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By using two co-evolving AI models, the R-Zero framework generates its own learning curriculum, moving beyond the need for labeled datasets.Read More

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Memp takes inspiration from human cognition to give LLM agents "procedural memory" that can adapt to new tasks and environments.Read More

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The open source framework provides the data and training recipe for building powerful computer-use agents that challenge proprietary systems.Read More

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Chain-of-Thought isn't a plug-and-play solution. For developers, this research offers a blueprint for LLM testing and strategic fine-tuning.Read More

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Moving beyond the slow, costly trial-and-error of RL, GEPA teaches AI systems to learn and improve using natural language.Read More

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CoAct-1 is an AI agent that combines GUI control with on-the-fly coding, making computer automation more robust and efficient.Read More

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A new study from Anthropic introduces "persona vectors," a technique for developers to monitor, predict and control unwanted LLM behaviors.Read More

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A diffusion model inspired by the human process of drafting, searching for information, and making iterative revisions.Read More

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A common AI fine-tuning practice could be unintentionally poisoning your models with hidden biases and risks, a new Anthropic study warns.Read More

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Hierarchical Reasoning Models (HRM) tackle complex reasoning tasks while being smaller, faster, and more data-efficient than large AI models.Read More

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Mixture-of-Recursions (MoR) is a new AI architecture that promises to cut LLM inference costs and memory use without sacrificing performance.Read More

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Google's new Gemini Embedding model now leads the MTEB benchmark. But it is facing fierce competition from closed and open source rivals.Read More

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A DeepMind study finds LLMs are both stubborn and easily swayed. This confidence paradox has key implications for building AI applications.Read More

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A new AI model learns to "think" longer on hard problems, achieving more robust reasoning and better generalization to novel, unseen tasks.Read More

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Katanemo Labs' new LLM routing framework aligns with human preferences and adapts to new models without retraining.Read More

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Sakana AI's new inference-time scaling technique uses Monte-Carlo Tree Search to orchestrate multiple LLMs to collaborate on complex tasks.Read More

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Mixus's "colleague-in-the-loop" model blends automation with human judgment for safe deployment of AI agents.Read More

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Forecasting is a fundamentally new capability that is missing from the current purview of generative AI. Here's how Kumo is changing that.Read More

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MIT researchers developed SEAL, a framework that lets language models continuously learn new knowledge and tasks.Read More

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Why is Google hiding Gemini's reasoning traces? The decision sparks a debate over black-box models versus the need for transparency.Read More