OpenRaiser/Intern-Atlas huggingface dataset:
A Systematic Literature Review of Infrastructure Studies in SIGCHI
Venue: arXiv cs.HC 2025
Year: 2025

Infrastructure is an indispensable part of human life. Over the past decades, the Human-Computer Interaction (HCI) community has paid increasing attention to human interactions with infrastructure. In this paper, we conducted a systematic literature review on infrastructure studies in SIGCHI, one of the most influential communities in HCI. We collected a total of 190 primary studies, covering works published between 2006 and 2024. Most of these studies are inspired by Susan Leigh Star's notion of infrastructure. We identify three major themes in infrastructure studies: growing infrastructure, appropriating infrastructure, and coping with infrastructure. Our review highlights a prevailing trend in SIGCHI's infrastructure research: a focus on informal infrastructural activities across various sociotechnical contexts. In particular, we examine studies that problematize infrastructure and alert the HCI community to its potentially harmful aspects.
Iterative Self-Training for Code Generation via Reinforced Re-Ranking
Venue: arXiv cs.CL 2025
Year: 2025

Generating high-quality code that solves complex programming tasks is challenging, especially with current decoder-based models that produce highly stochastic outputs. In code generation, even minor errors can easily break the entire solution. Leveraging multiple sampled solutions can significantly improve the overall output quality. One effective way to enhance code generation is by pairing a code generation model with a reranker model, which selects the best solution from the generated samples. We propose a novel iterative self-training approach for self-training reranker models using Proximal Policy Optimization (PPO), aimed at improving both reranking accuracy and the overall code generation process. Unlike traditional PPO approaches, where the focus is on optimizing a generative model with a reward model, our approach emphasizes the development of a robust reward/reranking model. This model improves the quality of generated code through reranking and addresses problems and errors that the reward model might overlook during PPO alignment with the reranker. Our method iteratively refines the training dataset by re-evaluating outputs, identifying high-scoring negative examples, and incorporating them into the training loop, that boosting model performance. Our evaluation on the MultiPL-E dataset demonstrates that our 13.4B parameter model outperforms a 33B model in code generation quality while being three times faster. Moreover, it achieves performance comparable to GPT-4 and surpasses it in one programming language.
RANSAC Revisited: An Improved Algorithm for Robust Subspace Recovery under Adversarial and Noisy Corruptions
Venue: arXiv cs.LG 2025
Year: 2025

In this paper, we study the problem of robust subspace recovery (RSR) in the presence of both strong adversarial corruptions and Gaussian noise. Specifically, given a limited number of noisy samples -- some of which are tampered by an adaptive and strong adversary -- we aim to recover a low-dimensional subspace that approximately contains a significant fraction of the uncorrupted samples, up to an error that scales with the Gaussian noise. Existing approaches to this problem often suffer from high computational costs or rely on restrictive distributional assumptions, limiting their applicability in truly adversarial settings. To address these challenges, we revisit the classical random sample consensus (RANSAC) algorithm, which offers strong robustness to adversarial outliers, but sacrifices efficiency and robustness against Gaussian noise and model misspecification in the process. We propose a two-stage algorithm, RANSAC+, that precisely pinpoints and remedies the failure modes of standard RANSAC. Our method is provably robust to both Gaussian and adversarial corruptions, achieves near-optimal sample complexity without requiring prior knowledge of the subspace dimension, and is more efficient than existing RANSAC-type methods.
Bridging Immutability with Flexibility: A Scheme for Secure and Efficient Smart Contract Upgrades
Venue: arXiv cs.CR 2025
Year: 2025

