OpenRaiser/Intern-Atlas huggingface dataset:
AutoEval: A Practical Framework for Autonomous Evaluation of Mobile Agents
Venue: arXiv cs.AI 2025
Year: 2025

Comprehensive evaluation of mobile agents can significantly advance their development and real-world applicability. However, existing benchmarks lack practicality and scalability due to the extensive manual effort in defining task reward signals and implementing evaluation codes. We propose AutoEval, an evaluation framework which tests mobile agents without any manual effort. Our approach designs a UI state change representation which can be used to automatically generate task reward signals, and employs a Judge System for autonomous evaluation. Evaluation shows AutoEval can automatically generate reward signals with high correlation to human-annotated signals, and achieve high accuracy (up to 94%) in autonomous evaluation comparable to human evaluation. Finally, we evaluate state-of-the-art mobile agents using our framework, providing insights into their performance and limitations.
A comparison of visual representations for real-world reinforcement learning in the context of vacuum gripping
Venue: arXiv cs.RO 2025
Year: 2025

When manipulating objects in the real world, we need reactive feedback policies that take into account sensor information to inform decisions. This study aims to determine how different encoders can be used in a reinforcement learning (RL) framework to interpret the spatial environment in the local surroundings of a robot arm. Our investigation focuses on comparing real-world vision with 3D scene inputs, exploring new architectures in the process. We built on the SERL framework, providing us with a sample efficient and stable RL foundation we could build upon, while keeping training times minimal. The results of this study indicate that spatial information helps to significantly outperform the visual counterpart, tested on a box picking task with a vacuum gripper. The code and videos of the evaluations are available at https://github.com/nisutte/voxel-serl.
The Inversion Paradox and Ranking Methods in Tournaments
Venue: arXiv cs.GT 2025
Year: 2025

This article deals with ranking methods. We study the situation where a tournament between $n$ players $P_1$, $P_2$, \ldots $P_n$ gives the ranking $P_1 \succ P_2 \succ \cdots \succ P_n$, but, if the results of $P_n$ are no longer taken into account (for example $P_n$ is suspended for doping), then the ranking becomes $P_{n-1} \succ P_{n-2} \succ \cdots \succ P_2 \succ P_1$. If such a situation arises, we call it an inversion paradox. In this article, we give a sufficient condition for the inversion paradox to occur. More precisely, we give an impossibility theorem. We prove that if a ranking method satisfies three reasonable properties (the ranking must be natural, reducible by Condorcet tournaments and satisfies the long tournament property) then we cannot avoid the inversion paradox, i.e., there are tournaments where the inversion paradox occurs. We then show that this paradox can occur when we use classical methods, e.g., Borda, Massey, Colley and Markov methods.
New centrality measure: ksi-centrality
Venue: arXiv cs.SI 2025
Year: 2025

We introduce new centrality measures, called ksi-centrality and normalized ksi-centrality measure the importance of a node up to the importance of its neighbors. First, we show that normalized ksi-centrality can be rewritten in terms of the Laplacian matrix such that its expression is similar to the local clustering coefficient. After that we introduce average normalized ksi-coefficient and show that for a random Erdos-Renyi graph it is almost the same as average clustering coefficient. It also shows behavior similar to the clustering coefficient for the Windmill and Wheel graphs. Finally, we show that the distributions of ksi centrality and normalized ksi centrality distinguish networks based on real data from artificial networks, including the Watts-Strogatz, Barabasi-Albert and Boccaletti-Hwang-Latora small-world networks. Furthermore, we show the relationship between normalized ksi centrality and the average normalized ksi coefficient and the algebraic connectivity of the graph and the Chegeer number.
Attack Tree Distance: a practical examination of tree difference measurement within cyber security
Venue: arXiv cs.CR 2025
Year: 2025

