OpenRaiser/Intern-Atlas huggingface dataset:
Modeling and Analysis of Coexistence Between MLO NSTR-based Wi-Fi 7 and Legacy Wi-Fi
Venue: arXiv cs.NI 2025
Year: 2025

Wi-Fi 7 introduces Multi-link operation (MLO) to enhance throughput and latency performance compared to legacy Wi-Fi standards. MLO enables simultaneous transmission and reception through multiple links, departing from conventional single-link operations (SLO). To fully exploit MLO's potential, it is essential to investigate Wi-Fi 7's coexistence performance with legacy Wi-Fi devices. Existing approaches, however, have overlooked some crucial aspects of MLO, necessitating the development of a standards-compliant analytical framework to model the actual channel access mechanism of MLO. Therefore, this paper tries to fill the gap by proposing a set of novel Markov chains (MC) to accurately model the MLO operation aligned with multi-link backoff behaviors specified by the standard. Specifically, we design two separate MCs for AP and non-AP multi-link devices (MLD) respectively, based on which transmit and collision probabilities are derived under the saturated traffic condition. Then, we also derive closed-form expressions for the throughput of various device types in the coexistence scenario between Wi-Fi 7 and legacy Wi-Fi, including AP MLD, non- AP MLD, and legacy devices. To validate the accuracy of our proposed models, we developed an ns-3 based simulator by implementing both STR(simultaneous transmission and reception) and NSTR(non-STR) based MLO operations. Our ns-3 based extensive simulations have demonstrated that the proposed analytic model provides accurate estimates on the per device throughput performance, while also revealing the dynamics of inter-WLAN coexistence scenarios.
Rethinking the Chain-of-Thought: The Roles of In-Context Learning and Pre-trained Priors
Venue: arXiv cs.CL 2025
Year: 2025

Chain-of-Thought reasoning has emerged as a pivotal methodology for enhancing model inference capabilities. Despite growing interest in Chain-of-Thought reasoning, its underlying mechanisms remain unclear. This paper explores the working mechanisms of Chain-of-Thought reasoning from the perspective of the dual relationship between in-context learning and pretrained priors. We first conduct a fine-grained lexical-level analysis of rationales to examine the model's reasoning behavior. Then, by incrementally introducing noisy exemplars, we examine how the model balances pretrained priors against erroneous in-context information. Finally, we investigate whether prompt engineering can induce slow thinking in large language models. Our extensive experiments reveal three key findings: (1) The model not only quickly learns the reasoning structure at the lexical level but also grasps deeper logical reasoning patterns, yet it heavily relies on pretrained priors. (2) Providing sufficient exemplars shifts the model's decision-making from pretrained priors to in-context signals, while misleading prompts introduce instability. (3) Long Chain-of-Thought prompting can induce the model to generate longer reasoning chains, thereby improving its performance on downstream tasks.
Towards Agentic OS: An LLM Agent Framework for Linux Schedulers
Venue: arXiv cs.AI 2025
Year: 2025

