Reasoning

@dataclass class AgentConfig: horizon: int = 6 replan_on_target_move: bool = True replan_on_obstacle_change: bool = True max_steps: int = 120 think_latency: float = 0.02 act_latency: float = 0.01 risk_gate: float = …

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 = result(‘uncertainty’) selected_idx = result(‘selected_index’) …

In the frantic arms race of ‘AI for code’, we have moved beyond the era of glorious autocomplete. Today, Anthropic is doubling down on a more ambitious vision: an AI …

Large language models (LLMs) are the world’s best imitators, but when it comes to the cold, hard logic of updating beliefs based on new evidence, they are surprisingly stubborn. A …

In this tutorial, we build an advanced tree-of-thoughts (TOT) multi-branch reasoning agent from scratch. Instead of relying on linear thought-chain logic, we design a system that generates multiple logic branches, …

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”: context.get(“goal”), “assumptions”: context.get(“assumptions”, ()), …

ByteDance Seed recently released research that could change the way we build reasoning AI. For years, developers and AI researchers have struggled to ‘cold-start’ large language models (LLMs). Long Chain …

In this tutorial, we build a self-organized memory system for an agent that goes beyond storing raw conversation history and instead structures conversations into persistent, meaningful knowledge units. We design …

Today, we are publishing a New open source sample chatbot It shows how to use feedback from automated reasoning checks to iterate on generated content, ask clarifying questions, and prove …

In this tutorial, we implement an agentic chain-of-thought pruning framework that generates multiple logic paths in parallel and dynamically prunes them using consensus signals and early stopping. We focus on …