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Managing Differentiated Secure Connectivity using Intents
Loay Abdelrazek, Filippo Rebecchi
https://arxiv.org/abs/2509.25462 https://arxiv.org/pdf/2509.254…

Managing Differentiated Secure Connectivity using Intents
Mobile networks in the 5G and 6G era require to rethink how to manage security due to the introduction of new services, use cases, each with its own security requirements, while simultaneously expanding the threat landscape. Although automation has emerged as a key enabler to address complexity in networks, existing approaches lack the expressiveness to define and enforce complex, goal-driven, and measurable security requirements. In this paper, we propose the concept of differentiated security…

BloomIntent: Automating Search Evaluation with LLM-Generated Fine-Grained User Intents
Yoonseo Choi, Eunhye Kim, Hyunwoo Kim, Donghyun Park, Honggu Lee, Jinyoung Kim, Juho Kim
https://arxiv.org/abs/2509.18641

BloomIntent: Automating Search Evaluation with LLM-Generated Fine-Grained User Intents
If 100 people issue the same search query, they may have 100 different goals. While existing work on user-centric AI evaluation highlights the importance of aligning systems with fine-grained user intents, current search evaluation methods struggle to represent and assess this diversity. We introduce BloomIntent, a user-centric search evaluation method that uses user intents as the evaluation unit. BloomIntent first generates a set of plausible, fine-grained search intents grounded on taxonomie…

Bridging Language Models and Formal Methods for Intent-Driven Optical Network Design
Anis Bekri, Amar Abane, Abdella Battou, Saddek Bensalem
https://arxiv.org/abs/2509.22834 htt…

Bridging Language Models and Formal Methods for Intent-Driven Optical Network Design
Intent-Based Networking (IBN) aims to simplify network management by enabling users to specify high-level goals that drive automated network design and configuration. However, translating informal natural-language intents into formally correct optical network topologies remains challenging due to inherent ambiguity and lack of rigor in Large Language Models (LLMs). To address this, we propose a novel hybrid pipeline that integrates LLM-based intent parsing, formal methods, and Optical Retrieval…

CoBel-World: Harnessing LLM Reasoning to Build a Collaborative Belief World for Optimizing Embodied Multi-Agent Collaboration
Zhimin Wang, Shaokang He, Duo Wu, Jinghe Wang, Linjia Kang, Jing Yu, Zhi Wang
https://arxiv.org/abs/2509.21981

CoBel-World: Harnessing LLM Reasoning to Build a Collaborative Belief World for Optimizing Embodied Multi-Agent Collaboration
Effective real-world multi-agent collaboration requires not only accurate planning but also the ability to reason about collaborators' intents -- a crucial capability for avoiding miscoordination and redundant communication under partial observable environments. Due to their strong planning and reasoning capabilities, large language models (LLMs) have emerged as promising autonomous agents for collaborative task solving. However, existing collaboration frameworks for LLMs overlook their reasoni…

Smart Contract Intent Detection with Pre-trained Programming Language Model
Youwei Huang, Jianwen Li, Sen Fang, Yao Li, Peng Yang, Bin Hu, Tao Zhang
https://arxiv.org/abs/2508.20086

Smart Contract Intent Detection with Pre-trained Programming Language Model
Malicious intent in smart contract development can lead to substantial economic losses. SmartIntentNN is a deep learning model specifically designed to identify unsafe intents in smart contracts. This model integrates the Universal Sentence Encoder, a K-means clustering-based intent highlighting mechanism, and a Bidirectional Long Short-Term Memory network for multi-label classification, achieving an F1 of 0.8633 in distinguishing ten different intent categories. In this study, we present an up…

BESPOKE: Benchmark for Search-Augmented Large Language Model Personalization via Diagnostic Feedback
Hyunseo Kim, Sangam Lee, Kwangwook Seo, Dongha Lee
https://arxiv.org/abs/2509.21106

BESPOKE: Benchmark for Search-Augmented Large Language Model Personalization via Diagnostic Feedback
Search-augmented large language models (LLMs) have advanced information-seeking tasks by integrating retrieval into generation, reducing users' cognitive burden compared to traditional search systems. Yet they remain insufficient for fully addressing diverse user needs, which requires recognizing how the same query can reflect different intents across users and delivering information in preferred forms. While recent systems such as ChatGPT and Gemini attempt personalization by leveraging user h…

