OpenRaiser/Intern-Atlas huggingface dataset:
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the Edge
Venue: arXiv cs.AR 2025
Year: 2025

Deploying large language models (LLMs) on edge devices is crucial for delivering fast responses and ensuring data privacy. However, the limited storage, weight, and power of edge devices make it difficult to deploy LLM-powered applications. These devices must balance latency requirements with energy consumption and model accuracy. In this paper, we first quantify the challenges of deploying LLMs on off-the-shelf edge devices and then we present CLONE, an in-depth algorithm-hardware co-design at both the model- and system-level that intelligently integrates real-time, energy optimization while maintaining robust generality. In order to maximize the synergistic benefits of these algorithms in always-on and intermediate edge computing settings, we specialize in a 28nm scalable hardware accelerator system. We implement and extensively evaluate CLONE on two off-the-shelf edge platforms. Experiments show that CLONE effectively accelerates the inference process up to 11.92x, and saves energy up to 7.36x, while maintaining high-generation.
Proportional Response Dynamics in Gross Substitutes Markets
Venue: arXiv cs.GT 2025
Year: 2025

Proportional response is a well-established distributed algorithm which has been shown to converge to competitive equilibria in both Fisher and Arrow-Debreu markets, for various sub-families of homogeneous utilities, including linear and constant elasticity of substitution utilities. We propose a natural generalization of proportional response for gross substitutes utilities, and prove that it converges to competitive equilibria in Fisher markets. This is the first convergence result of a proportional response style dynamics in Fisher markets for utilities beyond the homogeneous utilities covered by the Eisenberg-Gale convex program. We show an empirical convergence rate of $O(1/T)$ for the prices. Furthermore, we show that the allocations of a lazy version of the generalized proportional response dynamics converge to competitive equilibria in Arrow-Debreu markets.
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose Estimation
Venue: arXiv cs.CV 2025
Year: 2025

Action coordination in human structure is indispensable for the spatial constraints of 2D joints to recover 3D pose. Usually, action coordination is represented as a long-range dependence among body parts. However, there are two main challenges in modeling long-range dependencies. First, joints should not only be constrained by other individual joints but also be modulated by the body parts. Second, existing methods make networks deeper to learn dependencies between non-linked parts. They introduce uncorrelated noise and increase the model size. In this paper, we utilize a pyramid structure to better learn potential long-range dependencies. It can capture the correlation across joints and groups, which complements the context of the human sub-structure. In an effective cross-scale way, it captures the pyramid-structured long-range dependence. Specifically, we propose a novel Pyramid Graph Attention (PGA) module to capture long-range cross-scale dependencies. It concatenates information from various scales into a compact sequence, and then computes the correlation between scales in parallel. Combining PGA with graph convolution modules, we develop a Pyramid Graph Transformer (PGFormer) for 3D human pose estimation, which is a lightweight multi-scale transformer architecture. It encapsulates human sub-structures into self-attention by pooling. Extensive experiments show that our approach achieves lower error and smaller model size than state-of-the-art methods on Human3.6M and MPI-INF-3DHP datasets. The code is available at https://github.com/MingjieWe/PGFormer.
Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework
Venue: arXiv cs.CV 2025
Year: 2025

Although the Segment Anything Model (SAM) is highly effective in natural image segmentation, it requires dependencies on prompts, which limits its applicability to medical imaging where manual prompts are often unavailable. Existing efforts to fine-tune SAM for medical segmentation typically struggle to remove this dependency. We propose Hierarchical Self-Prompting SAM (HSP-SAM), a novel self-prompting framework that enables SAM to achieve strong performance in prompt-free medical image segmentation. Unlike previous self-prompting methods that remain limited to positional prompts similar to vanilla SAM, we are the first to introduce learning abstract prompts during the self-prompting process. This simple and intuitive self-prompting framework achieves superior performance on classic segmentation tasks such as polyp and skin lesion segmentation, while maintaining robustness across diverse medical imaging modalities. Furthermore, it exhibits strong generalization to unseen datasets, achieving improvements of up to 14.04% over previous state-of-the-art methods on some challenging benchmarks. These results suggest that abstract prompts encapsulate richer and higher-dimensional semantic information compared to positional prompts, thereby enhancing the model's robustness and generalization performance. All models and codes will be released upon acceptance.
Exploring listeners' perceptions of AI-generated and human-composed music for functional emotional applications
Venue: arXiv cs.HC 2025
Year: 2025

