OpenRaiser/Intern-Atlas huggingface dataset:
Preference-based Multi-Objective Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Multi-objective reinforcement learning (MORL) is a structured approach for optimizing tasks with multiple objectives. However, it often relies on pre-defined reward functions, which can be hard to design for balancing conflicting goals and may lead to oversimplification. Preferences can serve as more flexible and intuitive decision-making guidance, eliminating the need for complicated reward design. This paper introduces preference-based MORL (Pb-MORL), which formalizes the integration of preferences into the MORL framework. We theoretically prove that preferences can derive policies across the entire Pareto frontier. To guide policy optimization using preferences, our method constructs a multi-objective reward model that aligns with the given preferences. We further provide theoretical proof to show that optimizing this reward model is equivalent to training the Pareto optimal policy. Extensive experiments in benchmark multi-objective tasks, a multi-energy management task, and an autonomous driving task on a multi-line highway show that our method performs competitively, surpassing the oracle method, which uses the ground truth reward function. This highlights its potential for practical applications in complex real-world systems.
Glucose-ML: A collection of longitudinal diabetes datasets for development of robust AI solutions
Venue: arXiv cs.AI 2025
Year: 2025

Artificial intelligence (AI) algorithms are a critical part of state-of-the-art digital health technology for diabetes management. Yet, access to large high-quality datasets is creating barriers that impede development of robust AI solutions. To accelerate development of transparent, reproducible, and robust AI solutions, we present Glucose-ML, a collection of 10 publicly available diabetes datasets, released within the last 7 years (i.e., 2018 - 2025). The Glucose-ML collection comprises over 300,000 days of continuous glucose monitor (CGM) data with a total of 38 million glucose samples collected from 2500+ people across 4 countries. Participants include persons living with type 1 diabetes, type 2 diabetes, prediabetes, and no diabetes. To support researchers and innovators with using this rich collection of diabetes datasets, we present a comparative analysis to guide algorithm developers with data selection. Additionally, we conduct a case study for the task of blood glucose prediction - one of the most common AI tasks within the field. Through this case study, we provide a benchmark for short-term blood glucose prediction across all 10 publicly available diabetes datasets within the Glucose-ML collection. We show that the same algorithm can have significantly different prediction results when developed/evaluated with different datasets. Findings from this study are then used to inform recommendations for developing robust AI solutions within the diabetes or broader health domain. We provide direct links to each longitudinal diabetes dataset in the Glucose-ML collection and openly provide our code.
DPMT: Dual Process Multi-scale Theory of Mind Framework for Real-time Human-AI Collaboration
Venue: arXiv cs.LG 2025
Year: 2025

Real-time human-artificial intelligence (AI) collaboration is crucial yet challenging, especially when AI agents must adapt to diverse and unseen human behaviors in dynamic scenarios. Existing large language model (LLM) agents often fail to accurately model the complex human mental characteristics such as domain intentions, especially in the absence of direct communication. To address this limitation, we propose a novel dual process multi-scale theory of mind (DPMT) framework, drawing inspiration from cognitive science dual process theory. Our DPMT framework incorporates a multi-scale theory of mind (ToM) module to facilitate robust human partner modeling through mental characteristic reasoning. Experimental results demonstrate that DPMT significantly enhances human-AI collaboration, and ablation studies further validate the contributions of our multi-scale ToM in the slow system.
An Efficient Massively Parallel Constant-Factor Approximation Algorithm for the $k$-Means Problem
Venue: arXiv cs.DS 2025
Year: 2025

