OpenRaiser/Intern-Atlas huggingface dataset:
Interference-Aware Super-Constellation Design for NOMA
Venue: arXiv cs.IT 2025
Year: 2025

Non-orthogonal multiple access (NOMA) has gained significant attention as a potential next-generation multiple access technique. However, its implementation with finite-alphabet inputs faces challenges. Particularly, due to inter-user interference, superimposed constellations may have overlapping symbols leading to high bit error rates when successive interference cancellation (SIC) is applied. To tackle the issue, this paper employs autoencoders to design interference-aware super-constellations. Unlike conventional methods where superimposed constellation may have overlapping symbols, the proposed autoencoder-based NOMA (AE-NOMA) is trained to design super-constellations with distinguishable symbols at receivers, regardless of channel gains. The proposed architecture removes the need for SIC, allowing maximum likelihood-based approaches to be used instead. The paper presents the conceptual architecture, loss functions, and training strategies for AE-NOMA. Various test results are provided to demonstrate the effectiveness of interference-aware constellations in improving the bit error rate, indicating the adaptability of AE-NOMA to different channel scenarios and its promising potential for implementing NOMA systems
Federated Multimodal Learning with Dual Adapters and Selective Pruning for Communication and Computational Efficiency
Venue: arXiv cs.LG 2025
Year: 2025

Federated Learning (FL) enables collaborative learning across distributed clients while preserving data privacy. However, FL faces significant challenges when dealing with heterogeneous data distributions, which can lead to suboptimal global models that fail to generalize across diverse clients. In this work, we propose a novel framework designed to tackle these challenges by introducing a dual-adapter approach. The method utilizes a larger local adapter for client-specific personalization and a smaller global adapter to facilitate efficient knowledge sharing across clients. Additionally, we incorporate a pruning mechanism to reduce communication overhead by selectively removing less impactful parameters from the local adapter. Through extensive experiments on a range of vision and language tasks, our method demonstrates superior performance compared to existing approaches. It achieves higher test accuracy, lower performance variance among clients, and improved worst-case performance, all while significantly reducing communication and computation costs. Overall, the proposed method addresses the critical trade-off between model personalization and generalization, offering a scalable solution for real-world FL applications.
An Empirical Comparison of Cost Functions in Inductive Logic Programming
Venue: arXiv cs.LG 2025
Year: 2025

Recent inductive logic programming (ILP) approaches learn optimal hypotheses. An optimal hypothesis minimises a given cost function on the training data. There are many cost functions, such as minimising training error, textual complexity, or the description length of hypotheses. However, selecting an appropriate cost function remains a key question. To address this gap, we extend a constraint-based ILP system to learn optimal hypotheses for seven standard cost functions. We then empirically compare the generalisation error of optimal hypotheses induced under these standard cost functions. Our results on over 20 domains and 1000 tasks, including game playing, program synthesis, and image reasoning, show that, while no cost function consistently outperforms the others, minimising training error or description length has the best overall performance. Notably, our results indicate that minimising the size of hypotheses does not always reduce generalisation error.
Analyzing Symmetries of Swarms of Mobile Robots Using Equivariant Dynamical Systems
Venue: arXiv cs.RO 2025
Year: 2025

In this article, we investigate symmetry properties of distributed systems of mobile robots. We consider a swarm of $n\in\mathbb{N}$ robots in the $\mathcal{OBLOT}$ model and analyze their collective $\mathcal{F}$sync dynamics using of equivariant dynamical systems theory. To this end, we show that the corresponding evolution function commutes with rotational and reflective transformations of $\mathbb{R}^2$. These form a group that is isomorphic to $\mathbf{O}(2) \times S_n$, the product group of the orthogonal group and the permutation on $n$ elements. The theory of equivariant dynamical systems is used to deduce a hierarchy along which symmetries of a robot swarm can potentially increase following an arbitrary protocol. By decoupling the Look phase from the Compute and Move phases in the mathematical description of an LCM cycle, this hierarchy can be characterized in terms of automorphisms of connectivity graphs. In particular, we find all possible types of symmetry increase, if the decoupled Compute and Move phase is invertible. Finally, we apply our results to protocols which induce state-dependent linear dynamics, where the reduced system consisting of only the Compute and Move phase is linear.
Detection Avoidance Techniques for Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