This work investigates how listeners perceive and evaluate AI-generated as compared to human-composed music in the context of emotional resonance and regulation. Across a mixed-methods design, participants were exposed to both AI and human music under various labeling conditions (music correctly labeled as AI- or human-origin, music incorrectly labeled as AI- or human-origin, and unlabeled music) and emotion cases (Calm and Upbeat), and were asked to rate preference, efficacy of target emotion elicitation, and emotional impact. Participants were significantly more likely to rate human-composed music, regardless of labeling, as more effective at eliciting target emotional states, though quantitative analyses revealed no significant differences in emotional response. However, participants were significantly more likely to indicate preference for AI-generated music, yielding further questions regarding the impact of emotional authenticity and perceived authorship on musical appraisal. Qualitative data underscored this, with participants associating humanness with qualities such as imperfection, flow, and 'soul.' These findings challenge the assumption that preference alone signals success in generative music systems. Rather than positioning AI tools as replacements for human creativity or emotional expression, they point toward a more careful design ethos that acknowledges the limits of replication and prioritizes human values such as authenticity, individuality, and emotion regulation in wellness and affective technologies.
Surfer-H Meets Holo1: Cost-Efficient Web Agent Powered by Open Weights
Venue: arXiv cs.AI 2025
Year: 2025

We present Surfer-H, a cost-efficient web agent that integrates Vision-Language Models (VLM) to perform user-defined tasks on the web. We pair it with Holo1, a new open-weight collection of VLMs specialized in web navigation and information extraction. Holo1 was trained on carefully curated data sources, including open-access web content, synthetic examples, and self-produced agentic data. Holo1 tops generalist User Interface (UI) benchmarks as well as our new web UI localization benchmark, WebClick. When powered by Holo1, Surfer-H achieves a 92.2% state-of-the-art performance on WebVoyager, striking a Pareto-optimal balance between accuracy and cost-efficiency. To accelerate research advancement in agentic systems, we are open-sourcing both our WebClick evaluation dataset and the Holo1 model weights.
Token and Span Classification for Entity Recognition in French Historical Encyclopedias
Venue: arXiv cs.CL 2025
Year: 2025

Named Entity Recognition (NER) in historical texts presents unique challenges due to non-standardized language, archaic orthography, and nested or overlapping entities. This study benchmarks a diverse set of NER approaches, ranging from classical Conditional Random Fields (CRFs) and spaCy-based models to transformer-based architectures such as CamemBERT and sequence-labeling models like Flair. Experiments are conducted on the GeoEDdA dataset, a richly annotated corpus derived from 18th-century French encyclopedias. We propose framing NER as both token-level and span-level classification to accommodate complex nested entity structures typical of historical documents. Additionally, we evaluate the emerging potential of few-shot prompting with generative language models for low-resource scenarios. Our results demonstrate that while transformer-based models achieve state-of-the-art performance, especially on nested entities, generative models offer promising alternatives when labeled data are scarce. The study highlights ongoing challenges in historical NER and suggests avenues for hybrid approaches combining symbolic and neural methods to better capture the intricacies of early modern French text.
Hallucination to Consensus: Multi-Agent LLMs for End-to-End Test Generation
Venue: arXiv cs.SE 2025
Year: 2025