Operating system schedulers suffer from a fundamental semantic gap, where kernel policies fail to understand application-specific needs, leading to suboptimal performance. We introduce SchedCP, the first framework that enables fully autonomous Large Language Model (LLM) agents to safely and efficiently optimize Linux schedulers without human involvement. Our core insight is that the challenge is not merely to apply a better LLM, but to architect a decoupled control plane that separates the AI's role of semantic reasoning ("what to optimize") from the system's role of execution ("how to observe and act"), thereby separating the optimization problem into two stages: goal-inference and policy-synthesis. Implemented as Model Context Protocol(MCP) server, SchedCP provides a stable interface with three key services: a Workload Analysis Engine, an evolving Scheduler Policy Repository, and an Execution Verifier that validates all AI-generated code and configure before deployment with static and dynamic analysis. We demonstrate this architecture's power with sched-agent, a multi-agent system that autonomously analyzes workloads, synthesizes custom eBPF scheduling policies, and deploys them via the sched\_ext infrastructure. Our evaluation shows that SchedCP achieves up to an 1.79x performance improvement, and a 13x cost reduction compared to naive agentic approaches, all while maintaining high success rate. By bridging the semantic gap, SchedCP democratizes expert-level system optimization and represents a step towards creating truly self-optimizing, application-aware operating systems. The code is open-sourced in https://github.com/eunomia-bpf/schedcp
Building surrogate models using trajectories of agents trained by Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Sample efficiency in the face of computationally expensive simulations is a common concern in surrogate modeling. Current strategies to minimize the number of samples needed are not as effective in simulated environments with wide state spaces. As a response to this challenge, we propose a novel method to efficiently sample simulated deterministic environments by using policies trained by Reinforcement Learning. We provide an extensive analysis of these surrogate-building strategies with respect to Latin-Hypercube sampling or Active Learning and Kriging, cross-validating performances with all sampled datasets. The analysis shows that a mixed dataset that includes samples acquired by random agents, expert agents, and agents trained to explore the regions of maximum entropy of the state transition distribution provides the best scores through all datasets, which is crucial for a meaningful state space representation. We conclude that the proposed method improves the state-of-the-art and clears the path to enable the application of surrogate-aided Reinforcement Learning policy optimization strategies on complex simulators.
Aligning Requirement for Large Language Model's Code Generation
Venue: arXiv cs.SE 2025
Year: 2025

Code generation refers to the automatic generation of source code based on a given programming specification, which has garnered significant attention particularly with the advancement of large language models (LLMs). However, due to the inherent complexity of real-world problems, the LLM-generated code often fails to fully align with the provided specification. While state-of-the-art agent-based techniques have been proposed to enhance LLM code generation, they overlook the critical issue of specification perception, resulting in persistent misalignment issues. Given that accurate perception of programming specifications serves as the foundation of the LLM-based code generation paradigm, ensuring specification alignment is particularly crucial. In this work, we draw on software requirements engineering to propose Specine, a novel specification alignment technique for LLM code generation. Its key idea is to identify misaligned input specifications, lift LLM-perceived specifications, and align them to enhance the code generation performance of LLMs. Our comprehensive experiments on four state-of-the-art LLMs across five challenging competitive benchmarks by comparing with ten state-of-the-art baselines, demonstrate the effectiveness of Specine. For example, Specine outperforms the most effective baseline, achieving an average improvement of 29.60% across all subjects in terms of Pass@1.
Leveraging SystemC-TLM-based Virtual Prototypes for Embedded Software Fuzzing
Venue: arXiv cs.SE 2025
Year: 2025

SystemC-based virtual prototypes have emerged as widely adopted tools to test software ahead of hardware availability, reducing the time-to-market and improving software reliability. Recently, fuzzing has become a popular method for automated software testing due to its ability to quickly identify corner-case errors. However, its application to embedded software is still limited. Simulator tools can help bridge this gap by providing a more powerful and controlled execution environment for testing. Existing solutions, however, often tightly couple fuzzers with built-in simulators that lack support for hardware peripherals and offer limited flexibility, restricting their ability to test embedded software. To address these limitations, we present a framework that allows the integration of American-Fuzzy-Lop-based fuzzers and SystemC-based simulators. The framework provides a harness to decouple the adopted fuzzer and simulator. In addition, it intercepts peripheral accesses and queries the fuzzer for values, effectively linking peripheral behavior to the fuzzer. This solution enables flexible interchangeability of peripherals within the simulation environment and supports the interfacing of different SystemC-based virtual prototypes. The flexibility of the proposed solution is demonstrated by integrating the harness with different simulators and by testing various softwares.
Multitask Battery Management with Flexible Pretraining
Venue: arXiv cs.LG 2025
Year: 2025

