OpenRaiser/Intern-Atlas huggingface dataset:
Fundamentals of Building Autonomous LLM Agents
Venue: arXiv cs.AI 2025
Year: 2025

This paper reviews the architecture and implementation methods of agents powered by large language models (LLMs). Motivated by the limitations of traditional LLMs in real-world tasks, the research aims to explore patterns to develop "agentic" LLMs that can automate complex tasks and bridge the performance gap with human capabilities. Key components include a perception system that converts environmental percepts into meaningful representations; a reasoning system that formulates plans, adapts to feedback, and evaluates actions through different techniques like Chain-of-Thought and Tree-of-Thought; a memory system that retains knowledge through both short-term and long-term mechanisms; and an execution system that translates internal decisions into concrete actions. This paper shows how integrating these systems leads to more capable and generalized software bots that mimic human cognitive processes for autonomous and intelligent behavior.
Exploring User Risk Factors and Target Groups for Phishing Victimization in Pakistan
Venue: arXiv cs.CY 2025
Year: 2025

Phishing attacks pose a significant cybersecurity threat globally. This study investigates phishing susceptibility within the Pakistani population, examining the influence of demographic factors, technological aptitude and usage, previous phishing victimization, and email characteristics. Data was collected through convenient sampling; a total of 164 people completed the questionnaire. Contrary to some assumptions, the results indicate that men, individuals over 25, employed persons and frequent online shoppers have relatively high phishing susceptibility. The characteristics of email significantly affected phishing victimization, with authority and urgency signaling increasing susceptibility, while risk cues sometimes improved vigilance. In particular, users were more susceptible to emails from communication services such as Gmail and LinkedIn compared to government or social media sources. These findings highlight the need for targeted security awareness interventions tailored to specific demographics and email types. A multifaceted approach combining technology and education is crucial to combat phishing attacks.
Zero-shot image privacy classification with Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

While specialized learning-based models have historically dominated image privacy prediction, the current literature increasingly favours adopting large Vision-Language Models (VLMs) designed for generic tasks. This trend risks overlooking the performance ceiling set by purpose-built models due to a lack of systematic evaluation. To address this problem, we establish a zero-shot benchmark for image privacy classification, enabling a fair comparison. We evaluate the top-3 open-source VLMs, according to a privacy benchmark, using task-aligned prompts and we contrast their performance, efficiency, and robustness against established vision-only and multi-modal methods. Counter-intuitively, our results show that VLMs, despite their resource-intensive nature in terms of high parameter count and slower inference, currently lag behind specialized, smaller models in privacy prediction accuracy. We also find that VLMs exhibit higher robustness to image perturbations.
Obstacle Avoidance using Dynamic Movement Primitives and Reinforcement Learning
Venue: arXiv cs.RO 2025
Year: 2025

Learning-based motion planning can quickly generate near-optimal trajectories. However, it often requires either large training datasets or costly collection of human demonstrations. This work proposes an alternative approach that quickly generates smooth, near-optimal collision-free 3D Cartesian trajectories from a single artificial demonstration. The demonstration is encoded as a Dynamic Movement Primitive (DMP) and iteratively reshaped using policy-based reinforcement learning to create a diverse trajectory dataset for varying obstacle configurations. This dataset is used to train a neural network that takes as inputs the task parameters describing the obstacle dimensions and location, derived automatically from a point cloud, and outputs the DMP parameters that generate the trajectory. The approach is validated in simulation and real-robot experiments, outperforming a RRT-Connect baseline in terms of computation and execution time, as well as trajectory length, while supporting multi-modal trajectory generation for different obstacle geometries and end-effector dimensions. Videos and the implementation code are available at https://github.com/DominikUrbaniak/obst-avoid-dmp-pi2.
Placeit! A Framework for Learning Robot Object Placement Skills
Venue: arXiv cs.RO 2025
Year: 2025

