To ensure large language models (LLMs) are used safely, one must reduce their propensity to hallucinate or to generate unacceptable answers. A simple and often used strategy is to first let the LLM generate multiple hypotheses and then employ a reranker to choose the best one.

We introduce motion graph, a novel approach to the video prediction problem, which predicts future video frames from limited past data.

Image diffusion distillation achieves high-fidelity generation with very few sampling steps.

TGNN[24]introduce3D-RESby extending the bounding box annotations of ScanRefer [5] to masks by incorporating the instance masks from ScanNet and proposed a two-stage pipeline. Further, 3D-STMN [65] proposed an end-to-end method that matches the text and superpoints to get the 3D segmentation of the target object directly.

Autonomous agents operating in real-world scenarios frequently encounter uncertainty and make decisions based on incomplete information.

Through extensive empirical analysis using benchmarks in the Safety Gym suite, we show that our algorithm has similar or better performance than SoT A (non-episodic) algorithms adapted for the episodic setting.