Industrial-scale battery management involves various types of tasks, such as estimation, prediction, and system-level diagnostics. Each task employs distinct data across temporal scales, sensor resolutions, and data channels. Building task-specific methods requires a great deal of data and engineering effort, which limits the scalability of intelligent battery management. Here we present the Flexible Masked Autoencoder (FMAE), a flexible pretraining framework that can learn with missing battery data channels and capture inter-correlations across data snippets. FMAE learns unified battery representations from heterogeneous data and can be adopted by different tasks with minimal data and engineering efforts. Experimentally, FMAE consistently outperforms all task-specific methods across five battery management tasks with eleven battery datasets. On remaining life prediction tasks, FMAE uses 50 times less inference data while maintaining state-of-the-art results. Moreover, when real-world data lack certain information, such as system voltage, FMAE can still be applied with marginal performance impact, achieving comparable results with the best hand-crafted features. FMAE demonstrates a practical route to a flexible, data-efficient model that simplifies real-world multi-task management of dynamical systems.
Globally aware optimization with resurgence
Venue: arXiv cs.LG 2025
Year: 2025

Modern optimization faces a fundamental challenge: local gradient-based methods provide no global information about the objective function $L$ landscape, often leading to suboptimal convergence and sensitivity to initialization. We introduce a novel optimization framework that leverages resurgence theory from complex analysis to extract global structural information from divergent asymptotic series. Our key insight is that the factorially divergent perturbative expansions of parameter space partition functions encode precise information about all critical objective function value in the landscape through their Borel transform singularities. The algorithm works by computing the statistical mechanical partition function $Z(g) = \int e^{-L(θ)/g} dθ$ for small coupling $g\ll 1$, extracting its asymptotic series coefficients, and identifying Borel plane singularities that correspond one-to-one with critical objective function values. These target values provide global guidance to local optimizers, enabling principled learning rate adaptation and escape from suboptimal regions. Unlike heuristic adaptive methods, targets are theoretically grounded in the geometry of the optimization landscape.
Causal Sensitivity Identification using Generative Learning
Venue: arXiv cs.LG 2025
Year: 2025

In this work, we propose a novel generative method to identify the causal impact and apply it to prediction tasks. We conduct causal impact analysis using interventional and counterfactual perspectives. First, applying interventions, we identify features that have a causal influence on the predicted outcome, which we refer to as causally sensitive features, and second, applying counterfactuals, we evaluate how changes in the cause affect the effect. Our method exploits the Conditional Variational Autoencoder (CVAE) to identify the causal impact and serve as a generative predictor. We are able to reduce confounding bias by identifying causally sensitive features. We demonstrate the effectiveness of our method by recommending the most likely locations a user will visit next in their spatiotemporal trajectory influenced by the causal relationships among various features. Experiments on the large-scale GeoLife [Zheng et al., 2010] dataset and the benchmark Asia Bayesian network validate the ability of our method to identify causal impact and improve predictive performance.
M3Ret: Unleashing Zero-shot Multimodal Medical Image Retrieval via Self-Supervision
Venue: arXiv cs.CV 2025
Year: 2025

Medical image retrieval is essential for clinical decision-making and translational research, relying on discriminative visual representations. Yet, current methods remain fragmented, relying on separate architectures and training strategies for 2D, 3D, and video-based medical data. This modality-specific design hampers scalability and inhibits the development of unified representations. To enable unified learning, we curate a large-scale hybrid-modality dataset comprising 867,653 medical imaging samples, including 2D X-rays and ultrasounds, RGB endoscopy videos, and 3D CT scans. Leveraging this dataset, we train M3Ret, a unified visual encoder without any modality-specific customization. It successfully learns transferable representations using both generative (MAE) and contrastive (SimDINO) self-supervised learning (SSL) paradigms. Our approach sets a new state-of-the-art in zero-shot image-to-image retrieval across all individual modalities, surpassing strong baselines such as DINOv3 and the text-supervised BMC-CLIP. More remarkably, strong cross-modal alignment emerges without paired data, and the model generalizes to unseen MRI tasks, despite never observing MRI during pretraining, demonstrating the generalizability of purely visual self-supervision to unseen modalities. Comprehensive analyses further validate the scalability of our framework across model and data sizes. These findings deliver a promising signal to the medical imaging community, positioning M3Ret as a step toward foundation models for visual SSL in multimodal medical image understanding.
TopoNav: Topological Graphs as a Key Enabler for Advanced Object Navigation
Venue: arXiv cs.RO 2025
Year: 2025

