OpenRaiser/Intern-Atlas huggingface dataset:
V-STaR: Benchmarking Video-LLMs on Video Spatio-Temporal Reasoning
Venue: arXiv cs.CV 2025
Year: 2025

Human processes video reasoning in a sequential spatio-temporal reasoning logic, we first identify the relevant frames ("when") and then analyse the spatial relationships ("where") between key objects, and finally leverage these relationships to draw inferences ("what"). However, can Video Large Language Models (Video-LLMs) also "reason through a sequential spatio-temporal logic" in videos? Existing Video-LLM benchmarks primarily focus on assessing object presence, neglecting relational reasoning. Consequently, it is difficult to measure whether a model truly comprehends object interactions (actions/events) in videos or merely relies on pre-trained "memory" of co-occurrences as biases in generating answers. In this work, we introduce a Video Spatio-Temporal Reasoning (V-STaR) benchmark to address these shortcomings. The key idea is to decompose video understanding into a Reverse Spatio-Temporal Reasoning (RSTR) task that simultaneously evaluates what objects are present, when events occur, and where they are located while capturing the underlying Chain-of-thought (CoT) logic. To support this evaluation, we construct a dataset to elicit the spatial-temporal reasoning process of Video-LLMs. It contains coarse-to-fine CoT questions generated by a semi-automated GPT-4-powered pipeline, embedding explicit reasoning chains to mimic human cognition. Experiments from 14 Video-LLMs on our V-STaR reveal significant gaps between current Video-LLMs and the needs for robust and consistent spatio-temporal reasoning.
Cognitive Disentanglement for Referring Multi-Object Tracking
Venue: arXiv cs.CV 2025
Year: 2025

As a significant application of multi-source information fusion in intelligent transportation perception systems, Referring Multi-Object Tracking (RMOT) involves localizing and tracking specific objects in video sequences based on language references. However, existing RMOT approaches often treat language descriptions as holistic embeddings and struggle to effectively integrate the rich semantic information contained in language expressions with visual features. This limitation is especially apparent in complex scenes requiring comprehensive understanding of both static object attributes and spatial motion information. In this paper, we propose a Cognitive Disentanglement for Referring Multi-Object Tracking (CDRMT) framework that addresses these challenges. It adapts the "what" and "where" pathways from the human visual processing system to RMOT tasks. Specifically, our framework first establishes cross-modal connections while preserving modality-specific characteristics. It then disentangles language descriptions and hierarchically injects them into object queries, refining object understanding from coarse to fine-grained semantic levels. Finally, we reconstruct language representations based on visual features, ensuring that tracked objects faithfully reflect the referring expression. Extensive experiments on different benchmark datasets demonstrate that CDRMT achieves substantial improvements over state-of-the-art methods, with average gains of 6.0% in HOTA score on Refer-KITTI and 3.2% on Refer-KITTI-V2. Our approach advances the state-of-the-art in RMOT while simultaneously providing new insights into multi-source information fusion.
Exploring Typographic Visual Prompts Injection Threats in Cross-Modality Generation Models
Venue: arXiv cs.CV 2025
Year: 2025

Current Cross-Modality Generation Models (GMs) demonstrate remarkable capabilities in various generative tasks. Given the ubiquity and information richness of vision modality inputs in real-world scenarios, Cross-Vision tasks, encompassing Vision-Language Perception (VLP) and Image-to-Image (I2I), have attracted significant attention. Large Vision Language Models (LVLMs) and I2I Generation Models (GMs) are employed to handle VLP and I2I tasks, respectively. Previous research indicates that printing typographic words into input images significantly induces LVLMs and I2I GMs to produce disruptive outputs that are semantically aligned with those words. Additionally, visual prompts, as a more sophisticated form of typography, are also revealed to pose security risks to various applications of cross-vision tasks. However, the specific characteristics of the threats posed by visual prompts remain underexplored. In this paper, to comprehensively investigate the performance impact induced by Typographic Visual Prompt Injection (TVPI) in various LVLMs and I2I GMs, we propose the Typographic Visual Prompts Injection Dataset and thoroughly evaluate the TVPI security risks on various open-source and closed-source LVLMs and I2I GMs under visual prompts with different target semantics, deepening the understanding of TVPI threats.
Multi-robot coordination for connectivity recovery after unpredictable environment changes
Venue: arXiv cs.RO 2025
Year: 2025

