OpenRaiser/Intern-Atlas huggingface dataset:
Efficient and Accurate Scene Text Recognition with Cascaded-Transformers
Venue: arXiv cs.CV 2025
Year: 2025

In recent years, vision transformers with text decoder have demonstrated remarkable performance on Scene Text Recognition (STR) due to their ability to capture long-range dependencies and contextual relationships with high learning capacity. However, the computational and memory demands of these models are significant, limiting their deployment in resource-constrained applications. To address this challenge, we propose an efficient and accurate STR system. Specifically, we focus on improving the efficiency of encoder models by introducing a cascaded-transformers structure. This structure progressively reduces the vision token size during the encoding step, effectively eliminating redundant tokens and reducing computational cost. Our experimental results confirm that our STR system achieves comparable performance to state-of-the-art baselines while substantially decreasing computational requirements. In particular, for large-models, the accuracy remains same, 92.77 to 92.68, while computational complexity is almost halved with our structure.
SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild
Venue: arXiv cs.LG 2025
Year: 2025

DeepSeek-R1 has shown that long chain-of-thought (CoT) reasoning can naturally emerge through a simple reinforcement learning (RL) framework with rule-based rewards, where the training may directly start from the base models-a paradigm referred to as zero RL training. Most recent efforts to reproduce zero RL training have primarily focused on the Qwen2.5 model series, which may not be representative as we find the base models already exhibit strong instruction-following and self-reflection abilities. In this work, we investigate zero RL training across 10 diverse base models, spanning different families and sizes including LLama3-8B, Mistral-7B/24B, DeepSeek-Math-7B, Qwen2.5-math-7B, and all Qwen2.5 models from 0.5B to 32B. Leveraging several key design strategies-such as adjusting format reward and controlling query difficulty-we achieve substantial improvements in both reasoning accuracy and response length across most settings. However, by carefully monitoring the training dynamics, we observe that different base models exhibit distinct patterns during training. For instance, the increased response length does not always correlate with the emergence of certain cognitive behaviors such as verification (i.e., the "aha moment"). Notably, we observe the "aha moment" for the first time in small models not from the Qwen family. We share the key designs that enable successful zero RL training, along with our findings and practices. To facilitate further research, we open-source the code, models, and analysis tools.
Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection
Venue: arXiv cs.CV 2025
Year: 2025

Semi-supervised object detection (SSOD) based on pseudo-labeling significantly reduces dependence on large labeled datasets by effectively leveraging both labeled and unlabeled data. However, real-world applications of SSOD often face critical challenges, including class imbalance, label noise, and labeling errors. We present an in-depth analysis of SSOD under real-world conditions, uncovering causes of suboptimal pseudo-labeling and key trade-offs between label quality and quantity. Based on our findings, we propose four building blocks that can be seamlessly integrated into an SSOD framework. Rare Class Collage (RCC): a data augmentation method that enhances the representation of rare classes by creating collages of rare objects. Rare Class Focus (RCF): a stratified batch sampling strategy that ensures a more balanced representation of all classes during training. Ground Truth Label Correction (GLC): a label refinement method that identifies and corrects false, missing, and noisy ground truth labels by leveraging the consistency of teacher model predictions. Pseudo-Label Selection (PLS): a selection method for removing low-quality pseudo-labeled images, guided by a novel metric estimating the missing detection rate while accounting for class rarity. We validate our methods through comprehensive experiments on autonomous driving datasets, resulting in up to 6% increase in SSOD performance. Overall, our investigation and novel, data-centric, and broadly applicable building blocks enable robust and effective SSOD in complex, real-world scenarios. Code is available at https://mos-ks.github.io/publications.
Multi-Physics Inverse Design of Varifocal Optical Devices using Data-Driven Surrogates and Differential Modeling
Venue: arXiv cs.CE 2025
Year: 2025

Designing a new varifocal architecture in AR glasses poses significant challenges due to the complex interplay of multiple physics disciplines, including innovated piezo-electric material, solid mechanics, electrostatics, and optics. Traditional design methods, which treat each physics separately, are insufficient for this problem as they fail to establish the intricate relationships among design parameters in such a large and sensitive space, leading to suboptimal solutions. To address this challenge, we propose a novel design pipeline, mPhDBBs (multi-Physics Differential Building Blocks), that integrates these diverse physics through a graph neural network-based surrogate model and a differentiable ray tracing model. A hybrid optimization method combining evolutionary and gradient approaches is employed to efficiently determine superior design variables that achieve desired optical objectives, such as focal length and focusing quality. Our results demonstrate the effectiveness of mPhDBBs, achieving high accuracy with minimal training data and computational resources, resulting in a speedup of at least 1000 times compared to non-gradient-based methods. This work offers a promising paradigm shift in product design, enabling rapid and accurate optimization of complex multi-physics systems, and demonstrates its adaptability to other inverse design problems.
Is there a future for AI without representation?
Venue: arXiv cs.AI 2025
Year: 2025

