OpenRaiser/Intern-Atlas huggingface dataset:
Unified Flow Matching for Long Horizon Event Forecasting
Venue: arXiv cs.LG 2025
Year: 2025

Modeling long horizon marked event sequences is a fundamental challenge in many real-world applications, including healthcare, finance, and user behavior modeling. Existing neural temporal point process models are typically autoregressive, predicting the next event one step at a time, which limits their efficiency and leads to error accumulation in long-range forecasting. In this work, we propose a unified flow matching framework for marked temporal point processes that enables non-autoregressive, joint modeling of inter-event times and event types, via continuous and discrete flow matching. By learning continuous-time flows for both components, our method generates coherent long horizon event trajectories without sequential decoding. We evaluate our model on six real-world benchmarks and demonstrate significant improvements over autoregressive and diffusion-based baselines in both accuracy and generation efficiency.
A Refutation of Elmasry's $\tilde{O}(m \sqrt{n})$-Time Algorithm for Single-Source Shortest Paths
Venue: arXiv cs.DS 2025
Year: 2025

In this note we examine the recent paper "Breaking the Bellman-Ford Shortest-Path Bound" by Amr Elmasry, where he presents an algorithm for the single-source shortest path problem and claims that its running time complexity is $\tilde{O}(m\sqrt{n})$, where $n$ is the number of vertices and $m$ is the number of edges. We show that his analysis is incorrect, by providing an example of a weighted graph on which the running time of his algorithm is $Ω(mn)$.
Automated Bug Frame Retrieval from Gameplay Videos Using Vision-Language Models
Venue: arXiv cs.SE 2025
Year: 2025

Modern game studios deliver new builds and patches at a rapid pace, generating thousands of bug reports, many of which embed gameplay videos. To verify and triage these bug reports, developers must watch the submitted videos. This manual review is labour-intensive, slow, and hard to scale. In this paper, we introduce an automated pipeline that reduces each video to a single frame that best matches the reported bug description, giving developers instant visual evidence that pinpoints the bug. Our pipeline begins with FFmpeg for keyframe extraction, reducing each video to a median of just 1.90% of its original frames while still capturing bug moments in 98.79 of cases. These keyframes are then evaluated by a vision--language model (GPT-4o), which ranks them based on how well they match the textual bug description and selects the most representative frame. We evaluated this approach using real-world developer-submitted gameplay videos and JIRA bug reports from a popular First-Person Shooter (FPS) game. The pipeline achieves an overall F1 score of 0.79 and Accuracy of 0.89 for the top-1 retrieved frame. Performance is highest for the Lighting & Shadow (F1 = 0.94), Physics & Collision (0.86), and UI & HUD (0.83) bug categories, and lowest for Animation & VFX (0.51). By replacing video viewing with an immediately informative image, our approach dramatically reduces manual effort and speeds up triage and regression checks, offering practical benefits to quality assurance (QA) teams and developers across the game industry.
Revealing Temporal Label Noise in Multimodal Hateful Video Classification
Venue: arXiv cs.CV 2025
Year: 2025

The rapid proliferation of online multimedia content has intensified the spread of hate speech, presenting critical societal and regulatory challenges. While recent work has advanced multimodal hateful video detection, most approaches rely on coarse, video-level annotations that overlook the temporal granularity of hateful content. This introduces substantial label noise, as videos annotated as hateful often contain long non-hateful segments. In this paper, we investigate the impact of such label ambiguity through a fine-grained approach. Specifically, we trim hateful videos from the HateMM and MultiHateClip English datasets using annotated timestamps to isolate explicitly hateful segments. We then conduct an exploratory analysis of these trimmed segments to examine the distribution and characteristics of both hateful and non-hateful content. This analysis highlights the degree of semantic overlap and the confusion introduced by coarse, video-level annotations. Finally, controlled experiments demonstrated that time-stamp noise fundamentally alters model decision boundaries and weakens classification confidence, highlighting the inherent context dependency and temporal continuity of hate speech expression. Our findings provide new insights into the temporal dynamics of multimodal hateful videos and highlight the need for temporally aware models and benchmarks for improved robustness and interpretability. Code and data are available at https://github.com/Multimodal-Intelligence-Lab-MIL/HatefulVideoLabelNoise.
Test-Time Adaptation for Video Highlight Detection Using Meta-Auxiliary Learning and Cross-Modality Hallucinations
Venue: arXiv cs.CV 2025
Year: 2025

