Event-Centric Perception in Weak-Signal Physical Streams with Multimodal LLMs

Robotic and sensing systems operating in challenging environments often receive weak, noisy, and incomplete data streams, such as imaging sonar in underwater settings or thermal sensing under low visibility. In these scenarios, useful evidence is sparse over time, and the objective extends beyond frame-level analysis to reconstructing physically meaningful events. We study this problem through event-centric perception, which maps physical sensor streams into structured events with temporal support, spatial context, direction, count, and magnitude. Frontier multimodal LLMs, such as Qwen3.5-Plus and Gemini, have shown promising capability for event-centric inference under weak and temporally distributed evidence. However, with direct prompting, weak signals often remain below the models' effective decision boundary, leaving event-level structure underused. We address this problem with localized event reasoning, structured event records, and lightweight event-aligned adaptation. This moves weak signals from ignored observations into usable event evidence. In our experiments on a sonar benchmark, the adapted approach improves positive event recall from 0.041 to 0.914 and reduces normalized event error from 0.982 to 0.334. Thermal experiments further show that the approach extends to wildlife event reconstruction and temporally grounded occupancy reasoning. These results support event-centric perception as a principled framework for physical-stream intelligence.