The emergence of blockchain technology has revolutionized contract execution through the introduction of smart contracts. Ethereum, the leading blockchain platform, leverages smart contracts to power decentralized applications (DApps), enabling transparent and self-executing systems across various domains. While the immutability of smart contracts enhances security and trust, it also poses significant challenges for updates, defect resolution, and adaptation to changing requirements. Existing upgrade mechanisms are complex, resource-intensive, and costly in terms of gas consumption, often compromising security and limiting practical adoption. To address these challenges, we propose FlexiContracts+, a novel scheme that reimagines smart contracts by enabling secure, in-place upgrades on Ethereum while preserving historical data without relying on multiple contracts or extensive pre-deployment planning. FlexiContracts+ enhances security, simplifies development, reduces engineering overhead, and supports adaptable, expandable smart contracts. Comprehensive testing demonstrates that FlexiContracts+ achieves a practical balance between immutability and flexibility, advancing the capabilities of smart contract systems.
Nash Social Welfare with Submodular Valuations: Approximation Algorithms and Integrality Gaps
Venue: arXiv cs.GT 2025
Year: 2025

We study the problem of allocating items to agents with submodular valuations with the goal of maximizing the weighted Nash social welfare (NSW). The best-known results for unweighted and weighted objectives are the $(4+ε)$ approximation given by Garg, Husic, Li, Végh, and Vondrák~[STOC 2023] and the $(233+ε)$ approximation given by Feng, Hu, Li, and Zhang~[STOC 2025], respectively. In this work, we present a $(3.56+ε)$-approximation algorithm for weighted NSW maximization with submodular valuations, simultaneously improving the previous approximation ratios of both the weighted and unweighted NSW problems. Our algorithm solves the configuration LP of Feng, Hu, Li, and Zhang~[STOC 2025] via a stronger separation oracle that loses an $e/(e-1)$ factor only on small items, and then rounds the solution via a new bipartite multigraph construction. Some key technical ingredients of our analysis include a greedy proxy function, additive within each configuration, that preserves the LP value while lower-bounding the rounded solution, together with refined concentration bounds and a series of mathematical programs analyzed partly by computer assistance. On the hardness side, we prove that the configuration LP for weighted NSW with submodular valuations has an integrality gap of at least $(2^{\ln 2}-ε) \approx 1.617 - ε$, which is larger than the current best-known $e/(e-1)-ε\approx 1.582-ε$ hardness~[SODA 2020]. For additive valuations, we show an integrality gap of $(e^{1/e}-ε)$, which proves the tightness of the approximation ratio in~[ICALP 2024] for algorithms based on the configuration LP. For unweighted NSW with additive valuations, we show an integrality gap of $(2^{1/4}-ε) \approx 1.189-ε$, again larger than the current best-known $\sqrt{8/7} \approx 1.069$-hardness~[Math. Oper. Res. 2024].
Earthquake Simulation
Venue: arXiv cs.CE 2025
Year: 2025

This paper presents a seismic activity simulator that models the effects of fault lines on surface pressure. This project uses C programming to create a fully interactive learning resource intended to educate users on the mechanics of earthquakes. The motivation behind this project is to make studying seismic activity more accessible, engaging and cost effective.
SPOT: Spatio-Temporal Pattern Mining and Optimization for Load Consolidation in Freight Transportation Networks
Venue: arXiv cs.LG 2025
Year: 2025

Freight consolidation has significant potential to reduce transportation costs and mitigate congestion and pollution. An effective load consolidation plan relies on carefully chosen consolidation points to ensure alignment with existing transportation management processes, such as driver scheduling, personnel planning, and terminal operations. This complexity represents a significant challenge when searching for optimal consolidation strategies. Traditional optimization-based methods provide exact solutions, but their computational complexity makes them impractical for large-scale instances and they fail to leverage historical data. Machine learning-based approaches address these issues but often ignore operational constraints, leading to infeasible consolidation plans. This work proposes SPOT, an end-to-end approach that integrates the benefits of machine learning (ML) and optimization for load consolidation. The ML component plays a key role in the planning phase by identifying the consolidation points through spatio-temporal clustering and constrained frequent itemset mining, while the optimization selects the most cost-effective feasible consolidation routes for a given operational day. Extensive experiments conducted on industrial load data demonstrate that SPOT significantly reduces travel distance and transportation costs (by about 50% on large terminals) compared to the existing industry-standard load planning strategy and a neighborhood-based heuristic. Moreover, the ML component provides valuable tactical-level insights by identifying frequently recurring consolidation opportunities that guide proactive planning. In addition, SPOT is computationally efficient and can be easily scaled to accommodate large transportation networks.
GAMA: High-Performance GEMM Acceleration on AMD Versal ML-Optimized AI Engines
Venue: arXiv cs.AR 2025
Year: 2025

