Making AI "reason about space in words" might be backfiring 🧭 Here's a new approach that lets it imagine unseen viewpoints instead. Title: Imaginative Perception Tokens Enhance Spatial Reasoning in Multimodal Language Models URL: 🧭 Overview This work proposes Imaginative Perception Tokens (IPT) to strengthen spatial reasoning in vision language models (VLMs). Rather than forcing spatial logic through language, it keeps "what could be perceived under a different arrangement" as an intermediate perceptual representation. ❓ Challenges Solved VLMs struggle with spatial reasoning: inferring unobserved viewpoints, reasoning through occluded paths, and integrating partial observations. Prior work pushed this into textual chain-of-thought, but forcing visual reasoning through language alone hit a ceiling. 💡 Methodology & Proposed Approach ・Uses the unified VLM backbone BAGEL, trained with IPT supervision ・Formulates three tasks: Perspective Taking (PET), Path Tracing (PT), Multiview Counting (MVC) ・Builds a ~20,000-example dataset with ground truth, answers, and metrics The core idea is treating the perception itself ("if I moved here, I'd see this") as an intermediate representation. 📊 Experimental Results ・IPT improved Multiview Counting (MVC) accuracy by 3.4% ・Path Tracing (PT) reached performance competitive with closed-source models ・IPT supervision outperformed textual chain-of-thought training ・Conversely, textual CoT substantially degraded spatial reasoning #SpatialReasoning# #MultimodalLLM#