OpenRaiser/Intern-Atlas huggingface dataset:
Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector
Venue: arXiv cs.CL 2026
Year: 2026

Large language models have demonstrated impressive capabilities across various domains. However, their general-purpose nature often limits their effectiveness in specialized fields such as energy, where deep technical expertise and precise domain knowledge are essential. In this paper, we introduce EnergyGPT, a domain-specialized language model tailored for the energy sector, developed by fine-tuning the LLaMA 3.1-8B model on a high-quality, curated corpus of energy-related texts. We consider two adaptation strategies: a full-parameter Supervised Fine-Tuning variant and a parameter-efficient LoRA-based variant that updates only a small fraction of the model parameters. We present a complete development pipeline, including data collection and curation, model fine-tuning, benchmark design and LLM-judge choice, evaluation, and deployment. Through this work, we demonstrate that our training strategy enables improvements in domain relevance and performance without the need for large-scale infrastructure. By evaluating the performance of both EnergyGPT variants using domain-specific question-answering benchmarks, our results show that the adapted models consistently outperform the base model in most energy-related language understanding and generation tasks, with the LoRA variant achieving competitive gains at significantly reduced training cost.
Optimizing Task Scheduling in Fog Computing with Deadline Awareness
Venue: arXiv cs.DC 2026
Year: 2026

The rise of Internet of Things (IoT) devices has led to the development of numerous time-sensitive applications that require quick responses and low latency. Fog computing has emerged as a solution for processing these IoT applications, but it faces challenges such as resource allocation and job scheduling. Therefore, it is crucial to determine how to assign and schedule tasks on Fog nodes. This work aims to schedule tasks in IoT while minimizing the total energy consumption of nodes and enhancing the Quality of Service (QoS) requirements of IoT tasks, taking into account task deadlines. This paper classifies Fog nodes into two categories based on their traffic level: low and high. It schedules short-deadline tasks on low-traffic nodes using an Improved Golden Eagle Optimization (IGEO) algorithm, an enhancement that utilizes genetic operators for discretization. Long-deadline tasks are processed on high-traffic nodes using reinforcement learning (RL). This combined approach is called the Reinforcement Improved Golden Eagle Optimization (RIGEO) algorithm. Experimental results demonstrate that RIGEO achieves up to a 29% reduction in energy consumption, up to an 86% improvement in response time, and up to a 19% reduction in deadline violations compared to state-of-the-art algorithms.
FHIR-RAG-MEDS: Integrating HL7 FHIR with Retrieval-Augmented Large Language Models for Enhanced Medical Decision Support
Venue: arXiv cs.AI 2026
Year: 2026

In this study, we propose FHIR-RAG-MEDS system that aims to integrate Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) with a Retrieval-Augmented Generation (RAG)-based system to improve personalized medical decision support on evidence-based clinical guidelines, emphasizing the need for research in practical applications. In the evolving landscape of medical decision support systems, integrating advanced technologies such as RAG and HL7 FHIR can significantly enhance clinical decision-making processes. Despite the potential of these technologies, there is limited research on their integration in practical applications.
Learning-Based Planning for Improving Science Return of Earth Observation Satellites
Venue: arXiv cs.AI 2026
Year: 2026

Earth observing satellites are powerful tools for collecting scientific information about our planet, however they have limitations: they cannot easily deviate from their orbital trajectories, their sensors have a limited field of view, and pointing and operating these sensors can take a large amount of the spacecraft's resources. It is important for these satellites to optimize the data they collect and include only the most important or informative measurements. Dynamic targeting is an emerging concept in which satellite resources and data from a lookahead instrument are used to intelligently reconfigure and point a primary instrument. Simulation studies have shown that dynamic targeting increases the amount of scientific information gathered versus conventional sampling strategies. In this work, we present two different learning-based approaches to dynamic targeting, using reinforcement and imitation learning, respectively. These learning methods build on a dynamic programming solution to plan a sequence of sampling locations. We evaluate our approaches against existing heuristic methods for dynamic targeting, showing the benefits of using learning for this application. Imitation learning performs on average 10.0\% better than the best heuristic method, while reinforcement learning performs on average 13.7\% better. We also show that both learning methods can be trained effectively with small amounts of data.
Quadrotor Navigation using Reinforcement Learning with Privileged Information
Venue: arXiv cs.RO 2026
Year: 2026

