OpenRaiser/Intern-Atlas huggingface dataset:
Harnessing Adaptive Topology Representations for Zero-Shot Graph Question Answering
Venue: arXiv cs.CL 2025
Year: 2025

Large Multimodal Models (LMMs) have shown generalized zero-shot capabilities in diverse domain question-answering (QA) tasks, including graph QA that involves complex graph topologies. However, most current approaches use only a single type of graph representation, namely Topology Representation Form (TRF), such as prompt-unified text descriptions or style-fixed visual styles. Those "one-size-fits-all" approaches fail to consider the specific preferences of different models or tasks, often leading to incorrect or overly long responses. To address this, we first analyze the characteristics and weaknesses of existing TRFs, and then design a set of TRFs, denoted by $F_{ZS}$, tailored to zero-shot graph QA. We then introduce a new metric, Graph Response Efficiency (GRE), which measures the balance between the performance and the brevity in graph QA. Built on these, we develop the DynamicTRF framework, which aims to improve both the accuracy and conciseness of graph QA. To be specific, DynamicTRF first creates a TRF Preference (TRFP) dataset that ranks TRFs based on their GRE scores, to probe the question-specific TRF preferences. Then it trains a TRF router on the TRFP dataset, to adaptively assign the best TRF from $F_{ZS}$ for each question during the inference. Extensive experiments across 7 in-domain algorithmic graph QA tasks and 2 out-of-domain downstream tasks show that DynamicTRF significantly enhances the zero-shot graph QA of LMMs in terms of accuracy
ActivityDiff: A diffusion model with Positive and Negative Activity Guidance for De Novo Drug Design
Venue: arXiv cs.LG 2025
Year: 2025

Achieving precise control over a molecule's biological activity-encompassing targeted activation/inhibition, cooperative multi-target modulation, and off-target toxicity mitigation-remains a critical challenge in de novo drug design. However, existing generative methods primarily focus on producing molecules with a single desired activity, lacking integrated mechanisms for the simultaneous management of multiple intended and unintended molecular interactions. Here, we propose ActivityDiff, a generative approach based on the classifier-guidance technique of diffusion models. It leverages separately trained drug-target classifiers for both positive and negative guidance, enabling the model to enhance desired activities while minimizing harmful off-target effects. Experimental results show that ActivityDiff effectively handles essential drug design tasks, including single-/dual-target generation, fragment-constrained dual-target design, selective generation to enhance target specificity, and reduction of off-target effects. These results demonstrate the effectiveness of classifier-guided diffusion in balancing efficacy and safety in molecular design. Overall, our work introduces a novel paradigm for achieving integrated control over molecular activity, and provides ActivityDiff as a versatile and extensible framework.
Text as Any-Modality for Zero-Shot Classification by Consistent Prompt Tuning
Venue: arXiv cs.CV 2025
Year: 2025

The integration of prompt tuning with multimodal learning has shown significant generalization abilities for various downstream tasks. Despite advancements, existing methods heavily depend on massive modality-specific labeled data (e.g., video, audio, and image), or are customized for a single modality. In this study, we present Text as Any-Modality by Consistent Prompt Tuning (TaAM-CPT), a scalable approach for constructing a general representation model toward unlimited modalities using solely text data. TaAM-CPT comprises modality prompt pools, text construction, and modality-aligned text encoders from pre-trained models, which allows for extending new modalities by simply adding prompt pools and modality-aligned text encoders. To harmonize the learning across different modalities, TaAM-CPT designs intra- and inter-modal learning objectives, which can capture category details within modalities while maintaining semantic consistency across different modalities. Benefiting from its scalable architecture and pre-trained models, TaAM-CPT can be seamlessly extended to accommodate unlimited modalities. Remarkably, without any modality-specific labeled data, TaAM-CPT achieves leading results on diverse datasets spanning various modalities, including video classification, image classification, and audio classification. The code is available at https://github.com/Jinx630/TaAM-CPT.
End-to-End Text-to-SQL with Dataset Selection: Leveraging LLMs for Adaptive Query Generation
Venue: arXiv cs.LG 2025
Year: 2025