CONTEXT. Attack treesare a recommended threat modeling tool, but there is no established method to compare them. OBJECTIVE. We aim to establish a method to compare "real" attack trees, based on both the structure of the tree itself and the meaning of the node labels. METHOD. We define four methods of comparison (three novel and one established) and compare them to a dataset of attack trees created from a study run on students (n = 39). These attack trees all follow from the same scenario, but have slightly different labels. RESULTS. We find that applying semantic similarity as a means of comparing node labels is a valid approach. Further, we find that treeedit distance (established) and radical distance (novel) are themost promising methods of comparison in most circumstances. CONCLUSION. We show that these two methods are valid as means of comparing attack trees, and suggest a novel technique for using semantic similarity to compare node labels. We further suggest that these methods can be used to compare attack trees in a real-world scenario, and that they can be used to identify similar attack trees.
ROCKET-2: Steering Visuomotor Policy via Cross-View Goal Alignment
Venue: arXiv cs.AI 2025
Year: 2025

We aim to develop a goal specification method that is semantically clear, spatially sensitive, domain-agnostic, and intuitive for human users to guide agent interactions in 3D environments. Specifically, we propose a novel cross-view goal alignment framework that allows users to specify target objects using segmentation masks from their camera views rather than the agent's observations. We highlight that behavior cloning alone fails to align the agent's behavior with human intent when the human and agent camera views differ significantly. To address this, we introduce two auxiliary objectives: cross-view consistency loss and target visibility loss, which explicitly enhance the agent's spatial reasoning ability. According to this, we develop ROCKET-2, a state-of-the-art agent trained in Minecraft, achieving an improvement in the efficiency of inference 3x to 6x compared to ROCKET-1. We show that ROCKET-2 can directly interpret goals from human camera views, enabling better human-agent interaction. Remarkably, ROCKET-2 demonstrates zero-shot generalization capabilities: despite being trained exclusively on the Minecraft dataset, it can adapt and generalize to other 3D environments like Doom, DMLab, and Unreal through a simple action space mapping.
Q&C: When Quantization Meets Cache in Efficient Image Generation
Venue: arXiv cs.CV 2025
Year: 2025

Quantization and cache mechanisms are typically applied individually for efficient Diffusion Transformers (DiTs), each demonstrating notable potential for acceleration. However, the promoting effect of combining the two mechanisms on efficient generation remains under-explored. Through empirical investigation, we find that the combination of quantization and cache mechanisms for DiT is not straightforward, and two key challenges lead to severe catastrophic performance degradation: (i) the sample efficacy of calibration datasets in post-training quantization (PTQ) is significantly eliminated by cache operation; (ii) the combination of the above mechanisms introduces more severe exposure bias within sampling distribution, resulting in amplified error accumulation in the image generation process. In this work, we take advantage of these two acceleration mechanisms and propose a hybrid acceleration method by tackling the above challenges, aiming to further improve the efficiency of DiTs while maintaining excellent generation capability. Concretely, a temporal-aware parallel clustering (TAP) is designed to dynamically improve the sample selection efficacy for the calibration within PTQ for different diffusion steps. A variance compensation (VC) strategy is derived to correct the sampling distribution. It mitigates exposure bias through an adaptive correction factor generation. Extensive experiments have shown that our method has accelerated DiTs by 12.7x while preserving competitive generation capability. The code will be available at https://github.com/xinding-sys/Quant-Cache.
LTL Verification of Memoryful Neural Agents
Venue: arXiv cs.LO 2025
Year: 2025

We present a framework for verifying Memoryful Neural Multi-Agent Systems (MN-MAS) against full Linear Temporal Logic (LTL) specifications. In MN-MAS, agents interact with a non-deterministic, partially observable environment. Examples of MN-MAS include multi-agent systems based on feed-forward and recurrent neural networks or state-space models. Different from previous approaches, we support the verification of both bounded and unbounded LTL specifications. We leverage well-established bounded model checking techniques, including lasso search and invariant synthesis, to reduce the verification problem to that of constraint solving. To solve these constraints, we develop efficient methods based on bound propagation, mixed-integer linear programming, and adaptive splitting. We evaluate the effectiveness of our algorithms in single and multi-agent environments from the Gymnasium and PettingZoo libraries, verifying unbounded specifications for the first time and improving the verification time for bounded specifications by an order of magnitude compared to the SoA.
SpecInF: Exploiting Idle GPU Resources in Distributed DL Training via Speculative Inference Filling
Venue: arXiv cs.DC 2025
Year: 2025

Deep Learning (DL), especially with Large Language Models (LLMs), brings benefits to various areas. However, DL training systems usually yield prominent idling GPU resources due to many factors, such as resource allocation and collective communication. To improve GPU utilization, we present SpecInF, which adopts a Speculative Inference Filling method to exploit idle GPU resources. It collocates each primary training instance with additional inference instances on the same GPU, detects the training bubbles and adaptively fills with online or offline inference workloads. Our results show that SpecInF can effectively enhance GPU utilization under mainstream parallel training modes, delivering additional up to 14$\times$ offline inference throughputs than TGS and 67\% reduction in online inference p95 latency than MPS, while guaranteeing collocated training throughput.
TFHE-SBC: Software Designs for Fully Homomorphic Encryption over the Torus on Single Board Computers
Venue: arXiv cs.CR 2025
Year: 2025

Fully homomorphic encryption (FHE) is a technique that enables statistical processing and machine learning while protecting data, including sensitive information collected by single board computers (SBCs), on a cloud server. Among FHE schemes, the TFHE scheme is capable of homomorphic NAND operations and, unlike other FHE schemes, can perform various operations such as minimum, maximum, and comparison. However, TFHE requires Torus Learning With Error (TLWE) encryption, which encrypts one bit at a time, leading to less efficient encryption and larger ciphertext size compared to other schemes. Additionally, SBCs have a limited number of hardware accelerators compared to servers, making it challenging to achieve the same level of optimization as on servers. In this study, we propose a novel SBC-specific design, \textsf{TFHE-SBC}, to accelerate client-side TFHE operations and enhance communication and energy efficiency. Experimental results demonstrate that \textsf{TFHE-SBC} encryption is up to 2486 times faster, improves communication efficiency by 512 times, and achieves 12 to 2004 times greater energy efficiency than the state-of-the-art.
LLM-Safety Evaluations Lack Robustness
Venue: arXiv cs.CR 2025
Year: 2025

In this paper, we argue that current safety alignment research efforts for large language models are hindered by many intertwined sources of noise, such as small datasets, methodological inconsistencies, and unreliable evaluation setups. This can, at times, make it impossible to evaluate and compare attacks and defenses fairly, thereby slowing progress. We systematically analyze the LLM safety evaluation pipeline, covering dataset curation, optimization strategies for automated red-teaming, response generation, and response evaluation using LLM judges. At each stage, we identify key issues and highlight their practical impact. We also propose a set of guidelines for reducing noise and bias in evaluations of future attack and defense papers. Lastly, we offer an opposing perspective, highlighting practical reasons for existing limitations. We believe that addressing the outlined problems in future research will improve the field's ability to generate easily comparable results and make measurable progress.
Research on visual simultaneous localization and mapping technology based on near infrared light
Venue: arXiv cs.RO 2025
Year: 2025

In view of the problems that visual simultaneous localization and mapping (VSLAM) are susceptible to environmental light interference and luminosity inconsistency, the visual simultaneous localization and mapping technology based on near infrared perception (NIR-VSLAM) is proposed. In order to avoid ambient light interference, the near infrared light is innovatively selected as the light source. The luminosity parameter estimation of error energy function, halo factor and exposure time and the light source irradiance correction method are proposed in this paper, which greatly improves the positioning accuracy of Direct Sparse Odometry (DSO). The feasibility of the proposed method in four large scenes is verified, which provides the reference for visual positioning in automatic driving and mobile robot.
ARC-Flow : Articulated, Resolution-Agnostic, Correspondence-Free Matching and Interpolation of 3D Shapes Under Flow Fields
Venue: arXiv cs.CV 2025
Year: 2025

