Papers Intent Recognition
“Intent Recognition” 태그가 달린 논문 143편 · 필터 해제
Agent-Based ML-LLM Fusion with Self-Optimizing Prompts for Plateau Weather Alerts
To address insufficient contextualization, weak generalization, and poor scenario adaptation in tourism meteorological services, we propose SmartWeatherAgent--a unified three-stage architecture integrating intent recogni…
Intent RecognitionAslema at NADI 2026: Augmentation through Fewshot for SLU
We present Aslema, our system for NADI 2026 Shared Task 5, which consists of two subtasks: intent recognition and slot filling. We evaluate four omni LLMs in a zero-shot setting and compare them with fine-tuned models. O…
Intent RecognitionData AugmentationSlot FillingTemporal Posed and Spontaneous Gesture Recognition from Electromyography in the Rock-Paper-Scissors Game
The importance of gesture recognition has been acknowledged in many domains requiring real-time recognition systems. Two requirements for these are fast recognition in multiuser contexts. Therefore, we explored the tempo…
Gesture RecognitionIntent RecognitionEnd-to-End Voice Intent Recognition for Spontaneous Human-Drone Interaction with Naive Users
Voice control offers an intuitive alternative to manual drone piloting, yet most existing systems rely on rigid command vocabularies that fail to handle the spontaneous, disfluent speech of naive users. This paper addres…
Spoken Language UnderstandingSelf-Supervised LearningKnowledge DistillationIntent RecognitionHypoAgent: An Agentic Framework for Interactive Abductive Hypothesis Generation over Knowledge Graphs
Abductive reasoning over knowledge graphs aims to generate logical hypotheses that explain observed entities or facts. Existing controllable hypothesis generation methods allow users to guide this process with explicit c…
Semantic SimilarityIntent RecognitionKnowledge GraphsTeleCom-Bench: How Far Are Large Language Models from Industrial Telecommunication Applications?
While Large Language Models have achieved remarkable integration in various vertical scenarios, their deployment in the telecommunications domain remains exploratory due to the lack of a standardized evaluation framework…
Intent RecognitionEgoPro-Bench: Benchmarking Personalized Proactive Interaction in Egocentric Video Streams
Existing Multimodal Large Language Models (MLLMs) remain primarily reactive, failing to continuously perceive environments or proactively assist users. While emerging benchmarks address proactivity, they are largely conf…
Intent RecognitionInteractWeb-Bench: Can Multimodal Agent Escape Blind Execution in Interactive Website Generation?
With the advancement of multimodal large language models (MLLMs) and coding agents, the website development has shifted from manual programming to agent-based project-level code synthesis. Existing benchmarks rely on ide…
Intent RecognitionTopBench: A Benchmark for Implicit Predictive Reasoning in Tabular Question Answering
Large Language Models (LLMs) have advanced Table Question Answering, where most queries can be answered by extracting information or simple aggregation. However, a common class of real-world queries is implicitly predict…
Intent RecognitionQuestion AnsweringDecision MakingMISID: A Multimodal Multi-turn Dataset for Complex Intent Recognition in Strategic Deception Games
Understanding human intent in complex multi-turn interactions remains a fundamental challenge in human-computer interaction and behavioral analysis. While existing intent recognition datasets focus mainly on single utter…
Intent RecognitionIntent DetectionDyadic Partnership(DP): A Missing Link Towards Full Autonomy in Medical Robotics
For the past decades medical robotic solutions were mostly based on the concept of tele-manipulation. While their design was extremely intelligent, allowing for better access, improved dexterity, reduced tremor, and impr…
Intent RecognitionDecision MakingAdaptor: Advancing Assistive Teleoperation with Few-Shot Learning and Cross-Operator Generalization
Assistive teleoperation enhances efficiency via shared control, yet inter-operator variability, stemming from diverse habits and expertise, induces highly heterogeneous trajectory distributions that undermine intent reco…
Intent RecognitionFew-Shot LearningRemoteAgent: Bridging Vague Human Intents and Earth Observation with RL-based Agentic MLLMs
Earth Observation (EO) systems are essentially designed to support domain experts who often express their requirements through vague natural language rather than precise, machine-friendly instructions. Depending on the s…
Intent RecognitionTelcoAgent-Bench: A Multilingual Benchmark for Telecom AI Agents
The integration of large language model (LLM) agents into telecom networks introduces new challenges, related to intent recognition, tool execution, and resolution generation, while taking into consideration different op…
Intent RecognitionTATIC: Task-Aware Temporal Learning for Human Intent Inference from Physical Corrections in Human-Robot Collaboration
In human-robot collaboration (HRC), robots must adapt online to dynamic task constraints and evolving human intent. While physical corrections provide a natural, low-latency channel for operators to convey motion-level a…
Intent RecognitionGlance-Say: Multimodal Human-Robot Collaboration and Intent Recognition via Sticky Glance
Gaze and speech are promising interaction modalities for individuals with motor impairments, yet robust intent recognition in multi-object environments remains challenging due to micro-saccades, semantic ambiguity, and v…
Intent RecognitionFactored Reasoning with Inner Speech and Persistent Memory for Evidence-Grounded Human-Robot Interaction
Dialogue-based human-robot interaction requires robot cognitive assistants to maintain persistent user context, recover from underspecified requests, and ground responses in external evidence, while keeping intermediate …
Intent RecognitionSemantic RetrievalHow DDAIR you? Disambiguated Data Augmentation for Intent Recognition
Large Language Models (LLMs) are effective for data augmentation in classification tasks like intent detection. In some cases, they inadvertently produce examples that are ambiguous with regard to untargeted classes. We …
Intent RecognitionData AugmentationIntent DetectionEnhancing Cloud Network Resilience via a Robust LLM-Empowered Multi-Agent Reinforcement Learning Framework
While virtualization and resource pooling empower cloud networks with structural flexibility and elastic scalability, they inevitably expand the attack surface and challenge cyber resilience. Reinforcement Learning (RL)-…
Multi-agent Reinforcement LearningIntent RecognitionMTMCS-Bench: Evaluating Contextual Safety of Multimodal Large Language Models in Multi-Turn Dialogues
Multimodal large language models (MLLMs) are increasingly deployed as assistants that interact through text and images, making it crucial to evaluate contextual safety when risk depends on both the visual scene and the e…
Intent Recognition