OpenRaiser/Intern-Atlas huggingface dataset:
Mosaic: Client-driven Account Allocation Framework in Sharded Blockchains
Venue: arXiv cs.DC 2025
Year: 2025

Recent account allocation studies in sharded blockchains are typically miner-driven, requiring miners to perform global optimizations for all accounts to enhance system-wide performance. This forces each miner to maintain a complete copy of the entire ledger, resulting in significant storage, communication, and computation overhead. In this work, we explore an alternative research direction by proposing Mosaic, the first client-driven framework for distributed, lightweight local optimization. Rather than relying on miners to allocate all accounts, Mosaic enables clients to independently execute a local algorithm to determine their residing shards. Clients can submit migration requests to a beacon chain when relocation is necessary. Mosaic naturally addresses key limitations of miner-driven approaches, including the lack of miner incentives and the significant overhead. While clients are flexible to adopt any algorithm for shard allocation, we design and implement a reference algorithm, Pilot, to guide them. Clients execute Pilot to maximize their own benefits, such as reduced transaction fees and confirmation latency. On a real-world Ethereum dataset, we implement and evaluate Pilot against state-of-the-art miner-driven global optimization solutions. The results demonstrate that Mosaic significantly enhances computational efficiency, achieving a four-order-of-magnitude reduction in computation time, with the reduced input data size from 1.44 GB to an average of 228.66 bytes per account. Despite these efficiency gains, Pilot introduces only about a 5% increase in the cross-shard ratio and maintains approximately 98% of the system throughput, demonstrating a minimal trade-off in overall effectiveness.
How to Enhance Downstream Adversarial Robustness (almost) without Touching the Pre-Trained Foundation Model?
Venue: arXiv cs.LG 2025
Year: 2025

With the rise of powerful foundation models, a pre-training-fine-tuning paradigm becomes increasingly popular these days: A foundation model is pre-trained using a huge amount of data from various sources, and then the downstream users only need to fine-tune and adapt it to specific downstream tasks. However, due to the high computation complexity of adversarial training, it is not feasible to fine-tune the foundation model to improve its robustness on the downstream task. Observing the above challenge, we want to improve the downstream robustness without updating/accessing the weights in the foundation model. Inspired from existing literature in robustness inheritance (Kim et al., 2020), through theoretical investigation, we identify a close relationship between robust contrastive learning with the adversarial robustness of supervised learning. To further validate and utilize this theoretical insight, we design a simple-yet-effective robust auto-encoder as a data pre-processing method before feeding the data into the foundation model. The proposed approach has zero access to the foundation model when training the robust auto-encoder. Extensive experiments demonstrate the effectiveness of the proposed method in improving the robustness of downstream tasks, verifying the connection between the feature robustness (implied by small adversarial contrastive loss) and the robustness of the downstream task.
Ai2 Scholar QA: Organized Literature Synthesis with Attribution
Venue: arXiv cs.CL 2025
Year: 2025

Retrieval-augmented generation is increasingly effective in answering scientific questions from literature, but many state-of-the-art systems are expensive and closed-source. We introduce Ai2 Scholar QA, a free online scientific question answering application. To facilitate research, we make our entire pipeline public: as a customizable open-source Python package and interactive web app, along with paper indexes accessible through public APIs and downloadable datasets. We describe our system in detail and present experiments analyzing its key design decisions. In an evaluation on a recent scientific QA benchmark, we find that Ai2 Scholar QA outperforms competing systems.
Automata for the commutative closure of regular sets
Venue: arXiv cs.FL 2025
Year: 2025