Existing video highlight detection methods, although advanced, struggle to generalize well to all test videos. These methods typically employ a generic highlight detection model for each test video, which is suboptimal as it fails to account for the unique characteristics and variations of individual test videos. Such fixed models do not adapt to the diverse content, styles, or audio and visual qualities present in new, unseen test videos, leading to reduced highlight detection performance. In this paper, we propose Highlight-TTA, a test-time adaptation framework for video highlight detection that addresses this limitation by dynamically adapting the model during testing to better align with the specific characteristics of each test video, thereby improving generalization and highlight detection performance. Highlight-TTA is jointly optimized with an auxiliary task, cross-modality hallucinations, alongside the primary highlight detection task. We utilize a meta-auxiliary training scheme to enable effective adaptation through the auxiliary task while enhancing the primary task. During testing, we adapt the trained model using the auxiliary task on the test video to further enhance its highlight detection performance. Extensive experiments with three state-of-the-art highlight detection models and three benchmark datasets show that the introduction of Highlight-TTA to these models improves their performance, yielding superior results.
Toward Errorless Training ImageNet-1k
Venue: arXiv cs.CV 2025
Year: 2025

In this paper, we describe a feedforward artificial neural network trained on the ImageNet 2012 contest dataset [7] with the new method of [5] to an accuracy rate of 98.3% with a 99.69 Top-1 rate, and an average of 285.9 labels that are perfectly classified over the 10 batch partitions of the dataset. The best performing model uses 322,430,160 parameters, with 4 decimal places precision. We conjecture that the reason our model does not achieve a 100% accuracy rate is due to a double-labeling problem, by which there are duplicate images in the dataset with different labels.
A Metric for MLLM Alignment in Large-scale Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

Multimodal recommendation has emerged as a critical technique in modern recommender systems, leveraging content representations from advanced multimodal large language models (MLLMs). To ensure these representations are well-adapted, alignment with the recommender system is essential. However, evaluating the alignment of MLLMs for recommendation presents significant challenges due to three key issues: (1) static benchmarks are inaccurate because of the dynamism in real-world applications, (2) evaluations with online system, while accurate, are prohibitively expensive at scale, and (3) conventional metrics fail to provide actionable insights when learned representations underperform. To address these challenges, we propose the Leakage Impact Score (LIS), a novel metric for multimodal recommendation. Rather than directly assessing MLLMs, LIS efficiently measures the upper bound of preference data. We also share practical insights on deploying MLLMs with LIS in real-world scenarios. Online A/B tests on both Content Feed and Display Ads of Xiaohongshu's Explore Feed production demonstrate the effectiveness of our proposed method, showing significant improvements in user spent time and advertiser value.
CSRAP: Enhanced Canvas Attention Scheduling for Real-Time Mission Critical Perception
Venue: arXiv cs.CV 2025
Year: 2025

Real-time perception on edge platforms faces a core challenge: executing high-resolution object detection under stringent latency constraints on limited computing resources. Canvas-based attention scheduling was proposed in earlier work as a mechanism to reduce the resource demands of perception subsystems. It consolidates areas of interest in an input data frame onto a smaller area, called a canvas frame, that can be processed at the requisite frame rate. This paper extends prior canvas-based attention scheduling literature by (i) allowing for variable-size canvas frames and (ii) employing selectable canvas frame rates that may depart from the original data frame rate. We evaluate our solution by running YOLOv11, as the perception module, on an NVIDIA Jetson Orin Nano to inspect video frames from the Waymo Open Dataset. Our results show that the additional degrees of freedom improve the attainable quality/cost trade-offs, thereby allowing for a consistently higher mean average precision (mAP) and recall with respect to the state of the art.
Optimal Planning for Multi-Robot Simultaneous Area and Line Coverage Using Hierarchical Cyclic Merging Regulation
Venue: arXiv cs.RO 2025
Year: 2025