Object Navigation (ObjectNav) has made great progress with large language models (LLMs), but still faces challenges in memory management, especially in long-horizon tasks and dynamic scenes. To address this, we propose TopoNav, a new framework that leverages topological structures as spatial memory. By building and updating a topological graph that captures scene connections, adjacency, and semantic meaning, TopoNav helps agents accumulate spatial knowledge over time, retrieve key information, and reason effectively toward distant goals. Our experiments show that TopoNav achieves state-of-the-art performance on benchmark ObjectNav datasets, with higher success rates and more efficient paths. It particularly excels in diverse and complex environments, as it connects temporary visual inputs with lasting spatial understanding.
On the Alignment of Large Language Models with Global Human Opinion
Venue: arXiv cs.CL 2025
Year: 2025

Today's large language models (LLMs) are capable of supporting multilingual scenarios, allowing users to interact with LLMs in their native languages. When LLMs respond to subjective questions posed by users, they are expected to align with the views of specific demographic groups or historical periods, shaped by the language in which the user interacts with the model. Existing studies mainly focus on researching the opinions represented by LLMs among demographic groups in the United States or a few countries, lacking worldwide country samples and studies on human opinions in different historical periods, as well as lacking discussion on using language to steer LLMs. Moreover, they also overlook the potential influence of prompt language on the alignment of LLMs' opinions. In this study, our goal is to fill these gaps. To this end, we create an evaluation framework based on the World Values Survey (WVS) to systematically assess the alignment of LLMs with human opinions across different countries, languages, and historical periods around the world. We find that LLMs appropriately or over-align the opinions with only a few countries while under-aligning the opinions with most countries. Furthermore, changing the language of the prompt to match the language used in the questionnaire can effectively steer LLMs to align with the opinions of the corresponding country more effectively than existing steering methods. At the same time, LLMs are more aligned with the opinions of the contemporary population. To our knowledge, our study is the first comprehensive investigation of the topic of opinion alignment in LLMs across global, language, and temporal dimensions. Our code and data are publicly available at https://github.com/ku-nlp/global-opinion-alignment and https://github.com/nlply/global-opinion-alignment.
Body Ownership Affects the Processing of Sensorimotor Contingencies in Virtual Reality
Venue: arXiv cs.HC 2025
Year: 2025

Presence in virtual reality (VR), the subjective sense of "being there" in a virtual environment, is notoriously difficult to measure. Electroencephalography (EEG) may offer a promising, unobtrusive means of assessing a user's momentary state of presence. Unlike traditional questionnaires, EEG does not interrupt the experience or rely on users' retrospective self-reports, thereby avoiding interference with the very state it aims to capture. Previous research has attempted to quantify presence in virtual environments using event-related potentials (ERPs). We contend, however, that previous efforts have fallen short of fully realizing this goal, failing to either A) independently manipulate presence, B) validate their measure of presence against traditional techniques, C) adequately separate the constructs of presence and attention, and/or D) implement a realistic and immersive environment and task. We address these shortcomings in a preregistered ERP experiment in which participants play an engaging target shooting game in VR. ERPs are time-locked to the release of a ball from a sling. We induce breaks in presence (BIPs) by freezing the ball's release on a minority of trials. Embodiment is manipulated by allowing manual manipulation of the sling with a realistic avatar in one condition (embodied condition) and passive manipulation with only controllers in another (non-embodied condition). We support our predictions that the N2, the P3b, and the N400, are selectively sensitive towards specific components of these manipulations. The pattern of findings carries significant implications for theories of presence, which have been seldom addressed in previous ERP investigations on this topic.
The Geometry of Nonlinear Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Reward maximization, safe exploration, and intrinsic motivation are often studied as separate objectives in reinforcement learning (RL). We present a unified geometric framework, that views these goals as instances of a single optimization problem on the space of achievable long-term behavior in an environment. Within this framework, classical methods such as policy mirror descent, natural policy gradient, and trust-region algorithms naturally generalize to nonlinear utilities and convex constraints. We illustrate how this perspective captures robustness, safety, exploration, and diversity objectives, and outline open challenges at the interface of geometry and deep RL.
Quantum Brush: A quantum computing-based tool for digital painting
Venue: arXiv cs.GR 2025
Year: 2025