Text-to-SQL bridges the gap between natural language and structured database language, thus allowing non-technical users to easily query databases. Traditional approaches model text-to-SQL as a direct translation task, where a given Natural Language Query (NLQ) is mapped to an SQL command. Recent advances in large language models (LLMs) have significantly improved translation accuracy, however, these methods all require that the target database is pre-specified. This becomes problematic in scenarios with multiple extensive databases, where identifying the correct database becomes a crucial yet overlooked step. In this paper, we propose a three-stage end-to-end text-to-SQL framework to identify the user's intended database before generating SQL queries. Our approach leverages LLMs and prompt engineering to extract implicit information from natural language queries (NLQs) in the form of a ruleset. We then train a large db\_id prediction model, which includes a RoBERTa-based finetuned encoder, to predict the correct Database identifier (db\_id) based on both the NLQ and the LLM-generated rules. Finally, we refine the generated SQL by using critic agents to correct errors. Experimental results demonstrate that our framework outperforms the current state-of-the-art models in both database intent prediction and SQL generation accuracy.
Text Embedded Swin-UMamba for DeepLesion Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Segmentation of lesions on CT enables automatic measurement for clinical assessment of chronic diseases (e.g., lymphoma). Integrating large language models (LLMs) into the lesion segmentation workflow has the potential to combine imaging features with descriptions of lesion characteristics from the radiology reports. In this study, we investigate the feasibility of integrating text into the Swin-UMamba architecture for the task of lesion segmentation. The publicly available ULS23 DeepLesion dataset was used along with short-form descriptions of the findings from the reports. On the test dataset, our method achieved a high Dice score of 82.64, and a low Hausdorff distance of 6.34 pixels was obtained for lesion segmentation. The proposed Text-Swin-U/Mamba model outperformed prior approaches: 37.79% improvement over the LLM-driven LanGuideMedSeg model (p < 0.001), and surpassed the purely image-based XLSTM-UNet and nnUNet models by 2.58% and 1.01%, respectively. The dataset and code can be accessed at https://github.com/ruida/LLM-Swin-UMamba
The Problem of Atypicality in LLM-Powered Psychiatry
Venue: arXiv cs.CY 2025
Year: 2025

Large language models (LLMs) are increasingly proposed as scalable solutions to the global mental health crisis. But their deployment in psychiatric contexts raises a distinctive ethical concern: the problem of atypicality. Because LLMs generate outputs based on population-level statistical regularities, their responses -- while typically appropriate for general users -- may be dangerously inappropriate when interpreted by psychiatric patients, who often exhibit atypical cognitive or interpretive patterns. We argue that standard mitigation strategies, such as prompt engineering or fine-tuning, are insufficient to resolve this structural risk. Instead, we propose dynamic contextual certification (DCC): a staged, reversible and context-sensitive framework for deploying LLMs in psychiatry, inspired by clinical translation and dynamic safety models from artificial intelligence governance. DCC reframes chatbot deployment as an ongoing epistemic and ethical process that prioritises interpretive safety over static performance benchmarks. Atypicality, we argue, cannot be eliminated -- but it can, and must, be proactively managed.
Accessibility Literacy: Increasing accessibility awareness among young content creators
Venue: arXiv cs.HC 2025
Year: 2025

The proliferation of audiovisual and web content has created an increasing need for media accessibility education in various fields. However, accessibility remains a low priority in university curricula. This project explores the feasibility of an alternative learning experience aimed at increasing the accessibility literacy of young content creators, taking web accessibility as a case study. We propose a mini module that uses simple, easy-to-use training materials, such as infographics and short quizzes, and can be easily incorporated in educational programmes along existing courses. A survey was conducted to investigate the participants' accessibility literacy before and after training. The findings show that young content creators generally have limited accessibility literacy but even brief exposure to accessibility materials contributed to a shift in perceptions. After training, participants expressed more willingness to implement accessibility tools in their content, with ways varying depending on content type and purpose. This suggests that small, yet targeted interventions could be an alternative for integrating accessibility training into formal education across various disciplines. While some responses reflected traces of the medical model of disability and a particularlist view of accessibility, accessibility was recognised as important for increasing inclusion, improving content, and shaping a fairer society.
AutoMashup: Automatic Music Mashups Creation
Venue: arXiv cs.SD 2025
Year: 2025