The double coverage problem focuses on determining efficient, collision-free routes for multiple robots to simultaneously cover linear features (e.g., surface cracks or road routes) and survey areas (e.g., parking lots or local regions) in known environments. In these problems, each robot carries two functional roles: service (linear feature footprint coverage) and exploration (complete area coverage). Service has a smaller operational footprint but incurs higher costs (e.g., time) compared to exploration. We present optimal planning algorithms for the double coverage problems using hierarchical cyclic merging regulation (HCMR). To reduce the complexity for optimal planning solutions, we analyze the manifold attachment process during graph traversal from a Morse theory perspective. We show that solutions satisfying minimum path length and collision-free constraints must belong to a Morse-bounded collection. To identify this collection, we introduce the HCMR algorithm. In HCMR, cyclic merging search regulates traversal behavior, while edge sequence back propagation converts these regulations into graph edge traversal sequences. Incorporating balanced partitioning, the optimal sequence is selected to generate routes for each robot. We prove the optimality of the HCMR algorithm under a fixed sweep direction. The multi-robot simulation results demonstrate that the HCMR algorithm significantly improves planned path length by at least 10.0%, reduces task time by at least 16.9% in average, and ensures conflict-free operation compared to other state-of-the-art planning methods.
Situated Epistemic Infrastructures: A Diagnostic Framework for Post-Coherence Knowledge
Venue: arXiv cs.HC 2025
Year: 2025

Large Language Models (LLMs) such as ChatGPT have rendered visible the fragility of contemporary knowledge infrastructures by simulating coherence while bypassing traditional modes of citation, authority, and validation. This paper introduces the Situated Epistemic Infrastructures (SEI) framework as a diagnostic tool for analyzing how knowledge becomes authoritative across hybrid human-machine systems under post-coherence conditions. Rather than relying on stable scholarly domains or bounded communities of practice, SEI traces how credibility is mediated across institutional, computational, and temporal arrangements. Integrating insights from infrastructure studies, platform theory, and epistemology, the framework foregrounds coordination over classification, emphasizing the need for anticipatory and adaptive models of epistemic stewardship. The paper contributes to debates on AI governance, knowledge production, and the ethical design of information systems by offering a robust alternative to representationalist models of scholarly communication.
Disentangling Bias by Modeling Intra- and Inter-modal Causal Attention for Multimodal Sentiment Analysis
Venue: arXiv cs.LG 2025
Year: 2025

Multimodal sentiment analysis (MSA) aims to understand human emotions by integrating information from multiple modalities, such as text, audio, and visual data. However, existing methods often suffer from spurious correlations both within and across modalities, leading models to rely on statistical shortcuts rather than true causal relationships, thereby undermining generalization. To mitigate this issue, we propose a Multi-relational Multimodal Causal Intervention (MMCI) model, which leverages the backdoor adjustment from causal theory to address the confounding effects of such shortcuts. Specifically, we first model the multimodal inputs as a multi-relational graph to explicitly capture intra- and inter-modal dependencies. Then, we apply an attention mechanism to separately estimate and disentangle the causal features and shortcut features corresponding to these intra- and inter-modal relations. Finally, by applying the backdoor adjustment, we stratify the shortcut features and dynamically combine them with the causal features to encourage MMCI to produce stable predictions under distribution shifts. Extensive experiments on several standard MSA datasets and out-of-distribution (OOD) test sets demonstrate that our method effectively suppresses biases and improves performance.
The Docking Game: Loop Self-Play for Fast, Dynamic, and Accurate Prediction of Flexible Protein-Ligand Binding
Venue: arXiv cs.AI 2025
Year: 2025