In the present paper we develop a distributed method to reconnect a multi-robot team after connectivity failures, caused by unpredictable environment changes, i.e. appearance of new obstacles. After the changes, the team is divided into different groups of robots. The groups have a limited communication range and only a partial information in their field of view about the current scenario. Their objective is to form a chain from a static base station to a goal location. In the proposed distributed replanning approach, the robots predict new plans for the other groups from the new observed information by each robot in the changed scenario, to restore the connectivity with a base station and reach the initial joint objective. If a solution exists, the method achieves the reconnection of all the groups in a unique chain. The proposed method is compared with other two cases: 1) when all the agents have full information of the environment, and 2) when some robots must move to reach other waiting robots for reconnection. Numerical simulations are provided to evaluate the proposed approach in the presence of unpredictable scenario changes.
FLASHμ: Fast Localizing And Sizing of Holographic Microparticles
Venue: arXiv cs.CV 2025
Year: 2025

Reconstructing the 3D location and size of microparticles from diffraction images - holograms - is a computationally expensive inverse problem that has traditionally been solved using physics-based reconstruction methods. More recently, researchers have used machine learning methods to speed up the process. However, for small particles in large sample volumes the performance of these methods falls short of standard physics-based reconstruction methods. Here we designed a two-stage neural network architecture, FLASH$μ$, to detect small particles (6-100$μ$m) from holograms with large sample depths up to 20cm. Trained only on synthetic data with added physical noise, our method reliably detects particles of at least 9$μ$m diameter in real holograms, comparable to the standard reconstruction-based approaches while operating on smaller crops, at quarter of the original resolution and providing roughly a 600-fold speedup. In addition to introducing a novel approach to a non-local object detection or signal demixing problem, our work could enable low-cost, real-time holographic imaging setups.
VERIFY: A Benchmark of Visual Explanation and Reasoning for Investigating Multimodal Reasoning Fidelity
Venue: arXiv cs.CV 2025
Year: 2025

Visual reasoning is central to human cognition, enabling individuals to interpret and abstractly understand their environment. Although recent Multimodal Large Language Models (MLLMs) have demonstrated impressive performance across language and vision-language tasks, existing benchmarks primarily measure recognition-based skills and inadequately assess true visual reasoning capabilities. To bridge this critical gap, we introduce VERIFY, a benchmark explicitly designed to isolate and rigorously evaluate the visual reasoning capabilities of state-of-the-art MLLMs. VERIFY compels models to reason primarily from visual information, providing minimal textual context to reduce reliance on domain-specific knowledge and linguistic biases. Each problem is accompanied by a human-annotated reasoning path, making it the first to provide in-depth evaluation of model decision-making processes. Additionally, we propose novel metrics that assess visual reasoning fidelity beyond mere accuracy, highlighting critical imbalances in current model reasoning patterns. Our comprehensive benchmarking of leading MLLMs uncovers significant limitations, underscoring the need for a balanced and holistic approach to both perception and reasoning. For more teaser and testing, visit our project page (https://verify-eqh.pages.dev/).
Experimental evaluation of xApp Conflict Mitigation Framework in O-RAN: Insights from Testbed deployment in OTIC
Venue: arXiv cs.NI 2025
Year: 2025

Conflict Mitigation (CM) in Open Radio Access Network (O-RAN) is a topic that is gaining importance as commercial O-RAN deployments become more complex. Although research on CM is already covered in terms of simulated network scenarios, it lacks validation using real-world deployment and Over The Air (OTA) Radio Frequency (RF) transmission. Our objective is to conduct the first assessment of the Conflict Mitigation Framework (CMF) for O-RAN using a real-world testbed and OTA RF transmission. This paper presents results of an experiment using a dedicated testbed built in an O-RAN Open Test and Integration Center (OTIC) to confirm the validity of one of the Conflict Resolution (CR) schemes proposed by existing research. The results show that the implemented conflict detection and resolution mechanisms allow a significant improvement in network operation stability by reducing the variability of the measured Downlink (DL) throughput by 78%.
Implicit Bias-Like Patterns in Reasoning Models
Venue: arXiv cs.CY 2025
Year: 2025

