Author(s): Shashwata Bhattacharjee Originally published on Towards AI. The narrative of solo founders building eight-figure SaaS businesses using AI tools has become increasingly prevalent in entrepreneurial discourse. While the surface-level story focuses on individual success, the underlying technical transformation represents something far more fundamental: a complete decomposition of the traditional software development stack, enabled by the convergence of large language models, code generation capabilities, and automation frameworks. This analysis examines the technical architecture, economic implications, and systemic changes […]

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arXiv:2602.12485v1 Announce Type: new Abstract: The success of businesses depends on their ability to convert consumers into loyal customers. A customer’s value proposition is a primary determinant in this process, requiring a balance between affordability and long-term brand equity. Broad marketing campaigns can erode perceived brand value and reduce return on investment, while existing economic algorithms often misidentify highly engaged customers as ideal targets, leading to inefficient engagement and conversion outcomes. This work introduces a two-stage multi-model architecture […]

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This is a different kind of post. Less philosophy, more mud and conduit. My partner and I are building a thing in the woods behind our house. It’s called Orpheus — an autonomous research station that monitors wildlife, identifies bird species, tracks corvid vocalizations, counts visitors, makes pretty charts, monitors the weather, and eventually talks back. The hardware: an NVIDIA Jetson Orin NX, four cameras, four research-grade microphones, a hand-built weatherproof enclosure, and a 12-foot mast in Clare […]

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arXiv:2604.01310v1 Announce Type: new Abstract: Large vision-language models (VLMs) excel on general benchmarks but often lack robustness in medical imaging, where heterogeneous supervision induces cross-dataset interference and sensitivity to data regime (i.e., how the supervisory signals are mixed). In realistic clinical workflows, data and tasks arrive sequentially, so naive continual training further leads to catastrophic forgetting. To address these challenges, we propose MedQwen, a parameter-efficient medical VLM that couples a spectrally routed Mixture-of-Experts (MoE) with a theoretically grounded […]

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arXiv:2604.02408v1 Announce Type: new Abstract: Asynchronous inference has emerged as a prevalent paradigm in robotic manipulation, achieving significant progress in ensuring trajectory smoothness and efficiency. However, a systemic challenge remains unresolved, as inherent latency causes generated actions to inevitably lag behind the real-time environment. This issue is particularly exacerbated in dynamic scenarios, where such temporal misalignment severely compromises the policy’s ability to interpret and react to rapidly evolving surroundings. In this paper, we propose a novel framework that […]

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This work presents a series of interconnected mathematical emph{constructions} that take the zeros of the Riemann zeta function as primordial elements. Rather than seeking a conventional proof of the Riemann Hypothesis, we investigate: what kind of mathematical reality emerges when we emph{postulate} that these zeros form the spectrum of an operator within a specific geometric arena? Our constructions reveal a remarkable chain of coherence, linking geometry (minimal surfaces), topology (M”obius bands), statistics (GUE), and fundamental physical constants. Within […]

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Message Passing Neural Networks have recently become the most popular approach to graph machine learning tasks; however, their receptive field is limited by the number of message passing layers. To increase the receptive field, Graph Transformers with global attention have been proposed; however, global attention does not take into account the graph topology and thus lacks graph-structure-based inductive biases, which are typically very important for graph machine learning tasks. In this work, we propose an alternative approach: cluster […]

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arXiv:2604.13151v1 Announce Type: new Abstract: Language Model (LM) agents are increasingly used in complex open-ended decision-making tasks, from AI coding to physical AI. A core requirement in these settings is the ability to both explore the problem space and exploit acquired knowledge effectively. However, systematically distinguishing and quantifying exploration and exploitation from observed actions without access to the agent’s internal policy remains challenging. To address this, we design controllable environments inspired by practical embodied AI scenarios. Each environment […]

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arXiv:2602.09182v1 Announce Type: new Abstract: Machine learning relies on randomness as a fundamental component in various steps such as data sampling, data augmentation, weight initialization, and optimization. Most machine learning frameworks use pseudorandom number generators as the source of randomness. However, variations in design choices and implementations across different frameworks, software dependencies, and hardware backends along with the lack of statistical validation can lead to previously unexplored attack vectors on machine learning systems. Such attacks on randomness sources […]

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