Browsing: LLMs

Conclusion Several larger conclusions emerge from this test case. The two models that drew from curated databases of experimental literature, NotebookLM and our custom-built tool, outperformed…

In recent times, many developments in the agent ecosystem have focused on enabling AI agents to interact with external tools and access domain-specific knowledge more effectively.…

Google has officially released Android Bench, a new leaderboard and evaluation framework designed to measure how Large Language Models (LLMs) perform specifically on Android development tasks.…

Organizations increasingly deploy custom large language models (LLMs) on Amazon SageMaker AI real-time endpoints using their preferred serving frameworks—such as SGLang, vLLM, or TorchServe—to help gain…

Evaluating LLMs’ Bayesian capabilities As with humans, to be effective, an LLM’s user interactions require continual updates to its probabilistic estimates of the user’s preferences based…

def executor_agent(step: Dict[str, Any], context: Dict[str, Any]) -> StepResult: step_id = int(step.get(“id”, 0)) title = step.get(“title”, f”Step {step_id}”) tool = step.get(“tool”, “llm”) ctx_compact = { “goal”:…

Customizing Large Language Models (LLMs) currently presents a significant engineering trade-off between the flexibility of In-Context Learning (ICL) and the efficiency of Context Distillation (CD) or…

The generative AI race has long been a game of ‘bigger is better.’ But as the industry hits the limits of power consumption and memory bottlenecks,…

This post is cowritten with Remi Louf, CEO and technical founder of Dottxt. Structured output in AI applications refers to AI-generated responses conforming to formats that…

Large language models like ChatGPT, Claude are made to follow user instructions. But following user instructions indiscriminately creates a serious weakness. Attackers can slip in hidden…