February 2026

In the rapidly evolving landscape of 2026, “AI-first” is no longer a buzzword — it is an architectural requirement. For fintech institutions, the ability to automate credit decisions while maintaining strict compliance is the holy grail In this deep dive, we will build a production-grade Bank Loan Approval Workflow. We won’t just move entities from “Draft” to “Approved.” We will inject a cognitive layer into the state machine using symfony/workflow and symfony/ai-bundle. Our system will automatically score loan applications and dynamically route them: high-scoring applications get […]

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Hiring talented people with the right attitude and right skills is not enough, they also need the right environment to thrive—are leaders in the organization only focused on business deliverables or do they also invest in their team’s learning and growth? Do they encourage people to speak up and share their disagreements or do they shut down voices that differ with their own perspective? Are people in the team clear on their priorities and aligned with a long-term […]

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We demonstrate that modern machine-learning methods can autonomously reconstruct several flagship analytic structures in scattering amplitudes directly from numerical on-shell data. In particular, we show that the Kawai–Lewellen–Tye (KLT) relations can be rediscovered using symbolic regression applied to colour-ordered Yang–Mills amplitudes with Mandelstam invariants as input features. Using standard feature-selection techniques, specifically column-pivoted QR factorisation, we simultaneously recover the Kleiss–Kuijf and Bern–Carrasco–Johansson (BCJ) relations, identifying a minimal basis of partial amplitudes without any group-theoretic input. We obtain the […]

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Federated learning (FL) enables distributed model training across edge devices while preserving data locality. This decentralized approach has emerged as a promising solution for collaborative learning on sensitive user data, effectively addressing the longstanding privacy concerns inherent in centralized systems. However, the decentralized nature of FL exposes new security vulnerabilities, especially backdoor attacks that threaten model integrity. To investigate this critical concern, this paper presents the Layer Smoothing Attack (LSA), a novel backdoor attack that exploits layer-specific vulnerabilities […]

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Learning from electronic health records (EHRs) time series is challenging due to irregular sam- pling, heterogeneous missingness, and the resulting sparsity of observations. Prior self-supervised meth- ods either impute before learning, represent missingness through a dedicated input signal, or optimize solely for imputation, reducing their capacity to efficiently learn representations that support clinical downstream tasks. We propose the Augmented-Intrinsic Dual-Masked Autoencoder (AID-MAE), which learns directly from incomplete time series by applying an intrinsic missing mask to represent naturally […]

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Quantum unitary synthesis addresses the problem of translating abstract quantum algorithms into sequences of hardware-executable quantum gates. Solving this task exactly is infeasible in general due to the exponential growth of the underlying combinatorial search space. Existing approaches suffer from misaligned optimization objectives, substantial training costs and limited generalization across different qubit counts. We mitigate these limitations by using supervised learning to approximate the minimum description length of residual unitaries and combining this estimate with stochastic beam search […]

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Quantum unitary synthesis addresses the problem of translating abstract quantum algorithms into sequences of hardware-executable quantum gates. Solving this task exactly is infeasible in general due to the exponential growth of the underlying combinatorial search space. Existing approaches suffer from misaligned optimization objectives, substantial training costs and limited generalization across different qubit counts. We mitigate these limitations by using supervised learning to approximate the minimum description length of residual unitaries and combining this estimate with stochastic beam search […]

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Hey guys i have recently started learning about RL, dont know much in depth but focused more on implementing it in the stock market. I am not looking for some crazy unrealistic returns… just want to make something that can perform better than the market and want to learn along the way. My current roadmap is to just test how different models are performing on a basic level. I’d appreciate any kind of help or suggestion come my […]

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