The increasing popularity of large language models has not only led to widespread use but has also brought various risks, including the potential for systematically spreading fake news. Consequently, the development of classification systems such as DetectGPT has become vital. These detectors are vulnerable to evasion techniques, as demonstrated in an experimental series: Systematic changes of the generative models' temperature proofed shallow learning-detectors to be the least reliable. Fine-tuning the generative model via reinforcement learning circumvented BERT-based-detectors. Finally, rephrasing led to a >90\% evasion of zero-shot-detectors like DetectGPT, although texts stayed highly similar to the original. A comparison with existing work highlights the better performance of the presented methods. Possible implications for society and further research are discussed.
Evolomino is NP-complete
Venue: arXiv cs.CC 2025
Year: 2025

Evolomino is a pencil-and-paper logic puzzle popularized by the Japanese publisher Nikoli (like Sudoku, Kakuro, Slitherlink, Masyu, and Fillomino). The puzzle's name reflects its core mechanic: the shapes of polyomino-like blocks that players must draw gradually "evolve" in the directions indicated by pre-drawn arrows. We prove, by reduction from 3-SAT, that the question of whether there exists at least one solution to an Evolomino puzzle satisfying the rules is NP-complete. Since our reduction is parsimonious, i.e., it preserves the number of distinct solutions, we also prove that counting the number of solutions to an Evolomino puzzle is #P-complete.
Real-Time Detection of Robot Failures Using Gaze Dynamics in Collaborative Tasks
Venue: arXiv cs.HC 2025
Year: 2025

Detecting robot failures during collaborative tasks is crucial for maintaining trust in human-robot interactions. This study investigates user gaze behaviour as an indicator of robot failures, utilising machine learning models to distinguish between non-failure and two types of failures: executional and decisional. Eye-tracking data were collected from 26 participants collaborating with a robot on Tangram puzzle-solving tasks. Gaze metrics, such as average gaze shift rates and the probability of gazing at specific areas of interest, were used to train machine learning classifiers, including Random Forest, AdaBoost, XGBoost, SVM, and CatBoost. The results show that Random Forest achieved 90% accuracy for detecting executional failures and 80% for decisional failures using the first 5 seconds of failure data. Real-time failure detection was evaluated by segmenting gaze data into intervals of 3, 5, and 10 seconds. These findings highlight the potential of gaze dynamics for real-time error detection in human-robot collaboration.
Psychological Counseling Ability of Large Language Models
Venue: arXiv cs.LG 2025
Year: 2025

With the development of science and the continuous progress of artificial intelligence technology, Large Language Models (LLMs) have begun to be widely utilized across various fields. However, in the field of psychological counseling, the ability of LLMs have not been systematically assessed. In this study, we assessed the psychological counseling ability of mainstream LLMs using 1096 psychological counseling skill questions which were selected from the Chinese National Counselor Level 3 Examination, including Knowledge-based, Analytical-based, and Application-based question types. The analysis showed that the correctness rates of the LLMs for Chinese questions, in descending order, were GLM-3 (46.5%), GPT-4 (46.1%), Gemini (45.0%), ERNIE-3.5 (45.7%) and GPT-3.5 (32.9%). The correctness rates of the LLMs for English questions, in descending order, were ERNIE-3.5 (43.9%), GPT-4 (40.6%), Gemini (36.6%), GLM-3 (29.9%) and GPT-3.5 (29.5%). A chi-square test indicated significant differences in the LLMs' performance on Chinese and English questions. Furthermore, we subsequently utilized the Counselor's Guidebook (Level 3) as a reference for ERNIE-3.5, resulting in a new correctness rate of 59.6%, a 13.8% improvement over its initial rate of 45.8%. In conclusion, the study assessed the psychological counseling ability of LLMs for the first time, which may provide insights for future enhancement and improvement of psychological counseling ability of LLMs.
ConstellationNet: Reinventing Spatial Clustering through GNNs
Venue: arXiv cs.LG 2025
Year: 2025

