OpenRaiser/Intern-Atlas huggingface dataset:
Art3D: Training-Free 3D Generation from Flat-Colored Illustration
Venue: arXiv cs.CV 2025
Year: 2025

Large-scale pre-trained image-to-3D generative models have exhibited remarkable capabilities in diverse shape generations. However, most of them struggle to synthesize plausible 3D assets when the reference image is flat-colored like hand drawings due to the lack of 3D illusion, which are often the most user-friendly input modalities in art content creation. To this end, we propose Art3D, a training-free method that can lift flat-colored 2D designs into 3D. By leveraging structural and semantic features with pre- trained 2D image generation models and a VLM-based realism evaluation, Art3D successfully enhances the three-dimensional illusion in reference images, thus simplifying the process of generating 3D from 2D, and proves adaptable to a wide range of painting styles. To benchmark the generalization performance of existing image-to-3D models on flat-colored images without 3D feeling, we collect a new dataset, Flat-2D, with over 100 samples. Experimental results demonstrate the performance and robustness of Art3D, exhibiting superior generalizable capacity and promising practical applicability. Our source code and dataset will be publicly available on our project page: https://joy-jy11.github.io/ .
InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
Venue: arXiv cs.CV 2025
Year: 2025

We introduce InternVL3, a significant advancement in the InternVL series featuring a native multimodal pre-training paradigm. Rather than adapting a text-only large language model (LLM) into a multimodal large language model (MLLM) that supports visual inputs, InternVL3 jointly acquires multimodal and linguistic capabilities from both diverse multimodal data and pure-text corpora during a single pre-training stage. This unified training paradigm effectively addresses the complexities and alignment challenges commonly encountered in conventional post-hoc training pipelines for MLLMs. To further improve performance and scalability, InternVL3 incorporates variable visual position encoding (V2PE) to support extended multimodal contexts, employs advanced post-training techniques such as supervised fine-tuning (SFT) and mixed preference optimization (MPO), and adopts test-time scaling strategies alongside an optimized training infrastructure. Extensive empirical evaluations demonstrate that InternVL3 delivers superior performance across a wide range of multi-modal tasks. In particular, InternVL3-78B achieves a score of 72.2 on the MMMU benchmark, setting a new state-of-the-art among open-source MLLMs. Its capabilities remain highly competitive with leading proprietary models, including ChatGPT-4o, Claude 3.5 Sonnet, and Gemini 2.5 Pro, while also maintaining strong pure-language proficiency. In pursuit of open-science principles, we will publicly release both the training data and model weights to foster further research and development in next-generation MLLMs.
Exploring Generative AI Techniques in Government: A Case Study
Venue: arXiv cs.IR 2025
Year: 2025

The swift progress of Generative Artificial intelligence (GenAI), notably Large Language Models (LLMs), is reshaping the digital landscape. Recognizing this transformative potential, the National Research Council of Canada (NRC) launched a pilot initiative to explore the integration of GenAI techniques into its daily operation for performance excellence, where 22 projects were launched in May 2024. Within these projects, this paper presents the development of the intelligent agent Pubbie as a case study, targeting the automation of performance measurement, data management and insight reporting at the NRC. Cutting-edge techniques are explored, including LLM orchestration and semantic embedding via RoBERTa, while strategic fine-tuning and few-shot learning approaches are incorporated to infuse domain knowledge at an affordable cost. The user-friendly interface of Pubbie allows general government users to input queries in natural language and easily upload or download files with a simple button click, greatly reducing manual efforts and accessibility barriers.
Exposure to Content Written by Large Language Models Can Reduce Stigma Around Opioid Use Disorder in Online Communities
Venue: arXiv cs.SI 2025
Year: 2025

Widespread stigma, both in the offline and online spaces, acts as a barrier to harm reduction efforts in the context of opioid use disorder (OUD). This stigma is prominently directed towards clinically approved medications for addiction treatment (MAT), people with the condition, and the condition itself. Given the potential of artificial intelligence based technologies in promoting health equity, and facilitating empathic conversations, this work examines whether large language models (LLMs) can help abate OUD-related stigma in online communities. To answer this, we conducted a series of pre-registered randomized controlled experiments, where participants read LLM-generated, human-written, or no responses to help seeking OUD-related content in online communities. The experiment was conducted under two setups, i.e., participants read the responses either once (N = 2,141), or repeatedly for 14 days (N = 107). We found that participants reported the least stigmatized attitudes toward MAT after consuming LLM-generated responses under both the setups. This study offers insights into strategies that can foster inclusive online discourse on OUD, e.g., based on our findings LLMs can be used as an education-based intervention to promote positive attitudes and increase people's propensity toward MAT.
Integrating Emotion Distribution Networks and Textual Message Analysis for X User Emotional State Classification
Venue: arXiv cs.SI 2025
Year: 2025