Sources: Apple is testing upgraded App Intents with Uber, Temu, Amazon, YouTube, WhatsApp, and other apps to transform Siri into a true voice-first interface (Mark Gurman/Bloomberg)
https://www.bloomberg.com/news/newsletters

Automating Conflict-Aware ACL Configurations with Natural Language Intents
Wenlong Ding, Jianqiang Li, Zhixiong Niu, Huangxun Chen, Yongqiang Xiong, Hong Xu
https://arxiv.org/abs/2508.17990

Automating Conflict-Aware ACL Configurations with Natural Language Intents
ACL configuration is essential for managing network flow reachability, yet its complexity grows significantly with topologies and pre-existing rules. To carry out ACL configuration, the operator needs to (1) understand the new configuration policies or intents and translate them into concrete ACL rules, (2) check and resolve any conflicts between the new and existing rules, and (3) deploy them across the network. Existing systems rely heavily on manual efforts for these tasks, especially for th…

Cyber Physical Awareness via Intent-Driven Threat Assessment: Enhanced Space Networks with Intershell Links
Selen Gecgel Cetin, Tolga Ovatman, Gunes Karabulut Kurt
https://arxiv.org/abs/2508.16314

Cyber Physical Awareness via Intent-Driven Threat Assessment: Enhanced Space Networks with Intershell Links
This letter addresses essential aspects of threat assessment by proposing intent-driven threat models that incorporate both capabilities and intents. We propose a holistic framework for cyber physical awareness (CPA) in space networks, pointing out that analyzing reliability and security separately can lead to overfitting on system-specific criteria. We structure our proposed framework in three main steps. First, we suggest an algorithm that extracts characteristic properties of the received si…

RubikSQL: Lifelong Learning Agentic Knowledge Base as an Industrial NL2SQL System
Zui Chen, Han Li, Xinhao Zhang, Xiaoyu Chen, Chunyin Dong, Yifeng Wang, Xin Cai, Su Zhang, Ziqi Li, Chi Ding, Jinxu Li, Shuai Wang, Dousheng Zhao, Sanhai Gao, Guangyi Liu
https://arxiv.org/abs/2508.17590

RubikSQL: Lifelong Learning Agentic Knowledge Base as an Industrial NL2SQL System
We present RubikSQL, a novel NL2SQL system designed to address key challenges in real-world enterprise-level NL2SQL, such as implicit intents and domain-specific terminology. RubikSQL frames NL2SQL as a lifelong learning task, demanding both Knowledge Base (KB) maintenance and SQL generation. RubikSQL systematically builds and refines its KB through techniques including database profiling, structured information extraction, agentic rule mining, and Chain-of-Thought (CoT)-enhanced SQL profiling.…

Session-Based Recommendation with Validated and Enriched LLM Intents
Gyuseok Lee, Yaokun Liu, Yifan Liu, Susik Yoon, Dong Wang, SeongKu Kang
https://arxiv.org/abs/2508.00570 htt…

Session-Based Recommendation with Validated and Enriched LLM Intents
Session-based recommendation (SBR) aims to predict the next item for an anonymous user in a timely manner. However, SBR suffers from data sparsity due to the short and anonymous nature of sessions. Recently, an emerging line of work has explored inferring the underlying user intents of a session using large language models (LLMs), with the generated intents serving as auxiliary training signals to enhance SBR models. Despite its promise, this approach faces three key challenges: validating inte…

Most American electoral districts operate on a winner-take-all basis, especially for congressional and state legislative elections. This system, known formally as single-member plurality (first-past-the-post), means that the candidate who receives the most votes in a district wins the seat, and all other votes are essentially disregarded in the final result.
Gerrymandering is to all intents and purposes the inevitable consequence of lacking a democratic system with proportional represe…

Utilizing Vision-Language Models as Action Models for Intent Recognition and Assistance
Cesar Alan Contreras, Manolis Chiou, Alireza Rastegarpanah, Michal Szulik, Rustam Stolkin
https://arxiv.org/abs/2508.11093

Utilizing Vision-Language Models as Action Models for Intent Recognition and Assistance
Human-robot collaboration requires robots to quickly infer user intent, provide transparent reasoning, and assist users in achieving their goals. Our recent work introduced GUIDER, our framework for inferring navigation and manipulation intents. We propose augmenting GUIDER with a vision-language model (VLM) and a text-only language model (LLM) to form a semantic prior that filters objects and locations based on the mission prompt. A vision pipeline (YOLO for object detection and the Segment An…

