OpenRaiser/Intern-Atlas huggingface dataset:
ChartEditor: A Reinforcement Learning Framework for Robust Chart Editing
Venue: arXiv cs.MM 2025
Year: 2025

Chart editing reduces manual effort in visualization design. Typical benchmarks limited in data diversity and assume access to complete chart code, which is seldom in real-world scenarios. To address this gap, we present ChartEditVista, a comprehensive benchmark consisting of 7,964 samples spanning 31 chart categories. It encompasses diverse editing instructions and covers nearly all editable chart elements. The inputs in ChartEditVista include only the original chart image and natural language editing instructions, without the original chart codes. ChartEditVista is generated through a fully automated pipeline that produces, edits, and verifies charts, ensuring high-quality chart editing data. Besides, we introduce two novel fine-grained, rule-based evaluation metrics: the layout metric, which evaluates the position, size and color of graphical components; and the text metric, which jointly assesses textual content and font styling. Building on top of ChartEditVista, we present ChartEditor, a model trained using a reinforcement learning framework that incorporates a novel rendering reward to simultaneously enforce code executability and visual fidelity. Through extensive experiments and human evaluations, we demonstrate that ChartEditVista provides a robust evaluation, while ChartEditor consistently outperforms models with similar-scale and larger-scale on chart editing tasks.
Look, Zoom, Understand: The Robotic Eyeball for Embodied Perception
Venue: arXiv cs.RO 2025
Year: 2025

In embodied AI perception systems, visual perception should be active: the goal is not to passively process static images, but to actively acquire more informative data within pixel and spatial budget constraints. Existing vision models and fixed RGB-D camera systems fundamentally fail to reconcile wide-area coverage with fine-grained detail acquisition, severely limiting their efficacy in open-world robotic applications. To address this issue, we propose EyeVLA, a robotic eyeball for active visual perception that can take proactive actions based on instructions, enabling clear observation of fine-grained target objects and detailed information across a wide spatial extent. EyeVLA discretizes action behaviors into action tokens and integrates them with vision-language models (VLMs) that possess strong open-world understanding capabilities, enabling joint modeling of vision, language, and actions within a single autoregressive sequence. By using the 2D bounding box coordinates to guide the reasoning chain and applying reinforcement learning to refine the viewpoint selection policy, we transfer the open-world scene understanding capability of the VLM to a vision language action (VLA) policy using only minimal real-world data. Experiments show that our system efficiently performs instructed scenes in real-world environments and actively acquires more accurate visual information through instruction-driven actions of rotation and zoom, thereby achieving strong environmental perception capabilities. EyeVLA introduces a novel robotic vision system that leverages detailed and spatially rich, large-scale embodied data, and actively acquires highly informative visual observations for downstream embodied tasks.
Adversarial Attack on Black-Box Multi-Agent by Adaptive Perturbation
Venue: arXiv cs.MA 2025
Year: 2025

Evaluating security and reliability for multi-agent systems (MAS) is urgent as they become increasingly prevalent in various applications. As an evaluation technique, existing adversarial attack frameworks face certain limitations, e.g., impracticality due to the requirement of white-box information or high control authority, and a lack of stealthiness or effectiveness as they often target all agents or specific fixed agents. To address these issues, we propose AdapAM, a novel framework for adversarial attacks on black-box MAS. AdapAM incorporates two key components: (1) Adaptive Selection Policy simultaneously selects the victim and determines the anticipated malicious action (the action would lead to the worst impact on MAS), balancing effectiveness and stealthiness. (2) Proxy-based Perturbation to Induce Malicious Action utilizes generative adversarial imitation learning to approximate the target MAS, allowing AdapAM to generate perturbed observations using white-box information and thus induce victims to execute malicious action in black-box settings. We evaluate AdapAM across eight multi-agent environments and compare it with four state-of-the-art and commonly-used baselines. Results demonstrate that AdapAM achieves the best attack performance in different perturbation rates. Besides, AdapAM-generated perturbations are the least noisy and hardest to detect, emphasizing the stealthiness.
MSA - Technique for Stiffness Modeling of Manipulators with Complex and Hybrid Structures
Venue: arXiv cs.RO 2025
Year: 2025