We introduce AutoMashup, a system for automatic mashup creation based on source separation, music analysis, and compatibility estimation. We propose using COCOLA to assess compatibility between separated stems and investigate whether general-purpose pretrained audio models (CLAP and MERT) can support zero-shot estimation of track pair compatibility. Our results show that mashup compatibility is asymmetric -- it depends on the role assigned to each track (vocals or accompaniment) -- and that current embeddings fail to reproduce the perceptual coherence measured by COCOLA. These findings underline the limitations of general-purpose audio representations for compatibility estimation in mashup creation.
Stinger Robot: A Self-Bracing Robotic Platform for Autonomous Drilling in Confined Underground Environments
Venue: arXiv cs.RO 2025
Year: 2025

The increasing demand for critical raw materials has revitalized interest in abandoned underground mines, which pose extreme challenges for conventional drilling machinery due to confined, unstructured, and infrastructure-less environments. This paper presents the Stinger Robot, a novel compact robotic platform specifically designed for autonomous high-force drilling in such settings. The robot features a mechanically self-locking tri-leg bracing mechanism that enables stable anchoring to irregular tunnel surfaces. A key innovation lies in its force-aware, closed-loop control strategy, which enables force interaction with unstructured environments during bracing and drilling. Implemented as a finite-state machine in ROS 2, the control policy dynamically adapts leg deployment based on real-time contact feedback and load thresholds, ensuring stability without external supports. We demonstrate, through simulation and preliminary hardware tests, that the Stinger Robot can autonomously stabilize and drill in conditions previously inaccessible to nowadays mining machines. This work constitutes the first validated robotic architecture to integrate distributed force-bracing and autonomous drilling in underground environments, laying the groundwork for future collaborative mining operations using modular robot systems.
The Art of Breaking Words: Rethinking Multilingual Tokenizer Design
Venue: arXiv cs.CL 2025
Year: 2025

While model architecture and training objectives are well-studied, tokenization, particularly in multilingual contexts, remains a relatively neglected aspect of Large Language Model (LLM) development. Existing tokenizers often exhibit high token-to-word ratios, inefficient use of context length, and slower inference. We present a systematic study that links vocabulary size, pre-tokenization rules, and training-corpus composition to both token-to-word efficiency and model quality. To ground our analysis in a linguistically diverse context, we conduct extensive experiments on Indic scripts, which present unique challenges due to their high script diversity and orthographic complexity. Drawing on the insights from these analyses, we propose a novel algorithm for data composition that balances multilingual data for tokenizer training. Our observations on pretokenization strategies significantly improve model performance, and our data composition algorithm reduces the average token-to-word ratio by approximately 6% with respect to the conventional data randomization approach. Our tokenizer achieves more than 40% improvement on average token-to-word ratio against stateof-the-art multilingual Indic models. This improvement yields measurable gains in both model performance and inference speed. This highlights tokenization alongside architecture and training objectives as a critical lever for building efficient, scalable multilingual LLMs
MILD: Multi-Layer Diffusion Strategy for Complex and Precise Multi-IP Aware Human Erasing
Venue: arXiv cs.CV 2025
Year: 2025