Unit testing plays a critical role in ensuring software correctness. However, writing unit tests manually is labor-intensive, especially for strongly typed languages like Java, motivating the need for automated approaches. Traditional methods primarily rely on search-based or randomized algorithms to achieve high code coverage and produce regression oracles, which are derived from the program's current behavior rather than its intended functionality. Recent advances in LLMs have enabled oracle generation from natural language descriptions, aligning better with user requirements. However, existing LLM-based methods often require fine-tuning or rely on external tools such as EvoSuite for test prefix generation, making them costly or cumbersome to apply in practice. In this work, we propose CANDOR, a novel prompt engineering-based LLM framework for automated unit test generation in Java. CANDOR orchestrates multiple specialized LLM agents to collaboratively generate complete tests. To mitigate the notorious hallucinations in LLMs and improve oracle correctness, we introduce a novel strategy that engages multiple reasoning LLMs in a panel discussion and generates accurate oracles based on consensus. Additionally, to reduce the verbosity of reasoning LLMs' outputs, we propose a novel dual-LLM pipeline to produce concise and structured oracle evaluations. Our experiments show that CANDOR is comparable with EvoSuite in generating tests with high code coverage and clearly superior in terms of mutation score. Moreover, our prompt engineering-based approach CANDOR significantly outperforms the SOTA fine-tuning-based oracle generator TOGLL by at least 21.1 percentage points in oracle correctness on both correct and faulty source code. Further ablation studies confirm the critical contributions of key agents in generating high-quality tests.
FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have achieved state-of-the-art results across diverse domains, yet their development remains reliant on vast amounts of publicly available data, raising concerns about data scarcity and the lack of access to domain-specific, sensitive information. Federated Learning (FL) presents a compelling framework to address these challenges by enabling decentralized fine-tuning on pre-trained LLMs without sharing raw data. However, the compatibility and performance of pre-trained LLMs in FL settings remain largely under explored. We introduce the FlowerTune LLM Leaderboard, a first-of-its-kind benchmarking suite designed to evaluate federated fine-tuning of LLMs across four diverse domains: general NLP, finance, medical, and coding. Each domain includes federated instruction-tuning datasets and domain-specific evaluation metrics. Our results, obtained through a collaborative, open-source and community-driven approach, provide the first comprehensive comparison across 26 pre-trained LLMs with different aggregation and fine-tuning strategies under federated settings, offering actionable insights into model performance, resource constraints, and domain adaptation. This work lays the foundation for developing privacy-preserving, domain-specialized LLMs for real-world applications.
Computation- and Communication-Efficient Online FL for Resource-Constrained Aerial Vehicles
Venue: arXiv cs.LG 2025
Year: 2025

Privacy-preserving distributed machine learning (ML) and aerial connected vehicle (ACV)-assisted edge computing have drawn significant attention lately. Since the onboard sensors of ACVs can capture new data as they move along their trajectories, the continual arrival of such 'newly' sensed data leads to online learning and demands carefully crafting the trajectories. Besides, as typical ACVs are inherently resource-constrained, computation- and communication-efficient ML solutions are needed. Therefore, we propose a computation- and communication-efficient online aerial federated learning (2CEOAFL) algorithm to take the benefits of continual sensed data and limited onboard resources of the ACVs. In particular, considering independently owned ACVs act as selfish data collectors, we first model their trajectories according to their respective time-varying data distributions. We then propose a 2CEOAFL algorithm that allows the flying ACVs to (a) prune the received dense ML model to make it shallow, (b) train the pruned model, and (c) probabilistically quantize and offload their trained accumulated gradients to the central server (CS). Our extensive simulation results show that the proposed 2CEOAFL algorithm delivers comparable performances to its non-pruned and nonquantized, hence, computation- and communication-inefficient counterparts.
HaploOmni: Unified Single Transformer for Multimodal Video Understanding and Generation
Venue: arXiv cs.CV 2025
Year: 2025

