The rapid growth of scientific publications has made it increasingly difficult to keep literature reviews comprehensive and up-to-date. Though prior work has focused on automating retrieval and screening, the writing phase of systematic reviews remains largely under-explored, especially with regard to readability and factual accuracy. To address this, we present LiRA (Literature Review Agents), a multi-agent collaborative workflow which emulates the human literature review process. LiRA utilizes specialized agents for content outlining, subsection writing, editing, and reviewing, producing cohesive and comprehensive review articles. Evaluated on SciReviewGen and a proprietary ScienceDirect dataset, LiRA outperforms current baselines such as AutoSurvey and MASS-Survey in writing and citation quality, while maintaining competitive similarity to human-written reviews. We further evaluate LiRA in real-world scenarios using document retrieval and assess its robustness to reviewer model variation. Our findings highlight the potential of agentic LLM workflows, even without domain-specific tuning, to improve the reliability and usability of automated scientific writing.

Despite the impressive performance achieved by pre-trained language-and-vision models in downstream tasks, it remains an open question whether this reflects a proper understanding of image-text interaction.In this work, we explore to what extent they handle basic linguistic constructions-active-passive voice, coordination, and relative clauses-that even preschool children can typically master.We present BLA, a novel, automatically constructed benchmark to evaluate multimodal models on these Basic Language Abilities.We show that different types of Transformer-based systems, such as CLIP, ViLBERT, and BLIP2, generally struggle with BLA in a zero-shot setting, in line with previous findings.Our experiments, in particular, show that most of the tested models only marginally benefit when fine-tuned or prompted with construction-specific samples.Yet, the generative BLIP2 shows promising trends, especially in an in-context learning setting.This opens the door to using BLA not only as an evaluation benchmark but also to improve models' basic language abilities.