OpenRaiser/Intern-Atlas huggingface dataset:
Learn to Teach: Sample-Efficient Privileged Learning for Humanoid Locomotion over Diverse Terrains
Venue: arXiv cs.RO 2025
Year: 2025

Humanoid robots promise transformative capabilities for industrial and service applications. While recent advances in Reinforcement Learning (RL) yield impressive results in locomotion, manipulation, and navigation, the proposed methods typically require enormous simulation samples to account for real-world variability. This work proposes a novel one-stage training framework-Learn to Teach (L2T)-which unifies teacher and student policy learning. Our approach recycles simulator samples and synchronizes the learning trajectories through shared dynamics, significantly reducing sample complexities and training time while achieving state-of-the-art performance. Furthermore, we validate the RL variant (L2T-RL) through extensive simulations and hardware tests on the Digit robot, demonstrating zero-shot sim-to-real transfer and robust performance over 12+ challenging terrains without depth estimation modules.
Using Mathlink Cubes to Introduce Data Wrangling with Examples in R
Venue: arXiv cs.HC 2025
Year: 2025

This paper explores an innovative approach to teaching data wrangling skills to students through hands-on activities before transitioning to coding. Data wrangling, a critical aspect of data analysis, involves cleaning, transforming, and restructuring data. We introduce the use of a physical tool, mathlink cubes, to facilitate a tangible understanding of data sets. This approach helps students grasp the concepts of data wrangling before implementing them in coding languages such as R. We detail a classroom activity that includes hands-on tasks paralleling common data wrangling processes such as filtering, selecting, and mutating, followed by their coding equivalents using R's `dplyr` package.
CAHSOR: Competence-Aware High-Speed Off-Road Ground Navigation in SE(3)
Venue: arXiv cs.RO 2025
Year: 2025

While the workspace of traditional ground vehicles is usually assumed to be in a 2D plane, i.e., SE(2), such an assumption may not hold when they drive at high speeds on unstructured off-road terrain: High-speed sharp turns on high-friction surfaces may lead to vehicle rollover; Turning aggressively on loose gravel or grass may violate the non-holonomic constraint and cause significant lateral sliding; Driving quickly on rugged terrain will produce extensive vibration along the vertical axis. Therefore, most offroad vehicles are currently limited to drive only at low speeds to assure vehicle stability and safety. In this work, we aim at empowering high-speed off-road vehicles with competence awareness in SE(3) so that they can reason about the consequences of taking aggressive maneuvers on different terrain with a 6-DoF forward kinodynamic model. The model is learned from visual and inertial Terrain Representation for Off-road Navigation (TRON) using multimodal, self-supervised vehicle-terrain interactions. We demonstrate the efficacy of our Competence-Aware High-Speed Off-Road (CAHSOR) navigation approach on a physical ground robot in both an autonomous navigation and a human shared-control setup and show that CAHSOR can efficiently reduce vehicle instability by 62% while only compromising 8.6% average speed with the help of TRON.
Unsupervised Discovery of Object-Centric Neural Fields
Venue: arXiv cs.CV 2025
Year: 2025

We study inferring 3D object-centric scene representations from a single image. While recent methods have shown potential in unsupervised 3D object discovery from simple synthetic images, they fail to generalize to real-world scenes with visually rich and diverse objects. This limitation stems from their object representations, which entangle objects' intrinsic attributes like shape and appearance with extrinsic, viewer-centric properties such as their 3D location. To address this bottleneck, we propose Unsupervised discovery of Object-Centric neural Fields (uOCF). uOCF focuses on learning the intrinsics of objects and models the extrinsics separately. Our approach significantly improves systematic generalization, thus enabling unsupervised learning of high-fidelity object-centric scene representations from sparse real-world images. To evaluate our approach, we collect three new datasets, including two real kitchen environments. Extensive experiments show that uOCF enables unsupervised discovery of visually rich objects from a single real image, allowing applications such as 3D object segmentation and scene manipulation. Notably, uOCF demonstrates zero-shot generalization to unseen objects from a single real image. Project page: https://red-fairy.github.io/uOCF/
Score-based Diffusion Models via Stochastic Differential Equations -- a Technical Tutorial
Venue: arXiv cs.LG 2025
Year: 2025

This is an expository article on the score-based diffusion models, with a particular focus on the formulation via stochastic differential equations (SDE). After a gentle introduction, we discuss the two pillars in the diffusion modeling -- sampling and score matching, which encompass the SDE/ODE sampling, score matching efficiency, the consistency models, and reinforcement learning. Short proofs are given to illustrate the main idea of the stated results. The article is primarily a technical introduction to the field, and practitioners may also find some analysis useful in designing new models or algorithms.
Efficient and Universal Watermarking for LLM-Generated Code Detection
Venue: arXiv cs.CR 2025
Year: 2025