With the advancement of language models, unified multimodal understanding and generation have made significant strides, with model architectures evolving from separated components to unified single-model frameworks. This paper explores an efficient training paradigm to build a single transformer for unified multimodal understanding and generation. Specifically, we propose a multimodal warmup strategy utilizing prior knowledge to extend capabilities. To address cross-modal compatibility challenges, we introduce feature pre-scaling and multimodal AdaLN techniques. Integrating the proposed technologies, we present the HaploOmni, a new single multimodal transformer. With limited training costs, HaploOmni achieves competitive performance across multiple image and video understanding and generation benchmarks over advanced unified models. All codes will be made public at https://github.com/Tencent/HaploVLM.
Deep Learning for Retinal Degeneration Assessment: A Comprehensive Analysis of the MARIO Challenge
Venue: arXiv cs.CV 2025
Year: 2025

The MARIO challenge, held at MICCAI 2024, focused on advancing the automated detection and monitoring of age-related macular degeneration (AMD) through the analysis of optical coherence tomography (OCT) images. Designed to evaluate algorithmic performance in detecting neovascular activity changes within AMD, the challenge incorporated unique multi-modal datasets. The primary dataset, sourced from Brest, France, was used by participating teams to train and test their models. The final ranking was determined based on performance on this dataset. An auxiliary dataset from Algeria was used post-challenge to evaluate population and device shifts from submitted solutions. Two tasks were involved in the MARIO challenge. The first one was the classification of evolution between two consecutive 2D OCT B-scans. The second one was the prediction of future AMD evolution over three months for patients undergoing anti-vascular endothelial growth factor (VEGF) therapy. Thirty-five teams participated, with the top 12 finalists presenting their methods. This paper outlines the challenge's structure, tasks, data characteristics, and winning methodologies, setting a benchmark for AMD monitoring using OCT, infrared imaging, and clinical data (such as the number of visits, age, gender, etc.). The results of this challenge indicate that artificial intelligence (AI) performs as well as a physician in measuring AMD progression (Task 1) but is not yet able of predicting future evolution (Task 2).
On the Robustness of Tabular Foundation Models: Test-Time Attacks and In-Context Defenses
Venue: arXiv cs.LG 2025
Year: 2025

Recent tabular Foundational Models (FM) such as TabPFN and TabICL, leverage in-context learning to achieve strong performance without gradient updates or fine-tuning. However, their robustness to adversarial manipulation remains largely unexplored. In this work, we present a comprehensive study of the adversarial vulnerabilities of tabular FM, focusing on both their fragility to targeted test-time attacks and their potential misuse as adversarial tools. We show on three benchmarks in finance, cybersecurity and healthcare, that small, structured perturbations to test inputs can significantly degrade prediction accuracy, even when training context remain fixed. Additionally, we demonstrate that tabular FM can be repurposed to generate transferable evasion to conventional models such as random forests and XGBoost, and on a lesser extent to deep tabular models. To improve tabular FM, we formulate the robustification problem as an optimization of the weights (adversarial fine-tuning), or the context (adversarial in-context learning). We introduce an in-context adversarial training strategy that incrementally replaces the context with adversarial perturbed instances, without updating model weights. Our approach improves robustness across multiple tabular benchmarks. Together, these findings position tabular FM as both a target and a source of adversarial threats, highlighting the urgent need for robust training and evaluation practices in this emerging paradigm.
Facts Do Care About Your Language: Assessing Answer Quality of Multilingual LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Factuality is a necessary precursor to useful educational tools. As adoption of Large Language Models (LLMs) in education continues of grow, ensuring correctness in all settings is paramount. Despite their strong English capabilities, LLM performance in other languages is largely untested. In this work, we evaluate the correctness of the Llama3.1 family of models in answering factual questions appropriate for middle and high school students. We demonstrate that LLMs not only provide extraneous and less truthful information, but also exacerbate existing biases against rare languages.
Multi-Metric Adaptive Experimental Design under Fixed Budget with Validation
Venue: arXiv cs.LG 2025
Year: 2025

