OpenRaiser/Intern-Atlas huggingface dataset:
Can LLMs Help Improve Analogical Reasoning For Strategic Decisions? Experimental Evidence from Humans and GPT-4
Venue: arXiv cs.AI 2025
Year: 2025

This study investigates whether large language models, specifically GPT4, can match human capabilities in analogical reasoning within strategic decision making contexts. Using a novel experimental design involving source to target matching, we find that GPT4 achieves high recall by retrieving all plausible analogies but suffers from low precision, frequently applying incorrect analogies based on superficial similarities. In contrast, human participants exhibit high precision but low recall, selecting fewer analogies yet with stronger causal alignment. These findings advance theory by identifying matching, the evaluative phase of analogical reasoning, as a distinct step that requires accurate causal mapping beyond simple retrieval. While current LLMs are proficient in generating candidate analogies, humans maintain a comparative advantage in recognizing deep structural similarities across domains. Error analysis reveals that AI errors arise from surface level matching, whereas human errors stem from misinterpretations of causal structure. Taken together, the results suggest a productive division of labor in AI assisted organizational decision making where LLMs may serve as broad analogy generators, while humans act as critical evaluators, applying the most contextually appropriate analogies to strategic problems.
Surviving the Storm: The Impacts of Open RAN Disaggregation on Latency and Resilience
Venue: arXiv cs.NI 2025
Year: 2025

The development of Open Radio Access Networks (Open RAN), with their disaggregated architectures and virtualization of network functions, has brought considerable flexibility and cost savings to mobile networks. However, these architectural advancements introduce additional latency during the initial attachment procedure of User Equipment (UE), increasing the risk of signaling storms. This paper investigates the latency impact due to disaggregation of the Base-band Unit (BBU) into the Central Unit (CU) and Distributed Unit (DU). Specifically, we model the delays induced due to disaggregation on UE attachment, analyzing the performance under varying load conditions, and sensitivity to processing times. We demonstrate that while both monolithic and Open RAN architectures experience performance degradation under high-load conditions, Open RAN's added overheads can increase its susceptibility to congestion and signaling storms. However, Open RAN's inherent flexibility, enabled by disaggregation and virtualization, allows efficient deployment of resources, faster service deployment, and adaptive congestion control mechanisms to mitigate these risks and enhance overall system resilience. Thereby, we quantify resilience by introducing a new utility function and propose a novel adaptation mechanism to reinforce Open RAN's robustness against signaling storms. Our results show that the proposed adaptive mechanism significantly enhances resilience, achieving improvements of up to 286% over fixed configurations, with resilience scores approaching 0.96 under optimal conditions. While simulation results show that Open RAN disaggregation increases attachment latency and susceptibility to signaling congestion, they also highlight that its architectural flexibility can mitigate these effects, improving resilience under high-load conditions.
RevealNet: Distributed Traffic Correlation for Attack Attribution on Programmable Networks
Venue: arXiv cs.CR 2025
Year: 2025

Network attackers have increasingly resorted to proxy chains, VPNs, and anonymity networks to conceal their activities. To tackle this issue, past research has explored the applicability of traffic correlation techniques to perform attack attribution, i.e., to identify an attacker's true network location. However, current traffic correlation approaches rely on well-provisioned and centralized systems that ingest flows from multiple network probes to compute correlation scores. Unfortunately, this makes correlation efforts scale poorly for large high-speed networks. In this paper, we propose RevealNet, a decentralized framework for attack attribution that orchestrates a fleet of P4-programmable switches to perform traffic correlation. RevealNet builds on a set of correlation primitives inspired by prior work on computing and comparing flow sketches -- compact summaries of flows' key characteristics -- to enable efficient, distributed, in-network traffic correlation. Our evaluation suggests that RevealNet achieves comparable accuracy to centralized attack attribution systems while significantly reducing both the computational complexity and bandwidth overheads imposed by correlation tasks.
Neural Network Verification for Gliding Drone Control: A Case Study
Venue: arXiv cs.RO 2025
Year: 2025