MultiCOIN: Multi-Modal COntrollable Video INbetweening
Maham Tanveer, Yang Zhou, Simon Niklaus, Ali Mahdavi Amiri, Hao Zhang, Krishna Kumar Singh, Nanxuan Zhao
https://arxiv.org/abs/2510.08561

MultiCOIN: Multi-Modal COntrollable Video INbetweening
Video inbetweening creates smooth and natural transitions between two image frames, making it an indispensable tool for video editing and long-form video synthesis. Existing works in this domain are unable to generate large, complex, or intricate motions. In particular, they cannot accommodate the versatility of user intents and generally lack fine control over the details of intermediate frames, leading to misalignment with the creative mind. To fill these gaps, we introduce \modelname{}, a vi…

Changing the Paradigm from Dynamic Queries to LLM-generated SQL Queries with Human Intervention
Ambre Assor, Hyeon Jeon, Sungbok Shin, Jean-Daniel Fekete
https://arxiv.org/abs/2509.09461

Changing the Paradigm from Dynamic Queries to LLM-generated SQL Queries with Human Intervention
We propose leveraging Large Language Models (LLMs) as an interaction layer for medical visualization systems. In domains like healthcare, where users must navigate high-dimensional, coded, and heterogeneous datasets, LLM-generated queries enable expert medical users to express complex analytical intents in natural language. These intents are then translated into editable and executable queries, replacing the dynamic query interfaces used by traditional visualization systems built around sliders…

REG4Rec: Reasoning-Enhanced Generative Model for Large-Scale Recommendation Systems
Haibo Xing, Hao Deng, Yucheng Mao, Jinxin Hu, Yi Xu, Hao Zhang, Jiahao Wang, Shizhun Wang, Yu Zhang, Xiaoyi Zeng, Jing Zhang
https://arxiv.org/abs/2508.15308

REG4Rec: Reasoning-Enhanced Generative Model for Large-Scale Recommendation Systems
Sequential recommendation aims to predict a user's next action in large-scale recommender systems. While traditional methods often suffer from insufficient information interaction, recent generative recommendation models partially address this issue by directly generating item predictions. To better capture user intents, recent studies have introduced a reasoning process into generative recommendation, significantly improving recommendation performance. However, these approaches are constrained…

User-Intent-Driven Semantic Communication via Adaptive Deep Understanding
Peigen Ye, Jingpu Duan, Hongyang Du, Yulan Guo
https://arxiv.org/abs/2508.05884 https://

User-Intent-Driven Semantic Communication via Adaptive Deep Understanding
Semantic communication focuses on transmitting task-relevant semantic information, aiming for intent-oriented communication. While existing systems improve efficiency by extracting key semantics, they still fail to deeply understand and generalize users' real intentions. To overcome this, we propose a user-intention-driven semantic communication system that interprets diverse abstract intents. First, we integrate a multi-modal large model as semantic knowledge base to generate user-intention pr…

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…

AImoclips: A Benchmark for Evaluating Emotion Conveyance in Text-to-Music Generation
Gyehun Go, Satbyul Han, Ahyeon Choi, Eunjin Choi, Juhan Nam, Jeong Mi Park
https://arxiv.org/abs/2509.00813

AImoclips: A Benchmark for Evaluating Emotion Conveyance in Text-to-Music Generation
Recent advances in text-to-music (TTM) generation have enabled controllable and expressive music creation using natural language prompts. However, the emotional fidelity of TTM systems remains largely underexplored compared to human preference or text alignment. In this study, we introduce AImoclips, a benchmark for evaluating how well TTM systems convey intended emotions to human listeners, covering both open-source and commercial models. We selected 12 emotion intents spanning four quadrants …

LLM-Guided Semantic Relational Reasoning for Multimodal Intent Recognition
Qianrui Zhou, Hua Xu, Yifan Wang, Xinzhi Dong, Hanlei Zhang
https://arxiv.org/abs/2509.01337 https://

