OpenRaiser/Intern-Atlas huggingface dataset:
Bridging the Gap Between Theoretical and Practical Reinforcement Learning in Undergraduate Education
Venue: arXiv cs.CY 2025
Year: 2025

This innovative practice category paper presents an innovative framework for teaching Reinforcement Learning (RL) at the undergraduate level. Recognizing the challenges posed by the complex theoretical foundations of the subject and the need for hands-on algorithmic practice, the proposed approach integrates traditional lectures with interactive lab-based learning. Drawing inspiration from effective pedagogical practices in computer science and engineering, the framework engages students through real-time coding exercises using simulated environments such as OpenAI Gymnasium. The effectiveness of this approach is evaluated through student surveys, instructor feedback, and course performance metrics, demonstrating improvements in understanding, debugging, parameter tuning, and model evaluation. Ultimately, the study provides valuable insight into making Reinforcement Learning more accessible and engaging, thereby equipping students with essential problem-solving skills for real-world applications in Artificial Intelligence.
InterAct: A Large-Scale Dataset of Dynamic, Expressive and Interactive Activities between Two People in Daily Scenarios
Venue: arXiv cs.CV 2025
Year: 2025

We address the problem of accurate capture of interactive behaviors between two people in daily scenarios. Most previous works either only consider one person or solely focus on conversational gestures of two people, assuming the body orientation and/or position of each actor are constant or barely change over each interaction. In contrast, we propose to simultaneously model two people's activities, and target objective-driven, dynamic, and semantically consistent interactions which often span longer duration and cover bigger space. To this end, we capture a new multi-modal dataset dubbed InterAct, which is composed of 241 motion sequences where two people perform a realistic and coherent scenario for one minute or longer over a complete interaction. For each sequence, two actors are assigned different roles and emotion labels, and collaborate to finish one task or conduct a common interaction activity. The audios, body motions, and facial expressions of both persons are captured. InterAct contains diverse and complex motions of individuals and interesting and relatively long-term interaction patterns barely seen before. We also demonstrate a simple yet effective diffusion-based method that estimates interactive face expressions and body motions of two people from speech inputs. Our method regresses the body motions in a hierarchical manner, and we also propose a novel fine-tuning mechanism to improve the lip accuracy of facial expressions. To facilitate further research, the data and code is made available at https://hku-cg.github.io/interact/ .
Tiga: Accelerating Geo-Distributed Transactions with Synchronized Clocks [Technical Report]
Venue: arXiv cs.NI 2025
Year: 2025

This paper presents Tiga, a new design for geo-replicated and scalable transactional databases such as Google Spanner. Tiga aims to commit transactions within 1 wide-area roundtrip time, or 1 WRTT, for a wide range of scenarios, while maintaining high throughput with minimal computational overhead. Tiga consolidates concurrency control and consensus, completing both strictly serializable execution and consistent replication in a single round. It uses synchronized clocks to proactively order transactions by assigning each a future timestamp at submission. In most cases, transactions arrive at servers before their future timestamps and are serialized according to the designated timestamp, requiring 1 WRTT to commit. In rare cases, transactions are delayed and proactive ordering fails, in which case Tiga falls back to a slow path, committing in 1.5--2 WRTTs. Compared to state-of-the-art solutions, Tiga can commit more transactions at 1-WRTT latency, and incurs much less throughput overhead. Evaluation results show that Tiga outperforms all baselines, achieving 1.3--7.2$\times$ higher throughput and 1.4--4.6$\times$ lower latency. Tiga is open-sourced at https://github.com/New-Consensus-Concurrency-Control/Tiga.
3DPillars: Pillar-based two-stage 3D object detection
Venue: arXiv cs.CV 2025
Year: 2025