Molecular docking is a crucial aspect of drug discovery, as it predicts the binding interactions between small-molecule ligands and protein pockets. However, current multi-task learning models for docking often show inferior performance in ligand docking compared to protein pocket docking. This disparity arises largely due to the distinct structural complexities of ligands and proteins. To address this issue, we propose a novel game-theoretic framework that models the protein-ligand interaction as a two-player game called the Docking Game, with the ligand docking module acting as the ligand player and the protein pocket docking module as the protein player. To solve this game, we develop a novel Loop Self-Play (LoopPlay) algorithm, which alternately trains these players through a two-level loop. In the outer loop, the players exchange predicted poses, allowing each to incorporate the other's structural predictions, which fosters mutual adaptation over multiple iterations. In the inner loop, each player dynamically refines its predictions by incorporating its own predicted ligand or pocket poses back into its model. We theoretically show the convergence of LoopPlay, ensuring stable optimization. Extensive experiments conducted on public benchmark datasets demonstrate that LoopPlay achieves approximately a 10\% improvement in predicting accurate binding modes compared to previous state-of-the-art methods. This highlights its potential to enhance the accuracy of molecular docking in drug discovery.
Making Prompts First-Class Citizens for Adaptive LLM Pipelines
Venue: arXiv cs.DB 2025
Year: 2025

Modern LLM pipelines increasingly resemble data-centric systems: they retrieve external context, compose intermediate outputs, validate results, and adapt based on runtime feedback. Yet, the central element guiding this process -- the prompt -- remains a brittle, opaque string, disconnected from the surrounding dataflow. This disconnect limits reuse, optimization, and runtime control. In this paper, we describe our vision and an initial design for SPEAR, a language and runtime that fills this prompt management gap by making prompts structured, adaptive, and first-class components of the execution model. SPEAR enables (1) runtime prompt refinement -- modifying prompts dynamically in response to execution-time signals such as confidence, latency, or missing context; and (2) structured prompt management -- organizing prompt fragments into versioned views with support for introspection and logging. SPEAR defines a prompt algebra that governs how prompts are constructed and adapted within a pipeline. It supports multiple refinement modes (manual, assisted, and automatic), giving developers a balance between control and automation. By treating prompt logic as structured data, SPEAR enables optimizations such as operator fusion, prefix caching, and view reuse. Preliminary experiments quantify the behavior of different refinement modes compared to static prompts and agentic retries, as well as the impact of prompt-level optimizations such as operator fusion.
Theseus: A Distributed and Scalable GPU-Accelerated Query Processing Platform Optimized for Efficient Data Movement
Venue: arXiv cs.DC 2025
Year: 2025

Online analytical processing of queries on datasets in the many-terabyte range is only possible with costly distributed computing systems. To decrease the cost and increase the throughput, systems can leverage accelerators such as GPUs, which are now ubiquitous in the compute infrastructure. This introduces many challenges, the majority of which are related to when, where, and how to best move data around the system. We present Theseus -- a production-ready enterprise-scale distributed accelerator-native query engine designed to balance data movement, memory utilization, and computation in an accelerator-based system context. Specialized asynchronous control mechanisms are tightly coupled to the hardware resources for the purpose of network communication, data pre-loading, data spilling across memories and storage, and GPU compute tasks. The memory subsystem contains a mechanism for fixed-size page-locked host memory allocations to increase throughput and reduce memory fragmentation. For the TPC-H benchmarks at scale factors ranging from 1k to 30k on cloud infrastructure, Theseus outperforms Databricks Photon by up to $4\times$ at cost parity. Theseus is capable of processing all queries of the TPC-H and TPC-DS benchmarks at scale factor 100k (100 TB scale) with as few as 2 DGX A100 640GB nodes.
An ML-based Approach to Predicting Software Change Dependencies: Insights from an Empirical Study on OpenStack
Venue: arXiv cs.SE 2025
Year: 2025

