Spatio-Temporal Grounding of Large Language Models from Perception Streams

Jacob Anderson, Bardh Hoxha, Georgios Fainekos, HIDEKI OKAMOTO, Danil V. Prokhorov

Keywords: large language model, fine-tuning, spatio-temporal, regular expression

TL;DR: We present a framework for generating formal methods-based spatio-temporal perception streams for grounding large language models with spatial and temporal reasoning capabilities.

Abstract:

Embodied-AI agents must reason about how objects move and interact in 3-D space over time, yet existing smaller frontier Large Language Models (LLMs) still mis-handle fine-grained spatial relations, metric distances, and temporal orderings. We introduce the general framework Formally Explainable Spatio-Temporal Scenes (FESTS) that injects verifiable spatio-temporal supervision into an LLM by compiling natural-language queries into Spatial Regular Expression (SpRE) — a language combining regular expression syntax with S4u spatial logic and
extended here with universal and existential quantification. The pipeline matches each SpRE against any structured video log and exports aligned (query, frames, match, explanation) tuples, enabling unlimited training data without manual labels. Training a 3-billion-parameter model on 27k such tuples boosts frame-level F1 from 48.5% to 87.5%, matching GPT-4.1 on complex spatio-temporal reasoning while remaining two orders of magnitude smaller, and, hence, enabling spatio-temporal intelligence for Video LLM.

Submission Type: Long Research Paper (< 9 Pages)

Submission Number: 63