The paper presents a systematic approach for stiffness modeling of manipulators with complex and hybrid structures using matrix structural analysis. In contrast to previous results, it is suitable for mixed architectures containing closed-loops, flexible links, rigid connections, passive and elastic joints with external loadings and preloadings. The proposed approach produces the Cartesian stiffness matrices in a semi-analytical manner. It presents the manipulator stiffness model as a set of conventional equations describing the link elasticities that are supplemented by a set of constraints describing connections between links. Its allows user straightforward aggregation of stiffness model equations avoiding traditional column/row merging procedures in the extended stiffness matrix. Advantages of this approach are illustrated by stiffness analysis of NaVaRo manipulator.
Quant-Trim in Practice: Improved Cross-Platform Low-Bit Deployment on Edge NPUs
Venue: arXiv cs.LG 2025
Year: 2025

Specialized edge accelerators rely on low-bit quantization, but vendor compilers differ in scaling, clipping, and kernel support, often as black boxes. The same floating-point (FP) checkpoint can therefore yield inconsistent accuracy across backends, forcing practitioners to tweak flags or refactor models to vendor-friendly operator subsets. We introduce Quant-Trim, a training-phase method that produces a hardware-neutral checkpoint robust to backend and precision choices. It combines progressive fake quantization to align training with the deployed integer grid and reverse pruning to tame outlier-driven scale inflation while preserving learnability. Quant-Trim is agnostic to quantization schemes (symmetric/asymmetric, per-tensor/per-channel, INT8/INT4) and requires no vendor-specific graph changes. Across models and tasks, it narrows the FP-to-low-bit gap, reduces dependence on compiler heuristics/calibration, and avoids per-backend retraining. We report accuracy and edge metrics latency, throughput, energy per inference, and cost under static/dynamic activation scaling and varying operator coverage.
People readily follow personal advice from AI but it does not improve their well-being
Venue: arXiv cs.HC 2025
Year: 2025

People increasingly seek personal advice from large language models (LLMs), yet whether humans follow their advice, and its consequences for their well-being, remains unknown. In a longitudinal randomised controlled trial with a representative UK sample (N = 2,302), 75% of participants who had a 20-minute discussion with GPT-4o about health, careers or relationships subsequently reported following its advice. Based on autograder evaluations of chat transcripts, LLM advice rarely violated safety best practice. When queried 2-3 weeks later, participants who had interacted with personalised AI (with access to detailed user information) followed its advice more often in the real world and reported higher well-being than those advised by non-personalised AI. However, while receiving personal advice from AI temporarily reduced well-being, no differential long-term effects compared to a control emerged. Our results suggest that humans readily follow LLM advice about personal issues but doing so shows no additional well-being benefit over casual conversations.
BlueBottle: Fast and Robust Blockchains through Subsystem Specialization
Venue: arXiv cs.DC 2025
Year: 2025