General matrix-matrix multiplication (GEMM) is a fundamental operation in machine learning (ML) applications. We present the first comprehensive performance acceleration of GEMM workloads on AMD's second-generation AIE-ML (AIE2) architecture, which is specifically optimized for ML applications. Compared to AI-Engine (AIE1), AIE offers increased compute throughput and larger on-chip memory capacity. We propose a novel design that maximizes AIE memory utilization, incorporates custom buffer placement within the AIE2 and staggered kernel placement across the AIE2 array, significantly reducing performance bottlenecks such as memory stalls and routing congestion, resulting in improved performance and efficiency compared to the default compiler provided by AMD. We evaluate the performance benefits of our design at three levels: single AIE, pack of AIEs and the complete AIE array. GAMA achieves state-of-the-art performance, delivering up to 165 TOPS (85% of peak) for int8 precision and 83 TBFLOPS (86% of peak) for bfloat16 precision GEMM workloads. Our solution achieves 8.7%, 9%, 39% and 53.6% higher peak throughput efficiency compared to the state-of-the-art AIE1 frameworks AMA, MAXEVA, ARIES and CHARM, respectively.
ToolTipNet: A Segmentation-Driven Deep Learning Baseline for Surgical Instrument Tip Detection
Venue: arXiv cs.CV 2025
Year: 2025

In robot-assisted laparoscopic radical prostatectomy (RALP), the location of the instrument tip is important to register the ultrasound frame with the laparoscopic camera frame. A long-standing limitation is that the instrument tip position obtained from the da Vinci API is inaccurate and requires hand-eye calibration. Thus, directly computing the position of the tool tip in the camera frame using the vision-based method becomes an attractive solution. Besides, surgical instrument tip detection is the key component of other tasks, like surgical skill assessment and surgery automation. However, this task is challenging due to the small size of the tool tip and the articulation of the surgical instrument. Surgical instrument segmentation becomes relatively easy due to the emergence of the Segmentation Foundation Model, i.e., Segment Anything. Based on this advancement, we explore the deep learning-based surgical instrument tip detection approach that takes the part-level instrument segmentation mask as input. Comparison experiments with a hand-crafted image-processing approach demonstrate the superiority of the proposed method on simulated and real datasets.
InfoMAE: Pair-Efficient Cross-Modal Alignment for Multimodal Time-Series Sensing Signals
Venue: arXiv cs.AI 2025
Year: 2025

Standard multimodal self-supervised learning (SSL) algorithms regard cross-modal synchronization as implicit supervisory labels during pretraining, thus posing high requirements on the scale and quality of multimodal samples. These constraints significantly limit the performance of sensing intelligence in IoT applications, as the heterogeneity and the non-interpretability of time-series signals result in abundant unimodal data but scarce high-quality multimodal pairs. This paper proposes InfoMAE, a cross-modal alignment framework that tackles the challenge of multimodal pair efficiency under the SSL setting by facilitating efficient cross-modal alignment of pretrained unimodal representations. InfoMAE achieves \textit{efficient cross-modal alignment} with \textit{limited data pairs} through a novel information theory-inspired formulation that simultaneously addresses distribution-level and instance-level alignment. Extensive experiments on two real-world IoT applications are performed to evaluate InfoMAE's pairing efficiency to bridge pretrained unimodal models into a cohesive joint multimodal model. InfoMAE enhances downstream multimodal tasks by over 60% with significantly improved multimodal pairing efficiency. It also improves unimodal task accuracy by an average of 22%.
Adapting Robot's Explanation for Failures Based on Observed Human Behavior in Human-Robot Collaboration
Venue: arXiv cs.RO 2025
Year: 2025

