We introduce Agentic Reasoning, a framework that enhances large language model (LLM) reasoning by integrating external tool-using agents.Agentic Reasoning dynamically leverages web search, code execution, and structured memory to address complex problems requiring deep research.A key innovation in our framework is the Mind-Map agent, which constructs a structured knowledge graph to store reasoning context and track logical relationships, ensuring coherence in long reasoning chains with extensive tool usage.Additionally, we conduct a comprehensive exploration of the Web-Search agent, leading to a highly effective search mechanism that surpasses all prior approaches.When deployed on DeepSeek-R1, our method achieves a new state-of-the-art (SOTA) among public models and delivers performance comparable to OpenAI Deep Research, the leading proprietary model in this domain.Extensive ablation studies validate the optimal selection of agentic tools and confirm the effectiveness of our Mind-Map and Web-Search agents in enhancing LLM reasoning.Our code and data are publicly available.

Junde Wu, Jiayuan Zhu, Yunli Qi, Jingkun Chen, Min Xu, Filippo Menolascina, Yueming Jin, Vicente Grau. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

The severe shortage of medical doctors limits access to timely and reliable healthcare, leaving millions underserved. Large language models (LLMs) offer a potential solution but struggle in real-world clinical interactions. Many LLMs are not grounded in authoritative medical guidelines and fail to transparently manage diagnostic uncertainty. Their language is often rigid and mechanical, lacking the human-like qualities essential for patient trust. To address these challenges, we propose Ask Patients with Patience (APP), a multi-turn LLM-based medical assistant designed for grounded reasoning, transparent diagnoses, and human-centric interaction. APP enhances communication by eliciting user symptoms through empathetic dialogue, significantly improving accessibility and user engagement. It also incorporates Bayesian active learning to support transparent and adaptive diagnoses. The framework is built on verified medical guidelines, ensuring clinically grounded and evidence-based reasoning. To evaluate its performance, we develop a new benchmark that simulates realistic medical conversations using patient agents driven by profiles extracted from real-world consultation cases. We compare APP against SOTA one-shot and multi-turn LLM baselines. The results show that APP improves diagnostic accuracy, reduces uncertainty, and enhances user experience. By integrating medical expertise with transparent, human-like interaction, APP bridges the gap between AI-driven medical assistance and real-world clinical practice.

Medical image segmentation data inherently contain uncertainty. This can stem from both imperfect image quality and variability in labeling preferences on ambiguous pixels, which depend on annotator expertise and the clinical context of the annotations. For instance, a boundary pixel might be labeled as tumor in diagnosis to avoid under-estimation of severity, but as normal tissue in radiotherapy to prevent damage to sensitive structures. As segmentation preferences vary across downstream applications, it is often desirable for an image segmentation model to offer user-adaptable predictions rather than a fixed output. While prior uncertainty-aware and interactive methods offer adaptability, they are inefficient at test time: uncertainty-aware models require users to choose from numerous similar outputs, while interactive models demand significant user input through click or box prompts to refine segmentation. To address these challenges, we propose \textbf{SPA}, a new \textbf{S}egmentation \textbf{P}reference \textbf{A}lignment framework that efficiently adapts to diverse test-time preferences with minimal human interaction. By presenting users with a select few, distinct segmentation candidates that best capture uncertainties, it reduces the user workload to reach the preferred segmentation. To accommodate user preference, we introduce a probabilistic mechanism that leverages user feedback to adapt a model's segmentation preference. The proposed framework is evaluated on several medical image segmentation tasks: color fundus images, lung lesion and kidney CT scans, MRI scans of brain and prostate. SPA shows 1) a significant reduction in user time and effort compared to existing interactive segmentation approaches, 2) strong adaptability based on human feedback, and 3) state-of-the-art image segmentation performance across different imaging modalities and semantic labels.

Unlike general visual classification (CLS) tasks, certain CLS problems are significantly more challenging as they involve recognizing professionally categorized or highly specialized images. Fine-Grained Visual Classification (FGVC) has emerged as a broad solution to address this complexity. However, most existing methods have been predominantly evaluated on a limited set of homogeneous benchmarks, such as bird species or vehicle brands. Moreover, these approaches often train separate models for each specific task, which restricts their generalizability. This paper proposes a scalable and explainable foundational model designed to tackle a wide range of FGVC tasks from a unified and generalizable perspective. We introduce a novel architecture named Pro-NeXt and reveal that Pro-NeXt exhibits substantial generalizability across diverse professional fields such as fashion, medicine, and art areas, previously considered disparate. Our basic-sized Pro-NeXt-B surpasses all preceding task-specific models across 12 distinct datasets within 5 diverse domains. Furthermore, we find its good scaling property that scaling up Pro-NeXt in depth and width with increasing GFlops can consistently enhance its accuracy. Beyond scalability and adaptability, the intermediate features of Pro-NeXt achieve reliable object detection and segmentation performance without extra training, highlighting its solid explainability. We will release the code to promote further research in this area.

Recent research has highlighted the detection of human respiration rate using commodity WiFi devices. N everthe-less, these devices encounter challenges in accurately discerning human respiration amidst the prevailing human motion interference encountered in daily life. To tackle this predicament, this paper introduces a passive sensing and communication system designed specifically for respiration detection in the presence of robust human motion interference. Operating within the 60.48 GHz millimeter wave (mmWave) band, the proposed system aims to detect human respiration even when confronted with substantial human motion interference within close proximity. Subsequently, a neural network is trained using the collected data by us to enable human respiration detection. The experimental results demonstrate a consistently high accuracy rate over 95 % of the human respiration detection under interference, given an adequate sensing duration. Finally, the accuracy of the proposed passive detecion system of counting human respiration achieves 90% overall.

Environment matting is a technique to extract the environment matte which is used to describe how an object reflects and refracts the environment light. In this paper, we propose a novel environment matting method to obtain the environment matte of a real scene. Previous methods use different backdrops as the calibration patterns and search for the environment matte in the spatial domain. In our method, however, a series of background images displayed on a screen sequentially in time are interpreted as signals. The frequency similarity of these signals is used as the searching criterion. The frequencies of these signals are not changed when they interact with the foreground objects and thus can be used to extract the environment matte. While using correspondence in the spatial domain in existing approaches is prone to error, using frequency correspondence is not. Thus, our approach is robust to noise and can easily deal with some of the complex light transport phenomena which cannot be easily handled using current methods. The experimental results are very encouraging.