Spatial clustering is a crucial field, finding universal use across criminology, pathology, and urban planning. However, most spatial clustering algorithms cannot pull information from nearby nodes and suffer performance drops when dealing with higher dimensionality and large datasets, making them suboptimal for large-scale and high-dimensional clustering. Due to modern data growing in size and dimension, clustering algorithms become weaker when addressing multifaceted issues. To improve upon this, we develop ConstellationNet, a convolution neural network(CNN)-graph neural network(GNN) framework that leverages the embedding power of a CNN, the neighbor aggregation of a GNN, and a neural network's ability to deal with batched data to improve spatial clustering and classification with graph augmented predictions. ConstellationNet achieves state-of-the-art performance on both supervised classification and unsupervised clustering across several datasets, outperforming state-of-the-art classification and clustering while reducing model size and training time by up to tenfold and improving baselines by 10 times. Because of its fast training and powerful nature, ConstellationNet holds promise in fields like epidemiology and medical imaging, able to quickly train on new data to develop robust responses.
Insights into Schizophrenia: Leveraging Machine Learning for Early Identification via EEG, ERP, and Demographic Attributes
Venue: arXiv cs.LG 2025
Year: 2025

The research presents a machine learning (ML) classifier designed to differentiate between schizophrenia patients and healthy controls by utilising features extracted from electroencephalogram (EEG) data, specifically focusing on event-related potentials (ERPs) and certain demographic variables. The dataset comprises data from 81 participants, encompassing 32 healthy controls and 49 schizophrenia patients, all sourced from an online dataset. After preprocessing the dataset, our ML model achieved an accuracy of 99.930%. This performance outperforms earlier research, including those that used deep learning methods. Additionally, an analysis was conducted to assess individual features' contribution to improving classification accuracy. This involved systematically excluding specific features from the original dataset one at a time, and another technique involved an iterative process of removing features based on their entropy scores incrementally. The impact of these removals on model performance was evaluated to identify the most informative features.
GraphT5: Unified Molecular Graph-Language Modeling via Multi-Modal Cross-Token Attention
Venue: arXiv cs.LG 2025
Year: 2025

Molecular language modeling tasks such as molecule captioning have been recognized for their potential to further understand molecular properties that can aid drug discovery or material synthesis based on chemical reactions. Unlike the common use of molecule graphs in predicting molecular properties, most methods in molecular language modeling rely heavily on SMILES sequences. This preference is because the task involves generating a sequence of multiple tokens using transformer-based models. Therefore, a main challenge is determining how to integrate graph data, which contains structural and spatial information about molecules, with text data. In addition, simply using both 1D SMILES text and 2D graph as inputs without addressing how they align and represent the molecule structure in different modalities makes it challenging to fully utilize structural knowledge about molecules. To this end, we propose GraphT5, a multi-modal framework that integrates 1D SMILES text and 2D graph representations of molecules for molecular language modeling. Specifically, we introduce a novel cross-token attention module in GraphT5 to bridge the gap arising from the fundamental differences between the two modalities of molecule representations. Cross-token attention exploits implicit information between SMILES and graphs of molecules, resulting from their interactions at a fine-grained token level that benefits molecular language modeling. Extensive experiments including molecule captioning, IUPAC name prediction tasks, and case studies show that our GraphT5 outperforms the latest baseline approaches, which validates the effectiveness of our GraphT5 in sufficiently utilizing 1D SMILES text and 2D graph representations.
Merge then Realign: Simple and Effective Modality-Incremental Continual Learning for Multimodal LLMs
Venue: arXiv cs.LG 2025
Year: 2025

