Advancing Multi-Robot Networks via MLLM-Driven Sensing, Communication, and Computation: A Comprehensive Survey

Imagine advanced humanoid robots, powered by multimodal large language models (MLLMs), coordinating missions across industries like warehouse logistics, manufacturing, and safety rescue. While individual robots show local autonomy, realistic tasks demand coordination among multiple agents sharing vast streams of sensor data. Communication is indispensable, yet transmitting comprehensive data can overwhelm networks, especially when a system-level orchestrator or cloud-based MLLM fuses multimodal inputs for route planning or anomaly detection. These tasks are often initiated by high-level natural language instructions. This intent serves as a filter for resource optimization: by understanding the goal via MLLMs, the system can selectively activate relevant sensing modalities, dynamically allocate bandwidth, and determine computation placement. Thus, R2X is fundamentally an intent-to-resource orchestration problem where sensing, communication, and computation are jointly optimized to maximize task-level success under resource constraints. This survey examines how integrated design paves the way for multi-robot coordination under MLLM guidance. We review state-of-the-art sensing modalities, communication strategies, and computing approaches, highlighting how reasoning is split between on-device models and powerful edge/cloud servers. We present four end-to-end demonstrations (sense -> communicate -> compute -> act): (i) digital-twin warehouse navigation with predictive link context, (ii) mobility-driven proactive MCS control, (iii) a FollowMe robot with a semantic-sensing switch, and (iv) real-hardware open-vocabulary trash sorting via edge-assisted MLLM grounding. We emphasize system-level metrics -- payload, latency, and success -- to show why R2X orchestration outperforms purely on-device baselines.

Deeper Understanding of Black-box Predictions via Generalized Influence Functions

Influence functions (IFs) elucidate how learning data affects model behavior. However, growing non-convexity and the number of parameters in modern large-scale models lead to imprecise influence approximation and instability in computations. We highly suspect that the first-order approximation in large models causes such fragility, as IFs change all parameters including possibly nuisance parameters that are irrelevant to the examined data. Thus, we attempt to selectively analyze parameters associated with the data. However, simply computing influence from the chosen parameters can be misleading, as it fails to nullify the subliminal impact of unselected parameters. Our approach introduces generalized IFs, precisely estimating target parameters' influence while considering fixed parameters' effects. Unlike the classic IFs, we newly adopt a method to identify pertinent target parameters closely associated with the analyzed data. Furthermore, we tackle computational instability with a robust inverse-Hessian-vector product approximation. Remarkably, the proposed approximation algorithm guarantees convergence regardless of the network configurations. We evaluated our approach on ResNet-18 and VGG-11 for class removal and backdoor model recovery. Modifying just 10\% of the network yields results comparable to the network retrained from scratch. Aligned with our first guess, we also confirm that modifying an excessive number of parameters results in a decline in network utility. We believe our proposal can become a versatile tool for model analysis across various AI domains, appealing to both specialists and general readers. Codes are available at https://github.com/hslyu/GIF.