OpenRaiser/Intern-Atlas huggingface dataset:
PathFuzzing: Worst Case Analysis by Fuzzing Symbolic-Execution Paths
Venue: arXiv cs.SE 2025
Year: 2025

Estimating worst-case resource consumption is a critical task in software development. The worst-case analysis (WCA) problem is an optimization-based abstraction of this task. Fuzzing and symbolic execution are widely used techniques for addressing the WCA problem. However, improving code coverage in fuzzing or managing path explosion in symbolic execution within the context of WCA poses significant challenges. In this paper, we propose PathFuzzing, aiming to combine the strengths of both techniques to design a WCA method. The key idea is to transform a program into a symbolic one that takes an execution path (encoded as a binary string) and interprets the bits as branch decisions. PathFuzzing then applies evolutionary fuzzing techniques to the transformed program to search for binary strings that represent satisfiable path conditions and lead to high resource consumption. We evaluate the performance of PathFuzzing experimentally on a benchmark suite that consists of prior work's benchmarks and some added by us. Results show that PathFuzzing generally outperforms a fuzzing and a symbolic-execution baseline.
Green-LLM: Optimal Workload Allocation for Environmentally-Aware Distributed Inference
Venue: arXiv cs.NI 2025
Year: 2025

This letter investigates the optimal allocation of large language model (LLM) inference workloads across heterogeneous edge data centers (DCs) over time. Each DC features on-site renewable generation and faces dynamic electricity prices and spatiotemporal variability in renewable availability. The central question is: how can inference workloads be optimally distributed to the DCs to minimize energy consumption, carbon emissions, and water usage while enhancing user experience? This letter proposes a novel optimization model for LLM service providers to reduce operational costs and environmental impacts. Numerical results validate the efficacy of the proposed approach.
Ariel Explores: Vision-based underwater exploration and inspection via generalist drone-level autonomy
Venue: arXiv cs.RO 2025
Year: 2025

This work presents a vision-based underwater exploration and inspection autonomy solution integrated into Ariel, a custom vision-driven underwater robot. Ariel carries a $5$ camera and IMU based sensing suite, enabling a refraction-aware multi-camera visual-inertial state estimation method aided by a learning-based proprioceptive robot velocity prediction method that enhances robustness against visual degradation. Furthermore, our previously developed and extensively field-verified autonomous exploration and general visual inspection solution is integrated on Ariel, providing aerial drone-level autonomy underwater. The proposed system is field-tested in a submarine dry dock in Trondheim under challenging visual conditions. The field demonstration shows the robustness of the state estimation solution and the generalizability of the path planning techniques across robot embodiments.
Effects of structural properties of neural networks on machine learning performance
Venue: arXiv cs.LG 2025
Year: 2025

In recent years, graph-based machine learning techniques, such as reinforcement learning and graph neural networks, have garnered significant attention. While some recent studies have started to explore the relationship between the graph structure of neural networks and their predictive performance, they often limit themselves to a narrow range of model networks, particularly lacking mesoscale structures such as communities. Our work advances this area by conducting a more comprehensive investigation, incorporating realistic network structures characterized by heterogeneous degree distributions and community structures, which are typical characteristics of many real networks. These community structures offer a nuanced perspective on network architecture. Our analysis employs model networks such as random and scale-free networks, alongside a comparison with a biological neural network and its subsets for more detailed analysis. We examine the impact of these structural attributes on the performance of image classification tasks. Our findings reveal that structural properties do affect performance to some extent. Specifically, networks featuring coherent, densely interconnected communities demonstrate enhanced learning capabilities. The comparison with the biological neural network emphasizes the relevance of our findings to real-world structures, suggesting an intriguing connection worth further exploration. This study contributes meaningfully to network science and machine learning, providing insights that could inspire the design of more biologically informed neural networks.
Deep Hidden Cognition Facilitates Reliable Chain-of-Thought Reasoning
Venue: arXiv cs.AI 2025
Year: 2025

Chain of Thought (CoT) reasoning has demonstrated remarkable deep reasoning capabilities in both large language models (LLMs) and multimodal large language models (MLLMs). However, its reliability is often undermined by the accumulation of errors in intermediate steps. This paper introduces an novel approach to calibrate the CoT reasoning accuracy by leveraging the model's intrinsic veracity encoding. We discover that specific attention head activations reliably reflect the truthfulness of reasoning steps in CoT. Based on this insight, we train a confidence predictor to evaluate the correctness of each reasoning step using these truthfulness-sensitive activations, dynamically selecting the most plausible reasoning path via beam search. Experimental results demonstrate that our method significantly outperforms the state-of-the-art baselines (e.g., Few-Shot CoT, Self-Consistency, and Self-Evaluation Guided Beam Search) across the mathematical, symbolic, and commonsense reasoning tasks, exhibiting superior accuracy and reliability in both unimodal and multimodal settings. We further validate the approach on large reasoning models, confirming its applicability to specialized reasoning models. Additionally, we explore the role of the model's self-correction ability in CoT reasoning. This work provides a novel reliability improvement path for CoT reasoning with broad application potential.
Forecasting Coccidioidomycosis (Valley Fever) in Arizona: A Graph Neural Network Approach
Venue: arXiv cs.LG 2025
Year: 2025

