Research engineer Perspective-Grounded Spatial Reasoning in Multimodal Large Language Models

Mission confiée

Multimodal large language models (MLLMs) remain notably weak at spatial reasoning from video, particularly at perspective-taking where the model is asked to answer questions from a viewpoint other than the camera's own (e.g., identifying what lies to an observer's left from an allocentric frame). Existing approaches largely rely on either naive data augmentation or opaque latent reasoning tokens whose targets are not grounded in real geometry, both of which have known failure modes. This project aims to build a diagnostic benchmark that isolates genuine perspective-taking failures from confounds (e.g., left-right mirroring, temporal-order artifacts), and to develop a spatial reasoning representation that is explicitly grounded in 3D geometric structure via alignment with a pretrained 3D foundation model, rather than relying on self-consistency alone.

Principales activités

Main activities:

• Analyse and implement related work.
• Design novel innovative solutions.
• Write progress reports and papers.
• Present work at conferences.

Compétences

Technical skills and level required : programming skills are required.

Languages : English and possibly French.

Relational skills : Good communication skills.

Avantages

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage