ENTERPRISE AI ANALYSIS

ASF: Robust Step Recognition

The Adaptive Step Fusion (ASF) mechanism is crucial for MICA's robustness. It dynamically blends rule-based workflow constraints from a knowledge base with retrieval-based visual similarity, adapting online from natural speech feedback. ASF's key innovations include:

• Explicit Incorporation of Workflow Compatibility: Integrates non-jumping transitions into the fusion score, ensuring adherence to assembly procedures.
• Class-wise Fusion with Online Updates: Maintains per-class expert weights and biases, updating them based on user feedback without backpropagation, and freezing confident hits to prevent instability.
• Anti-collapse Regularization: Utilizes history-based scaling and weight floors to prevent fusion from collapsing into a single class, enhancing generalizability.

Experiments show ASF significantly improves robustness, reducing calibration error (ECE) and correcting systematic late-stage failures, with a 90.36 F1 improvement for complex steps (S4 from 0% to 95.34% accuracy).

Coordinated Multi-Agent Intelligence

MICA-core operates as a modular multi-agent reasoning framework, dynamically assigning queries to five specialized agents: Assembly Guide, Parts Advisor, Maintenance Advisor, Fault Handler, and General Agent. Each agent uses a Retrieval-Augmented Generation (RAG) paradigm, retrieving evidence from a structured knowledge base and refining responses iteratively.

A dedicated safety checker audits all agent outputs, enforcing rule-based assembly constraints, correct tool usage, assembly order, and hazard warnings, preventing unsafe recommendations. This dynamic routing, specialization, and safety auditing enable MICA-core to deliver contextually precise, semantically rich, and industrially safe responses.

In a benchmark against four multi-agent topologies (SharedMemory, CentralizedBroadcast, HierarchicalPipeline, DebateVoting), MICA-core achieved the highest task success (63.13%) and knowledge base alignment (19.12%), while maintaining the lowest latency (0.71s) and energy consumption (2.05kJ), demonstrating its superior balance of performance and efficiency for safety-critical industrial assistance.

Empirical Validation & Real-World Readiness

MICA consistently outperforms baseline multi-agent structures, showing improvements in task success, reliability, and responsiveness. The system achieves a unique balance of factual alignment, efficiency, and adaptivity crucial for real-world assistance.

• ASF Adaptation: Ten lightweight corrections per step drastically improve recognition accuracy and calibration, turning an initially brittle fusion into a calibrated, generalizable predictor.
• Coordination Efficacy: MICA's sparse activation yields approximately 0.71 seconds responsiveness and the lowest energy cost (2.05 kJ), highlighting its efficiency advantage over redundant agent activations in other topologies.
• Robustness & Safety: Perception grounding, router-based specialization, and safety auditing ensure accurate, safe, and interpretable responses, even when intent ambiguity or routing errors occur.

This work positions MICA as a deployable, privacy-preserving industrial assistant capable of adapting to dynamic factory workflows, running entirely on edge hardware.