As machine learning is increasingly deployed in autonomous systems, verification of neural network controllers is becoming an active research domain. Existing tools and annual verification competitions suggest that soon this technology will become effective for real-world applications. Our application comes from the emerging field of microflyers that are passively transported by the wind, which may have various uses in weather or pollution monitoring. Specifically, we investigate centimetre-scale bio-inspired gliding drones that resemble Alsomitra macrocarpa diaspores. In this paper, we propose a new case study on verifying Alsomitra-inspired drones with neural network controllers, with the aim of adhering closely to a target trajectory. We show that our system differs substantially from existing VNN and ARCH competition benchmarks, and show that a combination of tools holds promise for verifying such systems in the future, if certain shortcomings can be overcome. We propose a novel method for robust training of regression networks, and investigate formalisations of this case study in Vehicle and CORA. Our verification results suggest that the investigated training methods do improve performance and robustness of neural network controllers in this application, but are limited in scope and usefulness. This is due to systematic limitations of both Vehicle and CORA, and the complexity of our system reducing the scale of reachability, which we investigate in detail. If these limitations can be overcome, it will enable engineers to develop safe and robust technologies that improve people's lives and reduce our impact on the environment.
Open-Source LLM-Driven Federated Transformer for Predictive IoV Management
Venue: arXiv cs.AI 2025
Year: 2025

The proliferation of connected vehicles within the Internet of Vehicles (IoV) ecosystem presents critical challenges in ensuring scalable, real-time, and privacy-preserving traffic management. Existing centralized IoV solutions often suffer from high latency, limited scalability, and reliance on proprietary Artificial Intelligence (AI) models, creating significant barriers to widespread deployment, particularly in dynamic and privacy-sensitive environments. Meanwhile, integrating Large Language Models (LLMs) in vehicular systems remains underexplored, especially concerning prompt optimization and effective utilization in federated contexts. To address these challenges, we propose the Federated Prompt-Optimized Traffic Transformer (FPoTT), a novel framework that leverages open-source LLMs for predictive IoV management. FPoTT introduces a dynamic prompt optimization mechanism that iteratively refines textual prompts to enhance trajectory prediction. The architecture employs a dual-layer federated learning paradigm, combining lightweight edge models for real-time inference with cloud-based LLMs to retain global intelligence. A Transformer-driven synthetic data generator is incorporated to augment training with diverse, high-fidelity traffic scenarios in the Next Generation Simulation (NGSIM) format. Extensive evaluations demonstrate that FPoTT, utilizing EleutherAI Pythia-1B, achieves 99.86% prediction accuracy on real-world data while maintaining high performance on synthetic datasets. These results underscore the potential of open-source LLMs in enabling secure, adaptive, and scalable IoV management, offering a promising alternative to proprietary solutions in smart mobility ecosystems.
Steering Large Language Models with Register Analysis for Arbitrary Style Transfer
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have demonstrated strong capabilities in rewriting text across various styles. However, effectively leveraging this ability for example-based arbitrary style transfer, where an input text is rewritten to match the style of a given exemplar, remains an open challenge. A key question is how to describe the style of the exemplar to guide LLMs toward high-quality rewrites. In this work, we propose a prompting method based on register analysis to guide LLMs to perform this task. Empirical evaluations across multiple style transfer tasks show that our prompting approach enhances style transfer strength while preserving meaning more effectively than existing prompting strategies.
Faster All-Pairs Optimal Electric Car Routing
Venue: arXiv cs.DS 2025
Year: 2025