Implicit biases refer to automatic mental processes that shape perceptions, judgments, and behaviors. Previous research on "implicit bias'' in LLMs focused primarily on outputs rather than the processes underlying the outputs. We present the Reasoning Model Implicit Association Test (RM-IAT) to study implicit bias-like processing in reasoning models, which are LLMs that use step-by-step reasoning for complex tasks. Using RM-IAT, we find that reasoning models like o3-mini, DeepSeek-R1, gpt-oss-20b, and Qwen-3 8B consistently expend more reasoning tokens on association-incompatible tasks than association-compatible tasks, suggesting greater computational effort when processing counter-stereotypical information. In contrast, Claude 3.7 Sonnet exhibited reversed or inconsistent patterns, likely due to embedded safety mechanisms that flagged or rejected socially sensitive associations. These divergent behaviors highlight important differences in how alignment and safety processes shape model reasoning. As reasoning models become increasingly integrated into real-world decision-making, understanding their implicit bias-like patterns and how alignment methods influence them is crucial for ensuring fair and trustworthy AI systems.
Advancing 3D Gaussian Splatting Editing with Complementary and Consensus Information
Venue: arXiv cs.CV 2025
Year: 2025

We present a novel framework for enhancing the visual fidelity and consistency of text-guided 3D Gaussian Splatting (3DGS) editing. Existing editing approaches face two critical challenges: inconsistent geometric reconstructions across multiple viewpoints, particularly in challenging camera positions, and ineffective utilization of depth information during image manipulation, resulting in over-texture artifacts and degraded object boundaries. To address these limitations, we introduce: 1) A complementary information mutual learning network that enhances depth map estimation from 3DGS, enabling precise depth-conditioned 3D editing while preserving geometric structures. 2) A wavelet consensus attention mechanism that effectively aligns latent codes during the diffusion denoising process, ensuring multi-view consistency in the edited results. Through extensive experimentation, our method demonstrates superior performance in rendering quality and view consistency compared to state-of-the-art approaches. The results validate our framework as an effective solution for text-guided editing of 3D scenes.
From Score Matching to Diffusion: A Fine-Grained Error Analysis in the Gaussian Setting
Venue: arXiv cs.LG 2025
Year: 2025

Sampling from an unknown distribution, accessible only through discrete samples, is a fundamental problem at the core of generative AI. The current state-of-the-art methods follow a two-step process: first, estimating the score function (the gradient of a smoothed log-distribution) and then applying a diffusion-based sampling algorithm -- such as Langevin or Diffusion models. The resulting distribution's correctness can be impacted by four major factors: the generalization and optimization errors in score matching, and the discretization and minimal noise amplitude in the diffusion. In this paper, we make the sampling error explicit when using a diffusion sampler in the Gaussian setting. We provide a sharp analysis of the Wasserstein sampling error that arises from these four error sources. This allows us to rigorously track how the anisotropy of the data distribution (encoded by its power spectrum) interacts with key parameters of the end-to-end sampling method, including the number of initial samples, the stepsizes in both score matching and diffusion, and the noise amplitude. Notably, we show that the Wasserstein sampling error can be expressed as a kernel-type norm of the data power spectrum, where the specific kernel depends on the method parameters. This result provides a foundation for further analysis of the tradeoffs involved in optimizing sampling accuracy.
Tit-for-Tat: Safeguarding Large Vision-Language Models Against Jailbreak Attacks via Adversarial Defense
Venue: arXiv cs.CR 2025
Year: 2025