Robotics research has made significant strides in learning, yet mastering basic skills like object placement remains a fundamental challenge. A key bottleneck is the acquisition of large-scale, high-quality data, which is often a manual and laborious process. Inspired by Graspit!, a foundational work that used simulation to automatically generate dexterous grasp poses, we introduce Placeit!, an evolutionary-computation framework for generating valid placement positions for rigid objects. Placeit! is highly versatile, supporting tasks from placing objects on tables to stacking and inserting them. Our experiments show that by leveraging quality-diversity optimization, Placeit! significantly outperforms state-of-the-art methods across all scenarios for generating diverse valid poses. A pick&place pipeline built on our framework achieved a 90% success rate over 120 real-world deployments. This work positions Placeit! as a powerful tool for open-environment pick-and-place tasks and as a valuable engine for generating the data needed to train simulation-based foundation models in robotics.
Assessing the Impact of Post-Quantum Digital Signature Algorithms on Blockchains
Venue: arXiv cs.CR 2025
Year: 2025

The advent of quantum computing threatens the security of traditional encryption algorithms, motivating the development of post-quantum cryptography (PQC). In 2024, the National Institute of Standards and Technology (NIST) standardized several PQC algorithms, marking an important milestone in the transition toward quantum-resistant security. Blockchain systems fundamentally rely on cryptographic primitives to guarantee data integrity and transaction authenticity. However, widely used algorithms such as ECDSA, employed in Bitcoin, Ethereum, and other networks, are vulnerable to quantum attacks. Although adopting PQC is essential for long-term security, its computational overhead in blockchain environments remains largely unexplored. In this work, we propose a methodology for benchmarking both PQC and traditional cryptographic algorithms in blockchain contexts. We measure signature generation and verification times across diverse computational environments and simulate their impact at scale. Our evaluation focuses on PQC digital signature schemes (ML-DSA, Dilithium, Falcon, Mayo, SLH-DSA, SPHINCS+, and Cross) across security levels 1 to 5, comparing them to ECDSA, the current standard in Bitcoin and Ethereum. Our results indicate that PQC algorithms introduce only minor performance overhead at security level 1, while in some scenarios they significantly outperform ECDSA at higher security levels. For instance, ML-DSA achieves a verification time of 0.14 ms on an ARM-based laptop at level 5, compared to 0.88 ms for ECDSA. We also provide an open-source implementation to ensure reproducibility and encourage further research.
U-Turn: Enhancing Incorrectness Analysis by Reversing Direction
Venue: arXiv cs.LO 2025
Year: 2025

O'Hearn's Incorrectness Logic (IL) has sparked renewed interest in static analyses that aim to detect program errors rather than prove their absence, thereby avoiding false alarms -- a critical factor for practical adoption in industrial settings. As new incorrectness logics emerge to capture diverse error-related properties, a key question arises: can the combination of (in)correctness techniques enhance precision, expressiveness, automation, or scalability? Notable frameworks, such as outcome logic, UNTer, local completeness logic, and exact separation logic, unify multiple analyses within a single proof system. In this work, we adopt a complementary strategy. Rather than designing a unified logic, we combine IL, which identifies reachable error states, with Sufficient Incorrectness Logic (SIL), which finds input states potentially leading to those errors. As a result, we get a more informative and effective analysis than either logic in isolation. Rather than naively sequencing them, our key innovation is reusing heuristic choices from the first analysis to steer the second. In fact, both IL and SIL rely on under-approximation and thus their automation legitimates heuristics that avoid exhaustive path enumeration (e.g., selective disjunct pruning, loop unrolling). Concretely, we instrument the second logic's proof rules with derivations coming from the first to inductively guide rule selection and application. To our knowledge, this is the first rule format enabling such inter-analysis instrumentation. This combined analysis aids debugging and testing by revealing both reachable errors and their causes, and opens new avenues for embedding incorrectness insights into (a new kind of) scalable, expressive, automated code contracts.
Residual-Informed Learning of Solutions to Algebraic Loops
Venue: arXiv cs.LG 2025
Year: 2025

