Mechanism design is pivotal to federated learning (FL) for maximizing social welfare by coordinating self-interested clients. Existing mechanisms, however, often overlook the network effects of client participation and the diverse model performance requirements (i.e., generalization error) across applications, leading to suboptimal incentives and social welfare, or even inapplicability in real deployments. To address this gap, we explore incentive mechanism design for FL with network effects and application-specific requirements of model performance. We develop a theoretical model to quantify […]

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arXiv:2601.16443v1 Announce Type: new Abstract: Environments are the bottleneck for self-improving agents. Current terminal benchmarks were built for evaluation, not training; reinforcement learning requires a scalable pipeline, not just a dataset. We introduce Endless Terminals, a fully autonomous pipeline that procedurally generates terminal-use tasks without human annotation. The pipeline has four stages: generating diverse task descriptions, building and validating containerized environments, producing completion tests, and filtering for solvability. From this pipeline we obtain 3255 tasks spanning file operations, […]

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arXiv:2602.06146v1 Announce Type: cross Abstract: In recent work it is shown that Q-learning with linear function approximation is stable, in the sense of bounded parameter estimates, under the $(varepsilon,kappa)$-tamed Gibbs policy; $kappa$ is inverse temperature, and $varepsilon>0$ is introduced for additional exploration. Under these assumptions it also follows that there is a solution to the projected Bellman equation (PBE). Left open is uniqueness of the solution, and criteria for convergence outside of the standard tabular or linear MDP […]

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Working on a system where coding tasks flow through a multi-stage pipeline, with each stage receiving a curated context subset (full, summary, or omitted per field) rather than full project state. Key observations: focused context produces better code than noisy context. Bounded retry loops with differentiated prompts (first-pass vs remediation) resolve most test failures. Git worktree isolation enables true parallel execution. Model-agnostic (Claude, GPT, Ollama). swimcode.ai. Interested in whether others have measured scoped vs full context in multi-agent […]

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I have just recently released a free-to-use open source, local python implementation of a Multi Method Reinforcement Learning pipeline with no 3rd party paid requirements or sign-ups. It’s as simple as clone, configure, run. The repo contains full documentation and pipeline explanations, is made purely for consumer hardware compatibility, and works with any existing codebase or projects.Setup is as straightforward with extremely customizable configurations alongside the entire pipeline is one python file. Context and Motivations: I’m doing this […]

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arXiv:2601.05586v1 Announce Type: cross Abstract: Neural networks have shown state-of-the-art performances in various classification and regression tasks. Rectified linear units (ReLU) are often used as activation functions for the hidden layers in a neural network model. In this article, we establish the connection between the Poisson hyperplane processes (PHP) and two-layer ReLU neural networks. We show that the PHP with a Gaussian prior is an alternative probabilistic representation to a two-layer ReLU neural network. In addition, we show […]

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Large-scale Electronic Health Record (EHR) databases have become indispensable in supporting clinical decision-making through data-driven treatment recommendations. However, existing medication recommender methods often struggle with a user (i.e., patient) cold-start problem, where recommendations for new patients are usually unreliable due to the lack of sufficient prescription history for patient profiling. While prior studies have utilized medical knowledge graphs to connect medication concepts through pharmacological or chemical relationships, these methods primarily focus on mitigating the item cold-start issue and […]

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Key Highlights: Liquid AI Launches New On-Device AI Models for Phones, Cars, and Smart Devices AI startup Liquid AI announced on January 5, 2026, that it is launching a new family of models called LFM2.5-1.2B, designed to run artificial intelligence directly on devices such as smartphones, vehicles, and Internet-connected hardware. The launch was detailed in a blog post published by the company, outlining its latest push toward on-device AI systems. Instead of relying on cloud servers, these models […]

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arXiv:2603.00772v1 Announce Type: new Abstract: Score-based generative models (SGMs) aim at generating samples from a target distribution by approximating the reverse-time dynamics of a stochastic differential equation. Despite their strong empirical performance, classical samplers initialized from a Gaussian distribution require a long time horizon noising typically inducing a large number of discretization steps and high computational cost. In this work, we present a Kullback-Leibler convergence analysis of Variance Exploding diffusion samplers that highlights the critical role of the […]

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arXiv:2602.13301v1 Announce Type: new Abstract: Recent advances towards End-to-End Autonomous Driving (E2E-AD) have been often devoted on integrating modular designs into a unified framework for joint optimization e.g. UniAD, which follow a sequential paradigm (i.e., perception-prediction-planning) based on separable Transformer decoders and rely on dense BEV features to encode scene representations. However, such manual ordering design can inevitably cause information loss and cumulative errors, lacking flexible and diverse relation modeling among different modules and sensors. Meanwhile, insufficient training […]

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