OpenRaiser/Intern-Atlas huggingface dataset:
VitaGlyph: Vitalizing Artistic Typography with Flexible Dual-branch Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

Artistic typography is a technique to visualize the meaning of input character in an imaginable and readable manner. With powerful text-to-image diffusion models, existing methods directly design the overall geometry and texture of input character, making it challenging to ensure both creativity and legibility. In this paper, we introduce a dual-branch, training-free method called VitaGlyph, enabling flexible artistic typography with controllable geometry changes while maintaining the readability. The key insight of VitaGlyph is to treat input character as a scene composed of a Subject and its Surrounding, which are rendered with varying degrees of geometric transformation. To enhance the visual appeal and creativity of the generated artistic typography, the subject flexibly expresses the essential concept of the input character, while the surrounding enriches relevant background without altering the shape, thus maintaining overall readability. Specifically, we implement VitaGlyph through a three-phase framework: (i) Knowledge Acquisition leverages large language models to design text descriptions for the subject and surrounding. (ii) Regional Interpretation detects the part that most closely matches the subject description and refines the structure via Semantic Typography. (iii) Attentional Compositional Generation separately renders the textures of the Subject and Surrounding regions and blends them in an attention-based manner. Experimental results demonstrate that VitaGlyph not only achieves better artistry and readability but also manages to depict multiple customized concepts, facilitating more creative and pleasing artistic typography generation. Our code will be made publicly available.
From Text to Multimodality: Exploring the Evolution and Impact of Large Language Models in Medical Practice
Venue: arXiv cs.CY 2025
Year: 2025

Large Language Models (LLMs) have rapidly evolved from text-based systems to multimodal platforms, significantly impacting various sectors including healthcare. This comprehensive review explores the progression of LLMs to Multimodal Large Language Models (MLLMs) and their growing influence in medical practice. We examine the current landscape of MLLMs in healthcare, analyzing their applications across clinical decision support, medical imaging, patient engagement, and research. The review highlights the unique capabilities of MLLMs in integrating diverse data types, such as text, images, and audio, to provide more comprehensive insights into patient health. We also address the challenges facing MLLM implementation, including data limitations, technical hurdles, and ethical considerations. By identifying key research gaps, this paper aims to guide future investigations in areas such as dataset development, modality alignment methods, and the establishment of ethical guidelines. As MLLMs continue to shape the future of healthcare, understanding their potential and limitations is crucial for their responsible and effective integration into medical practice.
AI Conversational Interviewing: Transforming Surveys with LLMs as Adaptive Interviewers
Venue: arXiv cs.HC 2025
Year: 2025

Traditional methods for eliciting people's opinions face a trade-off between depth and scale: structured surveys enable large-scale data collection but limit respondents' ability to voice their opinions in their own words, while conversational interviews provide deeper insights but are resource-intensive. This study explores the potential of replacing human interviewers with large language models (LLMs) to conduct scalable conversational interviews. Our goal is to assess the performance of AI Conversational Interviewing and to identify opportunities for improvement in a controlled environment. We conducted a small-scale, in-depth study with university students who were randomly assigned to a conversational interview by either AI or human interviewers, both employing identical questionnaires on political topics. Various quantitative and qualitative measures assessed interviewer adherence to guidelines, response quality, participant engagement, and overall interview efficacy. The findings indicate the viability of AI Conversational Interviewing in producing quality data comparable to traditional methods, with the added benefit of scalability. We publish our data and materials for re-use and present specific recommendations for effective implementation.
Auction-Based Regulation for Artificial Intelligence
Venue: arXiv cs.GT 2025
Year: 2025

In an era of "moving fast and breaking things", regulators have moved slowly to pick up the safety, bias, and legal debris left in the wake of broken Artificial Intelligence (AI) deployment. While there is much-warranted discussion about how to address the safety, bias, and legal woes of state-of-the-art AI models, rigorous and realistic mathematical frameworks to regulate AI are lacking. Our paper addresses this challenge, proposing an auction-based regulatory mechanism that provably incentivizes devices (i) to deploy compliant models and (ii) to participate in the regulation process. We formulate AI regulation as an all-pay auction where enterprises submit models for approval. The regulator enforces compliance thresholds and further rewards models exhibiting higher compliance than their peers. We derive Nash Equilibria demonstrating that rational agents will submit models exceeding the prescribed compliance threshold. Empirical results show that our regulatory auction boosts compliance rates by 20% and participation rates by 15% compared to baseline regulatory mechanisms, outperforming simpler frameworks that merely impose minimum compliance standards.
Challenges and Future Directions of Data-Centric AI Alignment
Venue: arXiv cs.CL 2025
Year: 2025

