OpenRaiser/Intern-Atlas huggingface dataset:
Grounding Natural Language to SQL Translation with Data-Based Self-Explanations
Venue: arXiv cs.DB 2025
Year: 2025

Natural Language Interfaces for Databases empower non-technical users to interact with data using natural language (NL). Advanced approaches, utilizing either neural sequence-to-sequence or more recent sophisticated large-scale language models, typically implement NL to SQL (NL2SQL) translation in an end-to-end fashion. However, like humans, these end-to-end translation models may not always generate the best SQL output on their first try. In this paper, we propose CycleSQL, an iterative framework designed for end-to-end translation models to autonomously generate the best output through self-evaluation. The main idea of CycleSQL is to introduce data-grounded NL explanations of query results as self-provided feedback, and use the feedback to validate the correctness of the translation iteratively, hence improving the overall translation accuracy. Extensive experiments, including quantitative and qualitative evaluations, are conducted to study CycleSQL by applying it to seven existing translation models on five widely used benchmarks. The results show that 1) the feedback loop introduced in CycleSQL can consistently improve the performance of existing models, and in particular, by applying CycleSQL to RESDSQL, obtains a translation accuracy of 82.0% (+2.6%) on the validation set, and 81.6% (+3.2%) on the test set of Spider benchmark; 2) the generated NL explanations can also provide insightful information for users, aiding in the comprehension of translation results and consequently enhancing the interpretability of NL2SQL translation.
Region-Guided Attack on the Segment Anything Model (SAM)
Venue: arXiv cs.CV 2025
Year: 2025

The Segment Anything Model (SAM) is a cornerstone of image segmentation, demonstrating exceptional performance across various applications, particularly in autonomous driving and medical imaging, where precise segmentation is crucial. However, SAM is vulnerable to adversarial attacks that can significantly impair its functionality through minor input perturbations. Traditional techniques, such as FGSM and PGD, are often ineffective in segmentation tasks due to their reliance on global perturbations that overlook spatial nuances. Recent methods like Attack-SAM-K and UAD have begun to address these challenges, but they frequently depend on external cues and do not fully leverage the structural interdependencies within segmentation processes. This limitation underscores the need for a novel adversarial strategy that exploits the unique characteristics of segmentation tasks. In response, we introduce the Region-Guided Attack (RGA), designed specifically for SAM. RGA utilizes a Region-Guided Map (RGM) to manipulate segmented regions, enabling targeted perturbations that fragment large segments and expand smaller ones, resulting in erroneous outputs from SAM. Our experiments demonstrate that RGA achieves high success rates in both white-box and black-box scenarios, emphasizing the need for robust defenses against such sophisticated attacks. RGA not only reveals SAM's vulnerabilities but also lays the groundwork for developing more resilient defenses against adversarial threats in image segmentation.
Transformer-Based Fault-Tolerant Control for Fixed-Wing UAVs Using Knowledge Distillation and In-Context Adaptation
Venue: arXiv cs.RO 2025
Year: 2025

This study presents a transformer-based approach for fault-tolerant control in fixed-wing Unmanned Aerial Vehicles (UAVs), designed to adapt in real time to dynamic changes caused by structural damage or actuator failures. Unlike traditional Flight Control Systems (FCSs) that rely on classical control theory and struggle under severe alterations in dynamics, our method directly maps outer-loop reference values -- altitude, heading, and airspeed -- into control commands using the in-context learning and attention mechanisms of transformers, thus bypassing inner-loop controllers and fault-detection layers. Employing a teacher-student knowledge distillation framework, the proposed approach trains a student agent with partial observations by transferring knowledge from a privileged expert agent with full observability, enabling robust performance across diverse failure scenarios. Experimental results demonstrate that our transformer-based controller outperforms industry-standard FCS and state-of-the-art reinforcement learning (RL) methods, maintaining high tracking accuracy and stability in nominal conditions and extreme failure cases, highlighting its potential for enhancing UAV operational safety and reliability.
Conformance Games for Graded Semantics
Venue: arXiv cs.LO 2025
Year: 2025