This work aims to interpret human behavior to anticipate potential user confusion when a robot provides explanations for failure, allowing the robot to adapt its explanations for more natural and efficient collaboration. Using a dataset that included facial emotion detection, eye gaze estimation, and gestures from 55 participants in a user study, we analyzed how human behavior changed in response to different types of failures and varying explanation levels. Our goal is to assess whether human collaborators are ready to accept less detailed explanations without inducing confusion. We formulate a data-driven predictor to predict human confusion during robot failure explanations. We also propose and evaluate a mechanism, based on the predictor, to adapt the explanation level according to observed human behavior. The promising results from this evaluation indicate the potential of this research in adapting a robot's explanations for failures to enhance the collaborative experience.
ReadMe.LLM: A Framework to Help LLMs Understand Your Library
Venue: arXiv cs.SE 2025
Year: 2025

Large Language Models (LLMs) often struggle with code generation tasks involving niche software libraries. Existing code generation techniques with only human-oriented documentation can fail -- even when the LLM has access to web search and the library is documented online. To address this challenge, we propose ReadMe$.$LLM, LLM-oriented documentation for software libraries. By attaching the contents of ReadMe$.$LLM to a query, performance consistently improves to near-perfect accuracy, with one case study demonstrating up to 100% success across all tested models. We propose a software development lifecycle where LLM-specific documentation is maintained alongside traditional software updates. In this study, we present two practical applications of the ReadMe$.$LLM idea with diverse software libraries, highlighting that our proposed approach could generalize across programming domains.
CUT: Pruning Pre-Trained Multi-Task Models into Compact Models for Edge Devices
Venue: arXiv cs.LG 2025
Year: 2025

Multi-task learning has garnered widespread attention in the industry due to its efficient data utilization and strong generalization capabilities, making it particularly suitable for providing high-quality intelligent services to users. Edge devices, as the primary platforms directly serving users, play a crucial role in delivering multi-task services. However, current multi-task models are often large, and user task demands are increasingly diverse. Deploying such models directly on edge devices not only increases the burden on these devices but also leads to task redundancy. To address this issue, this paper innovatively proposes a pre-trained multi-task model pruning method specifically designed for edge computing. The goal is to utilize existing pre-trained multi-task models to construct a compact multi-task model that meets the needs of edge devices. The specific implementation steps are as follows: First, decompose the tasks within the pre-trained multi-task model and select tasks based on actual user needs. Next, while retaining the knowledge of the original pre-trained model, evaluate parameter importance and use a parameter fusion method to effectively integrate shared parameters among tasks. Finally, obtain a compact multi-task model suitable for edge devices. To validate the effectiveness of the proposed method, we conducted experiments on three public image datasets. The experimental results fully demonstrate the superiority and efficiency of this method, providing a new solution for multi-task learning on edge devices.
BO-SA-PINNs: Self-adaptive physics-informed neural networks based on Bayesian optimization for automatically designing PDE solvers
Venue: arXiv cs.CE 2025
Year: 2025