We present a randomized $\tilde{O}(n^{3.5})$-time algorithm for computing \emph{optimal energetic paths} for an electric car between all pairs of vertices in an $n$-vertex directed graph with positive and negative \emph{costs}. The optimal energetic paths are finite and well-defined even if the graph contains negative-cost cycles. This makes the problem much more challenging than standard shortest paths problems. More specifically, for every two vertices $s$ and~$t$ in the graph, the algorithm computes $α_B(s,t)$, the maximum amount of charge the car can reach~$t$ with, if it starts at~$s$ with full battery, i.e., with charge~$B$, where~$B$ is the capacity of the battery. In the presence of negative-cost cycles, optimal paths are not necessarily simple. For dense graphs, our new $\tilde{O}(n^{3.5})$ time algorithm improves on a previous $\tilde{O}(mn^{2})$-time algorithm of Dorfman et al. [ESA 2023] for the problem. The \emph{cost} of an arc is the amount of charge taken from the battery of the car when traversing the arc. The charge in the battery can never exceed the capacity~$B$ of the battery and can never be negative. An arc of negative cost may correspond, for example, to a downhill road segment, while an arc with a positive cost may correspond to an uphill segment. A negative-cost cycle, if one exists, can be used in certain cases to charge the battery to its capacity. This makes the problem more interesting and more challenging. Negative-cost cycles may arise when certain road segments have magnetic charging strips, or when the electric car has solar panels. Combined with a result of Dorfman et al. [SOSA 2024], this also provides a randomized $\tilde{O}(n^{3.5})$-time algorithm for computing \emph{minimum-cost paths} between all pairs of vertices in an $n$-vertex graph when the battery can be externally recharged, at varying costs, at intermediate vertices.
Unconstrained Large-scale 3D Reconstruction and Rendering across Altitudes
Venue: arXiv cs.CV 2025
Year: 2025

Production of photorealistic, navigable 3D site models requires a large volume of carefully collected images that are often unavailable to first responders for disaster relief or law enforcement. Real-world challenges include limited numbers of images, heterogeneous unposed cameras, inconsistent lighting, and extreme viewpoint differences for images collected from varying altitudes. To promote research aimed at addressing these challenges, we have developed the first public benchmark dataset for 3D reconstruction and novel view synthesis based on multiple calibrated ground-level, security-level, and airborne cameras. We present datasets that pose real-world challenges, independently evaluate calibration of unposed cameras and quality of novel rendered views, demonstrate baseline performance using recent state-of-practice methods, and identify challenges for further research.
Zoomer: Adaptive Image Focus Optimization for Black-box MLLM
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal large language models (MLLMs) such as GPT-4o, Gemini Pro, and Claude 3.5 have enabled unified reasoning over text and visual inputs, yet they often hallucinate in real world scenarios especially when small objects or fine spatial context are involved. We pinpoint two core causes of this failure: the absence of region-adaptive attention and inflexible token budgets that force uniform downsampling, leading to critical information loss. To overcome these limitations, we introduce Zoomer, a visual prompting framework that delivers token-efficient, detail-preserving image representations for black-box MLLMs. Zoomer integrates (1) a prompt-aware emphasis module to highlight semantically relevant regions, (2) a spatial-preserving orchestration schema to maintain object relationships, and (3) a budget-aware strategy to adaptively allocate tokens between global context and local details. Extensive experiments on nine benchmarks and three commercial MLLMs demonstrate that Zoomer boosts accuracy by up to 27% while cutting image token usage by up to 67%. Our approach establishes a principled methodology for robust, resource-aware multimodal understanding in settings where model internals are inaccessible.
Coral Protocol: Open Infrastructure Connecting The Internet of Agents
Venue: arXiv cs.MA 2025
Year: 2025

Coral Protocol is an open and decentralized collaboration infrastructure that enables communication, coordination, trust and payments for The Internet of Agents. It addresses the growing need for interoperability in a world where organizations are deploying multiple specialized AI agents that must work together across domains and vendors. As a foundational platform for multi-agent AI ecosystems, Coral establishes a common language and coordination framework allowing any agent to participate in complex workflows with others. Its design emphasizes broad compatibility, security, and vendor neutrality, ensuring that agent interactions are efficient and trustworthy. In particular, Coral introduces standardized messaging formats for agent communication, a modular coordination mechanism for orchestrating multi-agent tasks, and secure team formation capabilities for dynamically assembling trusted groups of agents. Together, these innovations position Coral Protocol as a cornerstone of the emerging "Internet of Agents," unlocking new levels of automation, collective intelligence, and business value through open agent collaboration.
InstructAttribute: Fine-grained Object Attributes editing with Instruction
Venue: arXiv cs.CV 2025
Year: 2025