We present Quantum Brush, an open-source digital painting tool that harnesses quantum computing to generate novel artistic expressions. The tool includes four different brushes that translate strokes into unique quantum algorithms, each highlighting a different way in which quantum effects can produce novel aesthetics. Each brush is designed to be compatible with the current noisy intermediate-scale quantum (NISQ) devices, as demonstrated by executing them on IQM's Sirius device.
5-axis Multi-material Desktop Additive Manufacturing of Conformal Antennas
Venue: arXiv cs.ET 2025
Year: 2025

This paper describes the novel use of low-cost, 5-axis, multi-material additive manufacturing to fabricate functional, complex conformal antennas. Using a customised open source 5-axis desktop printer incorporating conductive filaments, conformal S-band patch and Ultra-Wide Band antennas were fabricated and compared against planar-printed counterparts and electromagnetic simulations. Results show the potential of the approach for superior impedance matching, reduced fabrication time, and cost savings; highlighting the applicability of multi-axis multi-material prototyping of antennas with complex geometries.
Dissecting Atomic Facts: Visual Analytics for Improving Fact Annotations in Language Model Evaluation
Venue: arXiv cs.HC 2025
Year: 2025

Factuality evaluation of large language model (LLM) outputs requires decomposing text into discrete "atomic" facts. However, existing definitions of atomicity are underspecified, with empirical results showing high disagreement among annotators, both human and model-based, due to unresolved ambiguity in fact decomposition. We present a visual analytics concept to expose and analyze annotation inconsistencies in fact extraction. By visualizing semantic alignment, granularity and referential dependencies, our approach aims to enable systematic inspection of extracted facts and facilitate convergence through guided revision loops, establishing a more stable foundation for factuality evaluation benchmarks and improving LLM evaluation.
LLMHoney: A Real-Time SSH Honeypot with Large Language Model-Driven Dynamic Response Generation
Venue: arXiv cs.CR 2025
Year: 2025

Cybersecurity honeypots are deception tools for engaging attackers and gather intelligence, but traditional low or medium-interaction honeypots often rely on static, pre-scripted interactions that can be easily identified by skilled adversaries. This Report presents LLMHoney, an SSH honeypot that leverages Large Language Models (LLMs) to generate realistic, dynamic command outputs in real time. LLMHoney integrates a dictionary-based virtual file system to handle common commands with low latency while using LLMs for novel inputs, achieving a balance between authenticity and performance. We implemented LLMHoney using open-source LLMs and evaluated it on a testbed with 138 representative Linux commands. We report comprehensive metrics including accuracy (exact-match, Cosine Similarity, Jaro-Winkler Similarity, Levenshtein Similarity and BLEU score), response latency and memory overhead. We evaluate LLMHoney using multiple LLM backends ranging from 0.36B to 3.8B parameters, including both open-source models and a proprietary model(Gemini). Our experiments compare 13 different LLM variants; results show that Gemini-2.0 and moderately-sized models Qwen2.5:1.5B and Phi3:3.8B provide the most reliable and accurate responses, with mean latencies around 3 seconds, whereas smaller models often produce incorrect or out-of-character outputs. We also discuss how LLM integration improves honeypot realism and adaptability compared to traditional honeypots, as well as challenges such as occasional hallucinated outputs and increased resource usage. Our findings demonstrate that LLM-driven honeypots are a promising approach to enhance attacker engagement and collect richer threat intelligence.