LLM-Guided Semantic Relational Reasoning for Multimodal Intent Recognition
Understanding human intents from multimodal signals is critical for analyzing human behaviors and enhancing human-machine interactions in real-world scenarios. However, existing methods exhibit limitations in their modality-level reliance, constraining relational reasoning over fine-grained semantics for complex intent understanding. This paper proposes a novel LLM-Guided Semantic Relational Reasoning (LGSRR) method, which harnesses the expansive knowledge of large language models (LLMs) to est…

An automatic patent literature retrieval system based on LLM-RAG
Yao Ding, Yuqing Wu, Ziyang Ding
https://arxiv.org/abs/2508.14064 https://arxiv.org/pdf/25…

An automatic patent literature retrieval system based on LLM-RAG
With the acceleration of technological innovation efficient retrieval and classification of patent literature have become essential for intellectual property management and enterprise RD Traditional keyword and rulebased retrieval methods often fail to address complex query intents or capture semantic associations across technical domains resulting in incomplete and lowrelevance results This study presents an automated patent retrieval framework integrating Large Language Models LLMs with Retri…

Quantifying Distributional Robustness of Agentic Tool-Selection
Jehyeok Yeon, Isha Chaudhary, Gagandeep Singh
https://arxiv.org/abs/2510.03992 https://arxi…

Quantifying Distributional Robustness of Agentic Tool-Selection
Large language models (LLMs) are increasingly deployed in agentic systems where they map user intents to relevant external tools to fulfill a task. A critical step in this process is tool selection, where a retriever first surfaces candidate tools from a larger pool, after which the LLM selects the most appropriate one. This pipeline presents an underexplored attack surface where errors in selection can lead to severe outcomes like unauthorized data access or denial of service, all without modi…

Dark Patterns Meet GUI Agents: LLM Agent Susceptibility to Manipulative Interfaces and the Role of Human Oversight
Jingyu Tang, Chaoran Chen, Jiawen Li, Zhiping Zhang, Bingcan Guo, Ibrahim Khalilov, Simret Araya Gebreegziabher, Bingsheng Yao, Dakuo Wang, Yanfang Ye, Tianshi Li, Ziang Xiao, Yaxing Yao, Toby Jia-Jun Li
https://arxiv.org/abs/…

Dark Patterns Meet GUI Agents: LLM Agent Susceptibility to Manipulative Interfaces and the Role of Human Oversight
The dark patterns, deceptive interface designs manipulating user behaviors, have been extensively studied for their effects on human decision-making and autonomy. Yet, with the rising prominence of LLM-powered GUI agents that automate tasks from high-level intents, understanding how dark patterns affect agents is increasingly important. We present a two-phase empirical study examining how agents, human participants, and human-AI teams respond to 16 types of dark patterns across diverse scenario…

Performance Evaluation of Intent-Based Networking Scenarios: A GitOps and Nephio Approach
Saptarshi Ghosh, Ioannis Mavromatis, Konstantinos Antonakoglou, Konstantinos Katsaros
https://arxiv.org/abs/2509.13901

Performance Evaluation of Intent-Based Networking Scenarios: A GitOps and Nephio Approach
GitOps has emerged as a foundational paradigm for managing cloud-native infrastructures by enabling declarative configuration, version-controlled state, and automated reconciliation between intents and runtime deployments. Despite its widespread adoption, the performance and scalability of GitOps tools in Intent-Based Networking (IBN) scenarios are insufficiently evaluated. This paper presents a reproducible, metric-driven benchmarking, assessing the latency and resource overheads of three wide…

Exploring Direct Instruction and Summary-Mediated Prompting in LLM-Assisted Code Modification
Ningzhi Tang, Emory Smith, Yu Huang, Collin McMillan, Toby Jia-Jun Li
https://arxiv.org/abs/2508.01523

Exploring Direct Instruction and Summary-Mediated Prompting in LLM-Assisted Code Modification
This paper presents a study of using large language models (LLMs) in modifying existing code. While LLMs for generating code have been widely studied, their role in code modification remains less understood. Although "prompting" serves as the primary interface for developers to communicate intents to LLMs, constructing effective prompts for code modification introduces challenges different from generation. Prior work suggests that natural language summaries may help scaffold this process, yet s…

The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network
Oluwaseyi Giwa, Michael Adewole, Tobi Awodumila, Pelumi Aderinto
https://arxiv.org/abs/2509.10478

