Vision-language models (VLMs) can increasingly handle extended contexts and answer questions about long videos—but can they understand the narrative structure of movies as humans do? To investigate this question, we introduce MF² (Movie Facts and Fibs), a fully manually curated benchmark designed to evaluate whether models can identify, recall, and comprehend information central to the movie’s storyline, using both the visual and textual modalities. MF² includes over 50 open-licensed movies, each paired with manually constructed sets of claim pairs: one true claim (fact) and one plausible but false (fib), totaling over 850 pairs. Our analysis across 20 open-source and 2 proprietary models reveals a substantial gap relative to human performance. While subtitles provide a strong signal for narrative understanding, other textual sources (e.g., synopses) are less informative. In contrast, when visual information is distributed across the entire movie and jointly embedded with subtitles, models struggle to identify and effectively use the relevant content. However, when the visual input is restricted to claim-relevant visual content, models improve significantly, indicating substantial room for improvement in processing large multimodal contexts and distinguishing relevant from irrelevant information. Overall, our findings highlight persistent challenges in multimodal narrative understanding for current VLMs. We release our data and code to support further research in this direction.

Transformers are the current architecture of choice for NLP, but their attention layers do not scale well to long contexts. Recent works propose to replace attention with linear recurrent layers - this is the case for state space models, which enjoy efficient training and inference. However, it remains unclear whether these models are competitive with transformers in machine translation (MT). In this paper, we provide a rigorous and comprehensive experimental comparison between transformers and linear recurrent models for MT. Concretely, we experiment with RetNet, Mamba, and hybrid versions of Mamba which incorporate attention mechanisms. Our findings demonstrate that Mamba is highly competitive with transformers on sentence and paragraph-level datasets, where in the latter both models benefit from shifting the training distribution towards longer sequences. Further analysis show that integrating attention into Mamba improves translation quality, robustness to sequence length extrapolation, and the ability to recall named entities.

Abstract Meaning Representation (AMR) is a Semantic Parsing formalism that aims at providing a semantic graph abstraction representing a given text. Current approaches are based on autoregressive language models such as BART or T5, fine-tuned through Teacher Forcing to obtain a linearized version of the AMR graph from a sentence. In this paper, we present LeakDistill, a model and method that explores a modification to the Transformer architecture, using structural adapters to explicitly incorporate graph information into the learned representations and improve AMR parsing performance. Our experiments show how, by employing word-to-node alignment to embed graph structural information into the encoder at training time, we can obtain state-of-the-art AMR parsing through self-knowledge distillation, even without the use of additional data. We release the code at [http://www.github.com/sapienzanlp/LeakDistill](http://www.github.com/sapienzanlp/LeakDistill).