This paper investigates the prospects of AI without representation in general, and the proposals of Rodney Brooks in particular. What turns out to be characteristic of Brooks' proposal is the rejection of central control in intelligent agents; his systems has as much or as little representation as traditional AI. The traditional view that representation is necessary for intelligence presupposes that intelligence requires central control. However, much of recent cognitive science suggests that we should dispose of the image of intelligent agents as central representation processors. If this paradigm shift is achieved, Brooks' proposal for non-centralized cognition without representation appears promising for full-blown intelligent agents - though not for conscious agents and thus not for human-like AI.
Confronting Catastrophic Risk: The International Obligation to Regulate Artificial Intelligence
Venue: arXiv cs.CY 2025
Year: 2025

While artificial intelligence (AI) holds enormous promise, many experts in the field are warning that there is a non-trivial chance that the development of AI poses an existential threat to humanity. Existing regulatory initiative do not address this threat but merely instead focus on discrete AI-related risks such as consumer safety, cybersecurity, data protection, and privacy. In the absence of regulatory action to address the possible risk of human extinction by AI, the question arises: What legal obligations, if any, does public international law impose on states to regulate its development. Grounded in the precautionary principle, we argue that there exists an international obligation to mitigate the threat of human extinction by AI. Often invoked in relation to environmental regulation and the regulation of potentially harmful technologies, the principle holds that in situations where there is the potential for significant harm, even in the absence of full scientific certainty, preventive measures should not be postponed if delayed action may result in irreversible consequences. We argue that the precautionary principle is a general principle of international law and, therefore, that there is a positive obligation on states under the right to life within international human rights law to proactively take regulatory action to mitigate the potential existential risk of AI. This is significant because, if an international obligation to regulate the development of AI can be established under international law, then the basic legal framework would be in place to address this evolving threat.
A Physical and Mathematical Framework for the Semantic Theory of Evolution
Venue: arXiv cs.AI 2025
Year: 2025

The Semantic Theory of Evolution (STE) takes the existence of a number of arbitrary communication codes as a fundamental feature of life, from the genetic code to human cultural communication codes. Their arbitrariness enables, at each level, the selection of one out of several possible correspondences along with the generation of meaning. STE enables more novelties to emerge and suggests a greater variety of potential life forms. With this paper I ground STE on physical theories of meaningful information. Furthermore, I show that key features of the arbitrary communication codes employed by living organisms can be expressed by means of Evidence Theory (ET). In particular, I adapt ET to organisms that merely react to sequences of stimuli, explain its basics for organisms that are capable of prediction, and illustrate an unconventional version suitable for the most intricate communication codes employed by humans. Finally, I express the natural trend towards ambiguity reduction in terms of information entropy minimization along with thermodynamic entropy maximization.
Mist: Efficient Distributed Training of Large Language Models via Memory-Parallelism Co-Optimization
Venue: arXiv cs.DC 2025
Year: 2025

Various parallelism, such as data, tensor, and pipeline parallelism, along with memory optimizations like activation checkpointing, redundancy elimination, and offloading, have been proposed to accelerate distributed training for Large Language Models. To find the best combination of these techniques, automatic distributed training systems are proposed. However, existing systems only tune a subset of optimizations, due to the lack of overlap awareness, inability to navigate the vast search space, and ignoring the inter-microbatch imbalance, leading to sub-optimal performance. To address these shortcomings, we propose Mist, a memory, overlap, and imbalance-aware automatic distributed training system that comprehensively co-optimizes all memory footprint reduction techniques alongside parallelism. Mist is based on three key ideas: (1) fine-grained overlap-centric scheduling, orchestrating optimizations in an overlapped manner, (2) symbolic-based performance analysis that predicts runtime and memory usage using symbolic expressions for fast tuning, and (3) imbalance-aware hierarchical tuning, decoupling the process into an inter-stage imbalance and overlap aware Mixed Integer Linear Programming problem and an intra-stage Dual-Objective Constrained Optimization problem, and connecting them through Pareto frontier sampling. Our evaluation results show that Mist achieves an average of 1.28$\times$ (up to 1.73$\times$) and 1.27$\times$ (up to 2.04$\times$) speedup compared to state-of-the-art manual system Megatron-LM and state-of-the-art automatic system Aceso, respectively.
Clustering data by reordering them
Venue: arXiv cs.CV 2025
Year: 2025