Game-theoretic characterizations of process equivalences traditionally form a central topic in concurrency; for example, most equivalences on the classical linear-time / branching-time spectrum come with such characterizations. Recent work on so-called graded semantics has led to a generic behavioural equivalence game that covers the mentioned games on the linear-time~/ branching-time spectrum and moreover applies in coalgebraic generality, and thus instantiates also to equivalence games on systems with non-relational branching type (probabilistic, weighted, game-based etc.). In the present work, we generalize this approach to cover other types of process comparison beyond equivalence, such as behavioural preorders or pseudometrics. At the most general level, we abstract such notions of behavoiural conformance in terms of topological categories, and later specialize to conformances presented as relational structures to obtain a concrete syntax. We obtain a sound and complete generic game for behavioural conformances in this sense. We present a number of instantiations, obtaining game characterizations of, e.g., trace inclusion, probabilistic trace distance, bisimulation topologies, and simulation distances on metric labelled transition systems.
DiffLM: Controllable Synthetic Data Generation via Diffusion Language Models
Venue: arXiv cs.LG 2025
Year: 2025

Recent advancements in large language models (LLMs) have significantly enhanced their knowledge and generative capabilities, leading to a surge of interest in leveraging LLMs for high-quality data synthesis. However, synthetic data generation via prompting LLMs remains challenging due to LLMs' limited understanding of target data distributions and the complexity of prompt engineering, especially for structured formatted data. To address these issues, we introduce DiffLM, a controllable data synthesis framework based on variational autoencoder (VAE), which further (1) leverages diffusion models to reserve more information of original distribution and format structure in the learned latent distribution and (2) decouples the learning of target distribution knowledge from the LLM's generative objectives via a plug-and-play latent feature injection module. As we observed significant discrepancies between the VAE's latent representations and the real data distribution, the latent diffusion module is introduced into our framework to learn a fully expressive latent distribution. Evaluations on seven real-world datasets with structured formatted data (i.e., Tabular, Code, and Tool data) demonstrate that DiffLM generates high-quality data, with performance on downstream tasks surpassing that of real data by 2%-7% in certain cases. Data and code are available at https://github.com/bytedance/DiffLM.
xApp-Level Conflict Mitigation in O-RAN, a Mobility Driven Energy Saving Case
Venue: arXiv cs.NI 2025
Year: 2025

This paper investigates the emerging challenges of conflict detection and mitigation in Open Radio Access Network (O-RAN). Conflicts between xApps can arise that affect network performance and stability due to the disaggregated nature of O-RAN. This work provides a detailed theoretical framework of Extended Application (xApp)-level conflicts, i.e., direct, indirect, and implicit conflicts. Leveraging conflict graphs, we further highlight how conflicts impact Key Performance Indicators (KPIs) and explore strategies for conflict detection using Service Level Agreements (SLAs) and Quality of Service (QoS) thresholds. We evaluate the effectiveness of several mitigation strategies in a simulated environment with Mobility Robustness Optimization (MRO) and Energy Saving (ES) xApps and present experimental results showing comparisons among these strategies. The findings of this research provide significant insights for enhancing O-RAN deployments with flexible and efficient conflict management.
What Features in Prompts Jailbreak LLMs? Investigating the Mechanisms Behind Attacks
Venue: arXiv cs.CR 2025
Year: 2025