Blockchain consensus faces a trilemma of security, latency, and decentralization. High-throughput systems often require a reduction in decentralization or robustness against strong adversaries, while highly decentralized and secure systems tend to have lower performance. We present BlueBottle, a two-layer consensus architecture. The core layer, BB-Core, is an n=5f+1 protocol that trades some fault tolerance for a much lower finality latency with a medium-sized core validator set. Our experiments show that BB-Core reduces latency by 20-25% in comparison to Mysticeti. The guard layer, BB-Guard, provides decentralized timestamping, proactive misbehavior detection in BB-Core, and a synchronous recovery path. When it observes equivocations or liveness failures in the core -- while tolerating up to f<3n/5 faulty nodes in the primary layer -- guard validators disseminate evidence, agree on misbehaving parties for exclusion or slashing, and either restart the core protocol (for liveness violations) or select a canonical fork (for safety violations). Together, these layers enable optimistic sub-second finality at high throughput while maintaining strong safety and liveness under a mild synchrony assumption.
The Empowerment of Science of Science by Large Language Models: New Tools and Methods
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) have exhibited exceptional capabilities in natural language understanding and generation, image recognition, and multimodal tasks, charting a course towards AGI and emerging as a central issue in the global technological race. This manuscript conducts a comprehensive review of the core technologies that support LLMs from a user standpoint, including prompt engineering, knowledge-enhanced retrieval augmented generation, fine tuning, pretraining, and tool learning. Additionally, it traces the historical development of Science of Science (SciSci) and presents a forward looking perspective on the potential applications of LLMs within the scientometric domain. Furthermore, it discusses the prospect of an AI agent based model for scientific evaluation, and presents new research fronts detection and knowledge graph building methods with LLMs.
Breaking Expert Knowledge Limits: Self-Pruning for Large Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Large language models (LLMs) have achieved remarkable performance on a wide range of tasks, hindering real-world deployment due to their massive size. Existing pruning methods (e.g., Wanda) tailored for LLMs rely heavily on manual design pruning algorithms, thereby leading to \textit{huge labor costs} and \textit{requires expert knowledge}. Furthermore, we are the first to identify the serious \textit{outlier value issue} behind dramatic performance degradation under high pruning ratios that are caused by uniform sparsity, raising an additional concern about how to design adaptive pruning sparsity ideal for LLMs. Can LLMs prune by themselves? In this work, we introduce an affirmative answer by proposing a novel pruning method called \textbf{AutoPrune}, which first overcomes expert knowledge limits by leveraging LLMs to design optimal pruning algorithms for themselves automatically without any expert knowledge. Specifically, to mitigate the black-box nature of LLMs, we propose a Graph-driven Chain-of-Thought (GCoT) to optimize prompts, significantly enhancing the reasoning process in learning the pruning algorithm and enabling us to generate pruning algorithms with superior performance and interpretability in the next generation. Finally, grounded in insights of outlier value issue, we introduce Skew-aware Dynamic Sparsity Allocation (SDSA) to overcome the outlier value issue, mitigating performance degradation under high pruning ratios. We conduct extensive experiments on mainstream LLMs benchmarks, demonstrating the superiority of AutoPrune, which consistently excels state-of-the-art competitors. The code is available at: https://anonymous.4open.science/r/AutoPrune.
MutDafny: A Mutation-Based Approach to Assess Dafny Specifications
Venue: arXiv cs.SE 2025
Year: 2025

This paper explores the use of mutation testing to reveal weaknesses in formal specifications written in Dafny. In verification-aware programming languages, such as Dafny, despite their critical role, specifications are as prone to errors as implementations. Flaws in specs can result in formally verified programs that deviate from the intended behavior. We present MutDafny, a tool that increases the reliability of Dafny specifications by automatically signaling potential weaknesses. Using a mutation testing approach, we introduce faults (mutations) into the code and rely on formal specifications for detecting them. If a program with a mutant verifies, this may indicate a weakness in the specification. We extensively analyze mutation operators from popular tools, identifying the ones applicable to Dafny. In addition, we synthesize new operators tailored for Dafny from bugfix commits in publicly available Dafny projects on GitHub. Drawing from both, we equipped our tool with a total of 32 mutation operators. We evaluate MutDafny's effectiveness and efficiency in a dataset of 794 real-world Dafny programs and we manually analyze a subset of the resulting undetected mutants, identifying five weak real-world specifications (on average, one at every 241 lines of code) that would benefit from strengthening.
WarNav: An Autonomous Driving Benchmark for Segmentation of Navigable Zones in War Scenes
Venue: arXiv cs.CV 2025
Year: 2025

