Multi-view spatial reasoning in large vision-language models involves inferring view-specific spatial relations and integrating them into a consistent scene-centered representation. However, existing approaches often learn view-dependent relations tied to individual camera frames, which limits their ability to form viewpoint-invariant spatial representations. Motivated by cognitive models of spatial perspective-taking, we propose SCoRE (Spatial Consistency Reasoning), a method that formulates multi-view spatial reasoning as an egocentric-to-allocentric transformation problem. SCoRE first trains the model to infer egocentric object relations from single-view observations. It then applies reinforcement learning to encourage the model to estimate viewpoint orientation, transform egocentric relations into a shared scene-centered reference frame, and predict allocentric object relations. To enforce viewpoint-invariant reasoning, we optimize the model with a multi-view consistency reward that aligns predictions from different views in the common allocentric space. Experiments show that SCoRE improves spatial reasoning performance by +3.69%p on multi-view benchmarks and +4.72%p on general spatial reasoning benchmarks over prior state-of-the-art methods, demonstrating the effectiveness of explicit reference-frame transformation for robust multi-view spatial understanding.

Recent attempts to leverage large language models (LLMs) for reasoning and pre-trained knowledge in multi-modal reasoning focus on two main approaches: aligning image features with linguistic space, and converting images into textual cues to exploit the implicit reasoning capabilities of LLMs. Although they integrate visual information into the reasoning pipeline, they often treat visual perception and language reasoning as separate processes, limiting the potential for fully unified multi-modal reasoning. In this paper, we propose a novel method, Visual–Linguistic Abductive Reasoning (ViLA), inspired by human abductive reasoning processes. ViLA hypothesizes a plausible answer, generates the corresponding visual and textual premises, and employs fuzzy scoring to select the most coherent combination, thus deriving the final inference. This process integrates visual and linguistic modalities into interpretable abductive reasoning chains, enabling unified multi-modal reasoning. Without fine-tuning LLMs or retrieving external knowledge, ViLA improves performance by 2.31% on AOKVQA, 1.7% on OKVQA, and 1.7% on GQA over previous state-of-the-art models, while also improving interpretability and stability.

Negation-aware image generation remains challenging for text-to-image diffusion models, which often struggle with negation due to implicit linguistic biases that are deeply entangled within text representations. As a result, bias-aligned semantics frequently dominate over intended negative constraints. To overcome this limitation, we propose a simple training-free method of Decompose and RecOmpose text embedding (DeRO), which constructs a semantic-preserving subspace and a bias subspace using auxiliary prompts and performs semantic disentanglement based on bidirectional SVD. By decomposing entangled representations and selectively recomposing disentangled semantic components, DeRO realigns text representations toward intended negation semantics while restoring residual stylistic features to preserve semantic fidelity and image quality. As a model-agnostic inference-time framework operating solely on text representations, DeRO can be seamlessly applied to diverse diffusion architectures, including both U-Net and DiT based models. Extensive experiments on both object- and concept-level negation demonstrate consistent improvements in negation-aware image generation, achieving stronger text-image alignment, better semantic fidelity, and higher human preference scores.

Consistent reasoning about 3D spatial relations across changing viewpoints is fundamental for Embodied AI agents operating in dynamic environments. While Large Vision-Language Models (LVLMs) have advanced multimodal perception, their ability to maintain spatial consistency across diverse perspectives remains underexplored. Existing benchmarks primarily assess spatial capabilities from a static, single-view, and egocentric perspective, failing to capture the dynamic nature of real-world spatial cognition.To address this gap, we introduce SCOPE (Spatial COnsistency across PErspectives and Viewpoints), a comprehensive benchmark designed to rigorously diagnose spatial reasoning capabilities. Grounded in human cognitive theories of dual spatial representations, SCOPE discretizes the 360∘ field into multiview scenarios to systematically evaluate both allocentric and egocentric reasoning capabilities. Our dataset comprises 20.1K spatial VQA pairs derived from high-quality 3D environments. Through an extensive evaluation of 26 state-of-the-art LVLMs, we identify two fundamental limitations that prevent consistent spatial understanding across viewpoints.We hope SCOPE facilitates the diagnosis of spatial reasoning, serving as a stepping stone toward reliable embodied action.

Recent advances in large language models (LLMs) have improved logical reasoning by injecting formal logic or explicit structured representations. However, such methods often lose track of what is true now in multi-step reasoning, failing to maintain a coherent global state and its logical consequences. Motivated by Situation Model Theory in cognitive psychology, which views comprehension as constructing and updating a mental model of events along key dimensions (time, space, causality, intention, protagonist), we propose Situation Working Memory (SituW), a cognitively inspired method for contextual reasoning in LLMs. SituW first builds a situation representation by decomposing text along these five dimensions, then guides LLM inference with this evolving state. Keeping an explicit, dynamically updated situation memory instead of a static logical form encourages globally consistent reasoning over the situation model rather than raw text. Evaluated in both supervised and unsupervised settings, SituW improves accuracy by 23.3%p and 15.93%p while reducing “uncertain” predictions, suggesting that explicit situation modeling supports more globally consistent LLM reasoning.