This paper presents a reinforcement learning-based quadrotor navigation method that leverages efficient differentiable simulation, novel loss functions, and privileged information to navigate around large obstacles. Prior learning-based methods perform well in scenes that exhibit narrow obstacles, but struggle when the goal location is blocked by large walls or terrain. In contrast, the proposed method utilizes time-of-arrival (ToA) maps as privileged information and a yaw alignment loss to guide the robot around large obstacles. The policy is evaluated in photo-realistic simulation environments containing large obstacles, sharp corners, and dead-ends. Our approach achieves an 86% success rate and outperforms baseline strategies by 34%. We deploy the policy onboard a custom quadrotor in outdoor cluttered environments both during the day and night. The policy is validated across 20 flights, covering 589 meters without collisions at speeds up to 4 m/s.
A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving
Venue: arXiv cs.NE 2026
Year: 2026

Large Language Models (LLMs) possess substantial reasoning capabilities and are increasingly applied to optimization tasks, particularly in synergy with evolutionary computation. However, while recent surveys have explored specific aspects of this domain, they lack an integrative perspective that connects problem modeling with solving workflows. To address this gap, we present a systematic review of recent developments and organize them within a structured framework. First, we classify existing research into two primary stages: LLMs for optimization modeling and LLMs for optimization solving. Second, we divide the latter into three paradigms based on the role of the LLM: stand-alone optimizers, low-level components embedded within algorithms, and high-level managers for algorithm selection and generation. Third, for each category, we analyze representative methods, distill technical challenges, and examine their interplay with traditional approaches. Finally, we review interdisciplinary applications across the natural sciences, engineering, and machine learning. Based on this analysis, we highlight key limitations and point toward future directions for developing self-evolving agentic ecosystems. An up-to-date collection of related literature is maintained at https://github.com/ishmael233/LLM4OPT.
Silent Until Sparse: Backdoor Attacks on Semi-Structured Sparsity
Venue: arXiv cs.CR 2026
Year: 2026

Semi-structured (2:4) sparsity is a widely adopted pruning method in modern hardware and software ecosystems (e.g., NVIDIA Sparse Tensor Cores and PyTorch), achieving up to 2X faster inference and reduced memory footprint with negligible accuracy loss. It removes two out of every four contiguous weights, using permutations to ensure the largest-magnitude weights are retained. In this work, we show that this predictable mechanism can be exploited to design Silent Until Sparse (SUS), a novel compression-activated backdoor attack tailored to the 2:4 sparsity regime. SUS employs a two-phase training procedure that modifies (i) the weights that will be retained after pruning to embed the backdoor, and (ii) the weights that will be pruned to hide it in the dense model. SUS also provides formal guarantees that the attack will be successfully activated after sparsification. Experiments show that SUS is largely effective against semi-structured sparsification across both hardware-accelerated and software pipelines, outperforming existing compression-aware backdoor attacks, bypassing standard defenses, and even being robust to user-side fine-tuning.
Weaker Assumptions for Asymmetric Trust
Venue: arXiv cs.DC 2026
Year: 2026

In distributed systems with asymmetric trust, each participant is free to make its own trust assumptions about others, captured by an asymmetric quorum system. This contrasts with ordinary, symmetric quorum systems and threshold models, where trust assumptions are uniformly shared among participants. Fundamental problems like reliable broadcast and consensus are unsolvable in the asymmetric model if quorum systems satisfy only the classical properties of consistency and availability. Existing approaches overcome this by introducing stronger assumptions. We show that some of these assumptions are overly restrictive, so much so that they effectively eliminate the benefits of asymmetric trust. To address this, we propose a new approach to characterize asymmetric problems and, building upon it, present algorithms for reliable broadcast and consensus that require weaker assumptions than previous solutions. Our methods are general and can be extended to other core problems in systems with asymmetric trust.
Cognitive Affordances in Visualization: Related Constructs, Design Factors, and Framework
Venue: arXiv cs.HC 2026
Year: 2026

Classically, affordance research investigates how the shape of objects communicates actions to potential users. Cognitive affordances, a subset of this research, characterize how the design of objects influences cognitive actions, such as information processing. Within visualization, cognitive affordances inform how graphs' design decisions communicate information to their readers. Although several related concepts exist in visualization, a formal translation of affordance theory to visualization is still lacking. In this paper, we review and translate affordance theory to visualization by formalizing how cognitive affordances operate within a visualization context. We also review common methods and terms, and compare related constructs to cognitive affordances in visualization. Based on a synthesis of research from psychology, human computer interaction, and visualization, we propose a framework of cognitive affordances in visualization that enumerates design decisions and reader characteristics that influence a visualization's hierarchy of communicated information. Finally, we demonstrate how this framework can guide the evaluation and redesign of visualizations.
Generative Diffusion Contrastive Network for Multi-View Clustering
Venue: arXiv cs.CV 2026
Year: 2026

