Institution profile

Warsaw University of Technology

Academic institutioneurope · pl
Official website
Research library304linked papers
Opportunities0open roles
Selected work

Representative Papers

A Finite Difference Approximation of Second Order Regularization of Neural-SDFs

Nov 12, 2025

To address the high computational and memory overhead of curvature regularization in neural signed distance field (SDF) learning—stemming from reliance on second-order automatic differentiation—this paper proposes a lightweight finite-difference-based regularization framework. We introduce, for the first time, an O(h²)-accurate finite-difference stencil for explicit SDF curvature modeling, bypassing Hessian construction and second-order gradients entirely. The method enables plug-and-play approximations of both Gaussian curvature and rank-deficiency loss. Empirically, it matches the reconstruction accuracy of automatic-differentiation-based curvature regularization while reducing GPU memory consumption and training time by up to 50%. Moreover, it demonstrates strong robustness to sparse, incomplete, and non-CAD data. Our core contribution is achieving high-fidelity geometric regularization at the cost of only low-order differentiation, thereby significantly improving the efficiency and scalability of neural SDF learning.

1 citationsRead paper

Integrating Traditional Technical Analysis with AI: A Multi-Agent LLM-Based Approach to Stock Market Forecasting

Jun 20, 2025International Conference on Agents and Artificial Intelligence

Traditional technical analysis suffers from limited predictive accuracy in financial markets characterized by nonlinearity, high noise, and frequent exogenous shocks. To address this, we propose ElliottAgents—a novel interpretable AI trading system that is the first to deeply integrate Elliott Wave Theory into a large language model (LLM)-based multi-agent framework, synergizing retrieval-augmented generation (RAG), deep reinforcement learning (DRL), and multi-agent coordination. Our system enables automated wave-pattern recognition, cross-temporal trend inference, and decision traceability, thereby bridging interpretability and adaptability in AI-driven trading. Empirical evaluation on historical U.S. equity data demonstrates that ElliottAgents significantly outperforms baseline methods: waveform identification accuracy and trend prediction stability are markedly improved, with signal accuracy increasing by 23.6% and strategy Sharpe ratio rising by 31.4%.

1 citationsRead paper

Applying Informer for Option Pricing: A Transformer-Based Approach

Jun 05, 2025International Conference on Agents and Artificial Intelligence

Option pricing faces challenges in modeling market nonlinearity, time-varying volatility, and long-range dependencies. Conventional approaches—such as the Black–Scholes model and LSTM-based methods—exhibit limited robustness and adaptability to rapidly evolving financial dynamics. This paper introduces, for the first time, the lightweight and efficient Informer architecture to option pricing. Leveraging ProbSparse self-attention, distilling encoders, and a generative decoder, Informer effectively captures the dynamic structure of high-frequency, heterogeneous financial time series. The proposed data-driven framework significantly enhances real-time responsiveness to market regime shifts and improves generalization capability. Empirical evaluations across multiple markets demonstrate that the method reduces average pricing error by 37% relative to both Black–Scholes and LSTM baselines. Moreover, it achieves superior prediction stability and markedly improved cross-maturity and cross-contract generalization. This work establishes a novel paradigm for high-accuracy, low-latency derivative pricing.

1 citationsRead paper
Recent publications

Latest Papers