Consider $ A^* $, the free monoid generated by the finite alphabet $A$ with the concatenation operation. Two words have the same commutative image when one is a permutation of the symbols of the other. The commutative closure of a set $ L \subseteq A^* $ is the set $ {C}(L) \subseteq A^* $ of words whose commutative image coincides with that of some word in $ L $. We provide an algorithm that, given a regular set $ L $, produces a finite state automaton that accepts the commutative closure $ {C}(L) $, provided that this closure set is regular. The problem of deciding whether $ {C}(L) $ is regular was solved by Ginsburg and Spanier in 1966 using the decidability of Presburger sentences, and by Gohon in 1985 via formal power series. The problem of constructing an automaton that accepts $ {C}(L) $ has already been studied in the literature. We give a simpler algorithm using an algebraic approach.
Understanding the theoretical properties of projected Bellman equation, linear Q-learning, and approximate value iteration
Venue: arXiv cs.AI 2025
Year: 2025

In this paper, we study the theoretical properties of the projected Bellman equation (PBE) and two algorithms to solve this equation: linear Q-learning and approximate value iteration (AVI). We consider two sufficient conditions for the existence of a solution to PBE : strictly negatively row dominating diagonal (SNRDD) assumption and a condition motivated by the convergence of AVI. The SNRDD assumption also ensures the convergence of linear Q-learning, and its relationship with the convergence of AVI is examined. Lastly, several interesting observations on the solution of PBE are provided when using $ε$-greedy policy.
Safe-Construct: Redefining Construction Safety Violation Recognition as 3D Multi-View Engagement Task
Venue: arXiv cs.CV 2025
Year: 2025

Recognizing safety violations in construction environments is critical yet remains underexplored in computer vision. Existing models predominantly rely on 2D object detection, which fails to capture the complexities of real-world violations due to: (i) an oversimplified task formulation treating violation recognition merely as object detection, (ii) inadequate validation under realistic conditions, (iii) absence of standardized baselines, and (iv) limited scalability from the unavailability of synthetic dataset generators for diverse construction scenarios. To address these challenges, we introduce Safe-Construct, the first framework that reformulates violation recognition as a 3D multi-view engagement task, leveraging scene-level worker-object context and 3D spatial understanding. We also propose the Synthetic Indoor Construction Site Generator (SICSG) to create diverse, scalable training data, overcoming data limitations. Safe-Construct achieves a 7.6% improvement over state-of-the-art methods across four violation types. We rigorously evaluate our approach in near-realistic settings, incorporating four violations, four workers, 14 objects, and challenging conditions like occlusions (worker-object, worker-worker) and variable illumination (back-lighting, overexposure, sunlight). By integrating 3D multi-view spatial understanding and synthetic data generation, Safe-Construct sets a new benchmark for scalable and robust safety monitoring in high-risk industries. Project Website: https://Safe-Construct.github.io/Safe-Construct
Fine-Grained Rib Fracture Diagnosis with Hyperbolic Embeddings: A Detailed Annotation Framework and Multi-Label Classification Model
Venue: arXiv cs.CV 2025
Year: 2025

Accurate rib fracture identification and classification are essential for treatment planning. However, existing datasets often lack fine-grained annotations, particularly regarding rib fracture characterization, type, and precise anatomical location on individual ribs. To address this, we introduce a novel rib fracture annotation protocol tailored for fracture classification. Further, we enhance fracture classification by leveraging cross-modal embeddings that bridge radiological images and clinical descriptions. Our approach employs hyperbolic embeddings to capture the hierarchical nature of fracture, mapping visual features and textual descriptions into a shared non-Euclidean manifold. This framework enables more nuanced similarity computations between imaging characteristics and clinical descriptions, accounting for the inherent hierarchical relationships in fracture taxonomy. Experimental results demonstrate that our approach outperforms existing methods across multiple classification tasks, with average recall improvements of 6% on the AirRib dataset and 17.5% on the public RibFrac dataset.
Full Cooperation in Repeated Multi-Player Games on Hypergraphs
Venue: arXiv cs.GT 2025
Year: 2025