In recent years, Multi-View Clustering (MVC) has been significantly advanced under the influence of deep learning. By integrating heterogeneous data from multiple views, MVC enhances clustering analysis, making multi-view fusion critical to clustering performance. However, there is a problem of low-quality data in multi-view fusion. This problem primarily arises from two reasons: 1) Certain views are contaminated by noisy data. 2) Some views suffer from missing data. This paper proposes a novel Stochastic Generative Diffusion Fusion (SGDF) method to address this problem. SGDF leverages a multiple generative mechanism for the multi-view feature of each sample. It is robust to low-quality data. Building on SGDF, we further present the Generative Diffusion Contrastive Network (GDCN). Extensive experiments show that GDCN achieves the state-of-the-art results in deep MVC tasks. The source code is publicly available at https://github.com/HackerHyper/GDCN.
ButterflyQuant: Ultra-low-bit LLM Quantization through Learnable Orthogonal Butterfly Transforms
Venue: arXiv cs.LG 2026
Year: 2026

Large language models require massive memory footprints, severely limiting deployment on consumer hardware. Quantization reduces memory through lower numerical precision, but extreme 2-bit quantization suffers from catastrophic performance loss due to outliers in activations. Rotation-based methods such as QuIP and QuaRot apply orthogonal transforms to eliminate outliers before quantization, using computational invariance: $\mathbf{y} = \mathbf{Wx} = (\mathbf{WQ}^T)(\mathbf{Qx})$ for orthogonal $\mathbf{Q}$. However, these methods use fixed transforms--Hadamard matrices achieving optimal worst-case coherence $μ= 1/\sqrt{n}$--that cannot adapt to specific weight distributions. We identify that different transformer layers exhibit distinct outlier patterns, motivating layer-adaptive rotations rather than one-size-fits-all approaches. In this work, we propose ButterflyQuant, which replaces Hadamard rotations with learnable butterfly transforms parameterized by continuous Givens rotation angles. Unlike Hadamard's discrete $\{+1, -1\}$ entries that are non-differentiable and thus prohibit gradient-based learning, butterfly transforms' continuous parameterization enables smooth optimization while guaranteeing orthogonality by construction. This orthogonal constraint ensures theoretical guarantees in outlier suppression while achieving $O(n \log n)$ computational complexity with only $\frac{n \log n}{2}$ learnable parameters. We further introduce a uniformity regularization on post-transformation activations to promote smoother distributions amenable to quantization. Learning requires only 128 calibration samples and converges in minutes on a single GPU.
Faster and Memory-Efficient Training of Sequential Recommendation Models for Large Catalogs
Venue: arXiv cs.IR 2026
Year: 2026

Sequential recommendations (SR) with transformer-based architectures are widely adopted in real-world applications, where SR models require frequent retraining to adapt to ever-changing user preferences. However, training transformer-based SR models often encounters a high computational cost associated with scoring extensive item catalogs, often exceeding thousands of items. This occurs mainly due to the use of cross-entropy loss, where peak memory scales proportionally to catalog size, batch size, and sequence length. Recognizing this, practitioners in the field of recommendation systems typically address memory consumption by integrating the cross-entropy (CE) loss with negative sampling, thereby reducing the explicit memory demands of the final layer. However, a small number of negative samples would degrade model performance, and as we demonstrate in our work, increasing the number of negative samples and the batch size further improves the model's performance, but rapidly starts to exceed industrial GPUs' size (~40Gb). In this work, we introduce the CCE- method, which offers a GPU-efficient implementation of the CE loss with negative sampling. Our method accelerates training by up to two times while reducing memory consumption by more than 10 times. Leveraging the memory savings afforded by using CCE- for model training, it becomes feasible to enhance its accuracy on datasets with a large item catalog compared to those trained with original PyTorch-implemented loss functions. Finally, we perform an analysis of key memory-related hyperparameters and highlight the necessity of a delicate balance among these factors. We demonstrate that scaling both the number of negative samples and batch size leads to better results rather than maximizing only one of them. To facilitate further adoption of CCE-, we release a Triton kernel that efficiently implements the proposed method.
Enhancing LLM-based Specification Generation via Program Slicing and Logical Deletion
Venue: arXiv cs.SE 2026
Year: 2026