This paper presents a residual-informed machine learning approach for replacing algebraic loops in equation-based Modelica models with neural network surrogates. A feedforward neural network is trained using the residual (error) of the algebraic loop directly in its loss function, eliminating the need for a supervised dataset. This training strategy also resolves the issue of ambiguous solutions, allowing the surrogate to converge to a consistent solution rather than averaging multiple valid ones. Applied to the large-scale IEEE 14-Bus system, our method achieves a 60% reduction in simulation time compared to conventional simulations, while maintaining the same level of accuracy through error control mechanisms.
Safety Game: Balancing Safe and Informative Conversations with Blackbox Agentic AI using LP Solvers
Venue: arXiv cs.LG 2025
Year: 2025

Ensuring that large language models (LLMs) comply with safety requirements is a central challenge in AI deployment. Existing alignment approaches primarily operate during training, such as through fine-tuning or reinforcement learning from human feedback, but these methods are costly and inflexible, requiring retraining whenever new requirements arise. Recent efforts toward inference-time alignment mitigate some of these limitations but still assume access to model internals, which is impractical, and not suitable for third party stakeholders who do not have access to the models. In this work, we propose a model-independent, black-box framework for safety alignment that does not require retraining or access to the underlying LLM architecture. As a proof of concept, we address the problem of trading off between generating safe but uninformative answers versus helpful yet potentially risky ones. We formulate this dilemma as a two-player zero-sum game whose minimax equilibrium captures the optimal balance between safety and helpfulness. LLM agents operationalize this framework by leveraging a linear programming solver at inference time to compute equilibrium strategies. Our results demonstrate the feasibility of black-box safety alignment, offering a scalable and accessible pathway for stakeholders, including smaller organizations and entities in resource-constrained settings, to enforce safety across rapidly evolving LLM ecosystems.
Deep Learning to Identify the Spatio-Temporal Cascading Effects of Train Delays in a High-Density Network
Venue: arXiv cs.LG 2025
Year: 2025

The operational efficiency of railway networks, a cornerstone of modern economies, is persistently undermined by the cascading effects of train delays. Accurately forecasting this delay propagation is a critical challenge for real-time traffic management. While recent research has leveraged Graph Neural Networks (GNNs) to model the network structure of railways, a significant gap remains in developing frameworks that provide multi-step autoregressive forecasts at a network-wide scale, while simultaneously offering the live, interpretable explanations needed for decision support. This paper addresses this gap by developing and evaluating a novel XGeoAI framework for live, explainable, multi-step train delay forecasting. The core of this work is a two-stage, autoregressive Graph Attention Network (GAT) model, trained on a real-world dataset covering over 40% of the Dutch railway network. The model represents the system as a spatio-temporal graph of operational events (arrivals and departures) and is enriched with granular features, including platform and station congestion. To test its viability for live deployment, the model is rigorously evaluated using a sequential, k-step-ahead forecasting protocol that simulates real-world conditions where prediction errors can compound. The results demonstrate that while the proposed GATv2 model is challenged on pure error metrics (MAE) by a simpler Persistence baseline, it achieves consistently higher precision in classifying delay events -- a crucial advantage for a reliable decision support tool.
NL2GenSym: Natural Language to Generative Symbolic Rules for SOAR Cognitive Architecture via Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