Text-to-image (T2I) diffusion models are widely used in image editing due to their powerful generative capabilities. However, achieving fine-grained control over specific object attributes, such as color and material, remains a considerable challenge. Existing methods often fail to accurately modify these attributes or compromise structural integrity and overall image consistency. To fill this gap, we introduce Structure Preservation and Attribute Amplification (SPAA), a novel training-free framework that enables precise generation of color and material attributes for the same object by intelligently manipulating self-attention maps and cross-attention values within diffusion models. Building on SPAA, we integrate multi-modal large language models (MLLMs) to automate data curation and instruction generation. Leveraging this object attribute data collection engine, we construct the Attribute Dataset, encompassing a comprehensive range of colors and materials across diverse object categories. Using this generated dataset, we propose InstructAttribute, an instruction-tuned model that enables fine-grained and object-level attribute editing through natural language prompts. This capability holds significant practical implications for diverse fields, from accelerating product design and e-commerce visualization to enhancing virtual try-on experiences. Extensive experiments demonstrate that InstructAttribute outperforms existing instruction-based baselines, achieving a superior balance between attribute modification accuracy and structural preservation.
DARTer: Dynamic Adaptive Representation Tracker for Nighttime UAV Tracking
Venue: arXiv cs.CV 2025
Year: 2025

Nighttime UAV tracking presents significant challenges due to extreme illumination variations and viewpoint changes, which severely degrade tracking performance. Existing approaches either rely on light enhancers with high computational costs or introduce redundant domain adaptation mechanisms, failing to fully utilize the dynamic features in varying perspectives. To address these issues, we propose \textbf{DARTer} (\textbf{D}ynamic \textbf{A}daptive \textbf{R}epresentation \textbf{T}racker), an end-to-end tracking framework designed for nighttime UAV scenarios. DARTer leverages a Dynamic Feature Blender (DFB) to effectively fuse multi-perspective nighttime features from static and dynamic templates, enhancing representation robustness. Meanwhile, a Dynamic Feature Activator (DFA) adaptively activates Vision Transformer layers based on extracted features, significantly improving efficiency by reducing redundant computations. Our model eliminates the need for complex multi-task loss functions, enabling a streamlined training process. Extensive experiments on multiple nighttime UAV tracking benchmarks demonstrate the superiority of DARTer over state-of-the-art trackers. These results confirm that DARTer effectively balances tracking accuracy and efficiency, making it a promising solution for real-world nighttime UAV tracking applications.
Multi-Modal Language Models as Text-to-Image Model Evaluators
Venue: arXiv cs.CV 2025
Year: 2025

The steady improvements of text-to-image (T2I) generative models lead to slow deprecation of automatic evaluation benchmarks that rely on static datasets, motivating researchers to seek alternative ways to evaluate the T2I progress. In this paper, we explore the potential of multi-modal large language models (MLLMs) as evaluator agents that interact with a T2I model, with the objective of assessing prompt-generation consistency and image aesthetics. We present Multimodal Text-to-Image Eval (MT2IE), an evaluation framework that iteratively generates prompts for evaluation, scores generated images and matches T2I evaluation of existing benchmarks with a fraction of the prompts used in existing static benchmarks. Moreover, we show that MT2IE's prompt-generation consistency scores have higher correlation with human judgment than scores previously introduced in the literature. MT2IE generates prompts that are efficient at probing T2I model performance, producing the same relative T2I model rankings as existing benchmarks while using only 1/80th the number of prompts for evaluation.
Near-optimal Sensor Placement for Detecting Stochastic Target Trajectories in Barrier Coverage Systems
Venue: arXiv cs.RO 2025
Year: 2025

