Lifeboat Foundation

Traditional large language model (LLM) agent systems face significant challenges when deployed in real-world scenarios due to their limited flexibility and adaptability. Existing LLM agents typically select actions from a predefined set of possibilities at each decision point, a strategy that works well in closed environments with narrowly scoped tasks but falls short in more complex and dynamic settings. This static approach not only restricts the agent’s capabilities but also requires considerable human effort to anticipate and implement every potential action beforehand, which becomes impractical for complex or evolving environments. Consequently, these agents are unable to adapt effectively to new, unforeseen tasks or solve long-horizon problems, highlighting the need for more robust, self-evolving capabilities in LLM agents.

Researchers from the University of Maryland and Adobe introduce DynaSaur: an LLM agent framework that enables the dynamic creation and composition of actions online. Unlike traditional systems that rely on a fixed set of predefined actions, DynaSaur allows agents to generate, execute, and refine new Python functions in real-time whenever existing functions prove insufficient. The agent maintains a growing library of reusable functions, enhancing its ability to respond to diverse scenarios. This dynamic ability to create, execute, and store new tools makes AI agents more adaptable to real-world challenges.

The technical backbone of DynaSaur revolves around the use of Python functions as representations of actions. Each action is modeled as a Python snippet, which the agent generates, executes, and assesses in its environment. If existing functions do not suffice, the agent dynamically creates new ones and adds them to its library for future reuse. This system leverages Python’s generality and composability, allowing for a flexible approach to action representation. Furthermore, a retrieval mechanism allows the agent to fetch relevant actions from its accumulated library using embedding-based similarity search, addressing context length limitations and improving efficiency.

Combining quantum science with machine learning has led to a model that can accurately measure how surfaces feel to the touch.

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#physics #AI #NobelPrize

Physics, AI, and the nobel prize.

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Working with week-old zebrafish larva, researchers at Weill Cornell Medicine and colleagues decoded how the connections formed by a network of neurons in the brainstem guide the fishes’ gaze.

The study, published Nov. 22 in Nature Neuroscience, found that a simplified artificial circuit, based on the architecture of this neuronal system, can predict activity in the network. In addition to shedding light on how the brain handles short-term memory, the findings could lead to novel approaches for treating eye movement disorders.

Organisms are constantly taking in an array of sensory information about the environment that is changing from one moment to the next. To accurately assess a situation, the brain must retain these informational nuggets long enough to use them to form a complete picture—for instance, linking together the words in a sentence or allowing an animal to keep its eyes directed to an area of interest.

We present a DNA self-assembly based molecular data writing strategy to enable parallel movable-type printing for scalable DNA storage.

Researchers have managed to coax a quantum computer to pulse with a rhythm unlike any before—a rhythm that defies conventional physics. For the first time, scientists have transformed a quantum processor into a robust time crystal, a bizarre state of matter that ticks endlessly without external energy.

This achievement, the work of physicists from China and the United States, could mark a turning point for quantum computing. By stabilizing the delicate systems that underpin this cutting-edge technology, the experiment hints at a path toward practical quantum computers capable of solving problems far beyond the reach of traditional machines.

Unlike conventional phases, such as solids or liquids, time crystals exist in a state of perpetual motion. Let me explain.

Memorial Sloan Kettering Cancer Center-led researchers have identified a small molecule called gliocidin that kills glioblastoma cells without damaging healthy cells, potentially offering a new therapeutic avenue for this aggressive brain tumor.

Glioblastoma remains one of the most lethal primary brain tumors, with current therapies failing to significantly improve patient survival rates. Glioblastoma is difficult to treat for several reasons. The tumor consists of many different types of cells, making it difficult for treatments to target them all effectively.

There are few genetic changes in the cancer for drugs to target, and the tumor creates an environment that weakens the body’s immune response against it. Even getting medications near targets in the brain is challenging because the protective blood-brain barrier blocks entry for most potential drug treatments.

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Ten years ago, physicists discovered an anomaly that was dubbed the “ATOMKI anomaly”. The decays of certain atomic nuclei disagreed with our current understanding of physics. Particle physicists assigned the anomaly to a new particle, X17, often described as a fifth force. The anomaly was now tested by a follow-up experiment, but this is only the latest twist in a rather confusing story.

Continue reading “Huge Physics Anomaly Finally Put to Test, But That Just Makes it More Confusing” »

Protein PKMζ is a key molecular switch for long-term memory. Targeting PKMζ activity may enable new treatments for memory-related disorders.