@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 = 0.85 alt_search_depth: int = 2 @dataclass class StreamingDecisionAgent: cfg: AgentConfig world: DynamicGridWorld start_time: float = field(init=False, default_factory=time.time) step_id: int = field(init=False, default=0) current_plan: List[Coord]…

Curiosity-driven research has long sparked technological transformations. A century ago, curiosity about atoms led to quantum mechanics, and eventually the transistor at the heart of modern computing. Conversely, the steam engine was a practical breakthrough, but it took fundamental research in thermodynamics to fully harness its power. Today, artificial intelligence and science find themselves at…

The gap between proprietary frontier models and highly transparent open-source models is closing faster than ever. NVIDIA has officially pulled the curtain back on Nemotron 3 Super, a staggering 120 billion parameter reasoning model engineered specifically for complex multi-agent applications. Released today, Nemotron 3 Super sits perfectly between the lightweight 30 billion parameter Nemotron…

Young adults growing up in the attention economy — preparing for adult life, with social media and chatbots competing for their attention — can easily fall into unhealthy relationships with digital platforms. But what if chatbots weren’t mere distractions from real life? Could they be designed humanely, as moral partners whose digital goal is…

Google expanded its Gemini model family with the release of Gemini Embedding 2. This second-generation model succeeds the text-only gemini-embedding-001 and is designed specifically to address the high-dimensional storage and cross-modal retrieval challenges faced by AI developers building production-grade Retrieval-Augmented Generation (RAG) systems. The Gemini Embedding 2 release marks a significant technical shift in…

class MetaAgent: def __init__(self, llm: Optional[LocalLLM] = None): self.llm = llm or LocalLLM() def _capability_heuristics(self, task: str) -> Dict[str, Any]: t = task.lower() needs_data = any(k in t for k in [“csv”, “dataframe”, “pandas”, “dataset”, “table”, “excel”]) needs_math = any(k in t for k in [“calculate”, “compute”, “probability”, “equation”, “optimize”, “derivative”, “integral”]) needs_writing =…