Standard A/B tests in online experiments face statistical power challenges when testing multiple candidates simultaneously, while adaptive experimental designs (AED) alone fall short in inferring experiment statistics such as the average treatment effect, especially with many metrics (e.g., revenue, safety) and heterogeneous variances. This paper proposes a fixed-budget multi-metric AED framework with a two-phase structure: an adaptive exploration phase to identify the best treatment, and a validation phase with an A/B test to verify the treatment's quality and infer statistics. We propose SHRVar, which generalizes sequential halving (SH) (Karnin et al., 2013) with a novel relative-variance-based sampling and an elimination strategy built on reward z-values. It achieves a provable error probability that decreases exponentially, where the exponent generalizes the complexity measure for SH (Karnin et al., 2013) and SHVar (Lalitha et al., 2023) with homogeneous and heterogeneous variances, respectively. Numerical experiments verify our analysis and demonstrate the superior performance of this new framework.
GPU-Parallelizable Randomized Sketch-and-Precondition for Linear Regression using Sparse Sign Sketches
Venue: arXiv cs.DS 2025
Year: 2025

A litany of theoretical and numerical results have established the sketch-and-precondition paradigm as a powerful approach to solving large linear regression problems in standard computing environments. Perhaps surprisingly, much less work has been done on understanding how sketch-and-precondition performs on graphics processing unit (GPU) systems. We address this gap by benchmarking an implementation of sketch-and-precondition based on sparse sign-sketches on single and multi-GPU systems. In doing so, we describe a novel, easily parallelized, rejection-sampling based method for generating sparse sign sketches. Our approach, which is particularly well-suited for GPUs, is easily adapted to a variety of computing environments. Taken as a whole, our numerical experiments indicate that sketch-and-precondition with sparse sign sketches is particularly well-suited for GPUs, and may be suitable for use in black-box least-squares solvers.
DyTact: Capturing Dynamic Contacts in Hand-Object Manipulation
Venue: arXiv cs.CV 2025
Year: 2025

Reconstructing dynamic hand-object contacts is essential for realistic manipulation in AI character animation, XR, and robotics, yet it remains challenging due to heavy occlusions, complex surface details, and limitations in existing capture techniques. In this paper, we introduce DyTact, a markerless capture method for accurately capturing dynamic contact in hand-object manipulations in a non-intrusive manner. Our approach leverages a dynamic, articulated representation based on 2D Gaussian surfels to model complex manipulations. By binding these surfels to MANO meshes, DyTact harnesses the inductive bias of template models to stabilize and accelerate optimization. A refinement module addresses time-dependent high-frequency deformations, while a contact-guided adaptive sampling strategy selectively increases surfel density in contact regions to handle heavy occlusion. Extensive experiments demonstrate that DyTact not only achieves state-of-the-art dynamic contact estimation accuracy but also significantly improves novel view synthesis quality, all while operating with fast optimization and efficient memory usage. Project Page: https://oliver-cong02.github.io/DyTact.github.io/ .
Assessing Workers Neuro-physiological Stress Responses to Augmented Reality Safety Warnings in Immersive Virtual Roadway Work Zones
Venue: arXiv cs.HC 2025
Year: 2025

This paper presents a multi-stage experimental framework that integrates immersive Virtual Reality (VR) simulations, wearable sensors, and advanced signal processing to investigate construction workers neuro-physiological stress responses to multi-sensory AR-enabled warnings. Participants performed light- and moderate-intensity roadway maintenance tasks within a high-fidelity VR roadway work zone, while key stress markers of electrodermal activity (EDA), heart rate variability (HRV), and electroencephalography (EEG) were continuously measured. Statistical analyses revealed that task intensity significantly influenced physiological and neurological stress indicators. Moderate-intensity tasks elicited greater autonomic arousal, evidenced by elevated heart rate measures (mean-HR, std-HR, max-HR) and stronger electrodermal responses, while EEG data indicated distinct stress-related alpha suppression and beta enhancement. Feature-importance analysis further identified mean EDR and short-term HR metrics as discriminative for classifying task intensity. Correlation results highlighted a temporal lag between immediate neural changes and subsequent physiological stress reactions, emphasizing the interplay between cognition and autonomic regulation during hazardous tasks.