (OVRSIS), an emerging task that adapts Open-Vocabulary Segmentation (OVS) to the remote sensing (RS) domain, remains underexplored due to the absence of a unified evaluation benchmark and the domain gap between natural and RS images. To bridge these gaps, we first establish a standardized OVRSIS benchmark (\textbf{OVRSISBench}) based on widely-used RS segmentation datasets, enabling consistent evaluation across methods. Using this benchmark, we …

FIRESPARQL: A LLM-based Framework for SPARQL Query Generation over Scholarly Knowledge Graphs
Xueli Pan, Victor de Boer, Jacco van Ossenbruggen
https://arxiv.org/abs/2508.10467 …

FIRESPARQL: A LLM-based Framework for SPARQL Query Generation over Scholarly Knowledge Graphs
Question answering over Scholarly Knowledge Graphs (SKGs) remains a challenging task due to the complexity of scholarly content and the intricate structure of these graphs. Large Language Model (LLM) approaches could be used to translate natural language questions (NLQs) into SPARQL queries; however, these LLM-based approaches struggle with SPARQL query generation due to limited exposure to SKG-specific content and the underlying schema. We identified two main types of errors in the LLM-generat…

Think as a Doctor: An Interpretable AI Approach for ICU Mortality Prediction
Qingwen Li, Xiaohang Zhao, Xiao Han, Hailiang Huang, Lanjuan Liu
https://arxiv.org/abs/2510.11745 ht…

Think as a Doctor: An Interpretable AI Approach for ICU Mortality Prediction
Intensive Care Unit (ICU) mortality prediction, which estimates a patient's mortality status at discharge using EHRs collected early in an ICU admission, is vital in critical care. For this task, predictive accuracy alone is insufficient; interpretability is equally essential for building clinical trust and meeting regulatory standards, a topic that has attracted significant attention in information system research. Accordingly, an ideal solution should enable intrinsic interpretability and ali…

XplaiNLP at CheckThat! 2025: Multilingual Subjectivity Detection with Finetuned Transformers and Prompt-Based Inference with Large Language Models
Ariana Sahitaj, Jiaao Li, Pia Wenzel Neves, Fedor Splitt, Premtim Sahitaj, Charlott Jakob, Veronika Solopova, Vera Schmitt
https://arxiv.org/abs/2509.12130

XplaiNLP at CheckThat! 2025: Multilingual Subjectivity Detection with Finetuned Transformers and Prompt-Based Inference with Large Language Models
This notebook reports the XplaiNLP submission to the CheckThat! 2025 shared task on multilingual subjectivity detection. We evaluate two approaches: (1) supervised fine-tuning of transformer encoders, EuroBERT, XLM-RoBERTa, and German-BERT, on monolingual and machine-translated training data; and (2) zero-shot prompting using two LLMs: o3-mini for Annotation (rule-based labelling) and gpt-4.1-mini for DoubleDown (contrastive rewriting) and Perspective (comparative reasoning). The Annotation App…

On the Use of Hierarchical Vision Foundation Models for Low-Cost Human Mesh Recovery and Pose Estimation
Shuhei Tarashima, Yushan Wang, Norio Tagawa
https://arxiv.org/abs/2510.12660

On the Use of Hierarchical Vision Foundation Models for Low-Cost Human Mesh Recovery and Pose Estimation
In this work, we aim to develop simple and efficient models for human mesh recovery (HMR) and its predecessor task, human pose estimation (HPE). State-of-the-art HMR methods, such as HMR2.0 and its successors, rely on large, non-hierarchical vision transformers as encoders, which are inherited from the corresponding HPE models like ViTPose. To establish baselines across varying computational budgets, we first construct three lightweight HMR2.0 variants by adapting the corresponding ViTPose mode…

Uncertainty-Aware Tabular Prediction: Evaluating VBLL-Enhanced TabPFN in Safety-Critical Medical Data
Madhushan Ramalingam
https://arxiv.org/abs/2509.10048 https://

Uncertainty-Aware Tabular Prediction: Evaluating VBLL-Enhanced TabPFN in Safety-Critical Medical Data
Predictive models are being increasingly used across a wide range of domains, including safety-critical applications such as medical diagnosis and criminal justice. Reliable uncertainty estimation is a crucial task in such settings. Tabular Prior-data Fitted Network (TabPFN) is a recently proposed machine learning foundation model for tabular dataset, which uses a generative transformer architecture. Variational Bayesian Last Layers (VBLL) is a state-of-the-art lightweight variational formulati…