As AI systems become increasingly capable and influential, ensuring their alignment with human values, preferences, and goals has become a critical research focus. Current alignment methods primarily focus on designing algorithms and loss functions but often underestimate the crucial role of data. This paper advocates for a shift towards data-centric AI alignment, emphasizing the need to enhance the quality and representativeness of data used in aligning AI systems. In this position paper, we highlight key challenges associated with both human-based and AI-based feedback within the data-centric alignment framework. Through qualitative analysis, we identify multiple sources of unreliability in human feedback, as well as problems related to temporal drift, context dependence, and AI-based feedback failing to capture human values due to inherent model limitations. We propose future research directions, including improved feedback collection practices, robust data-cleaning methodologies, and rigorous feedback verification processes. We call for future research into these critical directions to ensure, addressing gaps that persist in understanding and improving data-centric alignment practices.
LLM+KG@VLDB'24 Workshop Summary
Venue: arXiv cs.DB 2025
Year: 2025

The unification of large language models (LLMs) and knowledge graphs (KGs) has emerged as a hot topic. At the LLM+KG'24 workshop, held in conjunction with VLDB 2024 in Guangzhou, China, one of the key themes explored was important data management challenges and opportunities due to the effective interaction between LLMs and KGs. This report outlines the major directions and approaches presented by various speakers during the LLM+KG'24 workshop.
Generating Symbolic Music from Natural Language Prompts using an LLM-Enhanced Dataset
Venue: arXiv cs.SD 2025
Year: 2025

Recent years have seen many audio-domain text-to-music generation models that rely on large amounts of text-audio pairs for training. However, symbolic-domain controllable music generation has lagged behind partly due to the lack of a large-scale symbolic music dataset with extensive metadata and captions. In this work, we present MetaScore, a new dataset consisting of 963K musical scores paired with rich metadata, including free-form user-annotated tags, collected from an online music forum. To approach text-to-music generation, We employ a pretrained large language model (LLM) to generate pseudo-natural language captions for music from its metadata tags. With the LLM-enhanced MetaScore, we train a text-conditioned music generation model that learns to generate symbolic music from the pseudo captions, allowing control of instruments, genre, composer, complexity and other free-form music descriptors. In addition, we train a tag-conditioned system that supports a predefined set of tags available in MetaScore. Our experimental results show that both the proposed text-to-music and tags-to-music models outperform a baseline text-to-music model in a listening test. While a concurrent work Text2MIDI also supports free-form text input, our models achieve comparable performance. Moreover, the text-to-music system offers a more natural interface than the tags-to-music model, as it allows users to provide free-form natural language prompts.
DomainLynx: Leveraging Large Language Models for Enhanced Domain Squatting Detection
Venue: arXiv cs.CR 2025
Year: 2025

Domain squatting poses a significant threat to Internet security, with attackers employing increasingly sophisticated techniques. This study introduces DomainLynx, an innovative compound AI system leveraging Large Language Models (LLMs) for enhanced domain squatting detection. Unlike existing methods focusing on predefined patterns for top-ranked domains, DomainLynx excels in identifying novel squatting techniques and protecting less prominent brands. The system's architecture integrates advanced data processing, intelligent domain pairing, and LLM-powered threat assessment. Crucially, DomainLynx incorporates specialized components that mitigate LLM hallucinations, ensuring reliable and context-aware detection. This approach enables efficient analysis of vast security data from diverse sources, including Certificate Transparency logs, Passive DNS records, and zone files. Evaluated on a curated dataset of 1,649 squatting domains, DomainLynx achieved 94.7\% accuracy using Llama-3-70B. In a month-long real-world test, it detected 34,359 squatting domains from 2.09 million new domains, outperforming baseline methods by 2.5 times. This research advances Internet security by providing a versatile, accurate, and adaptable tool for combating evolving domain squatting threats. DomainLynx's approach paves the way for more robust, AI-driven cybersecurity solutions, enhancing protection for a broader range of online entities and contributing to a safer digital ecosystem.
DomainDynamics: Lifecycle-Aware Risk Timeline Construction for Domain Names
Venue: arXiv cs.CR 2025
Year: 2025

The persistent threat posed by malicious domain names in cyber-attacks underscores the urgent need for effective detection mechanisms. Traditional machine learning methods, while capable of identifying such domains, often suffer from high false positive and false negative rates due to their extensive reliance on historical data. Conventional approaches often overlook the dynamic nature of domain names, the purposes and ownership of which may evolve, potentially rendering risk assessments outdated or irrelevant. To address these shortcomings, we introduce DomainDynamics, a novel system designed to predict domain name risks by considering their lifecycle stages. DomainDynamics constructs a timeline for each domain, evaluating the characteristics of each domain at various points in time to make informed, temporal risk determinations. In an evaluation experiment involving over 85,000 actual malicious domains from malware and phishing incidents, DomainDynamics demonstrated a significant improvement in detection rates, achieving an 82.58\% detection rate with a low false positive rate of 0.41\%. This performance surpasses that of previous studies and commercial services, improving detection capability substantially.
From Imitation to Exploration: End-to-end Autonomous Driving based on World Model
Venue: arXiv cs.AI 2025
Year: 2025