PointPillars is the fastest 3D object detector that exploits pseudo image representations to encode features for 3D objects in a scene. Albeit efficient, PointPillars is typically outperformed by state-of-the-art 3D detection methods due to the following limitations: 1) The pseudo image representations fail to preserve precise 3D structures, and 2) they make it difficult to adopt a two-stage detection pipeline using 3D object proposals that typically shows better performance than a single-stage approach. We introduce in this paper the first two-stage 3D detection framework exploiting pseudo image representations, narrowing the performance gaps between PointPillars and state-of-the-art methods, while retaining its efficiency. Our framework consists of two novel components that overcome the aforementioned limitations of PointPillars: First, we introduce a new CNN architecture, dubbed 3DPillars, that enables learning 3D voxel-based features from the pseudo image representation efficiently using 2D convolutions. The basic idea behind 3DPillars is that 3D features from voxels can be viewed as a stack of pseudo images. To implement this idea, we propose a separable voxel feature module that extracts voxel-based features without using 3D convolutions. Second, we introduce an RoI head with a sparse scene context feature module that aggregates multi-scale features from 3DPillars to obtain a sparse scene feature. This enables adopting a two-stage pipeline effectively, and fully leveraging contextual information of a scene to refine 3D object proposals. Experimental results on the KITTI and Waymo Open datasets demonstrate the effectiveness and efficiency of our approach, achieving a good compromise in terms of speed and accuracy.
Transformer-based Topology Optimization
Venue: arXiv cs.CE 2025
Year: 2025

Topology optimization enables the design of highly efficient and complex structures, but conventional iterative methods, such as SIMP-based approaches, often suffer from high computational costs and sensitivity to initial conditions. Although machine learning methods have recently shown promise for accelerating topology generation, existing models either remain iterative or struggle to match ground-truth performance. In this work, we propose a transformer-based machine learning model for topology optimization that embeds critical boundary and loading conditions directly into the tokenized domain representation via a class token mechanism. We implement this model on static and dynamic datasets, using transfer learning and FFT encoding of dynamic loads to improve our performance on the dynamic dataset. Auxiliary loss functions are introduced to promote the realism and manufacturability of the generated designs. We conduct a comprehensive evaluation of the model's performance, including compliance error, volume fraction error, floating material percentage, and load discrepancy error, and benchmark it against state-of-the-art non-iterative and iterative generative models. Our results demonstrate that the proposed model approaches the fidelity of diffusion-based models while remaining iteration-free, offering a significant step toward real-time, high-fidelity topology generation.
Simple Optimizers for Convex Aligned Multi-Objective Optimization
Venue: arXiv cs.LG 2025
Year: 2025

It is widely recognized in modern machine learning practice that access to a diverse set of tasks can enhance performance across those tasks. This observation suggests that, unlike in general multi-objective optimization, the objectives in many real-world settings may not be inherently conflicting. To address this, prior work introduced the Aligned Multi-Objective Optimization (AMOO) framework and proposed gradient-based algorithms with provable convergence guarantees. However, existing analysis relies on strong assumptions, particularly strong convexity, which implies the existence of a unique optimal solution. In this work, we relax this assumption and study gradient-descent algorithms for convex AMOO under standard smoothness or Lipschitz continuity conditions-assumptions more consistent with those used in deep learning practice. This generalization requires new analytical tools and metrics to characterize convergence in the convex AMOO setting. We develop such tools, propose scalable algorithms for convex AMOO, and establish their convergence guarantees. Additionally, we prove a novel lower bound that demonstrates the suboptimality of naive equal-weight approaches compared to our methods.
Augmenting Human-Centered Racial Covenant Detection and Georeferencing with Plug-and-Play NLP Pipelines
Venue: arXiv cs.HC 2025
Year: 2025

