Chain-of-thought (CoT) prompting makes language models write step-by-step explanations, but these steps may not match what the model actually used to choose its answer. Existing faithfulness checks often only test whether changing the written chain changes the answer, without verifying whether the steps are truly supported by the given evidence, or they require special prompts that do not generalize well. We present NSF-CoT, a neuro-symbolic formal verification method that checks CoT faithfulness step by step for contextual question answering. NSF-CoT (1) converts the provided context facts and each reasoning step into simple logical statements, (2) uses counterfactual attribution to estimate which context facts the model relied on while generating each step, and (3) verifies each step using a hybrid checker that combines an SMT solver with an LLM-based entailment judge. For every step, we score groundedness (supported by the full context), validity (supported by the facts the model relied on), and utility (helps reach the final answer), and combine them into a faithfulness score. Across OpenBookQA, QASC, and HotpotQA, NSF-CoT consistently outperforms causal mediation, perturbation probes, and behavioral monitoring, and it identifies reasoning steps that are not only unfaithful but also harmful to the model’s final decision.

Storytelling is the lifeline of the entertainment industrymovies, TV shows, and stand-up comedies, all need stories. A good and gripping script is the lifeline of storytelling and demands creativity and resource investment. Good scriptwriters are rare to find and often work under severe time pressure. Consequently, entertainment media are actively looking for automation. In this paper, we present an AIbased script-writing workbench called KUROSAWA which addresses the tasks of plot generation and script generation. Plot generation aims to generate a coherent and creative plot (600–800 words) given a prompt (15–40 words). Script generation, on the other hand, generates a scene (200–500 words) in a screenplay format from a brief description (15–40 words). Kurosawa needs data to train. We use a 4-act structure of storytelling to annotate the plot dataset manually. We create a dataset of 1000 manually annotated plots and their corresponding prompts/storylines and a gold-standard dataset of 1000 scenes with four main elements — scene headings, action lines, dialogues, and character names — tagged individually. We fine-tune GPT-3 with the above datasets to generate plots and scenes. These plots and scenes are first evaluated and then used by the scriptwriters of a large and famous media platform ErosNow. We release the annotated datasets and the models trained on these datasets as a working benchmark for automatic movie plot and script generation.