In recent years, end-to-end autonomous driving architectures have gained increasing attention due to their advantage in avoiding error accumulation. Most existing end-to-end autonomous driving methods are based on Imitation Learning (IL), which can quickly derive driving strategies by mimicking expert behaviors. However, IL often struggles to handle scenarios outside the training dataset, especially in high-dynamic and interaction-intensive traffic environments. In contrast, Reinforcement Learning (RL)-based driving models can optimize driving decisions through interaction with the environment, improving adaptability and robustness. To leverage the strengths of both IL and RL, we propose RAMBLE, an end-to-end world model-based RL method for driving decision-making. RAMBLE extracts environmental context information from RGB images and LiDAR data through an asymmetrical variational autoencoder. A transformer-based architecture is then used to capture the dynamic transitions of traffic participants. Next, an actor-critic structure reinforcement learning algorithm is applied to derive driving strategies based on the latent features of the current state and dynamics. To accelerate policy convergence and ensure stable training, we introduce a training scheme that initializes the policy network using IL, and employs KL loss and soft update mechanisms to smoothly transition the model from IL to RL. RAMBLE achieves state-of-the-art performance in route completion rate on the CARLA Leaderboard 1.0 and completes all 38 scenarios on the CARLA Leaderboard 2.0, demonstrating its effectiveness in handling complex and dynamic traffic scenarios. The model will be open-sourced upon paper acceptance at https://github.com/SCP-CN-001/ramble to support further research and development in autonomous driving.
Model-guided Fuzzing of Distributed Systems
Venue: arXiv cs.SE 2025
Year: 2025

We present a coverage-guided testing algorithm for distributed systems implementations. Our main innovation is the use of an abstract formal model of the system that is used to define coverage. Such abstract models are frequently developed in early phases of protocol design and verification but are infrequently used at testing time. We show that guiding random test generation using model coverage can be effective in covering interesting points in the implementation state space. We have implemented a fuzzer for distributed system implementations and abstract models written in TLA+. Our algorithm shows better coverage over purely random exploration as well as random exploration guided by different notions of scheduler coverage and mutation. In particular, we show consistently higher coverage and detect bugs faster on implementations of distributed consensus protocols such as those in Etcd-raft and RedisRaft. Moreover, we discovered 13 previously unknown bugs in their implementations, four of which could only be detected by model-guided fuzzing.
Post-edits Are Preferences Too
Venue: arXiv cs.CL 2025
Year: 2025

Preference Optimization (PO) techniques are currently one of the state of the art techniques for fine-tuning large language models (LLMs) on pairwise preference feedback from human annotators. However, in machine translation, this sort of feedback can be difficult to solicit. Additionally, Kreutzer et al. (2018) have shown that, for machine translation, pairwise preferences are less reliable than other forms of human feedback, such as 5-point ratings. We examine post-edits to see if they can be a source of reliable human preferences by construction. In PO, a human annotator is shown sequences $s_1$ and $s_2$ and asked for a preference judgment, %$s_1 > s_2$; while for post-editing, editors create $s_1$ and know that it should be better than $s_2$. We attempt to use these implicit preferences for PO and show that it helps the model move towards post-edit-like hypotheses and away from machine translation-like hypotheses. Furthermore, we show that best results are obtained by pre-training the model with supervised fine-tuning (SFT) on post-edits in order to promote post-edit-like hypotheses to the top output ranks.
EntryPrune: Neural Network Feature Selection using First Impressions
Venue: arXiv cs.LG 2025
Year: 2025

There is an ongoing effort to develop feature selection algorithms to improve interpretability, reduce computational resources, and minimize overfitting in predictive models. Neural networks stand out as architectures on which to build feature selection methods, and recently, neuron pruning and regrowth have emerged from the sparse neural network literature as promising new tools. We introduce EntryPrune, a novel supervised feature selection algorithm using a dense neural network with a dynamic sparse input layer. It employs entry-based pruning, a novel approach that compares neurons based on their relative change induced when they have entered the network. Extensive experiments on 13 different datasets show that our approach generally outperforms the current state-of-the-art methods, and in particular improves the average accuracy on low-dimensional datasets. Furthermore, we show that EntryPruning surpasses traditional techniques such as magnitude pruning within the EntryPrune framework and that EntryPrune achieves lower runtime than competing approaches. Our code is available at https://github.com/flxzimmer/entryprune.
SynCo: Synthetic Hard Negatives for Contrastive Visual Representation Learning
Venue: arXiv cs.CV 2025
Year: 2025