In this paper, we present an efficient massively parallel approximation algorithm for the $k$-means problem. Specifically, we provide an MPC algorithm that computes a constant-factor approximation to an arbitrary $k$-means instance in $O(\log\log n \cdot \log\log\log n)$ rounds. The algorithm uses $O(n^σ)$ bits of memory per machine, where $σ> 0$ is a constant that can be made arbitrarily small. The global memory usage is $O(n^{1+\varepsilon})$ bits for an arbitrarily small constant $\varepsilon > 0$, and is thus only slightly superlinear. Recently, Czumaj, Gao, Jiang, Krauthgamer, and Veselý showed that a constant-factor bicriteria approximation can be computed in $O(1)$ rounds in the MPC model. However, our algorithm is the first constant-factor approximation for the general $k$-means problem that runs in $o(\log n)$ rounds in the MPC model. Our approach builds upon the foundational framework of Jain and Vazirani. The core component of our algorithm is a constant-factor approximation for the related facility location problem. While such an approximation was already achieved in constant time in the work of Czumaj et al.\ mentioned above, our version additionally satisfies the so-called Lagrangian Multiplier Preserving (LMP) property. This property enables the transformation of a facility location approximation into a comparably good $k$-means approximation.
Generative AI-Driven High-Fidelity Human Motion Simulation
Venue: arXiv cs.AI 2025
Year: 2025

Human motion simulation (HMS) supports cost-effective evaluation of worker behavior, safety, and productivity in industrial tasks. However, existing methods often suffer from low motion fidelity. This study introduces Generative-AI-Enabled HMS (G-AI-HMS), which integrates text-to-text and text-to-motion models to enhance simulation quality for physical tasks. G-AI-HMS tackles two key challenges: (1) translating task descriptions into motion-aware language using Large Language Models aligned with MotionGPT's training vocabulary, and (2) validating AI-enhanced motions against real human movements using computer vision. Posture estimation algorithms are applied to real-time videos to extract joint landmarks, and motion similarity metrics are used to compare them with AI-enhanced sequences. In a case study involving eight tasks, the AI-enhanced motions showed lower error than human created descriptions in most scenarios, performing better in six tasks based on spatial accuracy, four tasks based on alignment after pose normalization, and seven tasks based on overall temporal similarity. Statistical analysis showed that AI-enhanced prompts significantly (p $<$ 0.0001) reduced joint error and temporal misalignment while retaining comparable posture accuracy.
Toward Temporal Causal Representation Learning with Tensor Decomposition
Venue: arXiv cs.LG 2025
Year: 2025

Temporal causal representation learning is a powerful tool for uncovering complex patterns in observational studies, which are often represented as low-dimensional time series. However, in many real-world applications, data are high-dimensional with varying input lengths and naturally take the form of irregular tensors. To analyze such data, irregular tensor decomposition is critical for extracting meaningful clusters that capture essential information. In this paper, we focus on modeling causal representation learning based on the transformed information. First, we present a novel causal formulation for a set of latent clusters. We then propose CaRTeD, a joint learning framework that integrates temporal causal representation learning with irregular tensor decomposition. Notably, our framework provides a blueprint for downstream tasks using the learned tensor factors, such as modeling latent structures and extracting causal information, and offers a more flexible regularization design to enhance tensor decomposition. Theoretically, we show that our algorithm converges to a stationary point. More importantly, our results fill the gap in theoretical guarantees for the convergence of state-of-the-art irregular tensor decomposition. Experimental results on synthetic and real-world electronic health record (EHR) datasets (MIMIC-III), with extensive benchmarks from both phenotyping and network recovery perspectives, demonstrate that our proposed method outperforms state-of-the-art techniques and enhances the explainability of causal representations.
OpenBEATs: A Fully Open-Source General-Purpose Audio Encoder
Venue: arXiv cs.SD 2025
Year: 2025

