OpenRaiser/Intern-Atlas huggingface dataset:
WebGen-Agent: Enhancing Interactive Website Generation with Multi-Level Feedback and Step-Level Reinforcement Learning
Venue: arXiv cs.CL 2025
Year: 2025

Agent systems powered by large language models (LLMs) have demonstrated impressive performance on repository-level code-generation tasks. However, for tasks such as website codebase generation, which depend heavily on visual effects and user-interaction feedback, current code agents rely only on simple code execution for feedback and verification. This approach fails to capture the actual quality of the generated code. In this paper, we propose WebGen-Agent, a novel website-generation agent that leverages comprehensive and multi-level visual feedback to iteratively generate and refine the website codebase. Detailed and expressive text descriptions and suggestions regarding the screenshots and GUI-agent testing of the websites are generated by a visual language model (VLM), together with scores that quantify their quality. The screenshot and GUI-agent scores are further integrated with a backtracking and select-best mechanism, enhancing the performance of the agent. Utilizing the accurate visual scores inherent in the WebGen-Agent workflow, we further introduce \textit{Step-GRPO with Screenshot and GUI-agent Feedback} to improve the ability of LLMs to act as the reasoning engine of WebGen-Agent. By using the screenshot and GUI-agent scores at each step as the reward in Step-GRPO, we provide a dense and reliable process supervision signal, which effectively improves the model's website-generation ability. On the WebGen-Bench dataset, WebGen-Agent increases the accuracy of Claude-3.5-Sonnet from 26.4% to 51.9% and its appearance score from 3.0 to 3.9, outperforming the previous state-of-the-art agent system. Additionally, our Step-GRPO training approach increases the accuracy of Qwen2.5-Coder-7B-Instruct from 38.9% to 45.4% and raises the appearance score from 3.4 to 3.7.
VoiceAssistant-Eval: Benchmarking AI Assistants across Listening, Speaking, and Viewing
Venue: arXiv cs.CL 2025
Year: 2025

The growing capabilities of large language models and multimodal systems have spurred interest in voice-first AI assistants, yet existing benchmarks are inadequate for evaluating the full range of these systems' capabilities. We introduce VoiceAssistant-Eval, a comprehensive benchmark designed to assess AI assistants across listening, speaking, and viewing. VoiceAssistant-Eval comprises 10,497 curated examples spanning 13 task categories. These tasks include natural sounds, music, and spoken dialogue for listening; multi-turn dialogue, role-play imitation, and various scenarios for speaking; and highly heterogeneous images for viewing. To demonstrate its utility, we evaluate 21 open-source models and GPT-4o-Audio, measuring the quality of the response content and speech, as well as their consistency. The results reveal three key findings: (1) proprietary models do not universally outperform open-source models; (2) most models excel at speaking tasks but lag in audio understanding; and (3) well-designed smaller models can rival much larger ones. Notably, the mid-sized Step-Audio-2-mini (7B) achieves more than double the listening accuracy of LLaMA-Omni2-32B-Bilingual. However, challenges remain: multimodal (audio plus visual) input and role-play voice imitation tasks are difficult for current models, and significant gaps persist in robustness and safety alignment. VoiceAssistant-Eval identifies these gaps and establishes a rigorous framework for evaluating and guiding the development of next-generation AI assistants. Code and data will be released at https://mathllm.github.io/VoiceAssistantEval/ .
See, Point, Fly: A Learning-Free VLM Framework for Universal Unmanned Aerial Navigation
Venue: arXiv cs.RO 2025
Year: 2025

