January 2026

arXiv:2601.18811v1 Announce Type: new Abstract: This paper presents a Quantum Reinforcement Learning (QRL) solution to the dynamic portfolio optimization problem based on Variational Quantum Circuits. The implemented QRL approaches are quantum analogues of the classical neural-network-based Deep Deterministic Policy Gradient and Deep Q-Network algorithms. Through an empirical evaluation on real-world financial data, we show that our quantum agents achieve risk-adjusted performance comparable to, and in some cases exceeding, that of classical Deep RL models with several orders of […]

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arXiv:2601.18803v1 Announce Type: new Abstract: This paper proposes a task-agnostic discovery layer for multivariate time series that constructs a relational hypothesis graph over entities without assuming linearity, stationarity, or a downstream objective. The method learns window-level sequence representations using an unsupervised sequence-to-sequence autoencoder, aggregates these representations into entity-level embeddings, and induces a sparse similarity network by thresholding a latent-space similarity measure. This network is intended as an analyzable abstraction that compresses the pairwise search space and exposes candidate […]

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arXiv:2601.18802v1 Announce Type: new Abstract: Augmented Reality (AR) is a powerful perceptual technology that can alter what users see, hear, feel, and experience throughout their daily lives. When combined with the speed and flexibility of context-aware generative AI, the power is greatly expanded, allowing individual users to be targeted with custom-generated AR experiences that are instantly tailored to who they are, where they are, and what they are doing. This can transform the physical world into a magical […]

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arXiv:2601.18800v1 Announce Type: new Abstract: Triad magnetometer components change with sensor attitude even when the IGRF total intensity target stays invariant. NavFormer forecasts this invariant target with rotation invariant scalar features and a Canonical SPD module that stabilizes the spectrum of window level second moments of the triads without sign discontinuities. The module builds a canonical frame from a Gram matrix per window and applies state dependent spectral scaling in the original coordinates. Experiments across five flights show […]

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arXiv:2601.18799v1 Announce Type: new Abstract: As digital twins (DTs) evolve to become more agentic through the integration of artificial intelligence (AI), they acquire capabilities that extend beyond dynamic representation of their target systems. This paper presents a taxonomy of agentic DTs organised around three fundamental dimensions: the locus of agency (external, internal, distributed), the tightness of coupling (loose, tight, constitutive), and model evolution (static, adaptive, reconstructive). From the resulting 27-configuration space, we identify nine illustrative configurations grouped into […]

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arXiv:2601.18798v1 Announce Type: new Abstract: ECG-Language Models (ELMs) extend recent progress in Multimodal Large Language Models (MLLMs) to automated ECG interpretation. However, most ELMs follow Vision-Language Model (VLM) designs and depend on pretrained ECG encoders, adding architectural and training complexity. Inspired by encoder-free VLMs, we introduce ELF, an encoder-free ELM that replaces the ECG encoder with a single projection layer trained jointly with the LLM. Across five datasets, ELF matches or exceeds state-of-the-art ELMs that use far more […]

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👩🏼‍❤️‍👨🏻 Dating as a timeless human topic Dating is one of the oldest human problems we continue to try to solve with technology, probably because it sits at the intersection of hope, vulnerability, ego, and timing, and rarely follows logic. Long before apps existed, people obsessed over mixed signals, situationships (before they had a name), and spiraling into why this person, why now, and why did it suddenly go cold? Technology didn’t invent this emotional chaos; it simply […]

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Artificial intelligence is making automation accelerate faster than previous waves. With AI as a propellant, the work of building automation increasingly becomes automatable itself. As it becomes cheaper and easier to mechanize tasks, automation spreads faster—and speeds up as it spreads. The result is that the current wave of AI adoption is likely to produce major disruption in the labor market. In earlier automation waves, the limiting factor was rarely whether a task could be automated in theory. The real […]

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Reinforcement learning has empowered large language models to act as intelligent agents, yet training them for long-horizon tasks remains challenging due to the scarcity of high-quality trajectories, especially under limited resources. Existing methods typically scale up rollout sizes and indiscriminately allocate computational resources among intermediate steps. Such attempts inherently waste substantial computation budget on trivial steps while failing to guarantee sample quality. To address this, we propose textbf{Spark} (textbf{S}trategic textbf{P}olicy-textbf{A}ware explotextbf{R}ation via textbf{K}ey-state dynamic branching), a novel framework […]

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Robust reinforcement learning methods typically focus on suppressing unreliable experiences or corrupted rewards, but they lack the ability to reason about the reliability of their own learning process. As a result, such methods often either overreact to noise by becoming overly conservative or fail catastrophically when uncertainty accumulates. In this work, we propose a meta-cognitive reinforcement learning framework that enables an agent to assess, regulate, and recover its learning behavior based on internally estimated reliability signals. The proposed […]

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