Tootfinder

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…

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…

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…

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…

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…

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…

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…

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…

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…