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

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…

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…

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 …

This https://arxiv.org/abs/2504.21398 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

In a Few Words: Comparing Weak Supervision and LLMs for Short Query Intent Classification
User intent classification is an important task in information retrieval. Previously, user intents were classified manually and automatically; the latter helped to avoid hand labelling of large datasets. Recent studies explored whether LLMs can reliably determine user intent. However, researchers have recognized the limitations of using generative LLMs for classification tasks. In this study, we empirically compare user intent classification into informational, navigational, and transactional c…

This https://arxiv.org/abs/2505.17433 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

MemeReaCon: Probing Contextual Meme Understanding in Large Vision-Language Models
Memes have emerged as a popular form of multimodal online communication, where their interpretation heavily depends on the specific context in which they appear. Current approaches predominantly focus on isolated meme analysis, either for harmful content detection or standalone interpretation, overlooking a fundamental challenge: the same meme can express different intents depending on its conversational context. This oversight creates an evaluation gap: although humans intuitively recognize ho…

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…

Enhancing Speech Instruction Understanding and Disambiguation in Robotics via Speech Prosody
David Sasu, Kweku Andoh Yamoah, Benedict Quartey, Natalie Schluter
https://arxiv.org/abs/2506.02057

Enhancing Speech Instruction Understanding and Disambiguation in Robotics via Speech Prosody
Enabling robots to accurately interpret and execute spoken language instructions is essential for effective human-robot collaboration. Traditional methods rely on speech recognition to transcribe speech into text, often discarding crucial prosodic cues needed for disambiguating intent. We propose a novel approach that directly leverages speech prosody to infer and resolve instruction intent. Predicted intents are integrated into large language models via in-context learning to disambiguate and …

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…

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…

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*

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…

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…

Rethinking Contrastive Learning in Session-based Recommendation
Xiaokun Zhang, Bo Xu, Fenglong Ma, Zhizheng Wang, Liang Yang, Hongfei Lin
https://arxiv.org/abs/2506.05044

Rethinking Contrastive Learning in Session-based Recommendation
Session-based recommendation aims to predict intents of anonymous users based on limited behaviors. With the ability in alleviating data sparsity, contrastive learning is prevailing in the task. However, we spot that existing contrastive learning based methods still suffer from three obstacles: (1) they overlook item-level sparsity and primarily focus on session-level sparsity; (2) they typically augment sessions using item IDs like crop, mask and reorder, failing to ensure the semantic consist…

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…

This https://arxiv.org/abs/2505.07843 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

PosterO: Structuring Layout Trees to Enable Language Models in Generalized Content-Aware Layout Generation
In poster design, content-aware layout generation is crucial for automatically arranging visual-textual elements on the given image. With limited training data, existing work focused on image-centric enhancement. However, this neglects the diversity of layouts and fails to cope with shape-variant elements or diverse design intents in generalized settings. To this end, we proposed a layout-centric approach that leverages layout knowledge implicit in large language models (LLMs) to create posters…

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…

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

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…

This https://arxiv.org/abs/2505.22299 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Logical Consistency is Vital: Neural-Symbolic Information Retrieval for Negative-Constraint Queries
Information retrieval plays a crucial role in resource localization. Current dense retrievers retrieve the relevant documents within a corpus via embedding similarities, which compute similarities between dense vectors mainly depending on word co-occurrence between queries and documents, but overlook the real query intents. Thus, they often retrieve numerous irrelevant documents. Particularly in the scenarios of complex queries such as \emph{negative-constraint queries}, their retrieval perfo…

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…

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…

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…