Grouping elements into families to analyse them separately is a standard analysis procedure in many areas of sciences. We propose herein a new algorithm based on the simple idea that members from a family look like each other, and don't resemble elements foreign to the family. After reordering the data according to the distance between elements, the analysis is automatically performed with easily-understandable parameters. Noise is explicitly taken into account to deal with the variety of problems of a data-driven world. We applied the algorithm to sort biomolecules conformations, gene sequences, cells, images, and experimental conditions.
HingeRLC-GAN: Combating Mode Collapse with Hinge Loss and RLC Regularization
Venue: arXiv cs.LG 2025
Year: 2025

Recent advances in Generative Adversarial Networks (GANs) have demonstrated their capability for producing high-quality images. However, a significant challenge remains mode collapse, which occurs when the generator produces a limited number of data patterns that do not reflect the diversity of the training dataset. This study addresses this issue by proposing a number of architectural changes aimed at increasing the diversity and stability of GAN models. We start by improving the loss function with Wasserstein loss and Gradient Penalty to better capture the full range of data variations. We also investigate various network architectures and conclude that ResNet significantly contributes to increased diversity. Building on these findings, we introduce HingeRLC-GAN, a novel approach that combines RLC Regularization and the Hinge loss function. With a FID Score of 18 and a KID Score of 0.001, our approach outperforms existing methods by effectively balancing training stability and increased diversity.
Paving the way for scientific foundation models: enhancing generalization and robustness in PDEs with constraint-aware pre-training
Venue: arXiv cs.LG 2025
Year: 2025

Partial differential equations (PDEs) govern a wide range of physical systems, but solving them efficiently remains a major challenge. The idea of a scientific foundation model (SciFM) is emerging as a promising tool for learning transferable representations across diverse domains. However, SciFMs require large amounts of solution data, which may be scarce or computationally expensive to generate. To maximize generalization while reducing data dependence, we propose incorporating PDE residuals into pre-training either as the sole learning signal or in combination with data loss to compensate for limited or infeasible training data. We evaluate this constraint-aware pre-training across three key benchmarks: (i) generalization to new physics, where material properties, e.g., the diffusion coefficient, is shifted with respect to the training distribution; (ii) generalization to entirely new PDEs, requiring adaptation to different operators; and (iii) robustness against noisy fine-tuning data, ensuring stability in real-world applications. Our results show that pre-training with PDE constraints significantly enhances generalization, outperforming models trained solely on solution data across all benchmarks. These findings prove the effectiveness of our proposed constraint-aware pre-training as a crucial component for SciFMs, providing a scalable approach to data-efficient, generalizable PDE solvers.
Simplifier: A New Tool for Boolean Circuit Simplification
Venue: arXiv cs.LO 2025
Year: 2025

The Boolean circuit simplification problem involves finding a smaller circuit that computes the same function as a given Boolean circuit. This problem is closely related to several key areas with both theoretical and practical applications, such as logic synthesis, satisfiability, and verification. In this paper, we present Simplifier, a new open source tool for simplifying Boolean circuits. The tool optimizes subcircuits with three inputs and at most three outputs, seeking to improve each one. It is designed as a low-effort method that runs in just a few seconds for circuits of reasonable size. This efficiency is achieved by combining two key strategies. First, the tool utilizes a precomputed database of optimized circuits, generated with SAT solvers after carefully clustering Boolean functions with three inputs and up to three outputs. Second, we demonstrate that it is sufficient to check a linear number of subcircuits, relative to the size of the original circuit. This allows a single iteration of the tool to be executed in linear time. We evaluated the tool on a wide range of Boolean circuits, including both industrial and hand-crafted examples, in two popular formats: AIG and BENCH. For AIG circuits, after applying the state-of-the-art ABC framework, our tool achieved an additional 4% average reduction in size. For BENCH circuits, the tool reduced their size by an average of 30%.
Understanding and Improving Information Preservation in Prompt Compression for LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Recent advancements in large language models (LLMs) have enabled their successful application to a broad range of tasks. However, in information-intensive tasks, the prompt length can grow fast, leading to increased computational requirements, performance degradation, and induced biases from irrelevant or redundant information. Recently, various prompt compression techniques have been introduced to optimize the trade-off between reducing input length and retaining performance. We propose a holistic evaluation framework that allows for in-depth analysis of prompt compression methods. We focus on three key aspects, besides compression ratio: (i) downstream task performance, (ii) grounding in the input context, and (iii) information preservation. Using our framework, we analyze state-of-the-art soft and hard compression methods and show that some fail to preserve key details from the original prompt, limiting performance on complex tasks. By identifying these limitations, we are able to improve one soft prompting method by controlling compression granularity, achieving up to +23% in downstream performance, +8 BERTScore points in grounding, and 2.7x more entities preserved in compression. Ultimately, we find that the best effectiveness/compression rate trade-off is achieved with soft prompting combined with sequence-level training.The code is available at https://github.com/amazon-science/information-preservation-in-prompt-compression.
Cooperative Control of Multi-Quadrotors for Transporting Cable-Suspended Payloads: Obstacle-Aware Planning and Event-Based Nonlinear Model Predictive Control
Venue: arXiv cs.RO 2025
Year: 2025