Masked token prediction has emerged as a powerful pre-training objective across language, vision, and speech, offering the potential to unify these diverse modalities through a single pre-training task. However, its application for general audio understanding remains underexplored, with BEATs being the only notable example. BEATs has seen limited modifications due to the absence of open-source pre-training code. Furthermore, BEATs was trained only on AudioSet, restricting its broader downstream applicability. To address these gaps, we present OpenBEATs, an open-source framework that extends BEATs via multi-domain audio pre-training. We conduct comprehensive evaluations across six types of tasks, twenty five datasets, and three audio domains, including audio reasoning tasks such as audio question answering, entailment, and captioning. OpenBEATs achieves state-of-the-art performance on six bioacoustics datasets, two environmental sound datasets and five reasoning datasets, performing better than models exceeding a billion parameters at one-fourth their parameter size. These results demonstrate the effectiveness of multi-domain datasets and masked token prediction task to learn general-purpose audio representations. To promote further research and reproducibility, we release all pre-training and evaluation code, pretrained and fine-tuned checkpoints, and training logs at https://shikhar-s.github.io/OpenBEATs
SpeedLLM: An FPGA Co-design of Large Language Model Inference Accelerator
Venue: arXiv cs.AR 2025
Year: 2025

This paper introduces SpeedLLM, a neural network accelerator designed on the Xilinx Alevo U280 platform and optimized for the Tinyllama framework to enhance edge computing performance. Key innovations include data stream parallelism, a memory reuse strategy, and Llama2 operator fusion, which collectively reduce latency and energy consumption. SpeedLLM's data pipeline architecture optimizes the read-compute-write cycle, while the memory strategy minimizes FPGA resource demands. The operator fusion boosts computational density and throughput. Results show SpeedLLM outperforms traditional Tinyllama implementations, achieving up to 4.8* faster performance and 1.18* lower energy consumption, offering improvements in edge devices.
The Free Will Equation: Quantum Field Analogies for AGI
Venue: arXiv cs.AI 2025
Year: 2025

Artificial General Intelligence (AGI) research traditionally focuses on algorithms that optimize for specific goals under deterministic rules. Yet, human-like intelligence exhibits adaptive spontaneity - an ability to make unexpected choices or free decisions not strictly dictated by past data or immediate reward. This trait, often dubbed "free will" in a loose sense, might be crucial for creativity, robust adaptation, and avoiding ruts in problem-solving. This paper proposes a theoretical framework, called the Free Will Equation, that draws analogies from quantum field theory to endow AGI agents with a form of adaptive, controlled stochasticity in their decision-making process. The core idea is to treat an AI agent's cognitive state as a superposition of potential actions or thoughts, which collapses probabilistically into a concrete action when a decision is made - much like a quantum wavefunction collapsing upon measurement. By incorporating mechanisms analogous to quantum fields, along with intrinsic motivation terms, we aim to improve an agent's ability to explore novel strategies and adapt to unforeseen changes. Experiments in a non-stationary multi-armed bandit environment demonstrate that agents using this framework achieve higher rewards and policy diversity compared to baseline methods.
Predictive Representativity: Uncovering Racial Bias in AI-based Skin Cancer Detection
Venue: arXiv cs.LG 2025
Year: 2025

Artificial intelligence (AI) systems increasingly inform medical decision-making, yet concerns about algorithmic bias and inequitable outcomes persist, particularly for historically marginalized populations. This paper introduces the concept of Predictive Representativity (PR), a framework of fairness auditing that shifts the focus from the composition of the data set to outcomes-level equity. Through a case study in dermatology, we evaluated AI-based skin cancer classifiers trained on the widely used HAM10000 dataset and on an independent clinical dataset (BOSQUE Test set) from Colombia. Our analysis reveals substantial performance disparities by skin phototype, with classifiers consistently underperforming for individuals with darker skin, despite proportional sampling in the source data. We argue that representativity must be understood not as a static feature of datasets but as a dynamic, context-sensitive property of model predictions. PR operationalizes this shift by quantifying how reliably models generalize fairness across subpopulations and deployment contexts. We further propose an External Transportability Criterion that formalizes the thresholds for fairness generalization. Our findings highlight the ethical imperative for post-hoc fairness auditing, transparency in dataset documentation, and inclusive model validation pipelines. This work offers a scalable tool for diagnosing structural inequities in AI systems, contributing to discussions on equity, interpretability, and data justice and fostering a critical re-evaluation of fairness in data-driven healthcare.
Understanding Two-Layer Neural Networks with Smooth Activation Functions
Venue: arXiv cs.LG 2025
Year: 2025