Jailbreaks have been a central focus of research regarding the safety and reliability of large language models (LLMs), yet the mechanisms underlying these attacks remain poorly understood. While previous studies have predominantly relied on linear methods to detect jailbreak attempts and model refusals, we take a different approach by examining both linear and non-linear features in prompts that lead to successful jailbreaks. First, we introduce a novel dataset comprising 10,800 jailbreak attempts spanning 35 diverse attack methods. Leveraging this dataset, we train linear and non-linear probes on hidden states of open-weight LLMs to predict jailbreak success. Probes achieve strong in-distribution accuracy but transfer is attack-family-specific, revealing that different jailbreaks are supported by distinct internal mechanisms rather than a single universal direction. To establish causal relevance, we construct probe-guided latent interventions that systematically shift compliance in the predicted direction. Interventions derived from non-linear probes produce larger and more reliable effects than those from linear probes, indicating that features linked to jailbreak success are encoded non-linearly in prompt representations. Overall, the results surface heterogeneous, non-linear structure in jailbreak mechanisms and provide a prompt-side methodology for recovering and testing the features that drive jailbreak outcomes.
Efficient Learning of Object Placement with Intra-Category Transfer
Venue: arXiv cs.RO 2025
Year: 2025

Efficient learning from demonstration for long-horizon tasks remains an open challenge in robotics. While significant effort has been directed toward learning trajectories, a recent resurgence of object-centric approaches has demonstrated improved sample efficiency, enabling transferable robotic skills. Such approaches model tasks as a sequence of object poses over time. In this work, we propose a scheme for transferring observed object arrangements to novel object instances by learning these arrangements on canonical class frames. We then employ this scheme to enable a simple yet effective approach for training models from as few as five demonstrations to predict arrangements of a wide range of objects including tableware, cutlery, furniture, and desk spaces. We propose a method for optimizing the learned models to enable efficient learning of tasks such as setting a table or tidying up an office with intra-category transfer, even in the presence of distractors. We present extensive experimental results in simulation and on a real robotic system for table setting which, based on human evaluations, scored 73.3% compared to a human baseline. We make the code and trained models publicly available at https://oplict.cs.uni-freiburg.de.
Efficient Iterative Proximal Variational Inference Motion Planning
Venue: arXiv cs.RO 2025
Year: 2025

We cast motion planning under uncertainty as a stochastic optimal control problem, where the optimal posterior distribution has an explicit form. To approximate this posterior, this work frames an optimization problem in the space of Gaussian distributions by solving a Variational Inference (VI) in the path distribution space. For linear-Gaussian stochastic dynamics, a proximal algorithm is proposed to solve for an optimal Gaussian proposal iteratively. The computational bottleneck is evaluating the gradients with respect to the proposal over a dense trajectory. To tackle this issue, the sparse planning factor graph and Gaussian Belief Propagation (GBP) are exploited, allowing for parallel computation of these gradients on Graphics Processing Units (GPUs). We term the novel paradigm the \textit{Parallel Gaussian Variational Inference Motion Planning (P-GVIMP)}. Building on the efficient algorithm for linear Gaussian systems, we then propose an iterative paradigm based on Statistical Linear Regression (SLR) techniques to solve planning problems for nonlinear stochastic systems, where the P-GVIMP serves as a sub-routine for the linearized time-varying system at each iteration. The proposed framework is validated on various robotic systems, demonstrating significant speed acceleration achieved by leveraging parallel computation and successful planning solutions for nonlinear systems under uncertainty. An open-sourced implementation is presented at \href{https://github.com/hzyu17/VIMP}{https://github.com/hzyu17/VIMP}.
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation
Venue: arXiv cs.LG 2025
Year: 2025

Multi-objective Bayesian optimization has been widely adopted in scientific experiment design, including drug discovery and hyperparameter optimization. In practice, regulatory or safety concerns often impose additional thresholds on certain attributes of the experimental outcomes. Previous work has primarily focused on constrained single-objective optimization tasks or active search under constraints. The existing constrained multi-objective algorithms address the issue with heuristics and approximations, posing challenges to the analysis of the sample efficiency. We propose a novel constrained multi-objective Bayesian optimization algorithm COMBOO that balances active learning of the level-set defined on multiple unknowns with multi-objective optimization within the feasible region. We provide both theoretical analysis and empirical evidence, demonstrating the efficacy of our approach on various synthetic benchmarks and real-world applications.
PyroGuardian: An IoT-Enabled System for Health and Location Monitoring in High-Risk Firefighting Environments
Venue: arXiv cs.NI 2025
Year: 2025