We present See, Point, Fly (SPF), a training-free aerial vision-and-language navigation (AVLN) framework built atop vision-language models (VLMs). SPF is capable of navigating to any goal based on any type of free-form instructions in any kind of environment. In contrast to existing VLM-based approaches that treat action prediction as a text generation task, our key insight is to consider action prediction for AVLN as a 2D spatial grounding task. SPF harnesses VLMs to decompose vague language instructions into iterative annotation of 2D waypoints on the input image. Along with the predicted traveling distance, SPF transforms predicted 2D waypoints into 3D displacement vectors as action commands for UAVs. Moreover, SPF also adaptively adjusts the traveling distance to facilitate more efficient navigation. Notably, SPF performs navigation in a closed-loop control manner, enabling UAVs to follow dynamic targets in dynamic environments. SPF sets a new state of the art in DRL simulation benchmark, outperforming the previous best method by an absolute margin of 63%. In extensive real-world evaluations, SPF outperforms strong baselines by a large margin. We also conduct comprehensive ablation studies to highlight the effectiveness of our design choice. Lastly, SPF shows remarkable generalization to different VLMs. Project page: https://spf-web.pages.dev
Fairness for niche users and providers: algorithmic choice and profile portability
Venue: arXiv cs.IR 2025
Year: 2025

Ensuring fair outcomes for multiple stakeholders in recommender systems has been studied mostly in terms of algorithmic interventions: building new models with better fairness properties, or using reranking to improve outcomes from an existing algorithm. What has rarely been studied is structural changes in the recommendation ecosystem itself. Our work explores the fairness impact of algorithmic pluralism, the idea that the recommendation algorithm is decoupled from the platform through which users access content, enabling user choice in algorithms. Prior work using a simulation approach has shown that niche consumers and (especially) niche providers benefit from algorithmic choice. In this paper, we use simulation to explore the question of profile portability, to understand how different policies regarding the handling of user profiles interact with fairness outcomes for consumers and providers.
Security Friction Quotient for Zero Trust Identity Policy with Empirical Validation
Venue: arXiv cs.CR 2025
Year: 2025

We define a practical method to quantify the trade-off between security and operational friction in modern identity-centric programs. We introduce the Security Friction Quotient (SFQ), a bounded composite index that combines a residual-risk estimator with empirically grounded friction terms (latency, failure rate, and helpdesk impact). We establish clarity properties (boundedness, monotonic response, and weight identifiability) with short proofs, then evaluate widely used Conditional Access policies over a 12-week horizon using Monte Carlo simulation (n = 2,000 runs per policy/scenario) with effect sizes and 95% confidence intervals. We further assess rank stability under 10,000 random weight draws, finding 95.5% preservation of policy ordering. Finally, we provide a 12-week passkey field observation from an enterprise-scale cohort (N = 1,200) that directionally aligns with the simulation's phishing-resistant MFA gains. The SFQ framework is designed to be reproducible, interpretable, and directly actionable for Zero Trust identity policy decisions, with artifacts and parameter ranges provided to support policy design, review, and continuous improvement.
Security Issues on the OpenPLC project and corresponding solutions
Venue: arXiv cs.CR 2025
Year: 2025

As Programmable Logic Controller (PLC) became a useful device and rose as an interesting research topic but remained expensive, multiple PLC simulators/emulators were introduced for various purposes. Open-source Programmable Logic Controller (OpenPLC) software, one of the most popular PLC simulators, is designed to be vendor-neutral and run on almost any computer or low-cost embedded devices, e.g., Raspberry Pi, Arduino, and other controllers. The project succeeded in introducing itself as an affordable and practical solution for the high cost of real hardware PLCs. However, it still lacks appropriate securing methods, resulting in several vulnerabilities. Through a combination of threat modeling, vulnerability analysis, and practical experiments, this thesis provides valuable insights for developers, researchers, and engineers aiming to deploy OpenPLC securely in industrial environments. To this end, this work first conducts an in-depth analysis aimed to shed light on va! rious security challenges and vulnerabilities within the OpenPLC project. After that, an advanced control logic injection attack was performed. This attack modifies the user program maliciously, exploiting presented vulnerabilities. Finally, the work introduces a security-enhanced OpenPLC software called OpenPLC Aqua. The new software is equipped with a set of security solutions designed specifically to address the vulnerabilities to which current OpenPLC versions are prone.
Robust Object Detection for Autonomous Driving via Curriculum-Guided Group Relative Policy Optimization
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal Large Language Models (MLLMs) excel in vision-language reasoning but often struggle with structured perception tasks requiring precise localization and robustness. We propose a reinforcement learning framework that augments Group Relative Policy Optimization (GRPO) with curriculum-based data scheduling and difficulty-aware filtering. This approach stabilizes optimization under sparse, noisy rewards and enables progressive adaptation to complex samples. Evaluations on autonomous driving benchmarks demonstrate substantial improvements in detection accuracy and robustness. Ablation studies confirm the importance of reward design, KL regularization, and curriculum pacing for convergence stability and generalization. Our findings highlight reinforcement-driven optimization with structured data curricula as a scalable path toward robust and interpretable multimodal detection.
Sequential Token Merging: Revisiting Hidden States
Venue: arXiv cs.CV 2025
Year: 2025