Recent years have witnessed the success of diffusion models in image customization tasks. However, existing mask-guided human erasing methods still struggle in complex scenarios such as human-human occlusion, human-object entanglement, and human-background interference, mainly due to the lack of large-scale multi-instance datasets and effective spatial decoupling to separate foreground from background. To bridge these gaps, we curate the MILD dataset capturing diverse poses, occlusions, and complex multi-instance interactions. We then define the Cross-Domain Attention Gap (CAG), an attention-gap metric to quantify semantic leakage. On top of these, we propose Multi-Layer Diffusion (MILD), which decomposes the generation process into independent denoising pathways, enabling separate reconstruction of each foreground instance and the background. To enhance human-centric understanding, we introduce Human Morphology Guidance, a plug-and-play module that incorporates pose, parsing, and spatial relationships into the diffusion process to improve structural awareness and restoration quality. Additionally, we present Spatially-Modulated Attention, an adaptive mechanism that leverages spatial mask priors to modulate attention across semantic regions, further widening the CAG to effectively minimize boundary artifacts and mitigate semantic leakage. Experiments show that MILD significantly outperforms existing methods. Datasets and code are publicly available at: https://mild-multi-layer-diffusion.github.io/.
Slice or the Whole Pie? Utility Control for AI Models
Venue: arXiv cs.CV 2025
Year: 2025

Training deep neural networks (DNNs) has become an increasingly resource-intensive task, requiring large volumes of labeled data, substantial computational power, and considerable fine-tuning efforts to achieve optimal performance across diverse use cases. Although pre-trained models offer a useful starting point, adapting them to meet specific user needs often demands extensive customization, and infrastructure overhead. This challenge grows when a single model must support diverse appli-cations with differing requirements for performance. Traditional solutions often involve training multiple model versions to meet varying requirements, which can be inefficient and difficult to maintain. In order to overcome this challenge, we propose NNObfuscator, a novel utility control mechanism that enables AI models to dynamically modify their performance according to predefined conditions. It is different from traditional methods that need separate models for each user. Instead, NNObfuscator allows a single model to be adapted in real time, giving you controlled access to multiple levels of performance. This mechanism enables model owners set up tiered access, ensuring that free-tier users receive a baseline level of performance while premium users benefit from enhanced capabilities. The approach improves resource allocation, reduces unnecessary computation, and supports sustainable business models in AI deployment. To validate our approach, we conducted experiments on multiple tasks, including image classification, semantic segmentation, and text to image generation, using well-established models such as ResNet, DeepLab, VGG16, FCN and Stable Diffusion. Experimental results show that NNObfuscator successfully makes model more adaptable, so that a single trained model can handle a broad range of tasks without requiring a lot of changes.
AquaChat++: LLM-Assisted Multi-ROV Inspection for Aquaculture Net Pens with Integrated Battery Management and Thruster Fault Tolerance
Venue: arXiv cs.RO 2025
Year: 2025

Inspection of aquaculture net pens is essential for ensuring the structural integrity and sustainable operation of offshore fish farming systems. Traditional methods, typically based on manually operated or single-ROV systems, offer limited adaptability to real-time constraints such as energy consumption, hardware faults, and dynamic underwater conditions. This paper introduces AquaChat++, a novel multi-ROV inspection framework that uses Large Language Models (LLMs) to enable adaptive mission planning, coordinated task execution, and fault-tolerant control in complex aquaculture environments. The proposed system consists of a two-layered architecture. The high-level plan generation layer employs an LLM, such as ChatGPT-4, to translate natural language user commands into symbolic, multi-agent inspection plans. A task manager dynamically allocates and schedules actions among ROVs based on their real-time status and operational constraints, including thruster faults and battery levels. The low-level control layer ensures accurate trajectory tracking and integrates thruster fault detection and compensation mechanisms. By incorporating real-time feedback and event-triggered replanning, AquaChat++ enhances system robustness and operational efficiency. Simulated experiments in a physics-based aquaculture environment demonstrate improved inspection coverage, energy-efficient behavior, and resilience to actuator failures. These findings highlight the potential of LLM-driven frameworks to support scalable, intelligent, and autonomous underwater robotic operations within the aquaculture sector.
StyleTailor: Towards Personalized Fashion Styling via Hierarchical Negative Feedback
Venue: arXiv cs.CV 2025
Year: 2025