SOAR, a classic symbol-based cognitive architecture, has been fostering the development of general, human-like intelligent agents. Nevertheless, its practical adoption is hindered by the laborious manual rule coding. Emerging Large Language Models (LLMs) present the immense potential for efficient rules generation. However, there is a critical gap that current research predominantly focuses on conceptual frameworks and lacks robust experimental validation. To bridge this gap, we propose \textit{N}atural \textit{L}anguage to \textit{Gen}erative \textit{Sym}bolic Rules (NL2GenSym), a novel framework that integrates LLMs with SOAR to autonomously produce generative symbolic rules from natural language. Specifically, our framework introduces a novel Execution-Grounded Generator-Critic mechanism. The LLM-based Generator, guided by a Retrieval-Augmented Generation-accessed self-evolving domain knowledge base, proposes rules from natural language. Subsequently, these rules are immediately executed within the SOAR environment to rigorously validate their correctness. Based on this execution-grounded feedback, a reflective LLM-based Critic drives the iterative refinement of these rules. Experiments on our specialized Water Jug Problem (WJP) dataset, utilizing both Gemini and Qwen series models, validate the efficacy of our framework. It achieves a success rate over 86\% in generating rules from natural language. Crucially, the framework also generates novel heuristic rules, reducing average decision cycles for solving the WJP to 1.98 times the optimal solution and 1/1000 of baseline methods. Additionally, our initial experiments show that NL2GenSym enables smaller-parameter models to achieve better performance than larger counterparts.
Token-Level Policy Optimization: Linking Group-Level Rewards to Token-Level Aggregation via Markov Likelihood
Venue: arXiv cs.CL 2025
Year: 2025

Group Relative Policy Optimization (GRPO) has significantly advanced the reasoning ability of large language models (LLMs), particularly by boosting their mathematical performance. However, GRPO and related entropy-regularization methods still face challenges rooted in the sparse token rewards inherent to chain-of-thought (CoT). Current approaches often rely on undifferentiated token-level entropy adjustments, which frequently lead to entropy collapse or model collapse. In this work, we propose TEPO, a novel token-level framework that incorporates Markov Likelihood (sequence likelihood) links group-level rewards with tokens via token-level aggregation. Experiments show that TEPO consistently outperforms existing baselines across key metrics (including @k and accuracy). It not only sets a new state of the art on mathematical reasoning tasks but also significantly enhances training stability.
Challenges in designing ethical rules for Infrastructures in Internet of Vehicles
Venue: arXiv cs.CY 2025
Year: 2025

Vehicular Ad-hoc Networks (VANETs) have seen significant advancements in technology. Innovation in connectivity and communication has brought substantial capabilities to various components of VANETs such as vehicles, infrastructures, passengers, drivers and affiliated environmental sensors. Internet of Things (IoT) has brought the notion of Internet of Vehicles (IoV) to VANETs where each component of VANET is connected directly or indirectly to the Internet. Vehicles and infrastructures are key components of a VANET system that can greatly augment the overall experience of the network by integrating the competencies of Vehicle to Vehicle (V2V), Vehicle to Pedestrian (V2P), Vehicle to Sensor (V2S), Vehicle to Infrastructure (V2I) and Infrastructure to Infrastructure (I2I). Internet connectivity in Vehicles and Infrastructures has immensely expanded the potential of developing applications for VANETs under the broad spectrum of IoV. Advent in the use of technology in VANETs requires considerable efforts in scheming the ethical rules for autonomous systems. Currently, there is a gap in literature that focuses on the challenges involved in designing ethical rules or policies for infrastructures, sometimes referred to as Road Side Units (RSUs) for IoVs. This paper highlights the key challenges entailing the design of ethical rules for RSUs in IoV systems. Furthermore, the article also proposes major ethical principles for RSUs in IoV systems that would set foundation for modeling future IoV architectures.
TIT: A Tree-Structured Instruction Tuning Approach for LLM-Based Code Translation
Venue: arXiv cs.SE 2025
Year: 2025