Vision Mambas (ViMs) achieve remarkable success with sub-quadratic complexity, but their efficiency remains constrained by quadratic token scaling with image resolution. While existing methods address token redundancy, they overlook ViMs' intrinsic Limited Directional Sequential Dependence (LDSD) - a critical information flow mechanism revealed in our analysis. We further identify Mamba's selective scan enables gradual information aggregation in hidden states. Based on these insights, we propose Sequential Token Merging (STM), featuring: 1) Bidirectional nearest neighbor merging to preserve sequential dependencies through symmetric spatial aggregation, and 2) Hidden states protection to stabilize the hidden states around the class token. STM strategically leverages Mamba's layer-wise loss convergence to convert temporal forgetfulness into stability. Experiments demonstrate STM's superiority: 1.0% accuracy drop for ViM-Ti at 20% token reduction, and only 1.4% degradation for ViM-S at 40% reduction. Our method achieves state-of-the-art efficiency with minimal complexity, while providing new insights into state-space model dynamics. Codes will be released soon.
Localizing Adversarial Attacks To Produces More Imperceptible Noise
Venue: arXiv cs.LG 2025
Year: 2025

Adversarial attacks in machine learning traditionally focus on global perturbations to input data, yet the potential of localized adversarial noise remains underexplored. This study systematically evaluates localized adversarial attacks across widely-used methods, including FGSM, PGD, and C&W, to quantify their effectiveness, imperceptibility, and computational efficiency. By introducing a binary mask to constrain noise to specific regions, localized attacks achieve significantly lower mean pixel perturbations, higher Peak Signal-to-Noise Ratios (PSNR), and improved Structural Similarity Index (SSIM) compared to global attacks. However, these benefits come at the cost of increased computational effort and a modest reduction in Attack Success Rate (ASR). Our results highlight that iterative methods, such as PGD and C&W, are more robust to localization constraints than single-step methods like FGSM, maintaining higher ASR and imperceptibility metrics. This work provides a comprehensive analysis of localized adversarial attacks, offering practical insights for advancing attack strategies and designing robust defensive systems.
Beyond Western Politics: Cross-Cultural Benchmarks for Evaluating Partisan Associations in LLMs
Venue: arXiv cs.CY 2025
Year: 2025

Partisan bias in LLMs has been evaluated to assess political leanings, typically through a broad lens and largely in Western contexts. We move beyond identifying general leanings to examine harmful, adversarial representational associations around political leaders and parties. To do so, we create datasets \textit{NeutQA-440} (non-adversarial prompts) and \textit{AdverQA-440} (adversarial prompts), which probe models for comparative plausibility judgments across the USA and India. Results show high susceptibility to biased partisan associations and pronounced asymmetries (e.g., substantially more favorable associations for U.S. Democrats than Republicans) alongside mixed-polarity concentration around India's BJP, highlighting systemic risks and motivating standardized, cross-cultural evaluation.
RAR$^2$: Retrieval-Augmented Medical Reasoning via Thought-Driven Retrieval
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have shown promising performance on diverse medical benchmarks, highlighting their potential in supporting real-world clinical tasks. Retrieval-Augmented Generation (RAG) has emerged as a key approach for mitigating knowledge gaps and hallucinations by incorporating external medical information. However, RAG still struggles with complex medical questions that require intensive reasoning, as surface-level input often fails to reflect the true knowledge needs of the task. Existing methods typically focus on refining queries without explicitly modeling the reasoning process, limiting their ability to retrieve and integrate clinically relevant knowledge. In this work, we propose RAR$^2$, a joint learning framework that improves both Reasoning-Augmented Retrieval and Retrieval-Augmented Reasoning. RAR$^2$ constructs a thought process to uncover implicit knowledge requirements and uses it to guide retrieval and answer generation. We build a training dataset of mixed preference pairs and apply Direct Preference Optimization (DPO) to train the model. Moreover, we design two test-time scaling strategies to explore the boundaries of our framework. Experiments demonstrate the effectiveness of RAR$^2$ across several biomedical question answering datasets, outperforming RAG baselines with or without fine-tuning.
DiaMoE-TTS: A Unified IPA-Based Dialect TTS Framework with Mixture-of-Experts and Parameter-Efficient Zero-Shot Adaptation
Venue: arXiv cs.SD 2025
Year: 2025