As the popularity and reach of social networks continue to surge, a vast reservoir of opinions and sentiments across various subjects inundates these platforms. Among these, X social network (formerly Twitter) stands as a juggernaut, boasting approximately 420 million active users. Extracting users' emotional and mental states from their expressed opinions on social media has become a common pursuit. While past methodologies predominantly focused on the textual content of messages to analyze user sentiment, the interactive nature of these platforms suggests a deeper complexity. This study employs hybrid methodologies, integrating textual analysis, profile examination, follower analysis, and emotion dissemination patterns. Initially, user interactions are leveraged to refine emotion classification within messages, encompassing exchanges where users respond to each other. Introducing the concept of a communication tree, a model is extracted to map these interactions. Subsequently, users' bios and interests from this tree are juxtaposed with message text to enrich analysis. Finally, influential figures are identified among users' followers in the communication tree, categorized into different topics to gauge interests. The study highlights that traditional sentiment analysis methodologies, focusing solely on textual content, are inadequate in discerning sentiment towards significant events, notably the presidential election. Comparative analysis with conventional methods reveals a substantial improvement in accuracy with the incorporation of emotion distribution patterns and user profiles. The proposed approach yields a 12% increase in accuracy with emotion distribution patterns and a 15% increase when considering user profiles, underscoring its efficacy in capturing nuanced sentiment dynamics.
Where Should I Deploy My Contracts? A Practical Experience Report
Venue: arXiv cs.SE 2025
Year: 2025

Blockchain networks provide a reliable trust anchor to decentralized applications (DApps) backed by smart contracts. The Ethereum ecosystem now encompasses most blockchain networks that provide compatible support for smart contracts code. Recently, many Ethereum Layer 2 (L2) rollup solutions emerged, meant to scale the base Layer 1 (L1) network, consequently decreasing transaction fees and diversifying the usage scenarios. Furthermore, the number of blockchain providers that offer access to the network infrastructure for both L1 and L2 continuously increases. A developer is faced with a multitude of deployment options and must weigh between the gains in costs and the losses in trust that are still an issue with L2. A decisive factor in this trade-off can be the use case itself, depending on its security requirements. Still, the evaluation of costs and performance cannot be ignored and should rely on a set of measurable metrics, although choosing the right metrics can be complicated. In this practical experience report, we explore the relevance of several such metrics in choosing between different providers and rollups. For this purpose, we perform evaluations for two use cases of DApps: a voting DApp with high security demands, suited for L1 deployment, and a cost-sensitive supply chain DApp, where L2 can be an option. We analyze a set of basic metrics by comparing these between two highly used access providers, Alchemy and Infura, for the L1 deployment case, and between two of the most popular rollups, Arbitrum One and OP Mainnet (Optimism), for the L2 deployment scenario.
Distilling Transitional Pattern to Large Language Models for Multimodal Session-based Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

Session-based recommendation (SBR) predicts the next item based on anonymous sessions. Traditional SBR explores user intents based on ID collaborations or auxiliary content. To further alleviate data sparsity and cold-start issues, recent Multimodal SBR (MSBR) methods utilize simplistic pre-trained models for modality learning but have limitations in semantic richness. Considering semantic reasoning abilities of Large Language Models (LLM), we focus on the LLM-enhanced MSBR scenario in this paper, which leverages LLM cognition for comprehensive multimodal representation generation, to enhance downstream MSBR. Tackling this problem faces two challenges: i) how to obtain LLM cognition on both transitional patterns and inherent multimodal knowledge, ii) how to align both features into one unified LLM, minimize discrepancy while maximizing representation utility. To this end, we propose a multimodal LLM-enhanced framework TPAD, which extends a distillation paradigm to decouple and align transitional patterns for promoting MSBR. TPAD establishes parallel Knowledge-MLLM and Transfer-MLLM, where the former interprets item knowledge-reflected features and the latter extracts transition-aware features underneath sessions. A transitional pattern alignment module harnessing mutual information estimation theory unites two MLLMs, alleviating distribution discrepancy and distilling transitional patterns into modal representations. Extensive experiments on real-world datasets demonstrate the effectiveness of our framework.
AgMMU: A Comprehensive Agricultural Multimodal Understanding Benchmark
Venue: arXiv cs.CV 2025
Year: 2025