Deploying large vision-language models (LVLMs) introduces a unique vulnerability: susceptibility to malicious attacks via visual inputs. However, existing defense methods suffer from two key limitations: (1) They solely focus on textual defenses, fail to directly address threats in the visual domain where attacks originate, and (2) the additional processing steps often incur significant computational overhead or compromise model performance on benign tasks. Building on these insights, we propose ESIII (Embedding Security Instructions Into Images), a novel methodology for transforming the visual space from a source of vulnerability into an active defense mechanism. Initially, we embed security instructions into defensive images through gradient-based optimization, obtaining security instructions in the visual dimension. Subsequently, we integrate security instructions from visual and textual dimensions with the input query. The collaboration between security instructions from different dimensions ensures comprehensive security protection. Extensive experiments demonstrate that our approach effectively fortifies the robustness of LVLMs against such attacks while preserving their performance on standard benign tasks and incurring an imperceptible increase in time costs.
TRUTH DECAY: Quantifying Multi-Turn Sycophancy in Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Rapid improvements in large language models have unveiled a critical challenge in human-AI interaction: sycophancy. In this context, sycophancy refers to the tendency of models to excessively agree with or flatter users, often at the expense of factual accuracy. While previous studies have primarily analyzed this behavior in single-turn interactions, its persistence and evolution in multi-step conversations remain largely unexplored. We introduce TRUTH DECAY, a benchmark specifically designed to evaluate sycophancy in extended dialogues, where language models must navigate iterative user feedback, challenges, and persuasion. We prompt models to elicit four types of sycophantic biases. We then propose and test sycophancy reduction strategies, evaluating their effectiveness beyond single-step interactions.
Circuit Diagram Retrieval Based on Hierarchical Circuit Graph Representation
Venue: arXiv cs.AR 2025
Year: 2025

In the domain of analog circuit design, the retrieval of circuit diagrams has drawn a great interest, primarily due to its vital role in the consultation of legacy designs and the detection of design plagiarism. Existing image retrieval techniques are adept at handling natural images, which converts images into feature vectors and retrieval similar images according to the closeness of these vectors. Nonetheless, these approaches exhibit limitations when applied to the more specialized and intricate domain of circuit diagrams. This paper presents a novel approach to circuit diagram retrieval by employing a graph representation of circuit diagrams, effectively reformulating the retrieval task as a graph retrieval problem. The proposed methodology consists of two principal components: a circuit diagram recognition algorithm designed to extract the circuit components and topological structure of the circuit using proposed GAM-YOLO model and a 2-step connected domain filtering algorithm, and a hierarchical retrieval strategy based on graph similarity and different graph representation methods for analog circuits. Our methodology pioneers the utilization of graph representation in the retrieval of circuit diagrams, incorporating topological features that are commonly overlooked by standard image retrieval methods. The results of our experiments substantiate the efficacy of our approach in retrieving circuit diagrams across of different types.
Lorecast: Layout-Aware Performance and Power Forecasting from Natural Language
Venue: arXiv cs.AR 2025
Year: 2025

In chip design planning, obtaining reliable performance and power forecasts for various design options is of critical importance. Traditionally, this involves using system-level models, which often lack accuracy, or trial synthesis, which is both labor-intensive and time-consuming. We introduce a new methodology, called Lorecast, which accepts English prompts as input to rapidly generate layout-aware performance and power estimates. This approach bypasses the need for HDL code development and synthesis, making it both fast and user-friendly. Experimental results demonstrate that Lorecast achieves accuracy within a few percent of error compared to post-layout analysis, while significantly reducing turnaround time.
An LLM-Based Approach for Insight Generation in Data Analysis
Venue: arXiv cs.AI 2025
Year: 2025

Generating insightful and actionable information from databases is critical in data analysis. This paper introduces a novel approach using Large Language Models (LLMs) to automatically generate textual insights. Given a multi-table database as input, our method leverages LLMs to produce concise, text-based insights that reflect interesting patterns in the tables. Our framework includes a Hypothesis Generator to formulate domain-relevant questions, a Query Agent to answer such questions by generating SQL queries against a database, and a Summarization module to verbalize the insights. The insights are evaluated for both correctness and subjective insightfulness using a hybrid model of human judgment and automated metrics. Experimental results on public and enterprise databases demonstrate that our approach generates more insightful insights than other approaches while maintaining correctness.
Towards Efficient Flash Caches with Emerging NVMe Flexible Data Placement SSDs
Venue: arXiv cs.AR 2025
Year: 2025