Dialect speech embodies rich cultural and linguistic diversity, yet building text-to-speech (TTS) systems for dialects remains challenging due to scarce data, inconsistent orthographies, and complex phonetic variation. To address these issues, we present DiaMoE-TTS, a unified IPA-based framework that standardizes phonetic representations and resolves grapheme-to-phoneme ambiguities. Built upon the F5-TTS architecture, the system introduces a dialect-aware Mixture-of-Experts (MoE) to model phonological differences and employs parameter-efficient adaptation with Low-Rank Adaptors (LoRA) and Conditioning Adapters for rapid transfer to new dialects. Unlike approaches dependent on large-scale or proprietary resources, DiaMoE-TTS enables scalable, open-data-driven synthesis. Experiments demonstrate natural and expressive speech generation, achieving zero-shot performance on unseen dialects and specialized domains such as Peking Opera with only a few hours of data.
EditGRPO: Reinforcement Learning with Post-Rollout Edits for Clinically Accurate Chest X-Ray Report Generation
Venue: arXiv cs.CL 2025
Year: 2025

Radiology report generation requires advanced medical image analysis, effective temporal reasoning, and accurate text generation. Although recent innovations, particularly multimodal large language models, have shown improved performance, their supervised fine-tuning (SFT) objective is not explicitly aligned with clinical efficacy. In this work, we introduce EditGRPO, a mixed-policy reinforcement learning algorithm designed specifically to optimize the generation through clinically motivated rewards. EditGRPO integrates on-policy exploration with off-policy guidance by injecting sentence-level detailed corrections during training rollouts. This mixed-policy approach addresses the exploration dilemma and sampling efficiency issues typically encountered in RL. Applied to a Qwen2.5-VL-3B, EditGRPO outperforms both SFT and vanilla GRPO baselines, achieving an average improvement of 3.4\% in clinical metrics across four major datasets. Notably, EditGRPO also demonstrates superior out-of-domain generalization, with an average performance gain of 5.9\% on unseen datasets.
Bridging Language Models and Formal Methods for Intent-Driven Optical Network Design
Venue: arXiv cs.NI 2025
Year: 2025

Intent-Based Networking (IBN) aims to simplify network management by enabling users to specify high-level goals that drive automated network design and configuration. However, translating informal natural-language intents into formally correct optical network topologies remains challenging due to inherent ambiguity and lack of rigor in Large Language Models (LLMs). To address this, we propose a novel hybrid pipeline that integrates LLM-based intent parsing, formal methods, and Optical Retrieval-Augmented Generation (RAG). By enriching design decisions with domain-specific optical standards and systematically incorporating symbolic reasoning and verification techniques, our pipeline generates explainable, verifiable, and trustworthy optical network designs. This approach significantly advances IBN by ensuring reliability and correctness, essential for mission-critical networking tasks.
Empart: Interactive Convex Decomposition for Converting Meshes to Parts
Venue: arXiv cs.RO 2025
Year: 2025

