Google Titans Crushes Transformers: Neural Memory for Infinite Context

Google Titans Crushes Transformers: Neural Memory for Infinite Context

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Author(s): Divy Yadav Originally published on Towards AI. The powerful shift from the transformer to Titans Remember that time you walked into a room and completely forgot why you went there? That frustrating “brain fart” is your short-term memory failing you. By the AuthorResearchers at Google have introduced a new architecture called Titans, which aims to solve the memory issues that have historically plagued AI models. Unlike traditional architectures, Titans can learn to remember important information during inference, […]

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Microsoft sells OpenAI models in China. OpenAI and Anthropic won’t.

Microsoft sells OpenAI models in China. OpenAI and Anthropic won’t.

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Microsoft has quietly become the main supplier of OpenAI models in China, selling the technology to the country’s largest internet companies even as OpenAI and Anthropic keep their own models out of the market on intellectual-property and misuse grounds. The arrangement, detailed this week by Bloomberg, hands Microsoft a position no other American AI vendor holds: it sells the GPT series to Chinese firms that the model’s own creator will not deal with directly. The scale is not trivial. […]

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Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning

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Previous LLMs-based RL studies typically follow either supervised learning with high annotation costs, or unsupervised paradigms using voting or entropy-based rewards. However, their performance remains far from satisfactory due to the substantial annotation cost and issues such as model collapse or reward hacking. To address these issues, we introduce a new perspective inspired by cognitive learning theory and propose a novel approach called EasyRL. The core of EasyRL is to simulate the human cognitive acquisition curve by integrating […]

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A better method for planning complex visual tasks

A better method for planning complex visual tasks

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MIT researchers have developed a generative artificial intelligence-driven approach for planning long-term visual tasks, like robot navigation, that is about twice as effective as some existing techniques. Their method uses a specialized vision-language model to perceive the scenario in an image and simulate actions needed to reach a goal. Then a second model translates those simulations into a standard programming language for planning problems, and refines the solution. In the end, the system automatically generates a set of […]

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Write Symfony Commands Like You Write Controllers—Finally

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The evolution of the Symfony Console component has been a journey of consistent refinement. For years, developers grew accustomed to the ritual of extending the Command class, implementing the configure() method to define arguments and options and placing their logic inside execute(). It was robust, deterministic and verbose. With the advent of Symfony 5 and 6, we saw the introduction of Invokable Commands — a paradigm shift that allowed us to treat commands more like controllers. The __invoke() method became the new entry […]

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Riemannian Graph Learning & Structural Entropy: Deep Node Clustering in 2026

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Table of Links Abstract and 1. Introduction Related Work Preliminaries and Notations Differentiable Structural Information 4.1. A New Formulation 4.2. Properties 4.3. Differentiability & Deep Graph Clustering LSEnet 5.1. Embedding Leaf Nodes 5.2. Learning Parent Nodes 5.3. Hyperbolic Partitioning Tree Experiments 6.1. Graph Clustering 6.2. Discussion on Structural Entropy Conclusion, Broader Impact, and References Appendix A. Proofs B. Hyperbolic Space C. Technical Details D. Additional Results 2. Related Work Deep Graph Clustering. Deep models have achieved state-of-the-art results […]

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I Built 10 Pokemon Agents That Learn From Each Other Using Kafka and Flink

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I built an agent to play Pokemon. Took inspiration from ClaudePlaysPokemon and the AlphaEvolve paper from DeepMind. My initial approach was just brute force in a single threaded loop, but I quickly started exploring spinning up 10 agents at a time to speed run and then learn. Each subsequent run gets slightly better. Data Flow: Agent → Tapes Proxy → Kafka (agent.telemetry.raw) ├→ telemetry-consumer (JSONL) ├→ Flink SQL (anomaly detection) │ └→ Kafka (agent.telemetry.alerts) └→ DuckDB (ad-hoc queries) […]

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Authentication Customer Segmentation — BEACON: K-Means Clustering

Authentication Customer Segmentation — BEACON: K-Means Clustering

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What is BEACON? BEACON (Behavioral Evaluation for Authentication Cohorts & Outcomes Network) is a data-driven framework that segments customers based on their authentication behavior using machine learning (k-means clustering). It groups users along interpretable dimensions of engagement and risk to better understand login behavior, fraud patterns, and retention outcomes. How do customers engage with the product/system? Customers engage with BEACON indirectly through their authentication behaviors — such as login frequency, success rates, friction encountered (e.g., challenge verification), and security-related interactions. Their […]

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Can JavaScript Escape a CSP Meta Tag Inside an Iframe?

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Research: Can JavaScript Escape a CSP Meta Tag Inside an Iframe? In trying to build my own version of Claude Artifacts I got curious about options for applying CSP headers to content in sandboxed iframes without using a separate domain to host the files. Turns out you can inject <meta http-equiv=”Content-Security-Policy”…> tags at the top of the iframe content and they’ll be obeyed even if subsequent untrusted JavaScript tries to manipulate them. Tags: iframes, security, javascript, content-security-policy, sandboxing

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Forecasting as Rendering: A 2D Gaussian Splatting Framework for Time Series Forecasting

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arXiv:2603.02220v1 Announce Type: new Abstract: Time series forecasting (TSF) remains a challenging problem due to the intricate entanglement of intraperiod-fluctuations and interperiod-trends. While recent advances have attempted to reshape 1D sequences into 2D period-phase representations, they suffer from two principal limitations.Firstly, treating reshaped tensors as static images results in a topological mismatch, as standard spatial operators sever chronological continuity at grid boundaries. Secondly, relying on uniform fixed-size representations allocates modeling capacity inefficiently and fails to provide the adaptive […]

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