OpenRaiser/Intern-Atlas huggingface dataset:
On Zero-Shot Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Modern reinforcement learning (RL) systems capture deep truths about general, human problem-solving. In domains where new data can be simulated cheaply, these systems uncover sequential decision-making policies that far exceed the ability of any human. Society faces many problems whose solutions require this skill, but they are often in domains where new data cannot be cheaply simulated. In such scenarios, we can learn simulators from existing data, but these will only ever be approximately correct, and can be pathologically incorrect when queried outside of their training distribution. As a result, a misalignment between the environments in which we train our agents and the real-world in which we wish to deploy our agents is inevitable. Dealing with this misalignment is the primary concern of zero-shot reinforcement learning, a problem setting where the agent must generalise to a new task or domain with zero practice shots. Whilst impressive progress has been made on methods that perform zero-shot RL in idealised settings, new work is needed if these results are to be replicated in real-world settings. In this thesis, we argue that doing so requires us to navigate (at least) three constraints. First, the data quality constraint: real-world datasets are small and homogeneous. Second, the observability constraint: states, dynamics and rewards in the real-world are often only partially observed. And third, the data availability constraint: a priori access to data cannot always be assumed. This work proposes a suite of methods that perform zero-shot RL subject to these constraints. In a series of empirical studies we expose the failings of existing methods, and justify our techniques for remedying them. We believe these designs take us a step closer to RL methods that can be deployed to solve real-world problems.
Terrain Classification for the Spot Quadrupedal Mobile Robot Using Only Proprioceptive Sensing
Venue: arXiv cs.RO 2025
Year: 2025

Quadrupedal mobile robots can traverse a wider range of terrain types than their wheeled counterparts but do not perform the same on all terrain types. These robots are prone to undesirable behaviours like sinking and slipping on challenging terrains. To combat this issue, we propose a terrain classifier that provides information on terrain type that can be used in robotic systems to create a traversability map to plan safer paths for the robot to navigate. The work presented here is a terrain classifier developed for a Boston Dynamics Spot robot. Spot provides over 100 measured proprioceptive signals describing the motions of the robot and its four legs (e.g., foot penetration, forces, joint angles, etc.). The developed terrain classifier combines dimensionality reduction techniques to extract relevant information from the signals and then applies a classification technique to differentiate terrain based on traversability. In representative field testing, the resulting terrain classifier was able to identify three different terrain types with an accuracy of approximately 97%
Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation
Venue: arXiv cs.CV 2025
Year: 2025

Recent advancements in video generation have substantially improved visual quality and temporal coherence, making these models increasingly appealing for applications such as autonomous driving, particularly in the context of driving simulation and so-called "world models". In this work, we investigate the effects of existing fine-tuning video generation approaches on structured driving datasets and uncover a potential trade-off: although visual fidelity improves, spatial accuracy in modeling dynamic elements may degrade. We attribute this degradation to a shift in the alignment between visual quality and dynamic understanding objectives. In datasets with diverse scene structures within temporal space, where objects or perspective shift in varied ways, these objectives tend to highly correlated. However, the very regular and repetitive nature of driving scenes allows visual quality to improve by modeling dominant scene motion patterns, without necessarily preserving fine-grained dynamic behavior. As a result, fine-tuning encourages the model to prioritize surface-level realism over dynamic accuracy. To further examine this phenomenon, we show that simple continual learning strategies, such as replay from diverse domains, can offer a balanced alternative by preserving spatial accuracy while maintaining strong visual quality.
Quality control in sublinear time: a case study via random graphs
Venue: arXiv cs.DS 2025
Year: 2025

Many algorithms are designed to work well on average over inputs. When running such an algorithm on an arbitrary input, we must ask: Can we trust the algorithm on this input? We identify a new class of algorithmic problems addressing this, which we call "Quality Control Problems." These problems are specified by a (positive, real-valued) "quality function" $ρ$ and a distribution $D$ such that, with high probability, a sample drawn from $D$ is "high quality," meaning its $ρ$-value is near $1$. The goal is to accept inputs $x \sim D$ and reject potentially adversarially generated inputs $x$ with $ρ(x)$ far from $1$. The objective of quality control is thus weaker than either component problem: testing for "$ρ(x) \approx 1$" or testing if $x \sim D$, and offers the possibility of more efficient algorithms. In this work, we consider the sublinear version of the quality control problem, where $D \in Δ(\{0,1\}^N)$ and the goal is to solve the $(D ,ρ)$-quality problem with $o(N)$ queries and time. As a case study, we consider random graphs, i.e., $D = G_{n,p}$ (and $N = \binom{n}2$), and the $k$-clique count function $ρ_k := C_k(G)/\mathbb{E}_{G' \sim G_{n,p}}[C_k(G')]$, where $C_k(G)$ is the number of $k$-cliques in $G$. Testing if $G \sim G_{n,p}$ with one sample, let alone with sublinear query access to the sample, is of course impossible. Testing if $ρ_k(G)\approx 1$ requires $p^{-Ω(k^2)}$ samples. In contrast, we show that the quality control problem for $G_{n,p}$ (with $n \geq p^{-ck}$ for some constant $c$) with respect to $ρ_k$ can be tested with $p^{-O(k)}$ queries and time, showing quality control is provably superpolynomially more efficient in this setting. More generally, for a motif $H$ of maximum degree $Δ(H)$, the respective quality control problem can be solved with $p^{-O(Δ(H))}$ queries and running time.
Using Generative AI to Uncover What Drives Player Enjoyment in PC and VR Games
Venue: arXiv cs.HC 2025
Year: 2025

As video games continue to evolve, understanding what drives player enjoyment remains a key challenge. Player reviews provide valuable insights, but their unstructured nature makes large-scale analysis difficult. This study applies generative AI and machine learning, leveraging Microsoft Phi-4 small language model (SLM) and Google Cloud, to quantify and analyze game reviews from Steam and Meta Quest stores. The approach converts qualitative feedback into structured data, enabling comprehensive evaluation of key game design elements, monetization models, and platform-specific trends. The findings reveal distinct patterns in player preferences across PC and VR games, highlighting factors that contribute to higher player enjoyment. By using Google Cloud for large-scale data storage and processing, this study establishes a scalable framework for game review analysis. The study's insights offer actionable guidance for game developers, helping optimize game mechanics, pricing strategies, and player engagement.
RAST: A Retrieval Augmented Spatio-Temporal Framework for Traffic Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Traffic prediction is a cornerstone of modern intelligent transportation systems and a critical task in spatio-temporal forecasting. Although advanced Spatio-temporal Graph Neural Networks (STGNNs) and pre-trained models have achieved significant progress in traffic prediction, two key challenges remain: (i) limited contextual capacity when modeling complex spatio-temporal dependencies, and (ii) low predictability at fine-grained spatio-temporal points due to heterogeneous patterns. Inspired by Retrieval-Augmented Generation (RAG), we propose RAST, a universal framework that integrates retrieval-augmented mechanisms with spatio-temporal modeling to address these challenges. Our framework consists of three key designs: 1) Decoupled Encoder and Query Generator to capture decoupled spatial and temporal features and construct a fusion query via residual fusion; 2) Spatio-temporal Retrieval Store and Retrievers to maintain and retrieve vectorized fine-grained patterns; and 3) Universal Backbone Predictor that flexibly accommodates pre-trained STGNNs or simple MLP predictors. Extensive experiments on six real-world traffic networks, including large-scale datasets, demonstrate that RAST achieves superior performance while maintaining computational efficiency.
Learn to Memorize: Optimizing LLM-based Agents with Adaptive Memory Framework
Venue: arXiv cs.LG 2025
Year: 2025

LLM-based agents have been extensively applied across various domains, where memory stands out as one of their most essential capabilities. Previous memory mechanisms of LLM-based agents are manually predefined by human experts, leading to higher labor costs and suboptimal performance. In addition, these methods overlook the memory cycle effect in interactive scenarios, which is critical to optimizing LLM-based agents for specific environments. To address these challenges, in this paper, we propose to optimize LLM-based agents with an adaptive and data-driven memory framework by modeling memory cycles. Specifically, we design an MoE gate function to facilitate memory retrieval, propose a learnable aggregation process to improve memory utilization, and develop task-specific reflection to adapt memory storage. Our memory framework empowers LLM-based agents to learn how to memorize information effectively in specific environments, with both off-policy and on-policy optimization. In order to evaluate the effectiveness of our proposed methods, we conduct comprehensive experiments across multiple aspects. To benefit the research community in this area, we release our project at https://github.com/nuster1128/learn_to_memorize.
Few-shot Class-incremental Fault Diagnosis by Preserving Class-Agnostic Knowledge with Dual-Granularity Representations
Venue: arXiv cs.LG 2025
Year: 2025

Few-Shot Class-Incremental Fault Diagnosis (FSC-FD), which aims to continuously learn from new fault classes with only a few samples without forgetting old ones, is critical for real-world industrial systems. However, this challenging task severely amplifies the issues of catastrophic forgetting of old knowledge and overfitting on scarce new data. To address these challenges, this paper proposes a novel framework built upon Dual-Granularity Representations, termed the Dual-Granularity Guidance Network (DGGN). Our DGGN explicitly decouples feature learning into two parallel streams: 1) a fine-grained representation stream, which utilizes a novel Multi-Order Interaction Aggregation module to capture discriminative, class-specific features from the limited new samples. 2) a coarse-grained representation stream, designed to model and preserve general, class-agnostic knowledge shared across all fault types. These two representations are dynamically fused by a multi-semantic cross-attention mechanism, where the stable coarse-grained knowledge guides the learning of fine-grained features, preventing overfitting and alleviating feature conflicts. To further mitigate catastrophic forgetting, we design a Boundary-Aware Exemplar Prioritization strategy. Moreover, a decoupled Balanced Random Forest classifier is employed to counter the decision boundary bias caused by data imbalance. Extensive experiments on the TEP benchmark and a real-world MFF dataset demonstrate that our proposed DGGN achieves superior diagnostic performance and stability compared to state-of-the-art FSC-FD approaches. Our code is publicly available at https://github.com/MentaY/DGGN
Empirical Analysis of the Effect of Context in the Task of Automated Essay Scoring in Transformer-Based Models
Venue: arXiv cs.CY 2025
Year: 2025

Automated Essay Scoring (AES) has emerged to prominence in response to the growing demand for educational automation. Providing an objective and cost-effective solution, AES standardises the assessment of extended responses. Although substantial research has been conducted in this domain, recent investigations reveal that alternative deep-learning architectures outperform transformer-based models. Despite the successful dominance in the performance of the transformer architectures across various other tasks, this discrepancy has prompted a need to enrich transformer-based AES models through contextual enrichment. This study delves into diverse contextual factors using the ASAP-AES dataset, analysing their impact on transformer-based model performance. Our most effective model, augmented with multiple contextual dimensions, achieves a mean Quadratic Weighted Kappa score of 0.823 across the entire essay dataset and 0.8697 when trained on individual essay sets. Evidently surpassing prior transformer-based models, this augmented approach only underperforms relative to the state-of-the-art deep learning model trained essay-set-wise by an average of 3.83\% while exhibiting superior performance in three of the eight sets. Importantly, this enhancement is orthogonal to architecture-based advancements and seamlessly adaptable to any AES model. Consequently, this contextual augmentation methodology presents a versatile technique for refining AES capabilities, contributing to automated grading and evaluation evolution in educational settings.
GPU Acceleration for Faster Evolutionary Spatial Cyclic Game Systems
Venue: arXiv cs.DC 2025
Year: 2025

This dissertation presents the design, implementation and evaluation of GPU-accelerated simulation frameworks for Evolutionary Spatial Cyclic Games (ESCGs), a class of agent-based models used to study ecological and evolutionary dynamics. Traditional single-threaded ESCG simulations are computationally expensive and scale poorly. To address this, high-performance implementations were developed using Apple's Metal and Nvidia's CUDA, with a validated single-threaded C++ version serving as a baseline comparison point. Benchmarking results show that GPU acceleration delivers significant speedups, with the CUDA maxStep implementation achieving up to a 28x improvement. Larger system sizes, up to 3200x3200, became tractable, while Metal faced scalability limits. The GPU frameworks also enabled replication and critical extension of recent ESCG studies, revealing sensitivities to system size and runtime not fully explored in prior work. Overall, this project provides a configurable ESCG simulation platform that advances the computational toolkit for this field of research. This dissertation forms the basis for a paper accepted for publication and presentation at the European Modelling and Simulation Symposium.
Enabling Multi-Agent Systems as Learning Designers: Applying Learning Sciences to AI Instructional Design
Venue: arXiv cs.CY 2025
Year: 2025

K-12 educators are increasingly using Large Language Models (LLMs) to create instructional materials. These systems excel at producing fluent, coherent content, but often lack support for high-quality teaching. The reason is twofold: first, commercial LLMs, such as ChatGPT and Gemini which are among the most widely accessible to teachers, do not come preloaded with the depth of pedagogical theory needed to design truly effective activities; second, although sophisticated prompt engineering can bridge this gap, most teachers lack the time or expertise and find it difficult to encode such pedagogical nuance into their requests. This study shifts pedagogical expertise from the user's prompt to the LLM's internal architecture. We embed the well-established Knowledge-Learning-Instruction (KLI) framework into a Multi-Agent System (MAS) to act as a sophisticated instructional designer. We tested three systems for generating secondary Math and Science learning activities: a Single-Agent baseline simulating typical teacher prompts; a role-based MAS where agents work sequentially; and a collaborative MAS-CMD where agents co-construct activities through conquer and merge discussion. The generated materials were evaluated by 20 practicing teachers and a complementary LLM-as-a-judge system using the Quality Matters (QM) K-12 standards. While the rubric scores showed only small, often statistically insignificant differences between the systems, the qualitative feedback from educators painted a clear and compelling picture. Teachers strongly preferred the activities from the collaborative MAS-CMD, describing them as significantly more creative, contextually relevant, and classroom-ready. Our findings show that embedding pedagogical principles into LLM systems offers a scalable path for creating high-quality educational content.
Optimizing Hyper parameters in CNN for Soil Classification using PSO and Whale Optimization Algorithm
Venue: arXiv cs.CV 2025
Year: 2025

