🌟 Excited to share that our paper, RoboReason, has been accepted to EMNLP 2026! 🤖 We study how well Vision-Language Models (VLMs) perform on spatiotemporal ordering tasks that use real-world robotics images. Our key findings:
 🔹 Large VLMs often underperform previously reported results when evaluated on spatiotemporal ordering tasks using real-world robotics images.
 🔹 Overlaying specialized computer vision model outputs and increasing reasoning effort generally help improve large VLM performance. 
 🔹 Finetuning smaller models using GRPO and SFT on examples generated by specialized computer vision models often helps them achieve comparable results to much larger VLMs. 📍 Presenting with @sagnikanupam at @emnlpmeeting in Budapest 🇭🇺 tomorrow!
 🗓️ Poster Session C | 9:00–10:30 AM #EMNLP2026 #VLMs #LLMs #Robotics #AI