Browsing: Reasoning

class AgentAnalyzer: @staticmethod def plot_response_distribution(result: Dict): fig, axes = plt.subplots(2, 2, figsize=(14, 10)) fig.suptitle(‘Agent Response Analysis’, fontsize=16, fontweight=”bold”) responses = result[‘all_responses’] scores = result[‘critic_scores’] uncertainty =…

In the frantic arms race of ‘AI for code,’ we’ve moved past the era of the glorified autocomplete. Today, Anthropic is double-downing on a more ambitious…

Large Language Models (LLMs) are the world’s best mimics, but when it comes to the cold, hard logic of updating beliefs based on new evidence, they…

In this tutorial, we build an advanced Tree-of-Thoughts (ToT) multi-branch reasoning agent from scratch. Instead of relying on linear chain-of-thought reasoning, we design a system that…

President Trump offers new reasoning for the U.S. attack on Iran. And, results from the first midterm primary of 2026 are in, providing an outlook for…

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”:…

In this tutorial, we build an advanced Griptape-based customer support automation system that combines deterministic tooling with agentic reasoning to process real-world support tickets end-to-end. We…

ByteDance Seed recently dropped a research that might change how we build reasoning AI. For years, devs and AI researchers have struggled to ‘cold-start’ Large Language…

What you need to knowGoogle is previewing Gemini 3.1 Pro, with a focus on complex, multi-step problem-solving that stumps most chatbots.The model scored 77.1% on the…

Google has officially shifted the Gemini era into high gear with the release of Gemini 3.1 Pro, the first version update in the Gemini 3 series.…