This paper aims to understand the training solution, which is obtained by the back-propagation algorithm, of two-layer neural networks whose hidden layer is composed of the units with smooth activation functions, including the usual sigmoid type most commonly used before the advent of ReLUs. The mechanism contains four main principles: construction of Taylor series expansions, strict partial order of knots, smooth-spline implementation and smooth-continuity restriction. The universal approximation for arbitrary input dimensionality is proved and experimental verification is given, through which the mystery of ``black box'' of the solution space is largely revealed. The new proofs employed also enrich approximation theory.
ExCyTIn-Bench: Evaluating LLM agents on Cyber Threat Investigation
Venue: arXiv cs.CR 2025
Year: 2025

We present ExCyTIn-Bench, the first benchmark to Evaluate an LLM agent x on the task of Cyber Threat Investigation through security questions derived from investigation graphs. Real-world security analysts must sift through a large number of heterogeneous alert signals and security logs, follow multi-hop chains of evidence, and compile an incident report. With the developments of LLMs, building LLM-based agents for automatic thread investigation is a promising direction. To assist the development and evaluation of LLM agents, we construct a dataset from a controlled Azure tenant that covers 8 simulated real-world multi-step attacks, 57 log tables from Microsoft Sentinel and related services, and 589 automatically generated questions. We leverage security logs extracted with expert-crafted detection logic to build threat investigation graphs, and then generate questions with LLMs using paired nodes on the graph, taking the start node as background context and the end node as answer. Anchoring each question to these explicit nodes and edges not only provides automatic, explainable ground truth answers but also makes the pipeline reusable and readily extensible to new logs. This also enables the automatic generation of procedural tasks with verifiable rewards, which can be naturally extended to training agents via reinforcement learning. Our comprehensive experiments with different models confirm the difficulty of the task: with the base setting, the average reward across all evaluated models is 0.249, and the best achieved is 0.368, leaving substantial headroom for future research. Code and data are coming soon!
Mapping the Parasocial AI Market: User Trends, Engagement and Risks
Venue: arXiv cs.CY 2025
Year: 2025

A scan of 110 AI companion platforms reveals a rapidly growing global market for emotionally engaging, personalized AI interactions. While parasocial use of general-purpose AI (GPAI) tools currently dominates, a growing number of platforms are designed specifically for care, transactional, or mating companionship. In the UK alone, these platforms receive between 46 million and 91 million monthly visits (1.1-2.2 billion globally), with users spending an average of 3.5 minutes per session. For context, Instagram averaged 67.3 million UK visits per month between January and March 2025. Notably, mating-oriented AI companions make up 44% of UK visits (higher than the global average of 30%) but see lower session times and return rates than mixed-use platforms. As mating-oriented romantic AI offerings improve, increased engagement may follow, raising urgent concerns about online safety, particularly for children, given weak age safeguards. Meanwhile, GPAI tools are moving toward more emotionally intelligent, personalized interactions, making parasocial AI use increasingly mainstream. These trends highlight the need for the UK AI Security Institute (AISI) to monitor this sector and assess whether existing regulation sufficiently addresses emerging societal risks.
Using Modular Arithmetic Optimized Neural Networks To Crack Affine Cryptographic Schemes Efficiently
Venue: arXiv cs.CR 2025
Year: 2025

We investigate the cryptanalysis of affine ciphers using a hybrid neural network architecture that combines modular arithmetic-aware and statistical feature-based learning. Inspired by recent advances in interpretable neural networks for modular arithmetic and neural cryptanalysis of classical ciphers, our approach integrates a modular branch that processes raw ciphertext sequences and a statistical branch that leverages letter frequency features. Experiments on datasets derived from natural English text demonstrate that the hybrid model attains high key recovery accuracy for short and moderate ciphertexts, outperforming purely statistical approaches for the affine cipher. However, performance degrades for very long ciphertexts, highlighting challenges in model generalization.
Beyond Architectures: Evaluating the Role of Contextual Embeddings in Detecting Bipolar Disorder on Social Media
Venue: arXiv cs.CL 2025
Year: 2025