The advancement of intelligent agents has revolutionized problem-solving across diverse domains, yet solutions for personalized fashion styling remain underexplored, which holds immense promise for promoting shopping experiences. In this work, we present StyleTailor, the first collaborative agent framework that seamlessly unifies personalized apparel design, shopping recommendation, virtual try-on, and systematic evaluation into a cohesive workflow. To this end, StyleTailor pioneers an iterative visual refinement paradigm driven by multi-level negative feedback, enabling adaptive and precise user alignment. Specifically, our framework features two core agents, i.e., Designer for personalized garment selection and Consultant for virtual try-on, whose outputs are progressively refined via hierarchical vision-language model feedback spanning individual items, complete outfits, and try-on efficacy. Counterexamples are aggregated into negative prompts, forming a closed-loop mechanism that enhances recommendation quality. To assess the performance, we introduce a comprehensive evaluation suite encompassing style consistency, visual quality, face similarity, and artistic appraisal. Extensive experiments demonstrate StyleTailor's superior performance in delivering personalized designs and recommendations, outperforming strong baselines without negative feedback and establishing a new benchmark for intelligent fashion systems.
IRL-VLA: Training an Vision-Language-Action Policy via Reward World Model
Venue: arXiv cs.AI 2025
Year: 2025

Vision-Language-Action (VLA) models have demonstrated potential in autonomous driving. However, two critical challenges hinder their development: (1) Existing VLA architectures are typically based on imitation learning in open-loop setup which tends to capture the recorded behaviors in the dataset, leading to suboptimal and constrained performance, (2) Close-loop training relies heavily on high-fidelity sensor simulation, where domain gaps and computational inefficiencies pose significant barriers. In this paper, we introduce IRL-VLA, a novel close-loop Reinforcement Learning via \textbf{I}nverse \textbf{R}einforcement \textbf{L}earning reward world model with a self-built VLA approach. Our framework proceeds in a three-stage paradigm: In the first stage, we propose a VLA architecture and pretrain the VLA policy via imitation learning. In the second stage, we construct a lightweight reward world model via inverse reinforcement learning to enable efficient close-loop reward computation. To further enhance planning performance, finally, we design specialized reward world model guidence reinforcement learning via PPO(Proximal Policy Optimization) to effectively balance the safety incidents, comfortable driving, and traffic efficiency. Our approach achieves state-of-the-art performance in NAVSIM v2 end-to-end driving benchmark, 1st runner up in CVPR2025 Autonomous Grand Challenge. We hope that our framework will accelerate VLA research in close-loop autonomous driving.
A Federated Learning Framework for Handling Subtype Confounding and Heterogeneity in Large-Scale Neuroimaging Diagnosis
Venue: arXiv cs.LG 2025
Year: 2025

Computer-aided diagnosis (CAD) systems play a crucial role in analyzing neuroimaging data for neurological and psychiatric disorders. However, small-sample studies suffer from low reproducibility, while large-scale datasets introduce confounding heterogeneity due to multiple disease subtypes being labeled under a single category. To address these challenges, we propose a novel federated learning framework tailored for neuroimaging CAD systems. Our approach includes a dynamic navigation module that routes samples to the most suitable local models based on latent subtype representations, and a meta-integration module that combines predictions from heterogeneous local models into a unified diagnostic output. We evaluated our framework using a comprehensive dataset comprising fMRI data from over 1300 MDD patients and 1100 healthy controls across multiple study cohorts. Experimental results demonstrate significant improvements in diagnostic accuracy and robustness compared to traditional methods. Specifically, our framework achieved an average accuracy of 74.06\% across all tested sites, showcasing its effectiveness in handling subtype heterogeneity and enhancing model generalizability. Ablation studies further confirmed the importance of both the dynamic navigation and meta-integration modules in improving performance. By addressing data heterogeneity and subtype confounding, our framework advances reliable and reproducible neuroimaging CAD systems, offering significant potential for personalized medicine and clinical decision-making in neurology and psychiatry.