26 papers · ranked by Valyu relevance
Maryam Amirizaniani, Benjamin Charles Germain Lee, Jevin West, Nicholas Weber
Effective personalized question answering (PQA) in language models requires grounding responses in the user's underlying intent, where intent refers to the implicit ``why'' behind a query beyond its explicit wording. However, existing approaches to intent-aware personalization rely on multi-turn conversational context…
Hamed Rahimi, Clemence Grislain, Adrien Jacquet Cretides, Olivier Sigaud + 1 more
Improving the effectiveness of human-robot interaction requires social robots to accurately infer human goals through robust intention understanding. This challenge is particularly critical in multimodal settings, where agents must integrate heterogeneous signals including text, visual cues to form a coherent…
Jiangnan Xia, Xuansheng Wu, Yu Yang, Xin Wang + 1 more
Intent-based recommender systems have gained significant attention for improving accuracy and interpretability by modeling the underlying motivations behind user behaviors. Most existing models derive intents directly from user sequences via clustering or prototype learning. However, they are sensitive to sequence…
Shasvat Desai, Hong Yao, Utkarsh Porwal, Kuang-chih Lee
Walmart holds the largest share of the U.S. ecommerce grocery market, where food and beverage categories generate some of the highest search traffic and, consequently, drive a substantial portion of sponsored search revenue. At this scale, even small mismatches between user intent and retrieved products can lead to…
Alan Lindsay, Andrés A. Ramírez-Duque, Bart Craenen, David A. Robb + 4 more
The task of supporting a human operator to understand generated plans, and to explore the plan space, are important problems in automated planning. In this work, we consider the problem of plan explainability and plan space exploration in underwater autonomous vehicle missions. In this context, concepts that are useful…
Cristina Gena
User modeling has traditionally relied on inferring preferences, traits, or intents from observable behaviour. While effective in many adaptive systems, this paradigm treats behaviour as the primary object of modeling and leaves mental-state attribution implicit. This assumption becomes limiting in socially situated…
Xiaojian Liu, Yangyang Zhang, Chee Wei Tan, Wenyi Zhang
Code coverage-guided unit test generation (CGTG) and large language model-based test generation (LLMTG) are two principal approaches for the generation of unit tests. Each of these approaches has its inherent advantages and drawbacks. Tests generated by CGTG have been shown to exhibit high code coverage and high…
Na Cui
Accurately distinguishing between impulsive and planned purchasing behavior remains a critical challenge in e-commerce analytics, given the multidimensional and dynamic nature of consumer data. Existing machine learning approaches often rely on conventional hyperparameter tuning strategies, which may limit model…
Yuanyuan Han, Juanjuan Zhai, Ping Li, Rongbin Yang
To address the low efficiency of feature mining and limited prediction accuracy in enterprise service user intent prediction, a research proposes an enterprise service user intention prediction model that integrates heuristic variants inspired by Kmeans++and Stacking ensemble learning. The model improves traditional…
Yanzhao Pan, Lea Rabe, Thorsten O Zander, Marius Klug
Virtual reality (VR) interaction remains largely dependent on explicit motor input, limiting seamless and adaptive interaction. This study investigated whether electroencephalography (EEG)-based passive brain–computer interfaces (BCIs), combined with eye gaze, can decode interaction intent directly from its underlying…
Pegah Safari, Mehrnoush Shamsfard, Ying Shen
Extracting user-specific profiles that include general personal information, such as hobbies, occupation, or age, is a valuable asset for systems like recommendation engines or personalized chatbots. Currently, most approaches moved toward exploiting the capabilities of large language models (LLMs) on rich languages…
Authors not listed
Realizing the promise of artificial intelligence (AI) to accelerate scientific progress and deliver technological impact depends on how effectively AI can be integrated into real-world decision- making processes. As Peter Norvig states, “Somewhat remarkably, almost all AI research until very recently has assumed that…
Ophelie Saussus, Pinhao Song, Sofie De Schrijver, Irene Caprara + 3 more
Continuous invasive brain–computer interfaces (BCIs) translate neural activity into continuous control signals. During ongoing control, fluctuations in these signals can reflect either transient execution noise or genuine changes in user intent, yet most BCI control systems do not explicitly distinguish between these…