Bipolar disorder is a chronic mental illness frequently underdiagnosed due to subtle early symptoms and social stigma. This paper explores the advanced natural language processing (NLP) models for recognizing signs of bipolar disorder based on user-generated social media text. We conduct a comprehensive evaluation of transformer-based models (BERT, RoBERTa, ALBERT, ELECTRA, DistilBERT) and Long Short Term Memory (LSTM) models based on contextualized (BERT) and static (GloVe, Word2Vec) word embeddings. Experiments were performed on a large, annotated dataset of Reddit posts after confirming their validity through sentiment variance and judgmental analysis. Our results demonstrate that RoBERTa achieves the highest performance among transformer models with an F1 score of ~98% while LSTM models using BERT embeddings yield nearly identical results. In contrast, LSTMs trained on static embeddings fail to capture meaningful patterns, scoring near-zero F1. These findings underscore the critical role of contextual language modeling in detecting bipolar disorder. In addition, we report model training times and highlight that DistilBERT offers an optimal balance between efficiency and accuracy. In general, our study offers actionable insights for model selection in mental health NLP applications and validates the potential of contextualized language models to support early bipolar disorder screening.
CCL-XCoT: An Efficient Cross-Lingual Knowledge Transfer Method for Mitigating Hallucination Generation
Venue: arXiv cs.CL 2025
Year: 2025

Multilingual Large Language Models(MLLMs) demonstrate strong generalization across languages, yet they remain prone to hallucinations, especially in low-resource languages, due to training data imbalances. These hallucinations, which include inaccurate or fabricated outputs, are particularly problematic in domain-specific generation tasks (Chataigner et al., 2024). To address this challenge, we propose CCL-XCoT(Curriculum-based Contrastive Learning-based Cross-lingual Chain-of-Thought), a two-stage fine-tuning framework for mitigating hallucination in MLLMs. Our approach first enhances cross-lingual semantic alignment through curriculum-based contrastive learning combined with next-token prediction during continued pre-training. Building on this foundation, we then introduce a cross-lingual Chain-of-Thought (XCoT) prompting strategy during instruction fine-tuning, which guides the model to reason in a high-resource language before generating answers in the target low-resource language. Experimental results show that CCL-XCoT reduces hallucination rates by up to 62% and substantially improves factual knowledge transfer across language pairs, without relying on external retrieval or multi-model ensembles.
Real-Time Communication-Aware Ride-Sharing Route Planning for Urban Air Mobility: A Multi-Source Hybrid Attention Reinforcement Learning Approach
Venue: arXiv cs.RO 2025
Year: 2025

Urban Air Mobility (UAM) systems are rapidly emerging as promising solutions to alleviate urban congestion, with path planning becoming a key focus area. Unlike ground transportation, UAM trajectory planning has to prioritize communication quality for accurate location tracking in constantly changing environments to ensure safety. Meanwhile, a UAM system, serving as an air taxi, requires adaptive planning to respond to real-time passenger requests, especially in ride-sharing scenarios where passenger demands are unpredictable and dynamic. However, conventional trajectory planning strategies based on predefined routes lack the flexibility to meet varied passenger ride demands. To address these challenges, this work first proposes constructing a radio map to evaluate the communication quality of urban airspace. Building on this, we introduce a novel Multi-Source Hybrid Attention Reinforcement Learning (MSHA-RL) framework for the challenge of effectively focusing on passengers and UAM locations, which arises from the significant dimensional disparity between the representations. This model first generates the alignment among diverse data sources with large gap dimensions before employing hybrid attention to balance global and local insights, thereby facilitating responsive, real-time path planning. Extensive experimental results demonstrate that the approach enables communication-compliant trajectory planning, reducing travel time and enhancing operational efficiency while prioritizing passenger safety.