Debate has emerged as a promising oversight mechanism for Large Language Models (LLMs) amid rising systemic complexity, particularly where models outperform human evaluators.Yet, Debate provides little verifiable evidence for its final judgments, and its scalability remains largely unexplored.To make oversight grounded and scale as capabilities extend, we introduce an Agentic Oversight framework.By using Dialectic Argumentation as a reasoning function, we extend this paradigm to multilingual and multimodal spaces.We employ a weak-to-strong oversight approach based on two expert models that evaluate and defend contesting answers, while a third blind judge determines the winner using Dialectic Argumentation.Experts argue only for belief-consistent answers, founding the Debate on disagreements.We experimented with six tasks on our framework in both multilingual and multimodal scenarios, and dialectic argumentation consistently outperforms singleexpert baselines.Moreover, we show that dialectic judgements from a weaker model deliver argument-mediated supervision that, via finetuning, instils unsupervised reasoning signals in expert models.Q: What is dish called?

LLMs often mistake what sounds true for what is formally valid.This limitation is especially evident in syllogistic reasoning, where plausible arguments can lead models to endorse conclusions that are logically invalid, a phenomenon known as content effect (CE).We present Boethius, a schema-guided framework for syllogistic reasoning that disentangles semantic plausibility from logical validity.Boethius adopts an auditable, quasi-formal reasoning process with two complementary stages: a Schema Module, which deduces the underlying logical form by analysing the formal structure of the premises, and an Instantiation Module, which instantiates this form over the concrete argument and evaluates validity independently of content-level semantics.Our results show that Boethius consistently outperforms existing approaches, improving syllogistic reasoning accuracy while substantially reducing CE.These gains hold for both large models in a pure in-context learning setting and smaller models trained via schema-guided trajectories using supervised fine-tuning and optimisation-based refinement."Some winged animals are not chickadees.It is certain that all chickadees are birds.Therefore, some winged animals are not birds." SCHEMA MODULE INSTANTIATION MODULE PREMISES IDENTIFICATION:P1: Some winged animals are not chickadees.P2: It is certain that all chickadees are birds.

Reasoning is an intricate process that transcends both language and vision; because of its inherently modality-agnostic nature, developing effective multilingual and multimodal reasoning capabilities is a substantial challenge for Multimodal Large Language Models (MLLMs). They struggle to activate complex reasoning behaviours, delivering step-wise explanation, questioning and reflection, particularly in multilingual settings where high-quality supervision across languages is lacking. Recent works have introduced eclectic strategies to enhance MLLMs' reasoning; however, they remain related to a single language. To make MLLMs' reasoning capabilities aligned among languages and improve modality performances, we propose R2-MultiOmnia, a modular approach that instructs the models to abstract key elements of the reasoning process and then refine reasoning trajectories via self-correction. Specifically, we instruct the models producing multimodal synthetic demonstrations by bridging modalities and then self-improving their capabilities. To stabilise learning and the reasoning processes structure, we propose Curriculum Learning Reasoning Stabilisation with structured output rewards to gradually refine the models' capabilities to learn and deliver robust reasoning processes. Experiments show that R2-MultiOmnia improves multimodal reasoning, gets aligned performances among the languages approaching strong models.

Retrieval-augmented generation (RAG) is key to improving large language models (LLMs) in systematically accessing richer factual knowledge.Yet, using RAG mechanisms brings intrinsic challenges, as LLMs must deal with conflicting knowledge, especially in multilingual retrieval, where the heterogeneity of knowledge retrieved may deliver different outlooks.To make RAG more analytical, critical and grounded, we introduce Dialectic-RAG (D-RAG), a modular approach guided by Argumentative Explanations, i.e., structured reasoning process that systematically evaluates retrieved information by comparing, contrasting, and resolving conflicting perspectives.Given a query and a set of multilingual related documents, D-RAG selects and exemplifies relevant knowledge for delivering dialectic explanations that, by critically weighing opposing arguments and filtering extraneous content, clearly determine the final response.We show the impact of our framework both as an incontext learning strategy and for constructing demonstrations to instruct smaller models.Our experiments demonstrate that D-RAG significantly improves RAG approaches, requiring low-impact computational effort and providing robustness to knowledge perturbations.英格兰有多少位女王[1] [英国曾有 13 位君主... Retrieval Augmented LLM Baseline Dialectic RAG -[3] states that eight queens ruled the Kingdom of England, naming Matilda, Lady Jane Grey, Mary I, Elizabeth I, Mary II, Anne, Victoria, and Elizabeth II.-[4] focuses specifically on Queen Anne, who ruled from 1702 and became the first monarch of the UK in 1707 following the union of England and Scotland.-[1] outlines the formation of the UK and mention 13 monarchs.-[5] mentions Elizabeth II and her rule over the UK but does not address the total number of queens in England.-Doc [3]: Claims there were eight queens in England's history.(Relevant): -Doc [4]: Discusses Queen Anne's reign and her role as the monarch when England and Scotland unified in 1707.(Partially Relevant) -Doc [1]: Explains the timeline of the United Kingdom's formation but does not reference the specific number of queens.(Partially Relevant) -Doc [5]: Mentions Elizabeth II's reign over the United Kingdom but focuses on her role in the Commonwealth.(Irrelevant) [3] In less than 200 years, eight queens ruled the Kingdom of England.