Contrastive learning has become a dominant approach in self-supervised visual representation learning, but efficiently leveraging hard negatives, which are samples closely resembling the anchor, remains challenging. We introduce SynCo (Synthetic negatives in Contrastive learning), a novel approach that improves model performance by generating synthetic hard negatives on the representation space. Building on the MoCo framework, SynCo introduces six strategies for creating diverse synthetic hard negatives on-the-fly with minimal computational overhead. SynCo achieves faster training and strong representation learning, surpassing MoCo-v2 by +0.4% and MoCHI by +1.0% on ImageNet ILSVRC-2012 linear evaluation. It also transfers more effectively to detection tasks achieving strong results on PASCAL VOC detection (57.2% AP) and significantly improving over MoCo-v2 on COCO detection (+1.0% AP) and instance segmentation (+0.8% AP). Our synthetic hard negative generation approach significantly enhances visual representations learned through self-supervised contrastive learning.
Revealing the Inherent Instructability of Pre-Trained Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Instruction tuning -- supervised fine-tuning using instruction-response pairs -- is a key step in making pre-trained large language models (LLMs) instructable. Meanwhile, LLMs perform multitask learning during their pre-training, acquiring extensive knowledge and capabilities. We hypothesize that the pre-training stage can enable them to develop the ability to comprehend and address instructions. To verify this, we propose Response Tuning (RT), which removes the instruction and its corresponding mapping to the response from instruction tuning. Instead, it focuses solely on establishing a response distribution. Our experiments demonstrate that RT models, trained only on responses, can effectively respond to a wide range of instructions akin to their instruction-tuned counterparts. In addition, we observe that the models can recognize and reject unsafe queries after learning a safety policy only from the response data. Furthermore, we find that these observations extend to an in-context learning setting. These findings support our hypothesis, highlighting the extensive inherent capabilities of pre-trained LLMs.
Inverse Entropic Optimal Transport Solves Semi-supervised Learning via Data Likelihood Maximization
Venue: arXiv cs.LG 2025
Year: 2025

Learning conditional distributions $π^*(\cdot|x)$ is a central problem in machine learning, which is typically approached via supervised methods with paired data $(x,y) \sim π^*$. However, acquiring paired data samples is often challenging, especially in problems such as domain translation. This necessitates the development of $\textit{semi-supervised}$ models that utilize both limited paired data and additional unpaired i.i.d. samples $x \sim π^*_x$ and $y \sim π^*_y$ from the marginal distributions. The usage of such combined data is complex and often relies on heuristic approaches. To tackle this issue, we propose a new learning paradigm that integrates both paired and unpaired data $\textbf{seamlessly}$ using the data likelihood maximization techniques. We demonstrate that our approach also connects intriguingly with inverse entropic optimal transport (OT). This finding allows us to apply recent advances in computational OT to establish an $\textbf{end-to-end}$ learning algorithm to get $π^*(\cdot|x)$. In addition, we derive the universal approximation property, demonstrating that our approach can theoretically recover true conditional distributions with arbitrarily small error. Furthermore, we demonstrate through empirical tests that our method effectively learns conditional distributions using paired and unpaired data simultaneously.
GPT-4o as the Gold Standard: A Scalable and General Purpose Approach to Filter Language Model Pretraining Data
Venue: arXiv cs.CL 2025
Year: 2025

Large language models require vast amounts of high-quality training data, but effective filtering of web-scale datasets remains a significant challenge. This paper demonstrates that GPT-4o is remarkably effective at identifying high-quality training data, but its prohibitive cost makes it impractical at web-scale. We propose SIEVE, a lightweight alternative that matches GPT-4o accuracy at less than 1\% of the cost. SIEVE can perform up to 500 filtering operations for the cost of one GPT-4o filtering call. The key to SIEVE is a seamless integration of GPT-4o and lightweight text classification models, using active learning to fine-tune these models in the background with a small number of calls to GPT-4o. Once trained, it performs as well as GPT-4o at a tiny fraction of the cost. Through different filtering prompts, SIEVE can efficiently curate high quality data for general or specialized domains from web-scale corpora -- a valuable capability given the current scarcity of high-quality domain-specific datasets. Extensive experiments using automatic and human evaluation metrics show that SIEVE and GPT-4o achieve similar performance on five highly specific filtering prompts. In addition, when performing quality filtering on web crawl datasets, we demonstrate SIEVE can further improve over state-of-the-art quality filtering methods in the DataComp-LM challenge for selecting LLM pretraining data.