The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network
The management of future AI-native Next-Generation (NextG) Radio Access Networks (RANs), including 6G and beyond, presents a challenge of immense complexity that exceeds the capabilities of traditional automation. In response, we introduce the concept of the LLM-RAN Operator. In this paradigm, a Large Language Model (LLM) is embedded into the RAN control loop to translate high-level human intents into optimal network actions. Unlike prior empirical studies, we present a formal framework for an …

Reasoning-enhanced Query Understanding through Decomposition and Interpretation
Yunfei Zhong, Jun Yang, Yixing Fan, Jiafeng Guo, Lixin Su, Maarten de Rijke, Ruqing Zhang, Dawei Yin, Xueqi Cheng
https://arxiv.org/abs/2509.06544

Reasoning-enhanced Query Understanding through Decomposition and Interpretation
Accurate inference of user intent is crucial for enhancing document retrieval in modern search engines. While large language models (LLMs) have made significant strides in this area, their effectiveness has predominantly been assessed with short, keyword-based queries. As AI-driven search evolves, long-form queries with intricate intents are becoming more prevalent, yet they remain underexplored in the context of LLM-based query understanding (QU). To bridge this gap, we introduce ReDI: a Reaso…

Implicit Search Intent Recognition using EEG and Eye Tracking: Novel Dataset and Cross-User Prediction
Mansi Sharma, Shuang Chen, Philipp M\"uller, Maurice Rekrut, Antonio Kr\"uger
https://arxiv.org/abs/2508.01860

Implicit Search Intent Recognition using EEG and Eye Tracking: Novel Dataset and Cross-User Prediction
For machines to effectively assist humans in challenging visual search tasks, they must differentiate whether a human is simply glancing into a scene (navigational intent) or searching for a target object (informational intent). Previous research proposed combining electroencephalography (EEG) and eye-tracking measurements to recognize such search intents implicitly, i.e., without explicit user input. However, the applicability of these approaches to real-world scenarios suffers from two key li…

Generative AI for Intent-Driven Network Management in 6G: A Case Study on Hierarchical Learning Approach
Md Arafat Habib, Medhat Elsayed, Yigit Ozcan, Pedro Enrique Iturria-Rivera, Majid Bavand, Melike Erol-Kantarci
https://arxiv.org/abs/2508.06616

Generative AI for Intent-Driven Network Management in 6G: A Case Study on Hierarchical Learning Approach
With the emergence of 6G, mobile networks are becoming increasingly heterogeneous and dynamic, necessitating advanced automation for efficient management. Intent-Driven Networks (IDNs) address this by translating high-level intents into optimization policies. Large Language Models (LLMs) can enhance this process by understanding complex human instructions to enable adaptive, intelligent automation. Given the rapid advancements in Generative AI (GenAI), a comprehensive survey of LLM-based IDN ar…

A Simple but Effective Elaborative Query Reformulation Approach for Natural Language Recommendation
Qianfeng Wen, Yifan Liu, Justin Cui, Joshua Zhang, Anton Korikov, George-Kirollos Saad, Scott Sanner
https://arxiv.org/abs/2510.02656

A Simple but Effective Elaborative Query Reformulation Approach for Natural Language Recommendation
Natural Language (NL) recommender systems aim to retrieve relevant items from free-form user queries and item descriptions. Existing systems often rely on dense retrieval (DR), which struggles to interpret challenging queries that express broad (e.g., "cities for youth friendly activities") or indirect (e.g., "cities for a high school graduation trip") user intents. While query reformulation (QR) has been widely adopted to improve such systems, existing QR methods tend to focus only on expandin…

Beyond Negative Transfer: Disentangled Preference-Guided Diffusion for Cross-Domain Sequential Recommendation
Xiaoxin Ye, Chengkai Huang, Hongtao Huang, Lina Yao
https://arxiv.org/abs/2509.00389

Beyond Negative Transfer: Disentangled Preference-Guided Diffusion for Cross-Domain Sequential Recommendation
Cross-Domain Sequential Recommendation (CDSR) leverages user behaviors across domains to enhance recommendation quality. However, naive aggregation of sequential signals can introduce conflicting domain-specific preferences, leading to negative transfer. While Sequential Recommendation (SR) already suffers from noisy behaviors such as misclicks and impulsive actions, CDSR further amplifies this issue due to domain heterogeneity arising from diverse item types and user intents. The core challeng…