PricingLogic: Evaluating LLMs Reasoning on Complex Tourism Pricing Tasks
Yunuo Liu, Dawei Zhu, Zena Al-Khalili, Dai Cheng, Yanjun Chen, Dietrich Klakow, Wei Zhang, Xiaoyu Shen
https://arxiv.org/abs/2510.12409

PricingLogic: Evaluating LLMs Reasoning on Complex Tourism Pricing Tasks
We present PricingLogic, the first benchmark that probes whether Large Language Models(LLMs) can reliably automate tourism-related prices when multiple, overlapping fare rules apply. Travel agencies are eager to offload this error-prone task onto AI systems; however, deploying LLMs without verified reliability could result in significant financial losses and erode customer trust. PricingLogic comprises 300 natural-language questions based on booking requests derived from 42 real-world pricing p…

StyleDecipher: Robust and Explainable Detection of LLM-Generated Texts with Stylistic Analysis
Siyuan Li, Aodu Wulianghai, Xi Lin, Guangyan Li, Xiang Chen, Jun Wu, Jianhua Li
https://arxiv.org/abs/2510.12608

StyleDecipher: Robust and Explainable Detection of LLM-Generated Texts with Stylistic Analysis
With the increasing integration of large language models (LLMs) into open-domain writing, detecting machine-generated text has become a critical task for ensuring content authenticity and trust. Existing approaches rely on statistical discrepancies or model-specific heuristics to distinguish between LLM-generated and human-written text. However, these methods struggle in real-world scenarios due to limited generalization, vulnerability to paraphrasing, and lack of explainability, particularly w…

Medico 2025: Visual Question Answering for Gastrointestinal Imaging
Sushant Gautam, Vajira Thambawita, Michael Riegler, P{\aa}l Halvorsen, Steven Hicks
https://arxiv.org/abs/2508.10869

Medico 2025: Visual Question Answering for Gastrointestinal Imaging
The Medico 2025 challenge addresses Visual Question Answering (VQA) for Gastrointestinal (GI) imaging, organized as part of the MediaEval task series. The challenge focuses on developing Explainable Artificial Intelligence (XAI) models that answer clinically relevant questions based on GI endoscopy images while providing interpretable justifications aligned with medical reasoning. It introduces two subtasks: (1) answering diverse types of visual questions using the Kvasir-VQA-x1 dataset, and (2…

Stabilizing Long-term Multi-turn Reinforcement Learning with Gated Rewards
Zetian Sun, Dongfang Li, Zhuoen Chen, Yuhuai Qin, Baotian Hu
https://arxiv.org/abs/2508.10548 https://…

Stabilizing Long-term Multi-turn Reinforcement Learning with Gated Rewards
Reward sparsity in long-horizon reinforcement learning (RL) tasks remains a significant challenge, while existing outcome-based reward shaping struggles to define meaningful immediate rewards without introducing bias or requiring explicit task decomposition. Alternatively, verification-based reward shaping uses stepwise critics, but misalignment between immediate rewards and long-term objectives can lead to reward hacking and suboptimal policies. In this work, we address this problem in the con…

From <Answer> to <Think>: Multidimensional Supervision of Reasoning Process for LLM Optimization
Beining Wang, Weihang Su, Hongtao Tian, Tao Yang, Yujia Zhou, Ting Yao, Qingyao Ai, Yiqun Liu
https://arxiv.org/abs/2510.11457

From to : Multidimensional Supervision of Reasoning Process for LLM Optimization
Improving the multi-step reasoning ability of Large Language Models (LLMs) is a critical yet challenging task. The dominant paradigm, outcome-supervised reinforcement learning (RLVR), rewards only correct final answers, often propagating flawed reasoning and suffering from sparse reward signals. While process-level reward models (PRMs) provide denser, step-by-step feedback, they lack generalizability and interpretability, requiring task-specific segmentation of the reasoning process. To this en…

PRELUDE: A Benchmark Designed to Require Global Comprehension and Reasoning over Long Contexts
Mo Yu, Tsz Ting Chung, Chulun Zhou, Tong Li, Rui Lu, Jiangnan Li, Liyan Xu, Haoshu Lu, Ning Zhang, Jing Li, Jie Zhou
https://arxiv.org/abs/2508.09848

PRELUDE: A Benchmark Designed to Require Global Comprehension and Reasoning over Long Contexts
We introduce PRELUDE, a benchmark for evaluating long-context understanding through the task of determining whether a character's prequel story is consistent with the canonical narrative of the original book. Our task poses a stronger demand for global comprehension and deep reasoning than existing benchmarks -- as the prequels are not part of the original story, assessing their plausibility typically requires searching and integrating information that is only indirectly related. Empirically, 8…