Physics-informed neural networks (PINNs) is becoming a popular alternative method for solving partial differential equations (PDEs). However, they require dedicated manual modifications to the hyperparameters of the network, the sampling methods and loss function weights for different PDEs, which reduces the efficiency of the solvers. In this paper, we pro- pose a general multi-stage framework, i.e. BO-SA-PINNs to alleviate this issue. In the first stage, Bayesian optimization (BO) is used to select hyperparameters for the training process, and based on the results of the pre-training, the network architecture, learning rate, sampling points distribution and loss function weights suitable for the PDEs are automatically determined. The proposed hyperparameters search space based on experimental results can enhance the efficiency of BO in identifying optimal hyperparameters. After selecting the appropriate hyperparameters, we incorporate a global self-adaptive (SA) mechanism the second stage. Using the pre-trained model and loss information in the second-stage training, the exponential moving average (EMA) method is employed to optimize the loss function weights, and residual-based adaptive refinement with distribution (RAR-D) is used to optimize the sampling points distribution. In the third stage, L-BFGS is used for stable training. In addition, we introduce a new activation function that enables BO-SA-PINNs to achieve higher accuracy. In numerical experiments, we conduct comparative and ablation experiments to verify the performance of the model on Helmholtz, Maxwell, Burgers and high-dimensional Poisson equations. The comparative experiment results show that our model can achieve higher accuracy and fewer iterations in test cases, and the ablation experiments demonstrate the positive impact of every improvement.
See or Recall: A Sanity Check for the Role of Vision in Solving Visualization Question Answer Tasks with Multimodal LLMs
Venue: arXiv cs.HC 2025
Year: 2025

Recent developments in multimodal large language models (MLLM) have equipped language models to reason about vision and language jointly. This permits MLLMs to both perceive and answer questions about data visualization across a variety of designs and tasks. Applying MLLMs to a broad range of visualization tasks requires us to properly evaluate their capabilities, and the most common way to conduct evaluation is through measuring a model's visualization reasoning capability, analogous to how we would evaluate human understanding of visualizations (e.g., visualization literacy). However, we found that in the context of visualization question answering (VisQA), how an MLLM perceives and reasons about visualizations can be fundamentally different from how humans approach the same problem. During the evaluation, even without visualization, the model could correctly answer a substantial portion of the visualization test questions, regardless of whether any selection options were provided. We hypothesize that the vast amount of knowledge encoded in the language model permits factual recall that supersedes the need to seek information from the visual signal. It raises concerns that the current VisQA evaluation may not fully capture the models' visualization reasoning capabilities. To address this, we propose a comprehensive sanity check framework that integrates a rule-based decision tree and a sanity check table to disentangle the effects of "seeing" (visual processing) and "recall" (reliance on prior knowledge). This validates VisQA datasets for evaluation, highlighting where models are truly "seeing", positively or negatively affected by the factual recall, or relying on inductive biases for question answering. Our study underscores the need for careful consideration in designing future visualization understanding studies when utilizing MLLMs.
FATE: A Prompt-Tuning-Based Semi-Supervised Learning Framework for Extremely Limited Labeled Data
Venue: arXiv cs.CV 2025
Year: 2025

Semi-supervised learning (SSL) has achieved significant progress by leveraging both labeled data and unlabeled data. Existing SSL methods overlook a common real-world scenario when labeled data is extremely scarce, potentially as limited as a single labeled sample in the dataset. General SSL approaches struggle to train effectively from scratch under such constraints, while methods utilizing pre-trained models often fail to find an optimal balance between leveraging limited labeled data and abundant unlabeled data. To address this challenge, we propose Firstly Adapt, Then catEgorize (FATE), a novel SSL framework tailored for scenarios with extremely limited labeled data. At its core, the two-stage prompt tuning paradigm FATE exploits unlabeled data to compensate for scarce supervision signals, then transfers to downstream tasks. Concretely, FATE first adapts a pre-trained model to the feature distribution of downstream data using volumes of unlabeled samples in an unsupervised manner. It then applies an SSL method specifically designed for pre-trained models to complete the final classification task. FATE is designed to be compatible with both vision and vision-language pre-trained models. Extensive experiments demonstrate that FATE effectively mitigates challenges arising from the scarcity of labeled samples in SSL, achieving an average performance improvement of 33.74% across seven benchmarks compared to state-of-the-art SSL methods. Code is available at https://anonymous.4open.science/r/Semi-supervised-learning-BA72.