Though no longer legally enforceable, racial covenants in twentieth-century property deeds continue to shape spatial and socioeconomic inequalities. Understanding this legacy requires identifying racially restrictive language and geolocating affected properties. The Mapping Prejudice project addresses this by engaging volunteers on the Zooniverse crowdsourcing platform to transcribe covenants from scanned deeds and link them to modern parcel maps using transcribed legal descriptions. While the project has explored automation, it values crowdsourcing for its social impact and technical advantages. Historically, Mapping Prejudice relied on lexicon-based searching and, more recently, fuzzy matching to flag suspected covenants. However, fuzzy matching has increased false positives, burdening volunteers and raising scalability concerns. Additionally, while many properties can be mapped automatically, others still require time-intensive manual geolocation. We present a human-centered computing approach with two plug-and-play NLP pipelines: (1) a context-aware text labeling model that flags racially restrictive language with high precision and (2) a georeferencing module that extracts geographic descriptions from deeds and resolves them to real-world locations. Evaluated on historical deed documents from six counties in Minnesota and Wisconsin, our system reduces false positives in racial term detection by 25.96% while maintaining 91.73% recall and achieves 85.58% georeferencing accuracy within 1x1 square-mile ranges. These tools enhance document filtering and enrich spatial annotations, accelerating volunteer participation and reducing manual cleanup while strengthening public engagement.
Finetuning LLMs for Human Behavior Prediction in Social Science Experiments
Venue: arXiv cs.LG 2025
Year: 2025

Large language models (LLMs) offer a powerful opportunity to simulate the results of social science experiments. In this work, we demonstrate that finetuning LLMs directly on individual-level responses from past experiments meaningfully improves the accuracy of such simulations across diverse social science domains. We construct SocSci210 via an automatic pipeline, a dataset comprising 2.9 million responses from 400,491 participants in 210 open-source social science experiments. Through finetuning, we achieve multiple levels of generalization. In completely unseen studies, our strongest model, Socrates-Qwen-14B, produces predictions that are 26% more aligned with distributions of human responses to diverse outcome questions under varying conditions relative to its base model (Qwen2.5-14B), outperforming GPT-4o by 13%. By finetuning on a subset of conditions in a study, generalization to new unseen conditions is particularly robust, improving by 71%. Since SocSci210 contains rich demographic information, we reduce demographic parity difference, a measure of bias, by 10.6% through finetuning. Because social sciences routinely generate rich, topic-specific datasets, our findings indicate that finetuning on such data could enable more accurate simulations for experimental hypothesis screening. We release our data, models and finetuning code at stanfordhci.github.io/socrates.
Decoding Latent Attack Surfaces in LLMs: Prompt Injection via HTML in Web Summarization
Venue: arXiv cs.CR 2025
Year: 2025

Large Language Models (LLMs) are increasingly integrated into web-based systems for content summarization, yet their susceptibility to prompt injection attacks remains a pressing concern. In this study, we explore how non-visible HTML elements such as <meta>, aria-label, and alt attributes can be exploited to embed adversarial instructions without altering the visible content of a webpage. We introduce a novel dataset comprising 280 static web pages, evenly divided between clean and adversarial injected versions, crafted using diverse HTML-based strategies. These pages are processed through a browser automation pipeline to extract both raw HTML and rendered text, closely mimicking real-world LLM deployment scenarios. We evaluate two state-of-the-art open-source models, Llama 4 Scout (Meta) and Gemma 9B IT (Google), on their ability to summarize this content. Using both lexical (ROUGE-L) and semantic (SBERT cosine similarity) metrics, along with manual annotations, we assess the impact of these covert injections. Our findings reveal that over 29% of injected samples led to noticeable changes in the Llama 4 Scout summaries, while Gemma 9B IT showed a lower, yet non-trivial, success rate of 15%. These results highlight a critical and largely overlooked vulnerability in LLM driven web pipelines, where hidden adversarial content can subtly manipulate model outputs. Our work offers a reproducible framework and benchmark for evaluating HTML-based prompt injection and underscores the urgent need for robust mitigation strategies in LLM applications involving web content.
Distortion Minimization in Reverse Engineering for Additive Manufacturing: An Integrated 3D Scanning and Simulation Framework
Venue: arXiv cs.CE 2025
Year: 2025