Large language models (LLMs) have significantly enhanced the usability of AI-generated code, providing effective assistance to programmers. This advancement also raises ethical and legal concerns, such as academic dishonesty or the generation of malicious code. For accountability, it is imperative to detect whether a piece of code is AI-generated. Watermarking is broadly considered a promising solution and has been successfully applied to identify LLM-generated text. However, existing efforts on code are far from ideal, suffering from limited universality and excessive time and memory consumption. In this work, we propose a plug-and-play watermarking approach for AI-generated code detection, named ACW (AI Code Watermarking). ACW is training-free and works by selectively applying a set of carefully-designed, semantic-preserving and idempotent code transformations to LLM code outputs. The presence or absence of the transformations serves as implicit watermarks, enabling the detection of AI-generated code. Our experimental results show that ACW effectively detects AI-generated code, preserves code utility, and is resilient against code optimizations. Especially, ACW is efficient and is universal across different LLMs, addressing the limitations of existing approaches.
FinLLM-B: When Large Language Models Meet Financial Breakout Trading
Venue: arXiv cs.AI 2025
Year: 2025

Trading range breakout is a key method in the technical analysis of financial trading, widely employed by traders in financial markets such as stocks, futures, and foreign exchange. However, distinguishing between true and false breakout and providing the correct rationale cause significant challenges to investors. Traditional quantitative methods require large amounts of data and cannot directly present the reasoning process, making them less than perfect in this field. Recently, large language models have achieved success in various downstream applications, but their effectiveness in the domain of financial breakout detection has been subpar. The reason is that the unique data and specific knowledge are required in breakout detection. To address these issues, we create the first financial breakout dataset and introduce FinLLM-B, the premier large language model for financial breakout detection, which enhances the effectiveness of breakout trading strategies. Furthermore, we have developed a novel framework for large language models, namely multi-stage structure, effectively reducing mistakes in downstream applications. Experimental results indicate that compared to GPT-3.5, FinLLM-B improves the average accuracy of answers and rational by 49.97%, with the multi-stage structure contributing 9.72% to the improvement. Additionally, it outperforms ChatGPT-4 by 42.38%.
Topic Modeling as Multi-Objective Contrastive Optimization
Venue: arXiv cs.CL 2025
Year: 2025

Recent representation learning approaches enhance neural topic models by optimizing the weighted linear combination of the evidence lower bound (ELBO) of the log-likelihood and the contrastive learning objective that contrasts pairs of input documents. However, document-level contrastive learning might capture low-level mutual information, such as word ratio, which disturbs topic modeling. Moreover, there is a potential conflict between the ELBO loss that memorizes input details for better reconstruction quality, and the contrastive loss which attempts to learn topic representations that generalize among input documents. To address these issues, we first introduce a novel contrastive learning method oriented towards sets of topic vectors to capture useful semantics that are shared among a set of input documents. Secondly, we explicitly cast contrastive topic modeling as a gradient-based multi-objective optimization problem, with the goal of achieving a Pareto stationary solution that balances the trade-off between the ELBO and the contrastive objective. Extensive experiments demonstrate that our framework consistently produces higher-performing neural topic models in terms of topic coherence, topic diversity, and downstream performance.
Autonomous Data Selection with Zero-shot Generative Classifiers for Mathematical Texts
Venue: arXiv cs.CL 2025
Year: 2025

We present Autonomous Data Selection (AutoDS), a method that leverages base language models themselves as zero-shot "generative classifiers" to automatically curate high-quality mathematical texts. Unlike prior approaches that require human annotations or training a dedicated data filter, AutoDS relies solely on a model's logits to determine whether a given passage is mathematically informative and educational. By integrating AutoDS into a continual pretraining pipeline, we substantially boost downstream performance on challenging math benchmarks (MATH, GSM8K, and BBH) while using far fewer tokens than previous methods. Empirically, our approach achieves roughly a twofold improvement in pretraining token efficiency over strong baselines, underscoring the potential of self-directed data selection in enhancing mathematical reasoning. We release our curated AutoMathText dataset to facilitate future research in automated domain-specific data curation. The AutoMathText dataset is available at https://huggingface.co/datasets/math-ai/AutoMathText. The code is available at https://github.com/yifanzhang-pro/AutoMathText.
Had enough of experts? Quantitative knowledge retrieval from large language models
Venue: arXiv cs.IR 2025
Year: 2025

Large language models (LLMs) have been extensively studied for their abilities to generate convincing natural language sequences, however their utility for quantitative information retrieval is less well understood. Here we explore the feasibility of LLMs as a mechanism for quantitative knowledge retrieval to aid two data analysis tasks: elicitation of prior distributions for Bayesian models and imputation of missing data. We introduce a framework that leverages LLMs to enhance Bayesian workflows by eliciting expert-like prior knowledge and imputing missing data. Tested on diverse datasets, this approach can improve predictive accuracy and reduce data requirements, offering significant potential in healthcare, environmental science and engineering applications. We discuss the implications and challenges of treating LLMs as 'experts'.
Algorithmic Fairness and Color-blind Racism: Navigating the Intersection
Venue: arXiv cs.CY 2025
Year: 2025

