:::info Authors: Fernando Spade Oshani Seneviratne ::: Table of Contents Abstract Introduction Background The Sandbox The Data A: Transaction Graph B: Bow-tie Model C: The Sandbox Support D: The Sandbox Scandals E: Whales Methodology Results Related Work Conclusion Resource Contributions Acknowledgements and References ABSTRACT We explore the burgeoning field of GameFi through a detailed network analysis of The Sandbox, a prominent decentralized application (dApp) in this domain. Utilizing the bowtie model, we map out transaction data within The Sandbox, providing […]

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The allocation of scarce donor organs constitutes one of the most consequential algorithmic challenges in healthcare. While the field is rapidly transitioning from rigid, rule-based systems to machine learning and data-driven optimization, we argue that current approaches often overlook a fundamental barrier: incentives. In this position paper, we highlight that organ allocation is not merely an optimization problem, but rather a complex game involving organ procurement organizations, transplant centers, clinicians, patients, and regulators. Focusing on US adult heart […]

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The lightweight semi-supervised learning (LSL) strategy provides an effective approach of conserving labeled samples and minimizing model inference costs. Prior research has effectively applied knowledge transfer learning and co-training regularization from large to small models in LSL. However, such training strategies are computationally intensive and prone to local optima, thereby increasing the difficulty of finding the optimal solution. This has prompted us to investigate the feasibility of integrating three low-cost scenarios for text mining tasks: limited labeled supervision, […]

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Engineering orgs still assume they must choose: move fast and risk defects, or slow down to ensure software quality. Modern reality breaks this binary. AI-generation tools multiply code output, microservices create complexity, and distributed teams increase context gaps across growing codebases. What once worked for small teams no longer holds as systems and organizations scale. As Maria Vinokurskaya, Founding Engineer (AI) at PlayerZero, puts it: “When you’re early, you ship something as fast as you can, see how […]

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This position paper argues that recommender systems should now be designed towards agents. We use recommender systems towards agents (RSTA) to denote systems whose immediate consumer is an acting agent, an orchestration layer, or a multi-agent system; whose ranked objects are actionable interventions rather than human-viewable items; and whose success is measured by downstream trajectory utility under preference, cost, policy, and safety constraints. We advance three falsifiable claims: (1) priority-sensitive ranking can improve trajectory utility even when candidate […]

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This paper introduces Bayesian R-LayerNorm, a normalization layer that extends the previously proposed R-LayerNorm with uncertainty quantification. Building upon R-LayerNorm, we draw connections to statistical field theory, renormalization group methods, and infor-mation geometry to motivate the design. The method incorporates uncertainty estimation through a stable ψ-function, enabling adaptive noise suppression based on local entropy esti-mates. We provide theoretical analysis of numerical stability, gradient stability, and training convergence under standard assumptions. A key practical contribution is the integration of […]

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Across early-stage startups, I keep seeing the same pattern: engineers set up master and develop branches, formal release cycles, and staging environments. Not because the business needs them, but because that’s what they did at their last job. This is the third article in my series on mental models for startups (please check it out, there’s a lot of interesting material there). This time, we’re looking at release processes. It usually happens when the product is just getting […]

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arXiv:2601.12208v1 Announce Type: new Abstract: Evaluating conversational systems in multi-turn settings remains a fundamental challenge. Conventional pipelines typically rely on manually defined rubrics and fixed conversational context$-$a static approach that limits coverage and fails to capture the diverse, emergent behaviors of dialogue models. To address this, we introduce CoReflect (Conversational Evaluation via Co-Evolutionary Simulation and Reflective Rubric Refinement), which unifies dialogue simulation and evaluation into an adaptive, iterative process. CoReflect employs a conversation planner that generates structured templates […]

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