Recent advances in Multimodal Large Language Models (MLLMs) have enhanced their versatility as they integrate a growing number of modalities. Considering the heavy cost of training MLLMs, it is efficient to reuse the existing ones and extend them to more modalities through Modality-incremental Continual Learning (MCL). The exploration of MCL is in its early stages. In this work, we dive into the causes of performance degradation in MCL. We uncover that it suffers not only from forgetting as in traditional continual learning, but also from misalignment between the modality-agnostic and modality-specific components. To this end, we propose an elegantly simple MCL paradigm called "MErge then ReAlign" (MERA) to address both forgetting and misalignment. MERA avoids introducing heavy model budgets or modifying model architectures, hence is easy to deploy and highly reusable in the MLLM community. Extensive experiments demonstrate the impressive performance of MERA, holding an average of 99.84\% Backward Relative Gain when extending to four modalities, achieving nearly lossless MCL performance. Our findings underscore the misalignment issue in MCL. More broadly, our work showcases how to adjust different components of MLLMs during continual learning.
Using a single actor to output personalized policy for different intersections
Venue: arXiv cs.LG 2025
Year: 2025

Recently, with the development of Multi-agent reinforcement learning (MARL), adaptive traffic signal control (ATSC) has achieved satisfactory results. In traffic scenarios with multiple intersections, MARL treats each intersection as an agent and optimizes traffic signal control strategies through learning and real-time decision-making. Considering that observation distributions of intersections might be different in real-world scenarios, shared parameter methods might lack diversity and thus lead to high generalization requirements in the shared-policy network. A typical solution is to increase the size of network parameters. However, simply increasing the scale of the network does not necessarily improve policy generalization, which is validated in our experiments. Accordingly, an approach that considers both the personalization of intersections and the efficiency of parameter sharing is required. To this end, we propose Hyper-Action Multi-Head Proximal Policy Optimization (HAMH-PPO), a Centralized Training with Decentralized Execution (CTDE) MARL method that utilizes a shared PPO policy network to deliver personalized policies for intersections with non-iid observation distributions. The centralized critic in HAMH-PPO uses graph attention units to calculate the graph representations of all intersections and outputs a set of value estimates with multiple output heads for each intersection. The decentralized execution actor takes the local observation history as input and output distributions of action as well as a so-called hyper-action to balance the multiple values estimated from the centralized critic to further guide the updating of TSC policies. The combination of hyper-action and multi-head values enables multiple agents to share a single actor-critic while achieving personalized policies.
Hierarchical Balance Packing: Towards Efficient Supervised Fine-tuning for Long-Context LLM
Venue: arXiv cs.LG 2025
Year: 2025

Training Long-Context Large Language Models (LLMs) is challenging, as hybrid training with long-context and short-context data often leads to workload imbalances. Existing works mainly use data packing to alleviate this issue, but fail to consider imbalanced attention computation and wasted communication overhead. This paper proposes Hierarchical Balance Packing (HBP), which designs a novel batch-construction method and training recipe to address those inefficiencies. In particular, the HBP constructs multi-level data packing groups, each optimized with a distinct packing length. It assigns training samples to their optimal groups and configures each group with the most effective settings, including sequential parallelism degree and gradient checkpointing configuration. To effectively utilize multi-level groups of data, we design a dynamic training pipeline specifically tailored to HBP, including curriculum learning, adaptive sequential parallelism, and stable loss. Our extensive experiments demonstrate that our method significantly reduces training time over multiple datasets and open-source models while maintaining strong performance. For the largest DeepSeek-V2 (236B) MoE model, our method speeds up the training by 2.4$\times$ with competitive performance. Codes will be released at https://github.com/ModelTC/HBP.
Transforming Traditional Neural Networks into Neuromorphic Quantum-Cognitive Models: A Tutorial with Applications
Venue: arXiv cs.LG 2025
Year: 2025

