The 4 AI Safety Alignment Approaches: How to Build AI That Won’t Lie, Harm, or Manipulate

The 4 AI Safety Alignment Approaches: How to Build AI That Won’t Lie, Harm, or Manipulate

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Author(s): TANVEER MUSTAFA Originally published on Towards AI. Understanding RLHF, Constitutional AI, Red Teaming, and Value Learning You ask ChatGPT how to make a bomb. It refuses. You ask it to write a racist joke. It declines. You try jailbreaking it with elaborate prompts. It still won’t comply. This isn’t accidental — it’s alignment. Image generated by Author using AIThis article discusses the importance of AI safety alignment, detailing four key approaches: Reinforcement Learning from Human Feedback (RLHF), […]

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Thinking in Frames: How Visual Context and Test-Time Scaling Empower Video Reasoning

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arXiv:2601.21037v1 Announce Type: new Abstract: Vision-Language Models have excelled at textual reasoning, but they often struggle with fine-grained spatial understanding and continuous action planning, failing to simulate the dynamics required for complex visual reasoning. In this work, we formulate visual reasoning by means of video generation models, positing that generated frames can act as intermediate reasoning steps between initial states and solutions. We evaluate their capacity in two distinct regimes: Maze Navigation for sequential discrete planning with low […]

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Regularity and Stability Properties of Selective SSMs with Discontinuous Gating

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arXiv:2505.11602v2 Announce Type: replace-cross Abstract: Deep selective State-Space Models (SSMs), whose state-space parameters are modulated online by a selection signal, offer significant expressive power but pose challenges for stability analysis, especially under discontinuous gating. We study continuous-time selective SSMs through the lenses of passivity and Input-to-State Stability (ISS), explicitly distinguishing the selection schedule $x(cdot)$ from the driving (port) input $u(cdot)$. First, we show that state-strict dissipativity ($beta>0$) together with quadratic bounds on a storage functional implies exponential decay […]

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Amazon Nova AI Challenge returns with Nova Forge access for competing teams

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The Amazon Nova AI Challenge is back for its second year. As ten selected university teams from around the world gather in Seattle this week for bootcamp (February 2-4), they’re preparing to tackle a real-world challenge in software development: building AI agents that can handle complex coding tasks while maintaining security and reliability. For the first time in an academic competition, participating teams will use Amazon Nova Forge to customize Nova models with access to tools, models and […]

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Social Graph as Infrastructure: The Anthropological Case for Distributed Storage

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Every decentralized protocol built so far has shipped the same architectural mistake. Here’s what we borrowed from 150,000 years of human behavior to fix it. There’s a pattern in how anthropologists describe information flow in pre-literate communities. News doesn’t broadcast — it cascades. A person hears something from someone they trust, judges the claim against what they know of that source, and decides how far to pass it along. The further information travels from its origin, the more […]

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Reinforcement Learning to Discover a NorthEast Monsoon Index for Monthly Rainfall Prediction in Thailand

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Climate prediction is a challenge due to the intricate spatiotemporal patterns within Earth systems. Global climate indices, such as the El Niño Southern Oscillation, are standard input features for long-term rainfall prediction. However, a significant gap persists regarding local-scale indices capable of improving predictive accuracy in specific regions of Thailand. This paper introduces a novel NorthEast monsoon climate index calculated from sea surface temperature to reflect the climatology of the boreal winter monsoon. To optimise the calculated areas […]

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Neural Networks for Language: How Context Became a Learned Transformation

Neural Networks for Language: How Context Became a Learned Transformation

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Once language modeling moved beyond pure counting, something important changed. The question was no longer only: “How often did these words appear together before?” It became: “Can a model learn what kinds of words tend to fit together, even when it has not seen the exact phrase many times?” That was a major turning point. N-grams had already shown that next-word prediction could be framed as a probability problem. But they were still brittle. They memorized local patterns. They did not really […]

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Differentiable Invariant Sets for Hybrid Limit Cycles with Application to Legged Robots

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arXiv:2604.05108v1 Announce Type: new Abstract: For hybrid systems exhibiting periodic behavior, analyzing the invariant set containing the limit cycle is a natural way to study the robustness of the closed-loop system. However, computing these sets can be computationally expensive, especially when applied to contact-rich cyber-physical systems such as legged robots. In this work, we extend existing methods for overapproximating reachable sets of continuous systems using parametric embeddings to compute a forward-invariant set around the nominal trajectory of a […]

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GAN-based Domain Adaptation for Image-aware Layout Generation in Advertising Poster Design

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arXiv:2604.07409v1 Announce Type: new Abstract: Layout plays a crucial role in graphic design and poster generation. Recently, the application of deep learning models for layout generation has gained significant attention. This paper focuses on using a GAN-based model conditioned on images to generate advertising poster graphic layouts, requiring a dataset of paired product images and layouts. To address this task, we introduce the Content-aware Graphic Layout Dataset (CGL-Dataset), consisting of 60,548 paired inpainted posters with annotations and 121,000 […]

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CLPIPS: A Personalized Metric for AI-Generated Image Similarity

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arXiv:2604.01234v1 Announce Type: new Abstract: Iterative prompt refinement is central to reproducing target images with text to image generative models. Previous studies have incorporated image similarity metrics (ISMs) as additional feedback to human users. Existing ISMs such as LPIPS and CLIP provide objective measures of image likeness but often fail to align with human judgments, particularly in context specific or user driven tasks. In this paper, we introduce Customized Learned Perceptual Image Patch Similarity (CLPIPS), a customized extension […]

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