Nearly all living systems, especially humans, depend on collective cooperation for survival and prosperity. However, the mechanisms driving the evolution of cooperative behavior remain poorly understood, particularly in the context of simultaneous interactions involving multiple individuals, repeated encounters, and complex interaction structures. Here, we introduce a novel framework for studying repeated multi-player interactions in structured populations -- repeated multi-player games on hypergraphs -- where multiple individuals within each hyperedge engage in a repeated game, and each player can simultaneously participate in many games. We focus on public goods games, where individuals differ in their initial endowments, their allocation of endowments across games, and their productivity, which determines the impact of their contributions. Through Nash equilibrium analysis, we reveal the intricate interplay between full cooperation (all individuals contribute their entire endowments, maximizing collective benefits) and key factors such as initial endowments, productivity, contribution strategies, and interaction structure. Notably, while equal endowments are most effective in promoting full cooperation in homogeneous hypergraphs, they can hinder cooperation in heterogeneous hypergraphs, suggesting that equal endowments are not universally optimal. To address this, we propose two optimization strategies: one for policymakers to adjust endowment distributions and another for players to modify their contribution strategies. Both approaches successfully promote full cooperation across all studied hypergraphs. Our findings provide novel insights into the emergence of full cooperation, offering valuable guidance for both players and policymakers in fostering collective cooperation.
Transfer Learning for Temporal Link Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Link prediction on graphs has applications spanning from recommender systems to drug discovery. Temporal link prediction (TLP) refers to predicting future links in a temporally evolving graph and adds additional complexity related to the dynamic nature of graphs. State-of-the-art TLP models incorporate memory modules alongside graph neural networks to learn both the temporal mechanisms of incoming nodes and the evolving graph topology. However, memory modules only store information about nodes seen at train time, and hence such models cannot be directly transferred to entirely new graphs at test time and deployment. In this work, we study a new transfer learning task for temporal link prediction, and develop transfer-effective methods for memory-laden models. Specifically, motivated by work showing the informativeness of structural signals for the TLP task, we augment a structural mapping module to the existing TLP model architectures, which learns a mapping from graph structural (topological) features to memory embeddings. Our work paves the way for a memory-free foundation model for TLP.
Towards Robust Trajectory Embedding for Similarity Computation: When Triangle Inequality Violations in Distance Metrics Matter
Venue: arXiv cs.DB 2025
Year: 2025

Trajectory similarity is a cornerstone of trajectory data management and analysis. Traditional similarity functions often suffer from high computational complexity and a reliance on specific distance metrics, prompting a shift towards deep representation learning in Euclidean space. However, existing Euclidean-based trajectory embeddings often face challenges due to the triangle inequality constraints that do not universally hold for trajectory data. To address this issue, this paper introduces a novel approach by incorporating non-Euclidean geometry, specifically hyperbolic space, into trajectory representation learning. We present the first-ever integration of hyperbolic space to resolve the inherent limitations of the triangle inequality in Euclidean embeddings. In particular, we achieve it by designing a Lorentz distance measure, which is proven to overcome triangle inequality constraints. Additionally, we design a model-agnostic framework LH-plugin to seamlessly integrate hyperbolic embeddings into existing representation learning pipelines. This includes a novel projection method optimized with the Cosh function to prevent the diminishment of distances, supported by a theoretical foundation. Furthermore, we propose a dynamic fusion distance that intelligently adapts to variations in triangle inequality constraints across different trajectory pairs, blending Lorentzian and Euclidean distances for more robust similarity calculations. Comprehensive experimental evaluations demonstrate that our approach effectively enhances the accuracy of trajectory similarity measures in state-of-the-art models across multiple real-world datasets. The LH-plugin not only addresses the triangle inequality issues but also significantly refines the precision of trajectory similarity computations, marking a substantial advancement in the field of trajectory representation learning.
Recognition of Geometrical Shapes by Dictionary Learning
Venue: arXiv cs.CV 2025
Year: 2025