This paper introduces a novel methodology for the cooperative control of multiple quadrotors transporting cablesuspended payloads, emphasizing obstacle-aware planning and event-based Nonlinear Model Predictive Control (NMPC). Our approach integrates trajectory planning with real-time control through a combination of the A* algorithm for global path planning and NMPC for local control, enhancing trajectory adaptability and obstacle avoidance. We propose an advanced event-triggered control system that updates based on events identified through dynamically generated environmental maps. These maps are constructed using a dual-camera setup, which includes multi-camera systems for static obstacle detection and event cameras for high-resolution, low-latency detection of dynamic obstacles. This design is crucial for addressing fast-moving and transient obstacles that conventional cameras may overlook, particularly in environments with rapid motion and variable lighting conditions. When new obstacles are detected, the A* algorithm recalculates waypoints based on the updated map, ensuring safe and efficient navigation. This real-time obstacle detection and map updating integration allows the system to adaptively respond to environmental changes, markedly improving safety and navigation efficiency. The system employs SLAM and object detection techniques utilizing data from multi-cameras, event cameras, and IMUs for accurate localization and comprehensive environmental mapping. The NMPC framework adeptly manages the complex dynamics of multiple quadrotors and suspended payloads, incorporating safety constraints to maintain dynamic feasibility and stability. Extensive simulations validate the proposed approach, demonstrating significant enhancements in energy efficiency, computational resource management, and responsiveness.
On the Problem Characteristics of Multi-objective Pseudo-Boolean Functions in Runtime Analysis
Venue: arXiv cs.NE 2025
Year: 2025

Recently, there has been growing interest within the theoretical community in analytically studying multi-objective evolutionary algorithms. This runtime analysis-focused research can help formally understand algorithm behaviour, explain empirical observations, and provide theoretical insights to support algorithm development and exploration. However, the test problems commonly used in the theoretical analysis are predominantly limited to problems with heavy "artificial" characteristics (e.g., symmetric, homogeneous objectives and linear Pareto fronts), which may not be able to well represent realistic scenarios. In this paper, we first discuss commonly used multi-objective functions in the theory domain and systematically review their features, limitations and implications to practical use. Then, we present several new functions with more realistic features, such as heterogenous objectives, local optimality and nonlinearity of the Pareto front, through simply mixing and matching classical single-objective functions in the area (e.g., LeadingOnes, Jump and RoyalRoad). We hope these functions can enrich the existing test problem suites, and strengthen the connection between theoretic and practical research.
Mining--Gym: A Configurable RL Benchmarking Environment for Truck Dispatch Scheduling
Venue: arXiv cs.LG 2025
Year: 2025

Optimizing the mining process -- particularly truck dispatch scheduling -- is a key driver of efficiency in open-pit operations. However, the dynamic and stochastic nature of these environments, with uncertainties such as equipment failures, truck maintenance, and variable haul cycle times, challenges traditional optimization. While Reinforcement Learning (RL) shows strong potential for adaptive decision-making in mining logistics, practical deployment requires evaluation in realistic, customizable simulation environments. The lack of standardized benchmarking hampers fair algorithm comparison, reproducibility, and real-world applicability of RL solutions. To address this, we present Mining-Gym -- a configurable, open-source benchmarking environment for training, testing, and evaluating RL algorithms in mining process optimization. Built on Salabim-based Discrete Event Simulation (DES) and integrated with Gymnasium, Mining-Gym captures mining-specific uncertainties through an event-driven decision-point architecture. It offers a GUI for parameter configuration, data logging, and real-time visualization, supporting reproducible evaluation of RL strategies and heuristic baselines. We validate Mining-Gym by comparing classical heuristics with RL-based scheduling across six scenarios from normal operation to severe equipment failures. Results show it is an effective, reproducible testbed, enabling fair evaluation of adaptive decision-making and demonstrating the strong performance potential of RL-trained schedulers.
On Symmetries in Convolutional Weights
Venue: arXiv cs.CV 2025
Year: 2025

We explore the symmetry of the mean k x k weight kernel in each layer of various convolutional neural networks. Unlike individual neurons, the mean kernels in internal layers tend to be symmetric about their centers instead of favoring specific directions. We investigate why this symmetry emerges in various datasets and models, and how it is impacted by certain architectural choices. We show how symmetry correlates with desirable properties such as shift and flip consistency, and might constitute an inherent inductive bias in convolutional neural networks.