Long-term time series prediction is of great value in finance, meteorology, and industry. The early mainstream frameworks are statistic-based models such as ARIMA and Moving Average. Such models were gradually replaced by deep learning models because they could not handle complex nonlinear relationships. Well-known deep models include RNN, LSTM, GRU, etc. However, these models exhibit limitations in capturing long-term temporal dependencies. The recent success of Transformer in natural language processing (NLP) has inspired their adaptation for time series forecasting. Their attention mechanism can model dependencies at arbitrary time steps, which is theoretically suitable for long-term prediction tasks. However, studies have shown that a simple MLP model can outperform Transformer-based approaches, which has led to renewed interest in MLP-based forecasting frameworks. A limitation of existing methods is that they mainly focus on time domain features and ignore frequency domain information. Although some studies have attempted to use frequency domain information, they examine single variables in isolation, and fail to capture frequency domain correlations across variables. To address these challenges, we propose a model based on cross-spectral entropy filtering and causal dilated pyramid convolution, called CSConv. Specifically, CSConv uses cross-spectral entropy for adaptive frequency-domain filtering, preserving multivariate joint spectral characteristics. In addition, CSConv includes a time-weighted causal dilated pyramid convolution, which captures multi-scale dependencies through dilation structures and temporal decay weights. The final prediction is achieved through an MLP-based architecture that decomposes trend, cyclical, and residual components. We validate the performance of CSConv on eight real-world datasets and compare the results with advanced baseline models. Experiments show that our model achieves competitive results on multiple evaluation metrics.
Quantizing large language models (LLMs) significantly reduces memory usage and computational requirements, enabling efficient on-device inference. However, aggressive quantization can degrade model performance and exacerbate prediction uncertainty. To address this critical issue, we propose a logits-based calibration strategy where the model is restricted to generating a single token from a limited set of predefined decision tokens. By applying a temperature-scaled softmax directly on the logits corresponding to these tokens, we obtain calibrated and interpretable probability distributions, explicitly circumventing stochastic methods such as top-k sampling by directly leveraging deterministic logit values, revealing subtle behavioral shifts caused by quantization. Using Qwen-2.5 models ranging from 7 B to 72 B parameters at various quantization levels (2, 4, 6 and 8-bit), we evaluate our method across four recently released benchmarks encompassing regression (README++, CompLex-ZH, GIRAI) and classification (DarkBench) tasks. Thus, minimizing the risk of data leakage into pre-training data. Results indicate moderate quantization (4-bit) as optimal, particularly when combined with minimal few-shot prompting, enabling quantized LLMs to closely match or surpass proprietary models such as GPT-4o and GPT-4.1 in certain tasks. Our opensource toolkit facilitates straightforward deployment of reliable, uncertainty-aware quantized LLMs for privacy-preserving, ondevice inference, making them suitable for sensitive settings such as human-subject economic experiments and survey analysis.
With the rapid development of artificial intelligence (AI) and manufacturing, fault diagnosis (FD), as a critical technology for ensuring equipment safety and improving production efficiency, faces challenges such as increasing data complexity and higher requirements for diagnostic accuracy. Traditional FD methods primarily rely on expert experience and statistical learning, exhibiting limitations in handling multimodal data fusion, knowledge representation, and reasoning. Large language models (LLMs) have brought new opportunities to the field of FD. This review systematically examines the current applications, technical approaches, and development trends of LLMs in FD. Through comparative analysis of the advantages and disadvantages of different technical approaches, we summarize the main achievements and existing technical bottlenecks in current research. LLMs demonstrate clear advantages in FD, however, they also face challenges such as high data quality requirements, significant computational resource consumption, and limited domain adaptability. Future development trends are expected to focus on model lightweighting, domain specialization, multimodal fusion, and enhanced real-time diagnostic capabilities. This review offers systematic theoretical guidance and technical reference for researchers and engineering professionals.
This study proposes a multi-agent Large Language Model (LLM) framework for formulaic alpha generation and selection in quantitative finance. In this context, an alpha refers to a predictive trading signal or mathematical expression designed to capture excess returns beyond market benchmarks. While a single LLM can generate candidate alphas, relying on one model often results in redundancy, limited diversity, and suboptimal signal quality. To address this, we adopt a multi-agent approach where DeepSeek is responsible for generating a broad pool of formulaic alphas, and Gemini serves as an evaluator that selects the top ten most important alphas from this pool based on criteria such as predictive strength and diversity of signals. To assess the effectiveness of the framework, we compare two experimental setups: (1) an equal-weighted portfolio constructed from the top ten alphas selected by Gemini, and (2) an equal-weighted portfolio of all 50 alphas generated by DeepSeek. Empirical results demonstrate that the multi-agent framework can achieve better portfolio performance, delivering higher cumulative returns, improved risk-adjusted measures, and lower maximum drawdown relative to the full alpha pool. These findings highlight the potential of multi-agent LLM collaboration in enhancing alpha discovery and systematic trading strategies.
We present Cognitive SOC, a multi-agent system leveraging Large Language Models (LLMs) to transform raw security alerts into evidence-backed narratives with complete citation chains. The proposed architecture employs specialized Triage, Enrichment, and StoryWeaver agents that collaborate to analyze security incidents and gather contextual intelligence through integration with threat-intelligence frameworks, including MITRE ATT&CK, Sigma detection rules (via a curated subset), and CVE databases. Evaluated on 1,000 alerts from the UNSW-NB15 dataset, the system achieved 99.6% evidence linkage rate and 100% citation coverage while maintaining an exceptionally low hallucination rate of 0.1%. The Cognitive SOC demonstrated superior accountability compared to baseline approaches, with a mean time-to-recommendation of 1.860 seconds and comprehensive evidence tracking. The system generated an average of 3.8 evidence artifacts and 5.4 citations per alert, substantially outperforming single-agent RAG (3.0 evidence, 0 citations, 11.8% hallucination) and static runbook baselines (1.0 evidence, 0 citations). This work fundamentally reframes security operations as collaborative human-AI storytelling, shifting the analyst role from manual data correlation to narrative validation and strategic decision-making.
