Scenario Generation

TL;DR for operators AGENTS-LLM is not another attempt to make a language model dream up an entire traffic world and then hope the simulator forgives the hallucination. It does something narrower and more operationally useful: it takes an existing real-world driving scenario, accepts a natural-language instruction such as adding a parked vehicle, jaywalker, accident site, or construction zone, and produces an augmented scenario that can be executed in closed-loop autonomous-driving simulation.1 ...

TL;DR for operators Portfolio teams do not lack optimisation formulas. They lack enough relevant future scenarios. That is the problem this paper attacks. The paper proposes a diffusion-based market simulator that learns from historical time-series data, then generates conditional future paths based on the current market state.1 Those generated paths become the training environment for a reinforcement-learning portfolio agent. In plain terms: instead of asking an RL policy to learn from a thin archive of market history, the system first builds a synthetic scenario engine and lets the policy practise there. Sensible. Also dangerous, if the simulator hallucinates a market that conveniently rewards your model. ...