We introduce WarNav, a novel real-world dataset constructed from images of the open-source DATTALION repository, specifically tailored to enable the development and benchmarking of semantic segmentation models for autonomous ground vehicle navigation in unstructured, conflict-affected environments. This dataset addresses a critical gap between conventional urban driving resources and the unique operational scenarios encountered by unmanned systems in hazardous and damaged war-zones. We detail the methodological challenges encountered, ranging from data heterogeneity to ethical considerations, providing guidance for future efforts that target extreme operational contexts. To establish performance references, we report baseline results on WarNav using several state-of-the-art semantic segmentation models trained on structured urban scenes. We further analyse the impact of training data environments and propose a first step towards effective navigability in challenging environments with the constraint of having no annotation of the targeted images. Our goal is to foster impactful research that enhances the robustness and safety of autonomous vehicles in high-risk scenarios while being frugal in annotated data.
FairEnergy: Contribution-Based Fairness meets Energy Efficiency in Federated Learning
Venue: arXiv cs.LG 2025
Year: 2025

Federated learning (FL) enables collaborative model training across distributed devices while preserving data privacy. However, balancing energy efficiency and fair participation while ensuring high model accuracy remains challenging in wireless edge systems due to heterogeneous resources, unequal client contributions, and limited communication capacity. To address these challenges, we propose FairEnergy, a fairness-aware energy minimization framework that integrates a contribution score capturing both the magnitude of updates and their compression ratio into the joint optimization of device selection, bandwidth allocation, and compression level. The resulting mixed-integer non-convex problem is solved by relaxing binary selection variables and applying Lagrangian decomposition to handle global bandwidth coupling, followed by per-device subproblem optimization. Experiments on non-IID data show that FairEnergy achieves higher accuracy while reducing energy consumption by up to 79\% compared to baseline strategies.
How To Cook The Fragmented Rug Pull?
Venue: arXiv cs.CR 2025
Year: 2025

Existing rug pull detectors assume a simple workflow: the deployer keeps liquidity pool (LP) tokens and performs one or a few large sells (within a day) that collapse the pool and cash out. In practice, however, many real-world exits violate these assumptions by splitting the attack across both time and actor dimensions: attackers break total extraction into many low-impact trades and route proceeds through multiple non-owner addresses, producing low-visibility drains. We formalize this family of attacks as the fragmented rug pull (FRP) and offer a compact recipe for a slow-stewed beef special: (i) keep the lid on (to preserve LP control so on-chain extraction remains feasible), (ii) chop thin slices (to split the total exit volume into many low-impact micro-trades that individually fall below impact thresholds), and (iii) pass the ladle (to delegate sells across multiple wallets so that each participant takes a small share of the extraction). Technically, we define three atomic predicate groups and show that their orthogonal combinations yield evasive strategies overlooked by prior heuristics (USENIX Sec 19, USENIX Sec 23). We validate the model with large-scale measurements. Our corpus contains 303,614 LPs, among which 105,434 are labeled as FRP pools. The labeled subset includes 34,192,767 pool-related transactions and 401,838 inflated-seller wallets, involving 1,501,408 unique interacting addresses. Notably, owner-wallet participation in inflated selling among FRP-flagged LPs has declined substantially (33.1% of cases), indicating a shift in scam behavior: the liquidity drain is no longer held on the owner wallet. We also detected 127,252 wallets acting as serial scammers when repeatedly engaging in inflated selling across multiple FRP LPs. Our empirical findings demonstrate that the evasive strategies we define are widespread and operationally significant.
A Novel CustNetGC Boosted Model with Spectral Features for Parkinson's Disease Prediction
Venue: arXiv cs.SD 2025
Year: 2025