We present AgMMU, a challenging real-world benchmark for evaluating and advancing vision-language models (VLMs) in the knowledge-intensive domain of agriculture. Unlike prior datasets that rely on crowdsourced prompts, AgMMU is distilled from 116,231 authentic dialogues between everyday growers and USDA-authorized Cooperative Extension experts. Through a three-stage pipeline: automated knowledge extraction, QA generation, and human verification, we construct (i) AgMMU, an evaluation set of 746 multiple-choice questions (MCQs) and 746 open-ended questions (OEQs), and (ii) AgBase, a development corpus of 57,079 multimodal facts covering five high-stakes agricultural topics: insect identification, species identification, disease categorization, symptom description, and management instruction. Benchmarking 12 leading VLMs reveals pronounced gaps in fine-grained perception and factual grounding. Open-sourced models trail after proprietary ones by a wide margin. Simple fine-tuning on AgBase boosts open-sourced model performance on challenging OEQs for up to 11.6% on average, narrowing this gap and also motivating future research to propose better strategies in knowledge extraction and distillation from AgBase. We hope AgMMU stimulates research on domain-specific knowledge integration and trustworthy decision support in agriculture AI development.
Improving Upon the generalized c-mu rule: a Whittle approach
Venue: arXiv cs.PF 2025
Year: 2025

Scheduling a stream of jobs whose holding cost changes over time is a classic and practical problem. Specifically, each job is associated with a holding cost (penalty), where a job's instantaneous holding cost is some increasing function of its class and current age (the time it has spent in the system since its arrival). The goal is to schedule the jobs to minimize the time-average total holding cost across all jobs. The seminal paper on this problem, by Van Mieghem in 1995, introduced the generalized c-mu rule for scheduling jobs. Since then, this problem has attracted significant interest but remains challenging due to the absence of a finite-dimensional state space formulation. Consequently, subsequent works focus on more tractable versions of this problem. This paper returns to the original problem, deriving a heuristic that empirically improves upon the generalized c-mu rule and all existing heuristics. Our approach is to first translate the holding cost minimization problem to a novel Restless Multi-Armed Bandit (R-MAB) problem with a finite number of arms. Based on our R-MAB, we derive a novel Whittle Index policy, which is both elegant and intuitive.
Introducing Large Language Models as the Next Challenging Internet Traffic Source
Venue: arXiv cs.NI 2025
Year: 2025

This article explores the growing impact of large language models (LLMs) and Generative AI (GenAI) tools on Internet traffic, focusing on their role as a new and significant source of network load. As these AI tools continue to gain importance in applications ranging from virtual assistants to content generation, the volume of traffic they generate is expected to increase massively. These models use the Internet as the global infrastructure for delivering multimedia messages (text, voice, images, video, etc.) to users, by interconnecting users and devices with AI agents typically deployed in the cloud. We believe this represents a new paradigm that will lead to a considerable increase in network traffic, and network operators must be prepared to address the resulting demands. To support this claim, we provide a proof-of-concept and source code for measuring traffic in remote user-agent interactions, estimating the traffic generated per prompt for some of the most popular open-source LLMs in 2025. The average size of each prompt query and response is 7,593 bytes, with a standard deviation of 369 bytes. These numbers are comparable with email and web browsing traffic. However, we envision AI as the next "killer application" that will saturate networks with traffic, such as Peer-to-Peer traffic and Video-on-demand dominated in previous decades.
Load Balancing with Network Latencies via Distributed Gradient Descent
Venue: arXiv cs.DC 2025
Year: 2025

Motivated by the growing demand for serving large language model inference requests, we study distributed load balancing for global serving systems with network latencies. We consider a fluid model in which continuous flows of requests arrive at different frontends and need to be routed to distant backends for processing whose processing rates are workload dependent. Network latencies can lead to long travel times for requests and delayed feedback from backends. The objective is to minimize the average latency of requests, composed of the network latency and the serving latency at the backends. We introduce Distributed Gradient Descent Load Balancing (DGD-LB), a probabilistic routing algorithm in which each frontend adjusts the routing probabilities dynamically using gradient descent. Our algorithm is distributed: there is no coordination between frontends, except by observing the delayed impact other frontends have on shared backends. The algorithm uses an approximate gradient that measures the marginal impact of an additional request evaluated at a delayed system state. Equilibrium points of our algorithm minimize the centralized optimal average latencies, and we provide a novel local stability analysis showing that our algorithm converges to an optimal solution when started sufficiently close to that point. Moreover, we present sufficient conditions on the step-size of gradient descent that guarantee convergence in the presence of network latencies. Numerical experiments show that our algorithm is globally stable and optimal, confirm our stability conditions are nearly tight, and demonstrate that DGD-LB can lead to substantial gains relative to other load balancers studied in the literature when network latencies are large.
Optimising Intrusion Detection Systems in Cloud-Edge Continuum with Knowledge Distillation for Privacy-Preserving and Efficient Communication
Venue: arXiv cs.CR 2025
Year: 2025