This work presents a unified framework for the unsupervised prediction of physically plausible interpolations between two 3D articulated shapes and the automatic estimation of dense correspondence between them. Interpolation is modelled as a diffeomorphic transformation using a smooth, time-varying flow field governed by Neural Ordinary Differential Equations (ODEs). This ensures topological consistency and non-intersecting trajectories while accommodating hard constraints, such as volume preservation, and soft constraints, \eg physical priors. Correspondence is recovered using an efficient Varifold formulation, that is effective on high-fidelity surfaces with differing parameterisations. By providing a simple skeleton for the source shape only, we impose physically motivated constraints on the deformation field and resolve symmetric ambiguities. This is achieved without relying on skinning weights or any prior knowledge of the skeleton's target pose configuration. Qualitative and quantitative results demonstrate competitive or superior performance over existing state-of-the-art approaches in both shape correspondence and interpolation tasks across standard datasets.
Adapting Decoder-Based Language Models for Diverse Encoder Downstream Tasks
Venue: arXiv cs.CL 2025
Year: 2025

Decoder-based transformers, while revolutionizing language modeling and scaling to immense sizes, have not completely overtaken encoder-heavy architectures in natural language processing. Specifically, encoder-only models remain dominant in tasks like classification, regression, and ranking. This is primarily due to the inherent structure of decoder-based models, which limits their direct applicability to these tasks. In this paper, we introduce Gemma Encoder, adapting the powerful Gemma decoder model to an encoder architecture, thereby unlocking its potential for a wider range of non-generative applications. To optimize the adaptation from decoder to encoder, we systematically analyze various pooling strategies, attention mechanisms, and hyperparameters (e.g., dropout rate). Furthermore, we benchmark Gemma Encoder against established approaches on the GLUE benchmarks, and MS MARCO ranking benchmark, demonstrating its effectiveness and versatility.
On describing trees and quasi-trees from their leaves
Venue: arXiv cs.LO 2025
Year: 2025

Generalized trees, we call them O-trees, are defined as hierarchical partial orders, i.e., such that the elements larger than any one are linearly ordered. Quasi-trees are, roughly speaking, undirected O-trees. For O-trees and quasi-trees, we define relational structures on their leaves that characterize them up to isomorphism. These structures have characterizations by universal first-order sentences. Furthermore, we consider cases where O-trees and quasi-trees can be reconstructed from their leaves by CMSO-transductions. These transductions are transformations of relational structures defined by monadic second-order (MSO) formulas. The letter "C" for counting refers to the use of set predicates that count cardinalities of finite sets modulo fixed integers. O-trees and quasi-trees make it possible to define respectively, the modular decomposition and the rank-width of a countable graph. Their constructions from their leaves by transductions of different types apply to rank-decompositions, and to modular decomposition and to other canonical graph decompositions.
Towards Robust Expert Finding in Community Question Answering Platforms
Venue: arXiv cs.IR 2025
Year: 2025

This paper introduces TUEF, a topic-oriented user-interaction model for fair Expert Finding in Community Question Answering (CQA) platforms. The Expert Finding task in CQA platforms involves identifying proficient users capable of providing accurate answers to questions from the community. To this aim, TUEF improves the robustness and credibility of the CQA platform through a more precise Expert Finding component. The key idea of TUEF is to exploit diverse types of information, specifically, content and social information, to identify more precisely experts thus improving the robustness of the task. We assess TUEF through reproducible experiments conducted on a large-scale dataset from StackOverflow. The results consistently demonstrate that TUEF outperforms state-of-the-art competitors while promoting transparent expert identification.
S4D-Bio Audio Monitoring of Bone Cement Disintegration in Pulsating Fluid Jet Surgery under Laboratory Conditions
Venue: arXiv cs.LG 2025
Year: 2025

This study investigates a pulsating fluid jet as a novel precise, minimally invasive and cold technique for bone cement removal. We utilize the pulsating fluid jet device to remove bone cement from samples designed to mimic clinical conditions. The effectiveness of long nozzles was tested to enable minimally invasive procedures. Audio signal monitoring, complemented by the State Space Model (SSM) S4D-Bio, was employed to optimize the fluid jet parameters dynamically, addressing challenges like visibility obstruction from splashing. Within our experiments, we generate a comprehensive dataset correlating various process parameters and their equivalent audio signals to material erosion. The use of SSMs yields precise control over the predictive erosion process, achieving 98.93 \% accuracy. The study demonstrates on the one hand, that the pulsating fluid jet device, coupled with advanced audio monitoring techniques, is a highly effective tool for precise bone cement removal. On the other hand, this study presents the first application of SSMs in biomedical surgery technology, marking a significant advancement in the application. This research significantly advances biomedical engineering by integrating machine learning combined with pulsating fluid jet as surgical technology, offering a novel, minimally invasive, cold and adaptive approach for bone cement removal in orthopedic applications.
Catheter Detection and Segmentation in X-ray Images via Multi-task Learning
Venue: arXiv cs.CV 2025
Year: 2025