Simplifying complex 3D meshes is a crucial step in robotics applications to enable efficient motion planning and physics simulation. Common methods, such as approximate convex decomposition, represent a mesh as a collection of simple parts, which are computationally inexpensive to simulate. However, existing approaches apply a uniform error tolerance across the entire mesh, which can result in a sub-optimal trade-off between accuracy and performance. For instance, a robot grasping an object needs high-fidelity geometry in the vicinity of the contact surfaces but can tolerate a coarser simplification elsewhere. A uniform tolerance can lead to excessive detail in non-critical areas or insufficient detail where it's needed most. To address this limitation, we introduce Empart, an interactive tool that allows users to specify different simplification tolerances for selected regions of a mesh. Our method leverages existing convex decomposition algorithms as a sub-routine but uses a novel, parallelized framework to handle region-specific constraints efficiently. Empart provides a user-friendly interface with visual feedback on approximation error and simulation performance, enabling designers to iteratively refine their decomposition. We demonstrate that our approach significantly reduces the number of convex parts compared to a state-of-the-art method (V-HACD) at a fixed error threshold, leading to substantial speedups in simulation performance. For a robotic pick-and-place task, Empart-generated collision meshes reduced the overall simulation time by 69% compared to a uniform decomposition, highlighting the value of interactive, region-specific simplification for performant robotics applications.
Neighborhood Sampling Does Not Learn the Same Graph Neural Network
Venue: arXiv cs.LG 2025
Year: 2025

Neighborhood sampling is an important ingredient in the training of large-scale graph neural networks. It suppresses the exponential growth of the neighborhood size across network layers and maintains feasible memory consumption and time costs. While it becomes a standard implementation in practice, its systemic behaviors are less understood. We conduct a theoretical analysis by using the tool of neural tangent kernels, which characterize the (analogous) training dynamics of neural networks based on their infinitely wide counterparts -- Gaussian processes (GPs). We study several established neighborhood sampling approaches and the corresponding posterior GP. With limited samples, the posteriors are all different, although they converge to the same one as the sample size increases. Moreover, the posterior covariance, which lower-bounds the mean squared prediction error, is uncomparable, aligning with observations that no sampling approach dominates.
A benchmark for vericoding: formally verified program synthesis
Venue: arXiv cs.SE 2025
Year: 2025

We present and test the largest benchmark for vericoding, LLM-generation of formally verified code from formal specifications - in contrast to vibe coding, which generates potentially buggy code from a natural language description. Our benchmark contains 12,504 formal specifications, with 3,029 in Dafny, 2,334 in Verus/Rust and 7,141 in Lean. Of these, 6,174 are new unseen problems. We find vericoding success rates of 27% in Lean, 44% in Verus/Rust and 82% in Dafny using off-the-shelf LLMs. Adding natural-language descriptions does not significantly improve performance. We also find that LLM progress has improved progress on pure Dafny verification from 68% to 96% over the past year. The benchmark and vericoding results are shared at https://github.com/Beneficial-AI-Foundation/vericoding-benchmark
TY-RIST: Tactical YOLO Tricks for Real-time Infrared Small Target Detection
Venue: arXiv cs.CV 2025
Year: 2025

Infrared small target detection (IRSTD) is critical for defense and surveillance but remains challenging due to (1) target loss from minimal features, (2) false alarms in cluttered environments, (3) missed detections from low saliency, and (4) high computational costs. To address these issues, we propose TY-RIST, an optimized YOLOv12n architecture that integrates (1) a stride-aware backbone with fine-grained receptive fields, (2) a high-resolution detection head, (3) cascaded coordinate attention blocks, and (4) a branch pruning strategy that reduces computational cost by about 25.5% while marginally improving accuracy and enabling real-time inference. We also incorporate the Normalized Gaussian Wasserstein Distance (NWD) to enhance regression stability. Extensive experiments on four benchmarks and across 20 different models demonstrate state-of-the-art performance, improving mAP at 0.5 IoU by +7.9%, Precision by +3%, and Recall by +10.2%, while achieving up to 123 FPS on a single GPU. Cross-dataset validation on a fifth dataset further confirms strong generalization capability. Additional results and resources are available at https://www.github.com/moured/TY-RIST