Coccidioidomycosis, commonly known as Valley Fever, remains a significant public health concern in endemic regions of the southwestern United States. This study develops the first graph neural network (GNN) model for forecasting Valley Fever incidence in Arizona. The model integrates surveillance case data with environmental predictors using graph structures, including soil conditions, atmospheric variables, agricultural indicators, and air quality metrics. Our approach explores correlation-based relationships among variables influencing disease transmission. The model captures critical delays in disease progression through lagged effects, enhancing its capacity to reflect complex temporal dependencies in disease ecology. Results demonstrate that the GNN architecture effectively models Valley Fever trends and provides insights into key environmental drivers of disease incidence. These findings can inform early warning systems and guide resource allocation for disease prevention efforts in high-risk areas.
LLMShot: Reducing snapshot testing maintenance via LLMs
Venue: arXiv cs.SE 2025
Year: 2025

Snapshot testing has emerged as a critical technique for UI validation in modern software development, yet it suffers from substantial maintenance overhead due to frequent UI changes causing test failures that require manual inspection to distinguish between genuine regressions and intentional design changes. This manual triage process becomes increasingly burdensome as applications evolve, creating a need for automated analysis solutions. This paper introduces LLMShot, a novel framework that leverages Vision-Language Models (VLMs) to automatically analyze snapshot test failures through semantic classification of UI changes. To evaluate LLMShot's effectiveness, we developed a comprehensive dataset using a feature-rich iOS application with configurable feature flags, creating realistic scenarios that produce authentic snapshot differences representative of real development workflows. Our evaluation using Gemma3 models demonstrates strong classification performance, with the 12B variant achieving over 84% recall in identifying failure root causes while the 4B model offers practical deployment advantages with acceptable performance for continuous integration environments. However, our exploration of selective ignore mechanisms revealed significant limitations in current prompting-based approaches for controllable visual reasoning. LLMShot represents the first automated approach to semantic snapshot test analysis, offering developers structured insights that can substantially reduce manual triage effort and advance toward more intelligent UI testing paradigms.
Accelerating Automatic Program Repair with Dual Retrieval-Augmented Fine-Tuning and Patch Generation on Large Language Models
Venue: arXiv cs.SE 2025
Year: 2025

Automated Program Repair (APR) is essential for ensuring software reliability and quality while enhancing efficiency and reducing developers' workload. Although rule-based and learning-based APR methods have demonstrated their effectiveness, their performance was constrained by the defect type of repair, the quality of training data, and the size of model parameters. Recently, Large Language Models (LLMs) combined with Retrieval-Augmented-Generation (RAG) have been increasingly adopted in APR tasks. However, current code LLMs and RAG designs neither fully address code repair tasks nor consider code-specific features. To overcome these limitations, we propose SelRepair, a novel APR approach with integration of a fine-tuned LLM with a newly-designed dual RAG module. This approach uses a bug-fix pair dataset for fine-tuning and incorporates semantic and syntactic/structural similarity information through an RAG selection gate. This design ensures relevant information is retrieved efficiently, thereby reducing token length and inference time. Evaluations on Java datasets show SelRepair outperforms other APR methods, achieving 26.29% and 17.64% in terms of exact match (EM) on different datasets while reducing inference time by at least 6.42% with controlled input lengths.
Physics-Informed Neural Networks with Unscented Kalman Filter for Sensorless Joint Torque Estimation in Humanoid Robots
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents a novel framework for whole-body torque control of humanoid robots without joint torque sensors, designed for systems with electric motors and high-ratio harmonic drives. The approach integrates Physics-Informed Neural Networks (PINNs) for friction modeling and Unscented Kalman Filtering (UKF) for joint torque estimation, within a real-time torque control architecture. PINNs estimate nonlinear static and dynamic friction from joint and motor velocity readings, capturing effects like motor actuation without joint movement. The UKF utilizes PINN-based friction estimates as direct measurement inputs, improving torque estimation robustness. Experimental validation on the ergoCub humanoid robot demonstrates improved torque tracking accuracy, enhanced energy efficiency, and superior disturbance rejection compared to the state-of-the-art Recursive Newton-Euler Algorithm (RNEA), using a dynamic balancing experiment. The framework's scalability is shown by consistent performance across robots with similar hardware but different friction characteristics, without re-identification. Furthermore, a comparative analysis with position control highlights the advantages of the proposed torque control approach. The results establish the method as a scalable and practical solution for sensorless torque control in humanoid robots, ensuring torque tracking, adaptability, and stability in dynamic environments.
Probabilistic Human Intent Prediction for Mobile Manipulation: An Evaluation with Human-Inspired Constraints
Venue: arXiv cs.RO 2025
Year: 2025