Automated detection and segmentation of surgical devices, such as catheters or wires, in X-ray fluoroscopic images have the potential to enhance image guidance in minimally invasive heart surgeries. In this paper, we present a convolutional neural network model that integrates a resnet architecture with multiple prediction heads to achieve real-time, accurate localization of electrodes on catheters and catheter segmentation in an end-to-end deep learning framework. We also propose a multi-task learning strategy in which our model is trained to perform both accurate electrode detection and catheter segmentation simultaneously. A key challenge with this approach is achieving optimal performance for both tasks. To address this, we introduce a novel multi-level dynamic resource prioritization method. This method dynamically adjusts sample and task weights during training to effectively prioritize more challenging tasks, where task difficulty is inversely proportional to performance and evolves throughout the training process. Experiments on both public and private datasets have demonstrated that the accuracy of our method surpasses the existing state-of-the-art methods in both single segmentation task and in the detection and segmentation multi-task. Our approach achieves a good trade-off between accuracy and efficiency, making it well-suited for real-time surgical guidance applications.
From Metaphor to Mechanism: How LLMs Decode Traditional Chinese Medicine Symbolic Language for Modern Clinical Relevance
Venue: arXiv cs.CL 2025
Year: 2025

Metaphorical expressions are abundant in Traditional Chinese Medicine (TCM), conveying complex disease mechanisms and holistic health concepts through culturally rich and often abstract terminology. Bridging these metaphors to anatomically driven Western medical (WM) concepts poses significant challenges for both automated language processing and real-world clinical practice. To address this gap, we propose a novel multi-agent and chain-of-thought (CoT) framework designed to interpret TCM metaphors accurately and map them to WM pathophysiology. Specifically, our approach combines domain-specialized agents (TCM Expert, WM Expert) with a Coordinator Agent, leveraging stepwise chain-of-thought prompts to ensure transparent reasoning and conflict resolution. We detail a methodology for building a metaphor-rich TCM dataset, discuss strategies for effectively integrating multi-agent collaboration and CoT reasoning, and articulate the theoretical underpinnings that guide metaphor interpretation across distinct medical paradigms. We present a comprehensive system design and highlight both the potential benefits and limitations of our approach, while leaving placeholders for future experimental validation. Our work aims to support clinical decision-making, cross-system educational initiatives, and integrated healthcare research, ultimately offering a robust scaffold for reconciling TCM's symbolic language with the mechanistic focus of Western medicine.
ESSPI: ECDSA/Schnorr Signed Program Input for BitVMX
Venue: arXiv cs.CR 2025
Year: 2025

The BitVM and BitVMX protocols have long relied on inefficient one-time signature (OTS) schemes like Lamport and Winternitz for signing program inputs. These schemes exhibit significant storage overheads, hindering their practical application. This paper introduces ESSPI, an optimized method leveraging ECDSA/Schnorr signatures to sign the BitVMX program input. With Schnorr signatures we achieve an optimal 1:1 data expansion, compared to the current known best ratio of 1:200 based on Winternitz signatures. To accomplish this we introduce 4 innovations to BitVMX: (1) a modification of the BitVMX CPU, adding a challengeable hashing core to it, (2) a new partition-based search to detect fraud during hashing, (3) a new enhanced transaction DAG with added data-carrying transactions with a fraud-verifying smart-contract and (4) a novel timelock-based method for proving data availability to Bitcoin smart contracts. The enhanced BitVMX protocol enables the verification of uncompressed inputs such as SPV proofs, NiPoPoWs, or longer computation integrity proofs, such as STARKs.