Our focus lies at the intersection between two broader research perspectives: (1) the scientific study of algorithms and (2) the scholarship on race and racism. Many streams of research related to algorithmic fairness have been born out of interest at this intersection. We think about this intersection as the product of work derived from both sides. From (1) algorithms to (2) racism, the starting place might be an algorithmic question or method connected to a conceptualization of racism. On the other hand, from (2) racism to (1) algorithms, the starting place could be recognizing a setting where a legacy of racism is known to persist and drawing connections between that legacy and the introduction of algorithms into this setting. In either direction, meaningful disconnection can occur when conducting research at the intersection of racism and algorithms. The present paper urges collective reflection on research directions at this intersection. Despite being primarily motivated by instances of racial bias, research in algorithmic fairness remains mostly disconnected from scholarship on racism. In particular, there has not been an examination connecting algorithmic fairness discussions directly to the ideology of color-blind racism; we aim to fill this gap. We begin with a review of an essential account of color-blind racism then we review racial discourse within algorithmic fairness research and underline significant patterns, shifts and disconnects. Ultimately, we argue that researchers can improve the navigation of the landscape at the intersection by recognizing ideological shifts as such and iteratively re-orienting towards maintaining meaningful connections across interdisciplinary lines.
Differentially Private Zeroth-Order Methods for Scalable Large Language Model Finetuning
Venue: arXiv cs.LG 2025
Year: 2025

Fine-tuning on task-specific datasets is a widely-embraced paradigm of harnessing the powerful capability of pretrained LLMs for various downstream tasks. Due to the popularity of LLMs fine-tuning and its accompanying privacy concerns, differentially private (DP) fine-tuning of pretrained LLMs has been widely used to safeguarding the privacy of task-specific datasets. Lying at the design core of DP LLM fine-tuning methods is the satisfactory tradeoff among privacy, utility, and scalability. Most existing methods build upon the seminal work of DP-SGD. Despite pushing the scalability of DP-SGD to its limit, DP-SGD-based fine-tuning methods are unfortunately limited by the inherent inefficiency of SGD. In this paper, we investigate the potential of DP zeroth-order methods for LLM pretraining, which avoids the scalability bottleneck of SGD by approximating the gradient with the more efficient zeroth-order gradient. Rather than treating the zeroth-order method as a drop-in replacement for SGD, this paper presents a comprehensive study both theoretically and empirically. First, we propose the stagewise DP zeroth-order method (DP-ZOSO) that dynamically schedules key hyperparameters. This design is grounded on the synergy between DP random perturbation and the gradient approximation error of the zeroth-order method, and its effect on fine-tuning trajectory. We provide theoretical analysis for both proposed methods. We conduct extensive empirical analysis on both encoder-only masked language model and decoder-only autoregressive language model, achieving impressive results in terms of scalability and utility regardless of the class of tasks (compared with DPZero, DP-ZOPO improves $4.5\%$ on SST-5, $5.5\%$ on MNLI with RoBERTa-Large and 9.2\% on CB, 3.9\% on BoolQ with OPT-2.7b when $ε=4$, demonstrates more significant enhancement in performance on more complicated tasks).
From Data Creator to Data Reuser: Distance Matters
Venue: arXiv cs.HC 2025
Year: 2025

Sharing research data is necessary, but not sufficient, for data reuse. Open science policies focus more heavily on data sharing than on reuse, yet both are complex, labor-intensive, expensive, and require infrastructure investments by multiple stakeholders. The value of data reuse lies in relationships between creators and reusers. By addressing knowledge exchange, rather than mere transactions between stakeholders, investments in data management and knowledge infrastructures can be made more wisely. Drawing upon empirical studies of data sharing and reuse, we develop the metaphor of distance between data creator and data reuser, identifying six dimensions of distance that influence the ability to transfer knowledge effectively: domain, methods, collaboration, curation, purposes, and time and temporality. We explore how social and socio-technical aspects of these dimensions may decrease -- or increase -- distances to be traversed between creators and reusers. Our theoretical framing of the distance between data creators and prospective reusers leads to recommendations to four categories of stakeholders on how to make data sharing and reuse more effective: data creators, data reusers, data archivists, and funding agencies. 'It takes a village' to share research data -- and a village to reuse data. Our aim is to provoke new research questions, new research, and new investments in effective and efficient circulation of research data; and to identify criteria for investments at each stage of data and research life cycles.
The Blocklace: A Byzantine-repelling and Universal Conflict-free Replicated Data Type
Venue: arXiv cs.DC 2025
Year: 2025