A Stochastic Birth-and-Death Approach for Street Furniture Geolocation in Urban Environments
Evan Murphy, Marco Viola, Vladimir A. Krylov
https://arxiv.org/abs/2509.10310 https:…

A Stochastic Birth-and-Death Approach for Street Furniture Geolocation in Urban Environments
In this paper we address the problem of precise geolocation of street furniture in complex urban environments, which is a critical task for effective monitoring and maintenance of public infrastructure by local authorities and private stakeholders. To this end, we propose a probabilistic framework based on energy maps that encode the spatial likelihood of object locations. Representing the energy in a map-based geopositioned format allows the optimisation process to seamlessly integrate externa…

Foundational theory for optimal decision tree problems. II. Optimal hypersurface decision tree algorithm
Xi He
https://arxiv.org/abs/2509.12057 https://arx…

Foundational theory for optimal decision tree problems. II. Optimal hypersurface decision tree algorithm
Decision trees are a ubiquitous model for classification and regression tasks due to their interpretability and efficiency. However, solving the optimal decision tree (ODT) problem remains a challenging combinatorial optimization task. Even for the simplest splitting rules--axis-parallel hyperplanes--it is NP-hard to optimize. In Part I of this series, we rigorously defined the proper decision tree model through four axioms and, based on these, introduced four formal definitions of the ODT prob…

Abduct, Act, Predict: Scaffolding Causal Inference for Automated Failure Attribution in Multi-Agent Systems
Alva West, Yixuan Weng, Minjun Zhu, Zhen Lin, Yue Zhang
https://arxiv.org/abs/2509.10401

Abduct, Act, Predict: Scaffolding Causal Inference for Automated Failure Attribution in Multi-Agent Systems
Failure attribution in multi-agent systems -- pinpointing the exact step where a decisive error occurs -- is a critical yet unsolved challenge. Current methods treat this as a pattern recognition task over long conversation logs, leading to critically low step-level accuracy (below 17\%), which renders them impractical for debugging complex systems. Their core weakness is a fundamental inability to perform robust counterfactual reasoning: to determine if correcting a single action would have ac…

Beating Harmful Stereotypes Through Facts: RAG-based Counter-speech Generation
Greta Damo, Elena Cabrio, Serena Villata
https://arxiv.org/abs/2510.12316 https://

Beating Harmful Stereotypes Through Facts: RAG-based Counter-speech Generation
Counter-speech generation is at the core of many expert activities, such as fact-checking and hate speech, to counter harmful content. Yet, existing work treats counter-speech generation as pure text generation task, mainly based on Large Language Models or NGO experts. These approaches show severe drawbacks due to the limited reliability and coherence in the generated countering text, and in scalability, respectively. To close this gap, we introduce a novel framework to model counter-speech ge…

Detect Anything via Next Point Prediction
Qing Jiang, Junan Huo, Xingyu Chen, Yuda Xiong, Zhaoyang Zeng, Yihao Chen, Tianhe Ren, Junzhi Yu, Lei Zhang
https://arxiv.org/abs/2510.12798

Detect Anything via Next Point Prediction
Object detection has long been dominated by traditional coordinate regression-based models, such as YOLO, DETR, and Grounding DINO. Although recent efforts have attempted to leverage MLLMs to tackle this task, they face challenges like low recall rate, duplicate predictions, coordinate misalignment, etc. In this work, we bridge this gap and propose Rex-Omni, a 3B-scale MLLM that achieves state-of-the-art object perception performance. On benchmarks like COCO and LVIS, Rex-Omni attains performan…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/6]:
- Personalized Product Search Ranking: A Multi-Task Learning Approach with Tabular and Non-Tabular ...
Morishetti, Kumar, Scott, Nag, Sharma, Vashishtha, Sridhar, Chatter, Achan

BenchECG and xECG: a benchmark and baseline for ECG foundation models
Riccardo Lunelli, Angus Nicolson, Samuel Martin Pr\"oll, Sebastian Johannes Reinstadler, Axel Bauer, Clemens Dlaska
https://arxiv.org/abs/2509.10151

BenchECG and xECG: a benchmark and baseline for ECG foundation models
Electrocardiograms (ECGs) are inexpensive, widely used, and well-suited to deep learning. Recently, interest has grown in developing foundation models for ECGs - models that generalise across diverse downstream tasks. However, consistent evaluation has been lacking: prior work often uses narrow task selections and inconsistent datasets, hindering fair comparison. Here, we introduce BenchECG, a standardised benchmark comprising a comprehensive suite of publicly available ECG datasets and versati…

