Tootfinder

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

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…

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…

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…

IntentFlow: Interactive Support for Communicating Intent with LLMs in Writing Tasks
Yoonsu Kim, Brandon Chin, Kihoon Son, Seoyoung Kim, Juho Kim
https://arxiv.org/abs/2507.22134

IntentFlow: Interactive Support for Communicating Intent with LLMs in Writing Tasks
While large language models (LLMs) are widely used for writing, users often struggle to express their nuanced and evolving intents through prompt-based interfaces. Intents -- low-level strategies or preferences for achieving a writing goal -- are often vague, fluid, or even subconscious, making it difficult for users to articulate and adjust them. To address this, we present IntentFlow, which supports the communication of dynamically evolving intents throughout LLM-assisted writing. IntentFlow …

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…

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…

Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models
Huihan Liu, Rutav Shah, Shuijing Liu, Jack Pittenger, Mingyo Seo, Yuchen Cui, Yonatan Bisk, Roberto Mart\'in-Mart\'in, Yuke Zhu
https://arxiv.org/abs/2506.14727

Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models
Assistive teleoperation, where control is shared between a human and a robot, enables efficient and intuitive human-robot collaboration in diverse and unstructured environments. A central challenge in real-world assistive teleoperation is for the robot to infer a wide range of human intentions from user control inputs and to assist users with correct actions. Existing methods are either confined to simple, predefined scenarios or restricted to task-specific data distributions at training, limit…

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 …

ChartMark: A Structured Grammar for Chart Annotation
Yiyu Chen, Yifan Wu, Shuyu Shen, Yupeng Xie, Leixian Shen, Hui Xiong, Yuyu Luo
https://arxiv.org/abs/2507.21810 https://

ChartMark: A Structured Grammar for Chart Annotation
Chart annotations enhance visualization accessibility but suffer from fragmented, non-standardized representations that limit cross-platform reuse. We propose ChartMark, a structured grammar that separates annotation semantics from visualization implementations. ChartMark features a hierarchical framework mapping onto annotation dimensions (e.g., task, chart context), supporting both abstract intents and precise visual details. Our toolkit demonstrates converting ChartMark specifications into V…

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…

Horus: A Protocol for Trustless Delegation Under Uncertainty
David Shi, Kevin Joo
https://arxiv.org/abs/2507.00631 https://arxiv.org/…

Horus: A Protocol for Trustless Delegation Under Uncertainty
Correctness is an emergent property of systems where exposing error is cheaper than committing it. In dynamic, low-trust environments, autonomous AI agents benefit from delegating work to sub-agents, yet correctness cannot be assured through upfront specification or centralized oversight. We propose a protocol that enforces correctness through collateralized claims in a recursive verification game. Tasks are published as intents, and solvers compete to fulfill them. Selected solvers carry out t…

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…

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…

Reading how many leftists suddenly praise the mullah regime for its peacefulness and good intents while condemning the israeli airstrikes on Iran, because, y'know, it's Israel, gives me a sense why many Israelis voted for a right-wing extremist.
*Screaming in the void*

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…

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.…

Toward an Intent-Based and Ontology-Driven Autonomic Security Response in Security Orchestration Automation and Response
Zequan Huang, Jacques Robin, Nicolas Herbaut, Nourh\`ene Ben Rabah, B\'en\'edicte Le Grand
https://arxiv.org/abs/2507.12061

Toward an Intent-Based and Ontology-Driven Autonomic Security Response in Security Orchestration Automation and Response
Modern Security Orchestration, Automation, and Response (SOAR) platforms must rapidly adapt to continuously evolving cyber attacks. Intent-Based Networking has emerged as a promising paradigm for cyber attack mitigation through high-level declarative intents, which offer greater flexibility and persistency than procedural actions. In this paper, we bridge the gap between two active research directions: Intent-Based Cyber Defense and Autonomic Cyber Defense, by proposing a unified, ontology-driv…

Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation
Guanting Dong, Xiaoxi Li, Yuyao Zhang, Mengjie Deng
https://arxiv.org/abs/2506.21384

Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation
Real-world live retrieval-augmented generation (RAG) systems face significant challenges when processing user queries that are often noisy, ambiguous, and contain multiple intents. While RAG enhances large language models (LLMs) with external knowledge, current systems typically struggle with such complex inputs, as they are often trained or evaluated on cleaner data. This paper introduces Omni-RAG, a novel framework designed to improve the robustness and effectiveness of RAG systems in live, o…

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…

IdeaBlocks: Expressing and Reusing Exploratory Intents for Design Exploration with Generative AI
DaEun Choi, Kihoon Son, Jaesang Yu, Hyunjoon Jung, Juho Kim
https://arxiv.org/abs/2507.22163

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…

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…

CORMO-RAN: Lossless Migration of xApps in O-RAN
Antonio Calagna, Stefano Maxenti, Leonardo Bonati, Salvatore D'Oro, Tommaso Melodia, Carla Fabiana Chiasserini
https://arxiv.org/abs/2506.19760

CORMO-RAN: Lossless Migration of xApps in O-RAN
Open Radio Access Network (RAN) is a key paradigm to attain unprecedented flexibility of the RAN via disaggregation and Artificial Intelligence (AI)-based applications called xApps. In dense areas with many active RAN nodes, compute resources are engineered to support potentially hundreds of xApps monitoring and controlling the RAN to achieve operator's intents. However, such resources might become underutilized during low-traffic periods, where most cells are sleeping and, given the reduced RA…

A Third Paradigm for LLM Evaluation: Dialogue Game-Based Evaluation using clembench
David Schlangen, Sherzod Hakimov, Jonathan Jordan, Philipp Sadler
https://arxiv.org/abs/2507.08491

A Third Paradigm for LLM Evaluation: Dialogue Game-Based Evaluation using clembench
There are currently two main paradigms for evaluating large language models (LLMs), reference-based evaluation and preference-based evaluation. The first, carried over from the evaluation of machine learning models in general, relies on pre-defined task instances, for which reference task executions are available. The second, best exemplified by the LM-arena, relies on (often self-selected) users bringing their own intents to a site that routes these to several models in parallel, among whose r…

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…

NetIntent: Leveraging Large Language Models for End-to-End Intent-Based SDN Automation
Md. Kamrul Hossain, Walid Aljoby
https://arxiv.org/abs/2507.14398 ht…

NetIntent: Leveraging Large Language Models for End-to-End Intent-Based SDN Automation
Intent-Based Networking (IBN) often leverages the programmability of Software-Defined Networking (SDN) to simplify network management. However, significant challenges remain in automating the entire pipeline, from user-specified high-level intents to device-specific low-level configurations. Existing solutions often rely on rigid, rule-based translators and fixed APIs, limiting extensibility and adaptability. By contrast, recent advances in large language models (LLMs) offer a promising pathway…

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…

RIDAS: A Multi-Agent Framework for AI-RAN with Representation- and Intention-Driven Agents
Kuiyuan Ding, Caili Guo, Yang Yang, Jianzhang Guo
https://arxiv.org/abs/2507.13140

RIDAS: A Multi-Agent Framework for AI-RAN with Representation- and Intention-Driven Agents
Sixth generation (6G) networks demand tight integration of artificial intelligence (AI) into radio access networks (RANs) to meet stringent quality of service (QoS) and resource efficiency requirements. Existing solutions struggle to bridge the gap between high level user intents and the low level, parameterized configurations required for optimal performance. To address this challenge, we propose RIDAS, a multi agent framework composed of representation driven agents (RDAs) and an intention dr…