arXiv:2601.03286v1 Announce Type: new Abstract: In this report, we present HyperCLOVA X 32B Think, a vision-language model designed with particular emphasis on reasoning within the Korean linguistic and cultural context, as well as agentic ability. HyperCLOVA X 32B Think is pre-trained with a strong focus on reasoning capabilities and subsequently post-trained to support multimodal understanding, enhanced reasoning, agentic behaviors, and alignment with human preferences. Experimental evaluations against comparably sized models demonstrate that our model achieves strong performance on […]

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Most common-sense explanations of time quietly assume what they are supposed to explain. We say time moves forward because clocks tick. But clocks only measure something, they do not explain why it has a direction. n We say time moves forward because the universe started in a special state. But that just pushes the mystery to the beginning and calls it a solution. The arrow of time still looks like a narrative patch. A story we tell to […]

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Large Language Model (LLM) agents have shown stunning results in complex tasks, yet they often operate in isolation, failing to learn from past experiences. Existing memory-based methods primarily store raw trajectories, which are often redundant and noise-heavy. This prevents agents from extracting high-level, reusable behavioral patterns that are essential for generalization. In this paper, we propose SkillRL, a framework that bridges the gap between raw experience and policy improvement through automatic skill discovery and recursive evolution. Our approach […]

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Recent advances in world models have shown promise for modeling future dynamics of environmental states, enabling agents to reason and act without accessing real environments. Current methods mainly perform single-step or fixed-horizon rollouts, leaving their potential for complex task planning under-exploited. We propose Imagine-then-Plan (texttt{ITP}), a unified framework for agent learning via lookahead imagination, where an agent’s policy model interacts with the learned world model, yielding multi-step “imagined” trajectories. Since the imagination horizon may vary by tasks and […]

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arXiv:2602.13942v1 Announce Type: new Abstract: In the era of large language models (LLMs), fine-tuning pretrained models has become ubiquitous. Yet the theoretical underpinning remains an open question. A central question is why only a few epochs of fine-tuning are typically sufficient to achieve strong performance on many different tasks. In this work, we approach this question by developing a statistical framework, combining rigorous early stopping theory with the attention-based Neural Tangent Kernel (NTK) for LLMs, offering new theoretical […]

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arXiv:2603.08806v1 Announce Type: new Abstract: We present Test-Driven AI Agent Definition (TDAD), a methodology that treats agent prompts as compiled artifacts: engineers provide behavioral specifications, a coding agent converts them into executable tests, and a second coding agent iteratively refines the prompt until tests pass. Deploying tool-using LLM agents in production requires measurable behavioral compliance that current development practices cannot provide. Small prompt changes cause silent regressions, tool misuse goes undetected, and policy violations emerge only after deployment. […]

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arXiv:2601.22364v1 Announce Type: new Abstract: Large Language Models (LLMs) have been shown to organize the representations of input sequences into straighter neural trajectories in their deep layers, which has been hypothesized to facilitate next-token prediction via linear extrapolation. Language models can also adapt to diverse tasks and learn new structure in context, and recent work has shown that this in-context learning (ICL) can be reflected in representational changes. Here we bring these two lines of research together to […]

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We know that engaging developers isn’t easy. They despise flashy ads and can spot “marketing” from a mile away.  But there’s one thing developers consistently show up for: Hackathons. They’re hands-on. They’re competitive. They’re creative. They let builders actually use your tech, instead of just hearing about it, and create something useful from it.  Incentivization + gamification + awareness = win-win-win! :::tip Learn more about HackerNoon Hackathons ::: HackerNoon Remote Technology Hackathons: Built for Longevity (Not Weekend Noise) […]

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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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Introduction Creating distributed systems might need implementation of special retry mechanisms, state machines, dead letter queues, etc. Despite best practices in software design, unexpected issues may still arise in production systems, requiring manual intervention. Temporal’s approach to workflow orchestration is different from other solutions by providing durability and state management as part of the system. It has been adopted by companies such as Netflix, NVIDIA, Snap, and Airwallex in their production systems. While it is essential to understand […]

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