Classifying soil images contributes to better land management, increased agricultural output, and practical solutions for environmental issues. The development of various disciplines, particularly agriculture, civil engineering, and natural resource management, is aided by understanding of soil quality since it helps with risk reduction, performance improvement, and sound decision-making . Artificial intelligence has recently been used in a number of different fields. In this study, an intelligent model was constructed using Convolutional Neural Networks to classify soil kinds, and machine learning algorithms were used to enhance the performance of soil classification . To achieve better implementation and performance of the Convolutional Neural Networks algorithm and obtain valuable results for the process of classifying soil type images, swarm algorithms were employed to obtain the best performance by choosing Hyper parameters for the Convolutional Neural Networks network using the Whale optimization algorithm and the Particle swarm optimization algorithm, and comparing the results of using the two algorithms in the process of multiple classification of soil types. The Accuracy and F1 measures were adopted to test the system, and the results of the proposed work were efficient result
COVID19 Prediction Based On CT Scans Of Lungs Using DenseNet Architecture
Venue: arXiv cs.CV 2025
Year: 2025

COVID19 took the world by storm since December 2019. A highly infectious communicable disease, COVID19 is caused by the SARSCoV2 virus. By March 2020, the World Health Organization (WHO) declared COVID19 as a global pandemic. A pandemic in the 21st century after almost 100 years was something the world was not prepared for, which resulted in the deaths of around 1.6 million people worldwide. The most common symptoms of COVID19 were associated with the respiratory system and resembled a cold, flu, or pneumonia. After extensive research, doctors and scientists concluded that the main reason for lives being lost due to COVID19 was failure of the respiratory system. Patients were dying gasping for breath. Top healthcare systems of the world were failing badly as there was an acute shortage of hospital beds, oxygen cylinders, and ventilators. Many were dying without receiving any treatment at all. The aim of this project is to help doctors decide the severity of COVID19 by reading the patient's Computed Tomography (CT) scans of the lungs. Computer models are less prone to human error, and Machine Learning or Neural Network models tend to give better accuracy as training improves over time. We have decided to use a Convolutional Neural Network model. Given that a patient tests positive, our model will analyze the severity of COVID19 infection within one month of the positive test result. The severity of the infection may be promising or unfavorable (if it leads to intubation or death), based entirely on the CT scans in the dataset.
MedRepBench: A Comprehensive Benchmark for Medical Report Interpretation
Venue: arXiv cs.CV 2025
Year: 2025

Medical report interpretation plays a crucial role in healthcare, enabling both patient-facing explanations and effective information flow across clinical systems. While recent vision-language models (VLMs) and large language models (LLMs) have demonstrated general document understanding capabilities, there remains a lack of standardized benchmarks to assess structured interpretation quality in medical reports. We introduce MedRepBench, a comprehensive benchmark built from 1,900 de-identified real-world Chinese medical reports spanning diverse departments, patient demographics, and acquisition formats. The benchmark is designed primarily to evaluate end-to-end VLMs for structured medical report understanding. To enable controlled comparisons, we also include a text-only evaluation setting using high-quality OCR outputs combined with LLMs, allowing us to estimate the upper-bound performance when character recognition errors are minimized. Our evaluation framework supports two complementary protocols: (1) an objective evaluation measuring field-level recall of structured clinical items, and (2) an automated subjective evaluation using a powerful LLM as a scoring agent to assess factuality, interpretability, and reasoning quality. Based on the objective metric, we further design a reward function and apply Group Relative Policy Optimization (GRPO) to improve a mid-scale VLM, achieving up to 6% recall gain. We also observe that the OCR+LLM pipeline, despite strong performance, suffers from layout-blindness and latency issues, motivating further progress toward robust, fully vision-based report understanding.
WISCA: A Lightweight Model Transition Method to Improve LLM Training via Weight Scaling
Venue: arXiv cs.LG 2025
Year: 2025

Transformer architecture gradually dominates the LLM field. Recent advances in training optimization for Transformer-based large language models (LLMs) primarily focus on architectural modifications or optimizer adjustments. However, these approaches lack systematic optimization of weight patterns during training. Weight pattern refers to the distribution and relative magnitudes of weight parameters in a neural network. To address this issue, we propose a Weight Scaling method called WISCA to enhance training efficiency and model quality by strategically improving neural network weight patterns without changing network structures. By rescaling weights while preserving model outputs, WISCA indirectly optimizes the model's training trajectory. Experiments demonstrate that WISCA significantly improves convergence quality (measured by generalization capability and loss reduction), particularly in LLMs with Grouped Query Attention (GQA) architectures and LoRA fine-tuning tasks. Empirical results show 5.6% average improvement on zero-shot validation tasks and 2.12% average reduction in training perplexity across multiple architectures.