Tootfinder

DuetUI: A Bidirectional Context Loop for Human-Agent Co-Generation of Task-Oriented Interfaces
Yuan Xu, Shaowen Xiang, Yizhi Song, Ruoting Sun, Xin Tong
https://arxiv.org/abs/2509.13444

DuetUI: A Bidirectional Context Loop for Human-Agent Co-Generation of Task-Oriented Interfaces
Large Language Models are reshaping task automation, yet remain limited in complex, multi-step real-world tasks that require aligning with vague user intent and enabling dynamic user override. From a formative study with 12 participants, we found that end-users actively seek to shape generative interfaces rather than relying on one-shot outputs. To address this, we introduce the human-agent co-generation paradigm, materialized in DuetUI. This LLM-empowered system unfolds alongside task progress…

InfraMind: A Novel Exploration-based GUI Agentic Framework for Mission-critical Industrial Management
Liangtao Lin, Zhaomeng Zhu, Tianwei Zhang, Yonggang Wen
https://arxiv.org/abs/2509.13704

InfraMind: A Novel Exploration-based GUI Agentic Framework for Mission-critical Industrial Management
Mission-critical industrial infrastructure, such as data centers, increasingly depends on complex management software. Its operations, however, pose significant challenges due to the escalating system complexity, multi-vendor integration, and a shortage of expert operators. While Robotic Process Automation (RPA) offers partial automation through handcrafted scripts, it suffers from limited flexibility and high maintenance costs. Recent advances in Large Language Model (LLM)-based graphical user…

Reconfigurable Intelligent Surface-Assisted Multiuser Tracking and Signal Detection in ISAC
Weifeng Zhu, Junyuan Gao, Shuowen Zhang, Liang Liu
https://arxiv.org/abs/2509.13940 h…

Reconfigurable Intelligent Surface-Assisted Multiuser Tracking and Signal Detection in ISAC
This paper investigates the multiuser tracking and signal detection problem in integrated sensing and communication (ISAC) systems with the assistance of reconfigurable intelligent surfaces (RISs). Due to the diverse and high user mobility, the tracking and signal detection performance can be significantly deteriorated without choreographed user state (position and velocity) updating principle. To tackle this challenge, we manage to establish a comprehensive probabilistic signal model to charac…

SRUM: Fine-Grained Self-Rewarding for Unified Multimodal Models
Weiyang Jin, Yuwei Niu, Jiaqi Liao, Chengqi Duan, Aoxue Li, Shenghua Gao, Xihui Liu
https://arxiv.org/abs/2510.12784

SRUM: Fine-Grained Self-Rewarding for Unified Multimodal Models
Recently, remarkable progress has been made in Unified Multimodal Models (UMMs), which integrate vision-language generation and understanding capabilities within a single framework. However, a significant gap exists where a model's strong visual understanding often fails to transfer to its visual generation. A model might correctly understand an image based on user instructions, yet be unable to generate a faithful image from text prompts. This phenomenon directly raises a compelling question: …

Countermind: A Multi-Layered Security Architecture for Large Language Models
Dominik Schwarz
https://arxiv.org/abs/2510.11837 https://arxiv.org/pdf/2510.11…

Countermind: A Multi-Layered Security Architecture for Large Language Models
The security of Large Language Model (LLM) applications is fundamentally challenged by "form-first" attacks like prompt injection and jailbreaking, where malicious instructions are embedded within user inputs. Conventional defenses, which rely on post hoc output filtering, are often brittle and fail to address the root cause: the model's inability to distinguish trusted instructions from untrusted data. This paper proposes Countermind, a multi-layered security architecture intended to shift def…

Anthropic says Haiku 4.5 can serve as a subagent for Sonnet 4.5, which can break down problems into multistep plans and orchestrate a team of Haiku 4.5 agents (Anthropic)
https://www.anthropic.com/news/claude-haiku-4-5

Introducing Claude Haiku 4.5
Claude Haiku 4.5, our latest small model, is available today to all users.

Decoupled Multimodal Fusion for User Interest Modeling in Click-Through Rate Prediction
Alin Fan, Hanqing Li, Sihan Lu, Jingsong Yuan, Jiandong Zhang
https://arxiv.org/abs/2510.11066

Decoupled Multimodal Fusion for User Interest Modeling in Click-Through Rate Prediction
Modern industrial recommendation systems improve recommendation performance by integrating multimodal representations from pre-trained models into ID-based Click-Through Rate (CTR) prediction frameworks. However, existing approaches typically adopt modality-centric modeling strategies that process ID-based and multimodal embeddings independently, failing to capture fine-grained interactions between content semantics and behavioral signals. In this paper, we propose Decoupled Multimodal Fusion (…

Dual Actor DDPG for Airborne STAR-RIS Assisted Communications
Danish Rizvi, David Boyle
https://arxiv.org/abs/2509.13328 https://arxiv.org/pdf/2509.13328…

Dual Actor DDPG for Airborne STAR-RIS Assisted Communications
This study departs from the prevailing assumption of independent Transmission and Reflection Coefficients (TRC) in Airborne Simultaneous Transmit and Reflect Reconfigurable Intelligent Surface (STAR-RIS) research. Instead, we explore a novel multi-user downlink communication system that leverages a UAV-mounted STAR-RIS (Aerial-STAR) incorporating a coupled TRC phase shift model. Our key contributions include the joint optimization of UAV trajectory, active beamforming vectors at the base statio…

DeepMMSearch-R1: Empowering Multimodal LLMs in Multimodal Web Search
Kartik Narayan, Yang Xu, Tian Cao, Kavya Nerella, Vishal M. Patel, Navid Shiee, Peter Grasch, Chao Jia, Yinfei Yang, Zhe Gan
https://arxiv.org/abs/2510.12801

DeepMMSearch-R1: Empowering Multimodal LLMs in Multimodal Web Search
Multimodal Large Language Models (MLLMs) in real-world applications require access to external knowledge sources and must remain responsive to the dynamic and ever-changing real-world information in order to address information-seeking and knowledge-intensive user queries. Existing approaches, such as retrieval augmented generation (RAG) methods, search agents, and search equipped MLLMs, often suffer from rigid pipelines, excessive search calls, and poorly constructed search queries, which resu…

ProtoSiTex: Learning Semi-Interpretable Prototypes for Multi-label Text Classification
Utsav Kumar Nareti, Suraj Kumar, Soumya Pandey, Soumi Chattopadhyay, Chandranath Adak
https://arxiv.org/abs/2510.12534

ProtoSiTex: Learning Semi-Interpretable Prototypes for Multi-label Text Classification
The surge in user-generated reviews has amplified the need for interpretable models that can provide fine-grained insights. Existing prototype-based models offer intuitive explanations but typically operate at coarse granularity (sentence or document level) and fail to address the multi-label nature of real-world text classification. We propose ProtoSiTex, a semi-interpretable framework designed for fine-grained multi-label text classification. ProtoSiTex employs a dual-phase alternating traini…