The growth of the Internet of Things has amplified the need for secure data interactions in cloud-edge ecosystems, where sensitive information is constantly processed across various system layers. Intrusion detection systems are commonly used to protect such environments from malicious attacks. Recently, Federated Learning has emerged as an effective solution for implementing intrusion detection systems, owing to its decentralised architecture that avoids sharing raw data with a central server, thereby enhancing data privacy. Despite its benefits, Federated Learning faces criticism for high communication overhead from frequent model updates, especially in large-scale Cloud-Edge infrastructures. This paper explores Knowledge Distillation to reduce communication overhead in Cloud-Edge intrusion detection while preserving accuracy and data privacy. Experiments show significant improvements over state-of-the-art methods.
The Trie Measure, Revisited
Venue: arXiv cs.DS 2025
Year: 2025

In this paper, we study the following problem: given $n$ subsets $S_1, \dots, S_n$ of an integer universe $U = \{0,\dots, u-1\}$, having total cardinality $N = \sum_{i=1}^n |S_i|$, find a prefix-free encoding $enc : U \rightarrow \{0,1\}^+$ minimizing the so-called trie measure, i.e., the total number of edges in the $n$ binary tries $\mathcal T_1, \dots, \mathcal T_n$, where $\mathcal T_i$ is the trie packing the encoded integers $\{enc(x):x\in S_i\}$. We first observe that this problem is equivalent to that of merging $u$ sets with the cheapest sequence of binary unions, a problem which in [Ghosh et al., ICDCS 2015] is shown to be NP-hard. Motivated by the hardness of the general problem, we focus on particular families of prefix-free encodings. We start by studying the fixed-length shifted encoding of [Gupta et al., Theoretical Computer Science 2007]. Given a parameter $0\le a < u$, this encoding sends each $x \in U$ to $(x + a) \mod u$, interpreted as a bit-string of $\log u$ bits. We develop the first efficient algorithms that find the value of $a$ minimizing the trie measure when this encoding is used. Our two algorithms run in $O(u + N\log u)$ and $O(N\log^2 u)$ time, respectively. We proceed by studying ordered encodings (a.k.a. monotone or alphabetic), and describe an algorithm finding the optimal such encoding in $O(N+u^3)$ time. Within the same running time, we show how to compute the best shifted ordered encoding, provably no worse than both the optimal shifted and optimal ordered encodings. We provide implementations of our algorithms and discuss how these encodings perform in practice.
PDSP-Bench: A Benchmarking System for Parallel and Distributed Stream Processing
Venue: arXiv cs.DC 2025
Year: 2025

The paper introduces PDSP-Bench, a novel benchmarking system designed for a systematic understanding of performance of parallel stream processing in a distributed environment. Such an understanding is essential for determining how Stream Processing Systems (SPS) use operator parallelism and the available resources to process massive workloads of modern applications. Existing benchmarking systems focus on analyzing SPS using queries with sequential operator pipelines within a homogeneous centralized environment. Quite differently, PDSP-Bench emphasizes the aspects of parallel stream processing in a distributed heterogeneous environment and simultaneously allows the integration of machine learning models for SPS workloads. In our results, we benchmark a well-known SPS, Apache Flink, using parallel query structures derived from real-world applications and synthetic queries to show the capabilities of PDSP-Bench towards parallel stream processing. Moreover, we compare different learned cost models using generated SPS workloads on PDSP-Bench by showcasing their evaluations on model and training efficiency. We present key observations from our experiments using PDSP-Bench that highlight interesting trends given different query workloads, such as non-linearity and paradoxical effects of parallelism on the performance.
GestureCoach: Rehearsing for Engaging Talks with LLM-Driven Gesture Recommendations
Venue: arXiv cs.HC 2025
Year: 2025

This paper introduces GestureCoach, a system designed to help speakers deliver more engaging talks by guiding them to gesture effectively during rehearsal. GestureCoach combines an LLM-driven gesture recommendation model with a rehearsal interface that proactively cues speakers to gesture appropriately. Trained on experts' gesturing patterns from TED talks, the model consists of two modules: an emphasis proposal module, which predicts when to gesture by identifying gesture-worthy text segments in the presenter notes, and a gesture identification module, which determines what gesture to use by retrieving semantically appropriate gestures from a curated gesture database. Results of a model performance evaluation and user study (N=30) show that the emphasis proposal module outperforms off-the-shelf LLMs in identifying suitable gesture regions, and that participants rated the majority of these predicted regions and their corresponding gestures as highly appropriate. A subsequent user study (N=10) showed that rehearsing with GestureCoach encouraged speakers to gesture and significantly increased gesture diversity, resulting in more engaging talks. We conclude with design implications for future AI-driven rehearsal systems.