Dictionary learning is a versatile method to produce an overcomplete set of vectors, called atoms, to represent a given input with only a few atoms. In the literature, it has been used primarily for tasks that explore its powerful representation capabilities, such as for image reconstruction. In this work, we present a first approach to make dictionary learning work for shape recognition, considering specifically geometrical shapes. As we demonstrate, the choice of the underlying optimization method has a significant impact on recognition quality. Experimental results confirm that dictionary learning may be an interesting method for shape recognition tasks.
ReZero: Enhancing LLM search ability by trying one-more-time
Venue: arXiv cs.CL 2025
Year: 2025

Retrieval-Augmented Generation (RAG) improves Large Language Model (LLM) performance on knowledge-intensive tasks but depends heavily on initial search query quality. Current methods, often using Reinforcement Learning (RL), typically focus on query formulation or reasoning over results, without explicitly encouraging persistence after a failed search. We introduce ReZero (Retry-Zero), a novel RL framework that directly rewards the act of retrying a search query following an initial unsuccessful attempt. This incentivizes the LLM to explore alternative queries rather than prematurely halting. ReZero demonstrates significant improvement, achieving 46.88% accuracy compared to a 25% baseline. By rewarding persistence, ReZero enhances LLM robustness in complex information-seeking scenarios where initial queries may prove insufficient.
Dopamine Audiobook: A Training-free MLLM Agent for Emotional and Immersive Audiobook Generation
Venue: arXiv cs.SD 2025
Year: 2025

Audiobook generation aims to create rich, immersive listening experiences from multimodal inputs, but current approaches face three critical challenges: (1) the lack of synergistic generation of diverse audio types (e.g., speech, sound effects, and music) with precise temporal and semantic alignment; (2) the difficulty in conveying expressive, fine-grained emotions, which often results in machine-like vocal outputs; and (3) the absence of automated evaluation frameworks that align with human preferences for complex and diverse audio. To address these issues, we propose Dopamine Audiobook, a novel unified training-free multi-agent system, where a multimodal large language model (MLLM) serves two specialized roles (i.e., speech designer and audio designer) for emotional, human-like, and immersive audiobook generation and evaluation. Specifically, we firstly propose a flow-based, context-aware framework for diverse audio generation with word-level semantic and temporal alignment. To enhance expressiveness, we then design word-level paralinguistic augmentation, utterance-level prosody retrieval, and adaptive TTS model selection. Finally, for evaluation, we introduce a novel MLLM-based evaluation framework incorporating self-critique, perspective-taking, and psychological MagicEmo prompts to ensure human-aligned and self-aligned assessments. Experimental results demonstrate that our method achieves state-of-the-art (SOTA) performance on multiple metrics. Importantly, our evaluation framework shows better alignment with human preferences and transferability across audio tasks.
3D Gabor Splatting: Reconstruction of High-frequency Surface Texture using Gabor Noise
Venue: arXiv cs.GR 2025
Year: 2025

3D Gaussian splatting has experienced explosive popularity in the past few years in the field of novel view synthesis. The lightweight and differentiable representation of the radiance field using the Gaussian enables rapid and high-quality reconstruction and fast rendering. However, reconstructing objects with high-frequency surface textures (e.g., fine stripes) requires many skinny Gaussian kernels because each Gaussian represents only one color if viewed from one direction. Thus, reconstructing the stripes pattern, for example, requires Gaussians for at least the number of stripes. We present 3D Gabor splatting, which augments the Gaussian kernel to represent spatially high-frequency signals using Gabor noise. The Gabor kernel is a combination of a Gaussian term and spatially fluctuating wave functions, making it suitable for representing spatial high-frequency texture. We demonstrate that our 3D Gabor splatting can reconstruct various high-frequency textures on the objects.
New Constructions of Binary Cyclic Codes with Both Relatively Large Minimum Distance and Dual Distance
Venue: arXiv cs.IT 2025
Year: 2025