Large Language Models (LLMs) have shown strong performance in automated source-to-target code translation through pretraining on extensive code corpora. However, mainstream LLM-based code translation methods suffer from two critical limitations. First, they are highly sensitive to language-specific features, which often introduce source-language syntax or lexicon into the output, leading to syntactic confusion. Second, they lack fine-grained semantic alignment due to an over-reliance on function-level parallel datasets, resulting in semantic misalignment between the translated code and the original source. To overcome these limitations, we propose TIT, a Tree-structured Instruction Tuning paradigm for LLM-based code translation. Specifically, TIT consists of three modules. First, to mitigate syntactic confusion, the syntactic information representation module integrates language-agnostic syntactic features via structured parsing. Then, to generate high-quality fine-grained parallel data, the fine-grained parallel dataset augmentation module aligns nodes with code segments through statement-level segmentation and contrastive matching. Finally, we leverage the dual-stage tree instruction tuning module to alleviate the contextual processing burden on the LLM caused by the introduction of syntactic information. The first stage employs syntax-aware fine-tuning to enable the LLM to autonomously comprehend structured syntactic information, while the second stage utilizes code generation fine-tuning to guide the model in generating accurate target code based on function-level syntactic dependencies. The experimental results demonstrate that the proposed method significantly outperforms existing approaches in multiple LLMs, achieving a success rate 1.22x-1.75x higher in code translation while markedly reducing syntactic confusion.
KORMo: Korean Open Reasoning Model for Everyone
Venue: arXiv cs.CL 2025
Year: 2025

This work presents the first large-scale investigation into constructing a fully open bilingual large language model (LLM) for a non-English language, specifically Korean, trained predominantly on synthetic data. We introduce KORMo-10B, a 10.8B-parameter model trained from scratch on a Korean-English corpus in which 68.74% of the Korean portion is synthetic. Through systematic experimentation, we demonstrate that synthetic data, when carefully curated with balanced linguistic coverage and diverse instruction styles, does not cause instability or degradation during large-scale pretraining. Furthermore, the model achieves performance comparable to that of contemporary open-weight multilingual baselines across a wide range of reasoning, knowledge, and instruction-following benchmarks. Our experiments reveal two key findings: (1) synthetic data can reliably sustain long-horizon pretraining without model collapse, and (2) bilingual instruction tuning enables near-native reasoning and discourse coherence in Korean. By fully releasing all components including data, code, training recipes, and logs, this work establishes a transparent framework for developing synthetic data-driven fully open models (FOMs) in low-resource settings and sets a reproducible precedent for future multilingual LLM research.
FOGMACHINE -- Leveraging Discrete-Event Simulation and Scene Graphs for Modeling Hierarchical, Interconnected Environments under Partial Observations from Mobile Agents
Venue: arXiv cs.RO 2025
Year: 2025

Dynamic Scene Graphs (DSGs) provide a structured representation of hierarchical, interconnected environments, but current approaches struggle to capture stochastic dynamics, partial observability, and multi-agent activity. These aspects are critical for embodied AI, where agents must act under uncertainty and delayed perception. We introduce FOGMACHINE , an open-source framework that fuses DSGs with discrete-event simulation to model object dynamics, agent observations, and interactions at scale. This setup enables the study of uncertainty propagation, planning under limited perception, and emergent multi-agent behavior. Experiments in urban scenarios illustrate realistic temporal and spatial patterns while revealing the challenges of belief estimation under sparse observations. By combining structured representations with efficient simulation, FOGMACHINE establishes an effective tool for benchmarking, model training, and advancing embodied AI in complex, uncertain environments.
CRPS-LAM: Regional ensemble weather forecasting from matching marginals
Venue: arXiv cs.LG 2025
Year: 2025

Machine learning for weather prediction increasingly relies on ensemble methods to provide probabilistic forecasts. Diffusion-based models have shown strong performance in Limited-Area Modeling (LAM) but remain computationally expensive at sampling time. Building on the success of global weather forecasting models trained based on Continuous Ranked Probability Score (CRPS), we introduce CRPS-LAM, a probabilistic LAM forecasting model trained with a CRPS-based objective. By sampling and injecting a single latent noise vector into the model, CRPS-LAM generates ensemble members in a single forward pass, achieving sampling speeds up to 39 times faster than a diffusion-based model. We evaluate the model on the MEPS regional dataset, where CRPS-LAM matches the low errors of diffusion models. By retaining also fine-scale forecast details, the method stands out as an effective approach for probabilistic regional weather forecasting