Accurate inference of human intent enables human-robot collaboration without constraining human control or causing conflicts between humans and robots. We present GUIDER (Global User Intent Dual-phase Estimation for Robots), a probabilistic framework that enables a robot to estimate the intent of human operators. GUIDER maintains two coupled belief layers, one tracking navigation goals and the other manipulation goals. In the Navigation phase, a Synergy Map blends controller velocity with an occupancy grid to rank interaction areas. Upon arrival at a goal, an autonomous multi-view scan builds a local 3D cloud. The Manipulation phase combines U2Net saliency, FastSAM instance saliency, and three geometric grasp-feasibility tests, with an end-effector kinematics-aware update rule that evolves object probabilities in real-time. GUIDER can recognize areas and objects of intent without predefined goals. We evaluated GUIDER on 25 trials (five participants x five task variants) in Isaac Sim, and compared it with two baselines, one for navigation and one for manipulation. Across the 25 trials, GUIDER achieved a median stability of 93-100% during navigation, compared with 60-100% for the BOIR baseline, with an improvement of 39.5% in a redirection scenario (T5). During manipulation, stability reached 94-100% (versus 69-100% for Trajectron), with a 31.4% difference in a redirection task (T3). In geometry-constrained trials (manipulation), GUIDER recognized the object intent three times earlier than Trajectron (median remaining time to confident prediction 23.6 s vs 7.8 s). These results validate our dual-phase framework and show improvements in intent inference in both phases of mobile manipulation tasks.
Extending Defeasibility for Propositional Standpoint Logics
Venue: arXiv cs.LO 2025
Year: 2025

In this paper, we introduce a new defeasible version of propositional standpoint logic by integrating Kraus et al.'s defeasible conditionals, Britz and Varzinczak's notions of defeasible necessity and distinct possibility, along with Leisegang et al.'s approach to defeasibility into the standpoint logics of Gómez Álvarez and Rudolph. The resulting logical framework allows for the expression of defeasibility on the level of implications, standpoint modal operators, and standpoint-sharpening statements. We provide a preferential semantics for this extended language and propose a tableaux calculus, which is shown to be sound and complete with respect to preferential entailment. We also establish the computational complexity of the tableaux procedure to be in PSpace.
Large-Scale Graph Building in Dynamic Environments: Low Latency and High Quality
Venue: arXiv cs.DC 2025
Year: 2025

Learning and constructing large-scale graphs has attracted attention in recent decades, resulting in a rich literature that introduced various systems, tools, and algorithms. Grale is one of such tools that is designed for offline environments and is deployed in more than 50 different industrial settings at Google. Grale is widely applicable because of its ability to efficiently learn and construct a graph on datasets with multiple types of features. However, it is often the case that applications require the underlying data to evolve continuously and rapidly and the updated graph needs to be available with low latency. Such setting make the use of Grale prohibitive. While there are Approximate Nearest Neighbor (ANN) systems that handle dynamic updates with low latency, they are mostly limited to similarities over a single embedding. In this work, we introduce a system that inherits the advantages and the quality of Grale, and maintains a graph construction in a dynamic setting with tens of milliseconds of latency per request. We call the system Dynamic Grale Using ScaNN (Dynamic GUS). Our system has a wide range of applications with over 10 deployments at Google. One of the applications is in Android Security and Privacy, where Dynamic Grale Using ScaNN enables capturing harmful applications 4 times faster, before they can reach users.
A Coincidence of Wants Mechanism for Swap Trade Execution in Decentralized Exchanges
Venue: arXiv cs.GT 2025
Year: 2025

We propose a mathematically rigorous framework for identifying and completing Coincidence of Wants (CoW) cycles in decentralized exchange (DEX) aggregators. Unlike existing auction based systems such as CoWSwap, our approach introduces an asset matrix formulation that not only verifies feasibility using oracle prices and formal conservation laws but also completes partial CoW cycles of swap orders that are discovered using graph traversal and are settled using imbalance correction. We define bridging orders and show that the resulting execution is slippage free and capital preserving for LPs. Applied to real world Arbitrum swap data, our algorithm demonstrates efficient discovery of CoW cycles and supports the insertion of synthetic orders for atomic cycle closure. This work can be thought of as the detailing of a potential delta-neutral strategy by liquidity providing market makers: a structured CoW cycle execution.
Is Micro-expression Ethnic Leaning?
Venue: arXiv cs.CV 2025
Year: 2025