NVMe Flash-based SSDs are widely deployed in data centers to cache working sets of large-scale web services. As data centers face increasing sustainability demands, such as reduced carbon emissions, efficient management of Flash overprovisioning and endurance has become crucial. Our analysis demonstrates that mixing data with different lifetimes on Flash blocks results in high device garbage collection costs, which either reduce device lifetime or necessitate host overprovisioning. Targeted data placement on Flash to minimize data intermixing and thus device write amplification shows promise for addressing this issue. The NVMe Flexible Data Placement (FDP) proposal is a newly ratified technical proposal aimed at addressing data placement needs while reducing the software engineering costs associated with past storage interfaces, such as ZNS and Open-Channel SSDs. In this study, we explore the feasibility, benefits, and limitations of leveraging NVMe FDP primitives for data placement on Flash media in CacheLib, a popular open-source Flash cache widely deployed and used in Meta's software ecosystem as a caching building block. We demonstrate that targeted data placement in CacheLib using NVMe FDP SSDs helps reduce device write amplification, embodied carbon emissions, and power consumption with almost no overhead to other metrics. Using multiple production traces and their configurations from Meta and Twitter, we show that an ideal device write amplification of ~1 can be achieved with FDP, leading to improved SSD utilization and sustainable Flash cache deployments.
Direct-Write Printed Contacts to Layered and 2D Materials
Venue: arXiv cs.ET 2025
Year: 2025

Advancements in fabrication methods have shaped new computing device technologies. Among these methods, depositing electrical contacts to the channel material is fundamental to device characterization. Novel layered and two-dimensional (2D) materials are promising for next-generation computing electronic channel materials. Direct-write printing of conductive inks is introduced as a surprisingly effective, significantly faster, and cleaner method to contact different classes of layered materials, including graphene (semi-metal), MoS2 (semiconductor), Bi-2212 (superconductor), and Fe5GeTe2 (metallic ferromagnet). Based on the electrical response, the quality of the printed contacts is comparable to what is achievable with resist-based lithography techniques. These devices are tested by sweeping gate voltage, temperature, and magnetic field to show that the materials remain pristine post-processing. This work demonstrates that direct-write printing is an agile method for prototyping and characterizing the electrical properties of novel layered materials.
A Survey of Direct Preference Optimization
Venue: arXiv cs.LG 2025
Year: 2025

Large Language Models (LLMs) have demonstrated unprecedented generative capabilities, yet their alignment with human values remains critical for ensuring helpful and harmless deployments. While Reinforcement Learning from Human Feedback (RLHF) has emerged as a powerful paradigm for aligning LLMs with human preferences, its reliance on complex reward modeling introduces inherent trade-offs in computational efficiency and training stability. In this context, Direct Preference Optimization (DPO) has recently gained prominence as a streamlined alternative that directly optimizes LLMs using human preferences, thereby circumventing the need for explicit reward modeling. Owing to its theoretical elegance and computational efficiency, DPO has rapidly attracted substantial research efforts exploring its various implementations and applications. However, this field currently lacks systematic organization and comparative analysis. In this survey, we conduct a comprehensive overview of DPO and introduce a novel taxonomy, categorizing previous works into four key dimensions: data strategy, learning framework, constraint mechanism, and model property. We further present a rigorous empirical analysis of DPO variants across standardized benchmarks. Additionally, we discuss real-world applications, open challenges, and future directions for DPO. This work delivers both a conceptual framework for understanding DPO and practical guidance for practitioners, aiming to advance robust and generalizable alignment paradigms. All collected resources are available and will be continuously updated at https://github.com/liushunyu/awesome-direct-preference-optimization.