As software systems grow in complexity, accurately identifying and managing dependencies among changes becomes increasingly critical. For instance, a change that leverages a function must depend on the change that introduces it. Establishing such dependencies allows CI/CD pipelines to build and orchestrate changes effectively, preventing build failures and incomplete feature deployments. In modern software systems, dependencies often span multiple components across teams, creating challenges for development and deployment. They serve various purposes, from enabling new features to managing configurations, and can even involve traditionally independent changes like documentation updates. To address these challenges, we conducted a preliminary study on dependency management in OpenStack, a large-scale software system. Our study revealed that a substantial portion of software changes in OpenStack over the past 10 years are interdependent. Surprisingly, 51.08% of these dependencies are identified during the code review phase-after a median delay of 5.06 hours-rather than at the time of change creation. Developers often spend a median of 57.12 hours identifying dependencies, searching among a median of 463 other changes. To help developers proactively identify dependencies, we propose a semi-automated approach that leverages two ML models. The first model predicts the likelihood of dependencies among changes, while the second identifies the exact pairs of dependent changes. Our proposed models demonstrate strong performance, achieving average AUC scores of 79.33% and 91.89%, and Brier scores of 0.11 and 0.014, respectively. Indeed, the second model has a good top-k recall across all types of pairs, while the top-k precision has room for improvement.
Human-AI Schema Discovery and Application for Creative Problem Solving
Venue: arXiv cs.HC 2025
Year: 2025

Humans often rely on underlying structural patterns-schemas-to create, whether by writing stories, designing software, or composing music. Schemas help organize ideas and guide exploration, but they are often difficult to discover and apply, especially in complex or unfamiliar domains. My Ph.D. research develops a framework for human-AI schema discovery and application to support creative problem solving. I design systems that support users in sensemaking over examples to abstract schemas, and in operationalizing schemas into human-AI co-creative workflows for application. This research offers insights into how schema-guided interaction can make implicit knowledge more accessible and actionable, advancing more transparent and collaborative human-AI systems.
ULU: A Unified Activation Function
Venue: arXiv cs.LG 2025
Year: 2025

We propose \textbf{ULU}, a novel non-monotonic, piecewise activation function defined as $\{f(x;α_1),x<0; f(x;α_2),x>=0 \}$, where $f(x;α)=0.5x(tanh(αx)+1),α>0$. ULU treats positive and negative inputs differently. Extensive experiments demonstrate ULU significantly outperforms ReLU and Mish across image classification and object detection tasks. Its variant Adaptive ULU (\textbf{AULU}) is expressed as $\{f(x;β_1^2),x<0; f(x;β_2^2),x>=0 \}$, where $β_1$ and $β_2$ are learnable parameters, enabling it to adapt its response separately for positive and negative inputs. Additionally, we introduce the LIB (Like Inductive Bias) metric from AULU to quantitatively measure the inductive bias of the model.
A Desktop-Centric Design Space for Direct Object Examination and Visualization in Mixed-Reality Environments
Venue: arXiv cs.HC 2025
Year: 2025

Mixed reality (MR) environments are bound to become ubiquitous as MR technology becomes lighter, higher resolution, more affordable, and overall becomes a seamless extension of our current work and living spaces. For research scientists and clinicians focused on understanding 3D phenomena or patient pathologies within the context of the larger human anatomy, that means a necessary evolution of their workstations currently only utilizing 2D interfaces for everyday communication, logistics and data analysis. MR technologies bring forth immersive 3D representations coexisting in our natural spaces, while allowing for richer interconnected information displays, where 3D representations greatly aid in the detailed understanding of physical structures, spatial relationships, and 3D contextualization of 2D measurements, projections, abstractions, and other data details. We present a breakdown of the different interaction zones and modalities into a design space that best accommodates the creation of applications for users engaged through MR technologies in precise object-centric data analysis within the ergonomic confines of their desktop physical spaces.