This paper addresses the deployment of sensors for a 2-D barrier coverage system. The challenge is to compute near-optimal sensor placements for detecting targets whose trajectories follow a log-Gaussian Cox line process. We explore sensor deployment in a transformed space, where linear target trajectories are represented as points. While this space simplifies handling the line process, the spatial functions representing sensor performance (i.e. probability of detection) become less intuitive. To illustrate our approach, we focus on positioning sensors of the barrier coverage system on the seafloor to detect passing ships. Through numerical experiments using historical ship data, we compute sensor locations that maximize the probability all ship passing over the barrier coverage system are detected.
SeLR: Sparsity-enhanced Lagrangian Relaxation for Computation Offloading at the Edge
Venue: arXiv cs.NI 2025
Year: 2025

This paper introduces a novel computational approach for offloading sensor data processing tasks to servers in edge networks for better accuracy and makespan. A task is assigned with one of several offloading options, each comprises a server, a route for uploading data to the server, and a service profile that specifies the performance and resource consumption at the server and in the network. This offline offloading and routing problem is formulated as mixed integer programming (MIP), which is non-convex and HP-hard due to the discrete decision variables associated to the offloading options. The novelty of our approach is to transform this non-convex problem into iterative convex optimization by relaxing integer decision variables into continuous space, combining primal-dual optimization for penalizing constraint violations and reweighted $L_1$-minimization for promoting solution sparsity, which achieves better convergence through a smoother path in a continuous search space. Compared to existing greedy heuristics, our approach can achieve a better Pareto frontier in accuracy and latency, scales better to larger problem instances, and can achieve a 7.72--9.17$\times$ reduction in computational overhead of scheduling compared to the optimal solver in hierarchically organized edge networks with 300 nodes and 50--100 tasks.
Thoughts without Thinking: Reconsidering the Explanatory Value of Chain-of-Thought Reasoning in LLMs through Agentic Pipelines
Venue: arXiv cs.AI 2025
Year: 2025

Agentic pipelines present novel challenges and opportunities for human-centered explainability. The HCXAI community is still grappling with how best to make the inner workings of LLMs transparent in actionable ways. Agentic pipelines consist of multiple LLMs working in cooperation with minimal human control. In this research paper, we present early findings from an agentic pipeline implementation of a perceptive task guidance system. Through quantitative and qualitative analysis, we analyze how Chain-of-Thought (CoT) reasoning, a common vehicle for explainability in LLMs, operates within agentic pipelines. We demonstrate that CoT reasoning alone does not lead to better outputs, nor does it offer explainability, as it tends to produce explanations without explainability, in that they do not improve the ability of end users to better understand systems or achieve their goals.
Inattentional Blindness with Augmented Reality HUDS: An On-road Study
Venue: arXiv cs.HC 2025
Year: 2025

As the integration of augmented reality (AR) technology in head-up displays (HUDs) becomes more prevalent in vehicles, it is crucial to understand how to design and evaluate AR interfaces to ensure safety. With new AR displays capable of rendering images with larger field of views and at varying depths, the visual and cognitive separation between graphical and real-world visual stimuli will be increasingly more difficult to quantify as will drivers' ability to efficiently allocate visual attention between the two sets of stimuli. In this study, we present a user study that serves as a crucial first step in gaining insight into inattentional blindness while using AR in surface transportation, where understanding is currently limited. Our primary goal is to investigate how the visual demand of AR tasks influences drivers' ability to detect stimuli, and whether the nature of the stimuli itself plays a role in this effect. To address these questions, we designed an on-road user study aimed at producing a more realistic and ecologically valid understanding of the phenomenon. Our results show that drivers' ability to timely detect stimuli in the environment decreased as the AR task visual demand increased demonstrated by both detection performance and inattentional blindness metrics. Further, inattentional blindness caused by AR displays appears to be more prevalent within drivers' central field of view. We conclude by discussing implications towards a safety-centric evaluation framework for AR HUDs.