How much does ethnicity play its part in emotional expression? Emotional expression and micro-expression research probe into understanding human psychological responses to emotional stimuli, thereby revealing substantial hidden yet authentic emotions that can be useful in the event of diagnosis and interviews. While increased attention had been provided to micro-expression analysis, the studies were done under Ekman's assumption of emotion universality, where emotional expressions are identical across cultures and social contexts. Our computational study uncovers some of the influences of ethnic background in expression analysis, leading to an argument that the emotional universality hypothesis is an overgeneralization from the perspective of manual psychological analysis. In this research, we propose to investigate the level of influence of ethnicity in a simulated micro-expression scenario. We construct a cross-cultural micro-expression database and algorithmically annotate the ethnic labels to facilitate the investigation. With the ethnically annotated dataset, we perform a prima facie study to compare mono-ethnicity and stereo-ethnicity in a controlled environment, which uncovers a certain influence of ethnic bias via an experimental way. Building on this finding, we propose a framework that integrates ethnic context into the emotional feature learning process, yielding an ethnically aware framework that recognises ethnicity differences in micro-expression recognition. For improved understanding, qualitative analyses have been done to solidify the preliminary investigation into this new realm of research. Code is publicly available at https://github.com/IcedDoggie/ICMEW2025_EthnicMER
Fine-Grained Coordinated OFDMA With Fiber Backhaul Enabled by openwifi and White Rabbit
Venue: arXiv cs.NI 2025
Year: 2025

Proper coordination is needed to guarantee the performance of wireless networks in dense deployments. Contention-based systems suffer badly in terms of latency when multiple devices compete for the same resources. Coordinated Orthogonal Frequency Division Multiple Access (Co-OFDMA) is proposed for Wi-Fi 8 to remedy this, as it enables multiple Access Points (APs) to share spectrum more efficiently. However, fine-grained resource allocation, namely within 20MHz bandwidth, is argued to be impractical due to the over-the-air scheduling overhead and complexity in terms of physical layer signaling. A wired backhaul mitigates the need for over-the-air scheduling and synchronization, and it allows for coordination even if APs are not in each others' range. Furthermore, it forms the basis for more advanced multi-AP coordination schemes like coordinated beamforming and joint transmission. In this work we demonstrate the realization of Wi-Fi 6 compliant fine-grained Co-OFDMA using a fiber backhaul, enabled by the open-source platforms openwifi and White Rabbit. We show that the performance in terms of carrier frequency offset pre-compensation and time synchronization between two APs exceeds related wireless standard requirements. Furthermore, the quality of the received constellation of the Co-OFDMA frame as reported by a wireless connectivity tester is better than individual frames sent by the APs.
Dimensionality increase for error correction in the interaction between information space and the physical world
Venue: arXiv cs.IT 2025
Year: 2025

The evolution of human intelligence led to the huge amount of data in the information space. Accessing and processing this data helps in finding solutions to applied problems based on finite-dimensional models. We argue, that formally, such a mathematical model can be embedded into a higher-dimensional model inside of which a desired solution will exist. In our model, the physical world and the information space are submanifolds of infinite-dimensional Hilbert spaces, and the processes, including information transmission, are maps between the submanifolds of the physical world or of the information space. We discuss how our perspective fits in the context of existing literature. Our theorem states that a submanifold in the parameter space of the physical world can be deformed to a target submanifold outside that space, with an appropriate count of the deformation parameters. We interpret this assertion as an existence result for a class of problems and we discuss further steps.
An Empirical Study of Interaction Bugs in ROS-based Software
Venue: arXiv cs.SE 2025
Year: 2025

Modern robotic systems integrate multiple independent software and hardware components, each responsible for distinct functionalities such as perception, decision-making, and execution. These components interact extensively to accomplish complex end-to-end tasks. As a result, the overall system reliability depends not only on the correctness of individual components, but also on the correctness of their interactions. Failures often manifest at the boundaries between components, yet interaction-related reliability issues in robotics--referred to here as interaction bugs (iBugs)--remain underexplored. This work presents an empirical study of iBugs within robotic systems built using the Robot Operating System (ROS), a widely adopted open-source robotics framework. A total of 121 iBugs were analyzed across ten actively maintained and representative ROS projects. The identified iBugs are categorized into three major types: intra-system iBugs, hardware iBugs, and environmental iBugs, covering a broad range of interaction scenarios in robotics. The analysis includes an examination of root causes, fixing strategies, and the impact of these bugs. Several findingsa are derived that shed light on the nature of iBugs and suggest directions for improving their prevention and detection. These insights aim to inform the design of more robust and safer robotic systems.