Parkinson's disease is a neurodegenerative disorder that can be very tricky to diagnose and treat. Such early symptoms can include tremors, wheezy breathing, and changes in voice quality as critical indicators of neural damage. Notably, there has been growing interest in utilizing changes in vocal attributes as markers for the detection of PD early on. Based on this understanding, the present paper was designed to focus on the acoustic feature analysis based on voice recordings of patients diagnosed with PD and healthy controls (HC). In this paper, we introduce a novel classification and visualization model known as CustNetGC, combining a Convolutional Neural Network (CNN) with Custom Network Grad-CAM and CatBoost to enhance the efficiency of PD diagnosis. We use a publicly available dataset from Figshare, including voice recordings of 81 participants: 40 patients with PD and 41 healthy controls. From these recordings, we extracted the key spectral features: L-mHP and Spectral Slopes. The L-mHP feature combines three spectrogram representations: Log-Mel spectrogram, harmonic spectrogram, and percussive spectrogram, which are derived using Harmonic-Percussive Source Separation (HPSS). Grad-CAM was used to highlight the important regions in the data, thus making the PD predictions interpretable and effective. Our proposed CustNetGC model achieved an accuracy of 99.06% and precision of 95.83%, with the area under the ROC curve (AUC) recorded at 0.90 for the PD class and 0.89 for the HC class. Additionally, the combination of CatBoost, a gradient boosting algorithm, enhanced the robustness and the prediction performance by properly classifying PD and non-PD samples. Therefore, the results provide the potential improvement in the CustNetGC system in enhancing diagnostic accuracy and the interpretability of the Parkinson's Disease prediction model.
A Tensor Compiler for Processing-In-Memory Architectures
Venue: arXiv cs.AR 2025
Year: 2025

Processing-In-Memory (PIM) devices integrated with high-performance Host processors (e.g., GPUs) can accelerate memory-intensive kernels in Machine Learning (ML) models, including Large Language Models (LLMs), by leveraging high memory bandwidth at PIM cores. However, Host processors and PIM cores require different data layouts: Hosts need consecutive elements distributed across DRAM banks, while PIM cores need them within local banks. This necessitates data rearrangements in ML kernel execution that pose significant performance and programmability challenges, further exacerbated by the need to support diverse PIM backends. Current compilation approaches lack systematic optimization for diverse ML kernels across multiple PIM backends and may largely ignore data rearrangements during compute code optimization. We demonstrate that data rearrangements and compute code optimization are interdependent, and need to be jointly optimized during the tuning process. To address this, we design DCC, the first data-centric ML compiler for PIM systems that jointly co-optimizes data rearrangements and compute code in a unified tuning process. DCC integrates a multi-layer PIM abstraction that enables various data distribution and processing strategies on different PIM backends. DCC enables effective co-optimization by mapping data partitioning strategies to compute loop partitions, applying PIM-specific code optimizations and leveraging a fast and accurate performance prediction model to select optimal configurations. Our evaluations in various individual ML kernels demonstrate that DCC achieves up to 7.68x speedup (2.7x average) on HBM-PIM and up to 13.17x speedup (5.75x average) on AttAcc PIM backend over GPU-only execution. In end-to-end LLM inference, DCC on AttAcc accelerates GPT-3 and LLaMA-2 by up to 7.71x (4.88x average) over GPU.
Instruction-Based Coordination of Heterogeneous Processing Units for Acceleration of DNN Inference
Venue: arXiv cs.AR 2025
Year: 2025

This paper presents an instruction-based coordination architecture for Field-Programmable Gate Array (FPGA)-based systems with multiple high-performance Processing Units (PUs) for accelerating Deep Neural Network (DNN) inference. This architecture enables programmable multi-PU synchronization through instruction controller units coupled with peer-to-peer instruction synchronization units, utilizing instruction types organized into load, compute, and store functional groups. A compilation framework is presented that transforms DNN models into executable instruction programs, enabling flexible partitioning of DNN models into topologically contiguous subgraphs mapped to available PUs. Multiple deployment strategies are supported, enabling pipeline parallelism among PUs and batch-level parallelism across different PU subsets, with runtime switching among them without FPGA reconfiguration. The proposed approach enables design space exploration, supporting dynamic trade-offs between single-batch and multi-batch performance. Experimental results on ResNet-50 demonstrate notable compute efficiency, up to $98\%$, and throughput efficiency gains, up to $2.7\times$, over prior works across different configurations.