Conflict-free Replicated Data Types (CRDTs) are designed for replica convergence without global coordination or consensus. Recent work has achieved the same in a Byzantine environment, through DAG-like structures based on cryptographic hashes of content. The blocklace is a partially-ordered generalization of the blockchain in which each block has any finite number of signed hash pointers to preceding blocks. We show that the blocklace datatype, with the sole operation of adding a single block, is a CRDT: it is both a pure operation-based CRDT, with self-tagging; and a delta-state CRDT, under a slight generalization of the delta framework. Allowing arbitrary values as payload, the blocklace can also be seen as a universal Byzantine fault-tolerant implementation for arbitrary CRDTs, under the operation-based approach. Current approaches only care about CRDT convergence, being equivocation-tolerant (they do not detect or prevent equivocations), allowing a Byzantine node to cause an arbitrary amount of harm by polluting the CRDT state with an unbounded number of equivocations. We show that the blocklace can be used not only in an equivocation-tolerant way, but also so as to detect and eventually exclude Byzantine nodes, including equivocators, even under the presence of undetectable colluders. The blocklace CRDT protocol ensures that a Byzantine node may harm only a finite prefix of the computation.
A Meaningful Human Control Perspective on User Perception of Partially Automated Driving Systems: A Case Study of Tesla Users
Venue: arXiv cs.HC 2025
Year: 2025

The use of partially automated driving systems raises concerns about potential responsibility issues, posing risk to the system safety, acceptance, and adoption of these technologies. The concept of meaningful human control has emerged in response to the responsibility gap problem, requiring the fulfillment of two conditions, tracking and tracing. While this concept has provided important philosophical and design insights on automated driving systems, there is currently little knowledge on how meaningful human control relates to subjective experiences of actual users of these systems. To address this gap, our study aimed to investigate the alignment between the degree of meaningful human control and drivers' perceptions of safety and trust in a real-world partially automated driving system. We utilized previously collected data from interviews with Tesla "Full Self-Driving" (FSD) Beta users, investigating the alignment between the user perception and how well the system was tracking the users' reasons. We found that tracking of users' reasons for driving tasks (such as safe maneuvers) correlated with perceived safety and trust, albeit with notable exceptions. Surprisingly, failure to track lane changing and braking reasons was not necessarily associated with negative perceptions of safety. However, the failure of the system to track expected maneuvers in dangerous situations always resulted in low trust and perceived lack of safety. Overall, our analyses highlight alignment points but also possible discrepancies between perceived safety and trust on the one hand, and meaningful human control on the other hand. Our results can help the developers of automated driving technology to design systems under meaningful human control and are perceived as safe and trustworthy.
Variational Continual Test-Time Adaptation
Venue: arXiv cs.LG 2025
Year: 2025

Continual Test-Time Adaptation (CTTA) task investigates effective domain adaptation under the scenario of continuous domain shifts during testing time. Due to the utilization of solely unlabeled samples, there exists significant uncertainty in model updates, leading CTTA to encounter severe error accumulation issues. In this paper, we introduce VCoTTA, a variational Bayesian approach to measure uncertainties in CTTA. At the source stage, we transform a pretrained deterministic model into a Bayesian Neural Network (BNN) via a variational warm-up strategy, injecting uncertainties into the model. During the testing time, we employ a mean-teacher update strategy using variational inference for the student model and exponential moving average for the teacher model. Our novel approach updates the student model by combining priors from both the source and teacher models. The evidence lower bound is formulated as the cross-entropy between the student and teacher models, along with the Kullback-Leibler (KL) divergence of the prior mixture. Experimental results on three datasets demonstrate the method's effectiveness in mitigating error accumulation within the CTTA framework.
Causal Learning for Trustworthy Recommender Systems: A Survey
Venue: arXiv cs.IR 2025
Year: 2025

Recommender Systems (RS) have significantly advanced online content filtering and personalized decision-making. However, emerging vulnerabilities in RS have catalyzed a paradigm shift towards Trustworthy RS (TRS). Despite substantial progress on TRS, most efforts focus on data correlations while overlooking the fundamental causal nature of recommendations. This drawback hinders TRS from identifying the root cause of trustworthiness issues, leading to limited fairness, robustness, and explainability. To bridge this gap, causal learning emerges as a class of promising methods to augment TRS. These methods, grounded in reliable causality, excel in mitigating various biases and noise while offering insightful explanations for TRS. However, there is a lack of timely and dedicated surveys in this vibrant area. This paper creates an overview of TRS from the perspective of causal learning. We begin by presenting the advantages and common procedures of Causality-oriented TRS (CTRS). Then, we identify potential trustworthiness challenges at each stage and link them to viable causal solutions, followed by a classification of CTRS methods. Finally, we discuss several future directions for advancing this field.