First responders risk their lives to reduce property damage and prevent injuries during disasters. Among first responders, firefighters work with fires in residential properties, forests, or other locations where fire occurs. We built the PyroGuardian system that uses wearable modules to transmit unit information over Long Range (LoRa) to an Android tablet. The tablet runs our application, PyroPortal, to assign each firefighter's stats, such as body temperature, heart rate, and GPS location. PyroPortal displays this information on unit dashboards, and markers on Google Maps represent the firefighter's location and the direction they are facing. These dashboards can help the incident commander (IC) make more informed decisions on mission control operations and remove specific units whose health stats, such as oximeter and pulse, passed certain thresholds. PyroGuardian completes all these tasks at an affordable cost and in an impressive maximum range between the units and IC. In addition, PyroGuardian has various application scenarios, such as law enforcement and military operations, besides firefighting. We also conducted a sample mission inside a burning building while real firefighters watched. After the demonstration, they completed a survey on system usability and PyroGuardian's potential to meet their requirements.
Where Do We Stand with Implicit Neural Representations? A Technical and Performance Survey
Venue: arXiv cs.CV 2025
Year: 2025

Implicit Neural Representations (INRs) have emerged as a paradigm in knowledge representation, offering exceptional flexibility and performance across a diverse range of applications. INRs leverage multilayer perceptrons (MLPs) to model data as continuous implicit functions, providing critical advantages such as resolution independence, memory efficiency, and generalisation beyond discretised data structures. Their ability to solve complex inverse problems makes them particularly effective for tasks including audio reconstruction, image representation, 3D object reconstruction, and high-dimensional data synthesis. This survey provides a comprehensive review of state-of-the-art INR methods, introducing a clear taxonomy that categorises them into four key areas: activation functions, position encoding, combined strategies, and network structure optimisation. We rigorously analyse their critical properties, such as full differentiability, smoothness, compactness, and adaptability to varying resolutions while also examining their strengths and limitations in addressing locality biases and capturing fine details. Our experimental comparison offers new insights into the trade-offs between different approaches, showcasing the capabilities and challenges of the latest INR techniques across various tasks. In addition to identifying areas where current methods excel, we highlight key limitations and potential avenues for improvement, such as developing more expressive activation functions, enhancing positional encoding mechanisms, and improving scalability for complex, high-dimensional data. This survey serves as a roadmap for researchers, offering practical guidance for future exploration in the field of INRs. We aim to foster new methodologies by outlining promising research directions for INRs and applications.
The Root Shapes the Fruit: On the Persistence of Gender-Exclusive Harms in Aligned Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Natural-language assistants are designed to provide users with helpful responses while avoiding harmful outputs, largely achieved through alignment to human preferences. Yet there is limited understanding of whether alignment techniques may inadvertently perpetuate or even amplify harmful biases inherited from their pre-aligned base models. This issue is compounded by the choice of bias evaluation benchmarks in popular preference-finetuned models, which predominantly focus on dominant social categories, such as binary gender, thereby limiting insights into biases affecting underrepresented groups. Towards addressing this gap, we center transgender, nonbinary, and other gender-diverse identities to investigate how alignment procedures interact with pre-existing gender-diverse bias in LLMs. Our key contributions include: 1) a comprehensive survey of bias evaluation modalities across leading preference-finetuned LLMs, highlighting critical gaps in gender-diverse representation, 2) systematic evaluation of gender-diverse biases across 16 models spanning Direct Preference Optimization (DPO) stages, uncovering harms popular bias benchmarks fail to detect, and 3) a flexible framework for measuring harmful biases in implicit reward signals applicable to other social contexts. Our findings reveal that DPO-aligned models are particularly sensitive to supervised finetuning (SFT), and can amplify two forms of real-world gender-diverse harms from their base models: stigmatization and gender non-affirmative language. We conclude with recommendations tailored to DPO and broader alignment practices, advocating for the adoption of community-informed bias evaluation frameworks to more effectively identify and address underrepresented harms in LLMs.
Overcoming label shift with target-aware federated learning
Venue: arXiv cs.LG 2025
Year: 2025