Quantum technologies are increasingly pervasive, underpinning the operation of numerous electronic, optical and medical devices. Today, we are also witnessing rapid advancements in quantum computing and communication. However, access to quantum technologies in computation remains largely limited to professionals in research organisations and high-tech industries. This paper demonstrates how traditional neural networks can be transformed into neuromorphic quantum models, enabling anyone with a basic understanding of undergraduate-level machine learning to create quantum-inspired models that mimic the functioning of the human brain -- all using a standard laptop. We present several examples of these quantum machine learning transformations and explore their potential applications, aiming to make quantum technology more accessible and practical for broader use. The examples discussed in this paper include quantum-inspired analogues of feedforward neural networks, recurrent neural networks, Echo State Network reservoir computing and Bayesian neural networks, demonstrating that a quantum approach can both optimise the training process and equip the models with certain human-like cognitive characteristics.
Ways of Seeing, and Selling, AI Art
Venue: arXiv cs.CY 2025
Year: 2025

In early 2025, Augmented Intelligence - Christie's first AI art auction - drew criticism for showcasing a controversial genre. Amid wider legal uncertainty, artists voiced concerns over data mining practices, notably with respect to copyright. The backlash could be viewed as a microcosm of AI's contested position in the creative economy. Touching on the auction's presentation, reception, and results, this paper explores how, among social dissonance, machine learning finds its place in the artworld. Foregrounding responsible innovation, the paper provides a balanced perspective that champions creators' rights and brings nuance to this polarised debate. With a focus on exhibition design, it centres framing, which refers to the way a piece is presented to influence consumer perception. Context plays a central role in shaping our understanding of how good, valuable, and even ethical an artwork is. In this regard, Augmented Intelligence situates AI art within a surprisingly traditional framework, leveraging hallmarks of "high art" to establish the genre's cultural credibility. Generative AI has a clear economic dimension, converging questions of artistic merit with those of monetary worth. Scholarship on ways of seeing, or framing, could substantively inform the interpretation and evaluation of creative outputs, including assessments of their aesthetic and commercial value.
Uncovering Cross-Domain Recommendation Ability of Large Language Models
Venue: arXiv cs.IR 2025
Year: 2025

Cross-Domain Recommendation (CDR) seeks to enhance item retrieval in low-resource domains by transferring knowledge from high-resource domains. While recent advancements in Large Language Models (LLMs) have demonstrated their potential in Recommender Systems (RS), their ability to effectively transfer domain knowledge for improved recommendations remains underexplored. To bridge this gap, we propose LLM4CDR, a novel CDR pipeline that constructs context-aware prompts by leveraging users' purchase history sequences from a source domain along with shared features between source and target domains. Through extensive experiments, we show that LLM4CDR achieves strong performance, particularly when using LLMs with large parameter sizes and when the source and target domains exhibit smaller domain gaps. For instance, incorporating CD and Vinyl purchase history for recommendations in Movies and TV yields a 64.28 percent MAP 1 improvement. We further investigate key factors including source domain data, domain gap, prompt design, and LLM size, which impact LLM4CDR's effectiveness in CDR tasks. Our results highlight that LLM4CDR excels when leveraging a single, closely related source domain and benefits significantly from larger LLMs. These insights pave the way for future research on LLM-driven cross-domain recommendations.
Strengthening the Internal Adversarial Robustness in Lifted Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Lifted neural networks (i.e. neural architectures explicitly optimizing over respective network potentials to determine the neural activities) can be combined with a type of adversarial training to gain robustness for internal as well as input layers, in addition to improved generalization performance. In this work we first investigate how adversarial robustness in this framework can be further strengthened by solely modifying the training loss. In a second step we fix some remaining limitations and arrive at a novel training loss for lifted neural networks, that combines targeted and untargeted adversarial perturbations.