Qilu Zhu | Searcharxiv

arXiv2026

AlphaSeek: Trajectory-Level Self-Iterative Factor Mining Framework for Multi-Source Financial Data

With the rapid rise of large language models, LLM-driven quantitative factor mining has become an increasingly active research area. However, existing methods still suffer from subjective direction design, limited integration of up-to-date multi-source information, semantic drift, factor redundancy, and the absence of an end-to-end feedback loop from factor discovery to portfolio backtesting. To address these limitations, we propose AlphaSeek, an end-to-end factor mining framework for quantitative investment that integrates automated direction discovery, trajectory-level factor evolution mining and self-iterative portfolio optimization. AlphaSeek first collects and summarizes multi-source financial information to identify promising mining directions. It then performs trajectory-level factor mining by extending the optimization unit from a single factor expression to a complete research trajectory covering hypothesis generation, factor construction, validation, backtesting, and feedback. Based on this design, we introduce evolution operators - parallel direction expansion, mutation and crossover - to improve search diversity, refinement quality and factor robustness. Finally, AlphaSeek constructs a self-iterative factor portfolio, allowing newly discovered factors to interact with an existing state-of-the-art(SOTA) factor library under redundancy-aware constraints. Experiments on CSI300 show that AlphaSeek achieves the strongest overall strategy-level performance on CSI300 with ARR of 8.28%, IR of 1.29 and MDD of 6.28%, while remaining competitive on factor predictive metrics with IC of 0.0454, while factors mined on CSI300 also achieve strong time-series return performance on CSI500 than other models, suggesting promising cross-market transferability under a zero-shot setting.

cs.CE↗

arXiv2026

SpikeRestormer: Towards Energy-Efficient All-in-One Image Restoration via Unified Event Reasoning

ANN-based All-in-One image restoration (AiOIR) unifies diverse degradation handling but incurs high computational costs, limiting its real-time deployment. While Spiking Neural Networks (SNNs) offer a low-power alternative, applying them to static images remains challenging. This difficulty arises because explicit event signals are absent, and degradation cues are heavily entangled with scene structures, hindering the learning of reliable restoration-oriented spike events. To address these issues, we propose SpikeRestormer, an energy-efficient SNN for AiOIR that performs event reasoning over internally generated spike cues. Specifically, we propose a degradation-event perception process to extract spike-based degradation events through Subtractive Degradation Event Attention (SDEA). Moreover, we introduce Hierarchical Bayesian Skip Masking (HBSM) and Additive Restoration Event Attention (AREA) processes for event-reliability inference and restoration-event construction, respectively. By integrating these complementary processes, SpikeRestormer formulates restoration as a unified process of degradation-event perception, degradation-event reliability inference, and restoration-event construction, liberating the potential of SNNs for energy-efficient AiOIR. Extensive experiments show that SpikeRestormer delivers competitive performance against ANN-based methods and establishes new state-of-the-art results among SNN-based methods with significantly lower energy consumption.

cs.CV↗