Reverse engineering can be used to derive a 3D model of an existing physical part when such a model is not readily available. For parts that will be fabricated with subtractive and formative manufacturing processes, existing reverse engineering techniques can be readily applied, but parts produced with additive manufacturing can present new challenges due to the high level of process-induced distortions and unique part attributes. This paper introduces an integrated 3D scanning and process simulation data-driven framework to minimize distortions of reverse-engineered additively manufactured components. This framework employs iterative finite element simulations to predict geometric distortions to minimize errors between the predicted and measured geometrical deviations of the key dimensional characteristics of the part. The effectiveness of this approach is then demonstrated by reverse engineering two Inconel-718 components manufactured using laser powder bed fusion additive manufacturing. This paper presents a remanufacturing process that combines reverse engineering and additive manufacturing, leveraging geometric feature-based part compensation through process simulation. Our approach can generate both compensated STL and parametric CAD models, eliminating laborious experimentation during reverse engineering. We evaluate the merits of STL-based and CAD-based approaches by quantifying the errors induced at the different steps of the proposed approach and analyzing the influence of varying part geometries. Using the proposed CAD-based method, the average absolute percent error between simulation-predicted distorted dimensions and actual measured dimensions of the manufactured parts was 0.087%, with better accuracy than the STL-based method.
ZhiFangDanTai: Fine-tuning Graph-based Retrieval-Augmented Generation Model for Traditional Chinese Medicine Formula
Venue: arXiv cs.CL 2025
Year: 2025

Traditional Chinese Medicine (TCM) formulas play a significant role in treating epidemics and complex diseases. Existing models for TCM utilize traditional algorithms or deep learning techniques to analyze formula relationships, yet lack comprehensive results, such as complete formula compositions and detailed explanations. Although recent efforts have used TCM instruction datasets to fine-tune Large Language Models (LLMs) for explainable formula generation, existing datasets lack sufficient details, such as the roles of the formula's sovereign, minister, assistant, courier; efficacy; contraindications; tongue and pulse diagnosis-limiting the depth of model outputs. To address these challenges, we propose ZhiFangDanTai, a framework combining Graph-based Retrieval-Augmented Generation (GraphRAG) with LLM fine-tuning. ZhiFangDanTai uses GraphRAG to retrieve and synthesize structured TCM knowledge into concise summaries, while also constructing an enhanced instruction dataset to improve LLMs' ability to integrate retrieved information. Furthermore, we provide novel theoretical proofs demonstrating that integrating GraphRAG with fine-tuning techniques can reduce generalization error and hallucination rates in the TCM formula task. Experimental results on both collected and clinical datasets demonstrate that ZhiFangDanTai achieves significant improvements over state-of-the-art models. Our model is open-sourced at https://huggingface.co/tczzx6/ZhiFangDanTai1.0.
A General Framework for Low Soundness Homomorphism Testing
Venue: arXiv cs.CC 2025
Year: 2025

We introduce a general framework to design and analyze algorithms for the problem of testing homomorphisms between finite groups in the low-soundness regime. In this regime, we give the first constant-query tests for various families of groups. These include tests for: (i) homomorphisms between arbitrary cyclic groups, (ii) homomorphisms between any finite group and $\mathbb{Z}_p$, (iii) automorphisms of dihedral and symmetric groups, (iv) inner automorphisms of non-abelian finite simple groups and extraspecial groups, and (v) testing linear characters of $\mathrm{GL}_n(\mathbb{F}_q)$, and finite-dimensional Lie algebras over $\mathbb{F}_q$. We also recover the result of Kiwi [TCS'03] for testing homomorphisms between $\mathbb{F}_q^n$ and $\mathbb{F}_q$. Prior to this work, such tests were only known for abelian groups with a constant maximal order (such as $\mathbb{F}_q^n$). No tests were known for non-abelian groups. As an additional corollary, our framework gives combinatorial list decoding bounds for cyclic groups with list size dependence of $O(\varepsilon^{-2})$ (for agreement parameter $\varepsilon$). This improves upon the currently best-known bound of $O(\varepsilon^{-105})$ due to Dinur, Grigorescu, Kopparty, and Sudan [STOC'08], and Guo and Sudan [RANDOM'14].
Study of Iterative Detection, Decoding and Channel Estimation for RIS-Aided MIMO Networks
Venue: arXiv cs.IT 2025
Year: 2025