Federated learning enables multiple actors to collaboratively train models without sharing private data. Existing algorithms are successful and well-justified in this task when the intended target domain, where the trained model will be used, shares data distribution with the aggregate of clients, but this is often violated in practice. A common reason is label shift -- that the label distributions differ between clients and the target domain. We demonstrate empirically that this can significantly degrade performance. To address this problem, we propose FedPALS, a principled and practical model aggregation scheme that adapts to label shifts to improve performance in the target domain by leveraging knowledge of label distributions at the central server. Our approach ensures unbiased updates under federated stochastic gradient descent which yields robust generalization across clients with diverse, label-shifted data. Extensive experiments on image classification tasks demonstrate that FedPALS consistently outperforms baselines by aligning model aggregation with the target domain. Our findings reveal that conventional federated learning methods suffer severely in cases of extreme label sparsity on clients, highlighting the critical need for target-aware aggregation as offered by FedPALS.
Reconsidering the Performance of GAE in Link Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Recent advancements in graph neural networks (GNNs) for link prediction have introduced sophisticated training techniques and model architectures. However, reliance on outdated baselines may exaggerate the benefits of these new approaches. To tackle this issue, we systematically explore Graph Autoencoders (GAEs) by applying model-agnostic tricks in recent methods and tuning hyperparameters. We find that a well-tuned GAE can match the performance of recent sophisticated models while offering superior computational efficiency on widely-used link prediction benchmarks. Our approach delivers substantial performance gains on datasets where structural information dominates and feature data is limited. Specifically, our GAE achieves a state-of-the-art Hits@100 score of 78.41\% on the ogbl-ppa dataset. Furthermore, we examine the impact of various tricks to uncover the reasons behind our success and to guide the design of future methods. Our study emphasizes the critical need to update baselines for a more accurate assessment of progress in GNNs for link prediction. Our code is available at https://github.com/GraphPKU/Refined-GAE.
A First Look at Ethereum Blob Revolution: Market, Strategies, and Optimality
Venue: arXiv cs.DC 2025
Year: 2025

As a key enabler of Web3, Ethereum has long faced scalability challenges. The recent EIP-4844 upgrade aims to alleviate the scalability issue by introducing the ''blob'', a new data structure for Layer-2 rollups that enables off-chain storage with much reduced costs. Yet, this new mechanism's impact on Ethereum, and the wider Web3 ecosystem, remains largely underexplored. In this paper, we conduct the first large-scale empirical analysis of the post-EIP-4844 ecosystem, leveraging a dataset of 319.5 million transactions, out of which 1.3 million are blob-carrying. Our analysis reveals two major trends: (1) average block size has increased 2.5 times, from 150 KB to 400 KB, while the share of conventional transactions has shrunk from over $150$ KB to around 80 KB; (2) rollups are rapidly migrating from expensive calldata, falling from approximately 7,500 to nearly zero, toward cheap blobs, rising from zero to about 10,000. These shifts introduce a new economic game between block builders and rollups. Thus, we develop a game-theoretic model to characterize their equilibrium strategies: a profit-maximizing inclusion rule for builders, and a cost-minimizing blob batching strategy for rollups. Empirically, however, we find notable economic inefficiencies: for example, 29.48% of blob-containing blocks are built sub-optimally, yielding less revenue than available alternatives. These findings highlight the intricacies of the blob marketplace, and our work has established both methodological and empirical foundations to understand the evolving post-EIP4844 Ethereum ecosystem.