Angélica Gutiérrez Cisneros, Alice Foucart, Angèle Brunellière
Over the past 30 years, there has been significant development in the understanding of the brain mechanisms underlying pragmatic processing. The primary purpose of the present review is to delve into the origins of neuropragmatics, defined as the study of the neural basis of pragmatic processing, tracing its…
Mario A. Cypko, Muhammad Agus Salim, Aditya Kumar, Leonard Berliner + 3 more
Purpose: Bayesian networks (BNs) are valuable for clinical decision support due to their transparency and interpretability. However, BN modelling requires considerable manual effort. This study explores how integrating large language models (LLMs) with retrieval-augmented generation (RAG) can improve BN modelling by…
Alexis Ellis, Stacie Severyn, Fjollë Novakazi, Hadi Banaee + 1 more
As human-machine teaming becomes central to paradigms like Industry 5.0, a critical need arises for machines to safely and effectively interpret complex human behaviors. A research gap currently exists between techno-centric robotic frameworks, which often lack nuanced models of human behavior, and descriptive…
Rebecca Danning, Zheng Tracy Ke, Rong Ma, Xihong Lin
Count data are ubiquitous across many applications in which understanding hidden patterns, or latent structure, is of interest. Topic modeling is a powerful tool for detecting latent structure in count data. However, standard topic modeling methods are often constrained by their restrictive assumptions, susceptible to…
Jhon G. Botello, Jose J. Padilla, Erika Frydenlund, Krzysztof Rechowicz + 1 more
Discovering simulation models for reuse remains a fundamental challenge in Modeling and Simulation (M&S). When many models coexist, identifying those that align with a given modeling intent remains difficult. Recent advances in Artificial Intelligence (AI), particularly retrieval-based approaches, offer a promising…
Saran Pankaew, Vincent Noel, Loic Paulevé, Denis Thieffry + 2 more
Boolean networks (BNs) have emerged as versatile tools for modeling cellular regulatory mechanisms due to their ability to capture key biological features despite their simplicity. Multiple BN synthesis methods, which aim to infer BNs with dynamics that corresponding to the experimental data, have emerged in a recent…
Luna Xingyu Li, Yue Zhang, Boris Aguilar, Tazein Shah + 2 more
Logical gene regulatory network (GRN) models provide interpretable, mechanistic representations of cellular regulation and are widely used in systems biology. However, most existing models remain incomplete, context-specific, and difficult to extend to comprehensive GRNs, limiting their broader applicability to tasks…
Yicheng Gao, Weixu Wang, Yuheng Zhao, Kejing Dong + 7 more
Decoding cellular systems requires integrating diverse omics data, yet most models are trained from scratch on a single modality, restricting generalization. Here we present CellHermes, a biological language model that leverages pretrained large language models (LLMs) to integrate multimodal forms of omics data, such…
M.A. Lieftinck, T. Verlaan, M.J.T. Reinders
Deep Neural Networks (DNNs) are renowned for their high accuracy and versatility, which has led to their application in many fields of research, including biology. However, this accuracy often comes at the expense of interpretability, making it challenging to reason about the inner workings of most DNNs. Particularly…
Authors not listed
Accurate modeling of drug concentration--time (C--t) profiles is central to pharmacokinetics (PK) and plays a critical role in both early-stage compound selection and late-stage individualized dosing. Traditional PK model offer mechanistic interpretability but often rely on rigid assumptions, extensive…
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High-entropy layered double hydroxides (HE-LDHs) have shown great potential in oxygen evolution reaction (OER) catalysis due to their tunable compositions and electronic structures. However, the synergistic effects between multiple vacancies, such as metal and oxygen vacancies, remain poorly understood and challenging…
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Early prediction of drug-induced organ toxicity remains a major bottleneck in drug discovery and clinical pharmacotherapy. Most data-driven toxicity models behave as endpoint predictors: they output a label but provide limited transparency about why a compound is risky or which evidence channel dominated the decision.…
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Quantitative Structure-Activity Relationship (QSAR) modeling is a pillar of computational drug discovery. However, standard machine learning (ML) models are often confounded by the high-dimensional and intensely correlated nature of molecular descriptors. A model may identify a "bulk" property (e.g., molecular weight)…