February 2026

arXiv:2602.03851v1 Announce Type: new Abstract: The mastery of Hijaiyah letters is a crucial foundation for reading and comprehending the Quran, yet conventional pedagogical approaches based on repetitive memorization frequently struggle to maintain the engagement of young learners in contemporary educational contexts. This research presents the design and implementation of an innovative gamification-based methodology for Hijaiyah literacy acquisition, systematically developed through the ADDIE framework (Analysis, Design, Development, Implementation, Evaluation) to optimize student motivation, participation, and educational outcomes. The resulting […]

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arXiv:2602.03850v1 Announce Type: new Abstract: We present a vision-language model (VLM) that automatically edits website HTML to address Web Content Accessibility Guidelines 2 (WCAG2) violations. We formulate this as a supervised image-conditioned program synthesis task, where the model learns to correct HTML given the HTML and its rendering. We collected WebAccessVL, a new dataset with manually corrected accessibility violations, establishing paired training data. We then propose a violation-conditioned VLM that additionally conditions on the WCAG2 violation count to […]

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arXiv:2602.03849v1 Announce Type: new Abstract: The “AI Scientist” paradigm is transforming scientific research by automating key stages of the research process, from idea generation to scholarly writing. This shift is expected to accelerate discovery and expand the scope of scientific inquiry. However, a key question remains unclear: can AI scientists identify meaningful research questions? While Large Language Models (LLMs) have been applied successfully to task-specific ideation, their potential to conduct strategic, long-term assessments of past breakthroughs and future […]

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In the strange new landscape of AI agents, a peculiar experiment took place and made quite a bit of mess. A network called Moltbook emerged as a bots-only social platform, kinda like a digital ecosystem where AI agents could interact, share information, and theoretically operate free from human interference. It was designed as a pure testbed for autonomous agent behavior, a place where artificial intelligences could be themselves. However, and besides the whole “WOW” moment around it, within […]

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Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an indispensable paradigm for enhancing reasoning in Large Language Models (LLMs). However, standard policy optimization methods, such as Group Relative Policy Optimization (GRPO), often converge to low-entropy policies, leading to severe mode collapse and limited output diversity. We analyze this issue from the perspective of sampling probability dynamics, identifying that the standard objective disproportionately reinforces the highest-likelihood paths, thereby suppressing valid alternative reasoning chains. To address this, we propose […]

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Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an indispensable paradigm for enhancing reasoning in Large Language Models (LLMs). However, standard policy optimization methods, such as Group Relative Policy Optimization (GRPO), often converge to low-entropy policies, leading to severe mode collapse and limited output diversity. We analyze this issue from the perspective of sampling probability dynamics, identifying that the standard objective disproportionately reinforces the highest-likelihood paths, thereby suppressing valid alternative reasoning chains. To address this, we propose […]

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I used to treat Test-Driven Development (TDD) as busywork. Write a failing test. Watch it fail. Write code to pass it. Rinse and repeat. It always felt like it slowed me down. Then AI changed everything. Tools like GitHub Copilot can now generate implementation and comprehensive tests in seconds. Suddenly, the mechanical ritual of TDD seemed… optional. But the more I experimented, the more I realized something important: TDD’s real value was never the mechanics—it was the thinking […]

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There’s a very specific kind of productivity that only engineers know. It looks like progress. It feels like progress. It even *logs* like progress. But nothing ships. I know this because I’ve lived there. For weeks. Sometimes months. Writing code. Rewriting code. Refactoring code that hasn’t offended anyone yet. Stashing changes like they’re radioactive. Telling myself, *“I’m being careful.”* No. I was being scared. Let’s call it what it is. This isn’t engineering. This is avoidance in a […]

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Disentangled representation learning aims to capture the underlying explanatory factors of observed data, enabling a principled understanding of the data-generating process. Recent advances in generative modeling have introduced new paradigms for learning such representations. However, existing diffusion-based methods encourage factor independence via inductive biases, yet frequently lack strong semantic alignment. In this work, we propose a flow matching-based framework for disentangled representation learning, which casts disentanglement as learning factor-conditioned flows in a compact latent space. To enforce explicit […]

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