Traditional formal specification generation methods are typically tailored to specific specification types, and therefore suffer from limited generality. In recent years, large language model (LLM)-based specification generation approaches have emerged, offering a new direction for improving the universality of automated specification synthesis. However, when dealing with complex control flow, LLMs often struggle to precisely generate complete specifications that cover substructures. Moreover, the distinctive verification pipelines adopted by existing approaches may incorrectly discard logically correct specifications, while verification tools alone cannot reliably identify correct specifications. To address these issues, we propose SLD-Spec, an LLM-based specification generation method that combines program slicing and logical deletion. Specifically, SLD-Spec augments the conventional specification generation framework with two key stages: (1) a program slicing stage that decomposes the target function into several smaller code slices, enabling LLMs to focus on more localized semantic structures and thereby improving specification relevance and completeness; and (2) a logical deletion stage that leverages LLMs to perform logical reasoning and filtering over candidate specifications so as to retain logically correct ones. Experimental results show that SLD-Spec consistently outperforms existing methods on datasets containing programs of varying complexity, verifying more programs and generating specifications that are more relevant and more complete. Further ablation studies indicate that program slicing mainly improves specification relevance and completeness, whereas logical deletion plays a key role in increasing verification success rates.
Linguistic trajectories of bipolar disorder on social media
Venue: arXiv cs.CL 2026
Year: 2026

Language use offers valuable insight into affective disorders such as bipolar disorder (BD), yet past research has been cross-sectional and limited in scale. Here, we demonstrate that social media records can be leveraged to study longitudinal language change associated with BD on a large scale. Using a novel method to infer diagnosis timelines from user self-reports, we compared users self-identifying with BD, depression, or no mental health condition. The onset of BD diagnosis corresponded with widespread linguistic shifts reflecting mood disturbance, psychiatric comorbidity, substance abuse, hospitalization, medical comorbidities, interpersonal concerns, unusual thought content, and altered linguistic coherence. In the years following the diagnosis, discussions of mood symptoms were found to fluctuate periodically with a dominant 12-month cycle consistent with seasonal mood variation. These findings suggest that social media language captures linguistic and behavioral changes associated with BD and might serve as a valuable complement to traditional psychiatric cohort research.
AttnBoost: Retail Supply Chain Sales Insights via Gradient Boosting Perspective
Venue: arXiv cs.LG 2026
Year: 2026

Forecasting product demand in retail supply chains presents a complex challenge due to noisy, heterogeneous features and rapidly shifting consumer behavior. While traditional gradient boosting decision trees (GBDT) offer strong predictive performance on structured data, they often lack adaptive mechanisms to identify and emphasize the most relevant features under changing conditions. In this work, we propose AttnBoost, an interpretable learning framework that integrates feature-level attention into the boosting process to enhance both predictive accuracy and explainability. Specifically, the model dynamically adjusts feature importance during each boosting round via a lightweight attention mechanism, allowing it to focus on high-impact variables such as promotions, pricing, and seasonal trends. We evaluate AttnBoost on a large-scale retail sales dataset and demonstrate that it outperforms standard machine learning and deep tabular models, while also providing actionable insights for supply chain managers. An ablation study confirms the utility of the attention module in mitigating overfitting and improving interpretability. Our results suggest that attention-guided boosting represents a promising direction for interpretable and scalable AI in real-world forecasting applications.
ASL360: AI-Enabled Adaptive Streaming of Layered 360$^\circ$ Video over UAV-assisted Wireless Networks
Venue: arXiv cs.NI 2026
Year: 2026

We propose ASL360, an adaptive deep reinforcement learning-based scheduler for on-demand 360$^\circ$ video streaming to mobile VR users in next generation wireless networks. We aim to maximize the overall Quality of Experience (QoE) of the users served over a UAV-assisted 5G wireless network. Our system model comprises a macro base station (MBS) and a UAV-mounted base station which both deploy mm-Wave transmission to the users. The 360$^\circ$ video is encoded into dependent layers and segmented tiles, allowing a user to schedule downloads of each layer's segments. Furthermore, each user utilizes multiple buffers to store the corresponding video layer's segments. We model the scheduling decision as a Constrained Markov Decision Process (CMDP), where the agent selects Base or Enhancement layers to maximize the QoE and use a policy gradient-based method (PPO) to find the optimal policy. Additionally, we implement a dynamic adjustment mechanism for cost components, allowing the system to adaptively balance and prioritize the video quality, buffer occupancy, and quality change based on real-time network and streaming session conditions. We demonstrate that ASL360 significantly improves the QoE, achieving approximately 2 dB higher average video quality, 80% lower average rebuffering time, and 57% lower video quality variation, relative to competitive baseline methods. Our results show the effectiveness of our layered and adaptive approach in enhancing the QoE in immersive videostreaming applications, particularly in dynamic and challenging network environments.