Progressive Flow-inspired Unfolding for Spectral Compressive Imaging
Xiaodong Wang, Ping Wang, Zijun He, Mengjie Qin, Xin Yuan
https://arxiv.org/abs/2509.12079 https://

Progressive Flow-inspired Unfolding for Spectral Compressive Imaging
Coded aperture snapshot spectral imaging (CASSI) retrieves a 3D hyperspectral image (HSI) from a single 2D compressed measurement, which is a highly challenging reconstruction task. Recent deep unfolding networks (DUNs), empowered by explicit data-fidelity updates and implicit deep denoisers, have achieved the state of the art in CASSI reconstruction. However, existing unfolding approaches suffer from uncontrollable reconstruction trajectories, leading to abrupt quality jumps and non-gradual re…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[8/11]:
- Framing AI System Benchmarking as a Learning Task: FlexBench and the Open MLPerf Dataset
Grigori Fursin, Daniel Altunay

GNN-based Unified Deep Learning
Furkan Pala, Islem Rekik
https://arxiv.org/abs/2508.10583 https://arxiv.org/pdf/2508.10583…

GNN-based Unified Deep Learning
Deep learning models often struggle to maintain generalizability in medical imaging, particularly under domain-fracture scenarios where distribution shifts arise from varying imaging techniques, acquisition protocols, patient populations, demographics, and equipment. In practice, each hospital may need to train distinct models - differing in learning task, width, and depth - to match local data. For example, one hospital may use Euclidean architectures such as MLPs and CNNs for tabular or grid-…

Projected Coupled Diffusion for Test-Time Constrained Joint Generation
Hao Luan, Yi Xian Goh, See-Kiong Ng, Chun Kai Ling
https://arxiv.org/abs/2508.10531 https://

Projected Coupled Diffusion for Test-Time Constrained Joint Generation
Modifications to test-time sampling have emerged as an important extension to diffusion algorithms, with the goal of biasing the generative process to achieve a given objective without having to retrain the entire diffusion model. However, generating jointly correlated samples from multiple pre-trained diffusion models while simultaneously enforcing task-specific constraints without costly retraining has remained challenging. To this end, we propose Projected Coupled Diffusion (PCD), a novel te…

Promoting Efficient Reasoning with Verifiable Stepwise Reward
Chuhuai Yue, Chengqi Dong, Yinan Gao, Hang He, Jiajun Chai, Guojun Yin, Wei Lin
https://arxiv.org/abs/2508.10293 ht…

Promoting Efficient Reasoning with Verifiable Stepwise Reward
Large reasoning models (LRMs) have recently achieved significant progress in complex reasoning tasks, aided by reinforcement learning with verifiable rewards. However, LRMs often suffer from overthinking, expending excessive computation on simple problems and reducing efficiency. Existing efficient reasoning methods typically require accurate task assessment to preset token budgets or select reasoning modes, which limits their flexibility and reliability. In this work, we revisit the essence of…

Multitask finetuning and acceleration of chemical pretrained models for small molecule drug property prediction
Matthew Adrian, Yunsie Chung, Kevin Boyd, Saee Paliwal, Srimukh Prasad Veccham, Alan C. Cheng
https://arxiv.org/abs/2510.12719

Multitask finetuning and acceleration of chemical pretrained models for small molecule drug property prediction
Chemical pretrained models, sometimes referred to as foundation models, are receiving considerable interest for drug discovery applications. The general chemical knowledge extracted from self-supervised training has the potential to improve predictions for critical drug discovery endpoints, including on-target potency and ADMET properties. Multi-task learning has previously been successfully leveraged to improve predictive models. Here, we show that enabling multitasking in finetuning of chemic…

All that structure matches does not glitter
Maya M. Martirossyan, Thomas Egg, Philipp Hoellmer, George Karypis, Mark Transtrum, Adrian Roitberg, Mingjie Liu, Richard G. Hennig, Ellad B. Tadmor, Stefano Martiniani
https://arxiv.org/abs/2509.12178

All that structure matches does not glitter
Generative models for materials, especially inorganic crystals, hold potential to transform the theoretical prediction of novel compounds and structures. Advancement in this field depends critically on robust benchmarks and minimal, information-rich datasets that enable meaningful model evaluation. This paper critically examines common datasets and reported metrics for a crystal structure prediction task$\unicode{x2014}$generating the most likely structures given the chemical composition of a m…