This work proposes an iterative detection, decoding and channel estimation scheme for multiple-antenna systems assisted by multiple reflective intelligent surfaces (RIS). A novel channel estimation technique that exploits low-density parity-check (LDPC) codes and iterative processing is developed to enhance estimation accuracy while reducing the number of required pilot symbols. The key idea is to exploit encoded pilots to improve the iterative process, enabling the use of not only pilot bits but also parity bits from the coded packet to refine channel estimation. Simulations analyze a sub-6 GHz scenario where the channel propagation is not sparse and multiple RIS are deployed, considering both LOS and NLOS conditions. Numerical results show significant performance gains for the proposed scheme and estimator over competing approaches.
X-SQL: Expert Schema Linking and Understanding of Text-to-SQL with Multi-LLMs
Venue: arXiv cs.LG 2025
Year: 2025

With Large Language Models' (LLMs) emergent abilities on code generation tasks, Text-to-SQL has become one of the most popular downstream applications. Despite the strong results of multiple recent LLM-based Text-to-SQL frameworks, the research community often overlooks the importance of database schema information for generating high-quality SQL queries. We find that such schema information plays a significant or even dominant role in the Text-to-SQL task. To tackle this challenge, we propose a novel database schema expert with two components. We first introduce X-Linking, an LLM Supervised Finetuning (SFT)-based method that achieves superior Schema Linking results compared to existing open-source Text-to-SQL methods. In addition, we innovatively propose an X-Admin component that focuses on Schema Understanding by bridging the gap between abstract schema information and the user's natural language question. Aside from better learning with schema information, we experiment with Multi-LLMs for different components within the system to further boost its performance. By incorporating these techniques into our end-to-end framework, X-SQL, we have achieved Execution Accuracies of 84.9% on the Spider-Dev dataset and 82.5% on the Spider-Test dataset. This outstanding performance establishes X-SQL as the leading Text-to-SQL framework based on open-source models.
Enhancing the Robustness of Contextual ASR to Varying Biasing Information Volumes Through Purified Semantic Correlation Joint Modeling
Venue: arXiv cs.CL 2025
Year: 2025

Recently, cross-attention-based contextual automatic speech recognition (ASR) models have made notable advancements in recognizing personalized biasing phrases. However, the effectiveness of cross-attention is affected by variations in biasing information volume, especially when the length of the biasing list increases significantly. We find that, regardless of the length of the biasing list, only a limited amount of biasing information is most relevant to a specific ASR intermediate representation. Therefore, by identifying and integrating the most relevant biasing information rather than the entire biasing list, we can alleviate the effects of variations in biasing information volume for contextual ASR. To this end, we propose a purified semantic correlation joint modeling (PSC-Joint) approach. In PSC-Joint, we define and calculate three semantic correlations between the ASR intermediate representations and biasing information from coarse to fine: list-level, phrase-level, and token-level. Then, the three correlations are jointly modeled to produce their intersection, so that the most relevant biasing information across various granularities is highlighted and integrated for contextual recognition. In addition, to reduce the computational cost introduced by the joint modeling of three semantic correlations, we also propose a purification mechanism based on a grouped-and-competitive strategy to filter out irrelevant biasing phrases. Compared with baselines, our PSC-Joint approach achieves average relative F1 score improvements of up to 21.34% on AISHELL-1 and 28.46% on KeSpeech, across biasing lists of varying lengths.