# Seeing Together: Multi-Robot Cooperative Egocentric Spatial Reasoning with Multimodal Large Language Models

arXiv: https://arxiv.org/abs/2605.18431 · HTML: https://mlsanity.com/paper/2605.18431/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 24 of its 75 references that are in the map.
- First posted on arXiv 18 May 2026.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)
- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 24 of 24)

- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://mlsanity.com/paper/2508.18265/) (2025)
- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [OpenAI GPT-5 System Card](https://mlsanity.com/paper/2601.03267/) (2025)
- [VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding](https://mlsanity.com/paper/2501.13106/) (2025)
- [Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots](https://mlsanity.com/paper/2310.13724/) (2023)
- [PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-agent Tasks](https://mlsanity.com/paper/2411.00081/) (2024)
- [Solaris: Building a Multiplayer Video World Model in Minecraft](https://mlsanity.com/paper/2602.22208/) (2026)
- [MM-Ego: Towards Building Egocentric Multimodal LLMs for Video QA](https://mlsanity.com/paper/2410.07177/) (2024)
- [EgoExoBench: A Benchmark for First- and Third-person View Video Understanding in MLLMs](https://mlsanity.com/paper/2507.18342/) (2025)
- [MultiWorld: Scalable Multi-Agent Multi-View Video World Models](https://mlsanity.com/paper/2604.18564/) (2026)
- [TeamCraft: A Benchmark for Multi-Modal Multi-Agent Systems in Minecraft](https://mlsanity.com/paper/2412.05255/) (2024)
- [Empowering Multi-Robot Cooperation via Sequential World Models](https://mlsanity.com/paper/2509.13095/) (2025)
- [Co-NavGPT: Multi-Robot Cooperative Visual Semantic Navigation Using Vision Language Models](https://mlsanity.com/paper/2310.07937/) (2023)
- [EgoMe: A New Dataset and Challenge for Following Me via Egocentric View in Real World](https://mlsanity.com/paper/2501.19061/) (2025)
- [KTV: Keyframes and Key Tokens Selection for Efficient Training-Free Video LLMs](https://mlsanity.com/paper/2602.03615/) (2026)
- [EgoExo-Con: Exploring View-Invariant Video Temporal Understanding](https://mlsanity.com/paper/2510.26113/) (2025)
- [MA-EgoQA: Question Answering over Egocentric Videos from Multiple Embodied Agents](https://mlsanity.com/paper/2603.09827/) (2026)
- [CoMaTrack: Competitive Multi-Agent Game-Theoretic Tracking with Vision-Language-Action Models](https://mlsanity.com/paper/2603.22846/) (2026)
- [DeCoNav: Dialog enhanced Long-Horizon Collaborative Vision-Language Navigation](https://mlsanity.com/paper/2604.12486/) (2026)
- [OmniVLN: Omnidirectional 3D Perception and Token-Efficient LLM Reasoning for Visual-Language Navigation across Air and Ground Platforms](https://mlsanity.com/paper/2603.17351/) (2026)
- [Can a Robot Walk the Robotic Dog: Triple-Zero Collaborative Navigation for Heterogeneous Multi-Agent Systems](https://mlsanity.com/paper/2603.21723/) (2026)

## Built on by (most cited first, 4 of 4)

- [RoboFind: Multi-Agent Personalized Object Search for People Who Are Blind or Have Low Vision](https://mlsanity.com/paper/2609.20330/) (2026)
- [SpatialSkill: Self-Evolving Skills for Cross-View Spatial Reasoning](https://mlsanity.com/paper/2609.34124/) (2026)
- [Does Local Video Understanding Transfer Across Encounters? The EgoGears Benchmark](https://mlsanity.com/paper/2609.37938/) (2026)
- [Multics: Generative Multi-Player World Model with Hierarchical Communication](https://mlsanity.com/paper/2610.17201/) (2026)

## Abstract (arXiv)

Multimodal Large Language Models (MLLMs) have made substantial progress in egocentric video understanding, but their ability to reason cooperatively from multiple embodied viewpoints remains largely unexplored. We study this problem through multi-robot cooperative dynamic spatial reasoning, where a model must answer spatial, temporal, visibility, and coordination questions by integrating synchronized egocentric videos from a team of moving robots. To support this setting, we introduce CoopSR, the first benchmark for this task, together with EgoTeam, a multi-robot egocentric QA dataset. EgoTeam contains 114,227 QA pairs spanning 19 question types, four difficulty tiers, and three team sizes in Habitat and iGibson, along with a real-world test set of around 2,326 QAs collected using two quadruped robots. We further propose SP-CoR (Spectral and Physics-Informed Cooperative Reasoner), an MLLM framework for fine-grained cooperative spatial reasoning. SP-CoR combines dynamics-aware multi-robot frame sampling, spectral- and physics-guided view fusion, and physics-aligned prompt distillation, enabling the model to benefit from privileged robot-pose supervision during training while requiring only egocentric videos at test time. Across 22 MLLM baselines, SP-CoR consistently improves cooperative reasoning, outperforming the strongest fine-tuned baseline by +3.87% on Habitat and +7.12% on iGibson. It also shows stronger generalization to unseen team sizes and real-world robot tests. Code can be found at https://github.com/KPeng9510/seeing-together.git.