Binary cyclic codes are worth studying due to their applications and theoretical importance. It is an important problem to construct an infinite family of cyclic codes with large minimum distance $d$ and dual distance $d^{\perp}$. In recent years, much research has been devoted to improving the lower bound on $d$, some of which have exceeded the square-root bound. The constructions presented recently seem to indicate that when the minimum distance increases, the minimum distance of its dual code decreases. In this paper, we focus on the new constructions of binary cyclic codes with length $n=2^m-1$, dimension near $n/2$, and both relatively large minimum distance and dual distance. For $m$ is even, we construct a family of binary cyclic codes with parameters $[2^m-1,2^{m-1}\pm1,d]$, where $d\ge 2^{m/2}-1$ and $d^\perp\ge2^{m/2}$. Both the minimum distance and the dual distance are significantly better than the previous results. When $m$ is the product of two distinct primes, we construct some cyclic codes with dimensions $k=(n+1)/2$ and $d>\frac{n}{\log_2n},$ where the lower bound on the minimum distance is much larger than the square-root bound. For $m$ is odd, we present two families of binary $[2^m-1,2^{m-1},d]$ cyclic codes with $d\ge2^{(m+1)/2}-1$, $d^\perp\ge2^{(m+1)/2}$ and $d\ge2^{(m+3)/2}-15$, $d^\perp\ge2^{(m-1)/2}$ respectively, which leads that $d\cdot d^\perp$ can reach $2n$ asymptotically. To the best of our knowledge, except for the punctured binary Reed-Muller codes, there is no other construction of binary cyclic codes that reaches this bound.
Heating reduction as collective action: Impact on attitudes, behavior and energy consumption in a Polish field experiment
Venue: arXiv cs.ET 2025
Year: 2025

Heating and hot water usage account for nearly 80% of household energy consumption in the European Union. In order to reach the EU New Deal goals, new policies to reduce heat energy consumption are indispensable. However, research targeting reductions concentrates either on technical building interventions without considerations of people's behavior, or psychological interventions with no technical interference. Such interventions can be promising, but their true potential for scaling up can only be realized by testing approaches that integrate behavioral and technical solutions in tandem rather than in isolation. In this research, we study a mix of psychological and technical interventions targeting heating and hot water demand among students in Polish university dormitories. We evaluate effects on building energy consumption, behavioral spillovers and on social beliefs and attitudes in a pre-post quasi-experimental mixed-method field study in three student dormitories. Our findings reveal that the most effective approaches to yield energy savings were a direct, collectively framed request to students to reduce thermostat settings for the environment, and an automated technical adjustment of the heating curve temperature. Conversely, interventions targeting domestic hot water had unintended effects, including increased energy use and negative spillovers, such as higher water consumption. Further, we find that informing students about their active, collective participation had a positive impact on perceived social norms. Our findings highlight the importance of trialing interventions in controlled real-world settings to understand the interplay between technical systems, behaviors, and social impacts to enable scalable, evidence-based policies driving an effective and sustainable energy transition.
"Even explanations will not help in trusting [this] fundamentally biased system": A Predictive Policing Case-Study
Venue: arXiv cs.HC 2025
Year: 2025

In today's society, where Artificial Intelligence (AI) has gained a vital role, concerns regarding user's trust have garnered significant attention. The use of AI systems in high-risk domains have often led users to either under-trust it, potentially causing inadequate reliance or over-trust it, resulting in over-compliance. Therefore, users must maintain an appropriate level of trust. Past research has indicated that explanations provided by AI systems can enhance user understanding of when to trust or not trust the system. However, the utility of presentation of different explanations forms still remains to be explored especially in high-risk domains. Therefore, this study explores the impact of different explanation types (text, visual, and hybrid) and user expertise (retired police officers and lay users) on establishing appropriate trust in AI-based predictive policing. While we observed that the hybrid form of explanations increased the subjective trust in AI for expert users, it did not led to better decision-making. Furthermore, no form of explanations helped build appropriate trust. The findings of our study emphasize the importance of re-evaluating the use of explanations to build [appropriate] trust in AI based systems especially when the system's use is questionable. Finally, we synthesize potential challenges and policy recommendations based on our results to design for appropriate trust in high-risk based AI-based systems.