CARTA: Context-Aware Dual Retrieval and Chain-of-Thought Task Allocation for Environment-Grounded Elderly Care
Enabling elderly individuals to age independently at home requires intelligent assistive systems that can understand complex care needs and coordinate appropriate responses. While large language models (LLMs) show promise for adaptive assistance, current eldercare systems suffer from critical limitations: they generate physically infeasible actions due to weak environmental grounding, cannot coordinate multi-faceted care needs through single-agent processing, and lack safety mechanisms to prevent hallucination of non-existent resources. We present CARTA (Context-Aware Dual Retrieval and Chain-of-Thought Task Allocation), a framework that addresses these gaps through two innovations. First, our dual-path retrieval architecture combines semantic knowledge retrieval with graph-constrained retrieval-augmented reasoning, constructing an explicit environment graph encoding object availability, functional affordances, and spatial relationships. This grounds LLM reasoning in physical reality, improving the environmental executability of generated plans by constraining them to objects and affordances actually present in the scene. Second, our Chain-of-Thought multi-agent coordination enables specialized agents to deliberatively decompose ambiguous requests, negotiate responsibilities based on capabilities and constraints, and execute interventions concurrently. Experimental evaluation demonstrates substantial improvements: CARTA achieves 81.8% environment constraint satisfaction versus 65.5% for BM25 and 76.4% for dense retrieval, preventing resource hallucination. On multi-agent task execution, CARTA attains 78.1% success rate on complex tasks and 75.0% on vague commands-a 7.9% improvement over state-of-the-art planners on ambiguous scenarios requiring adaptive coordination. These results establish that explicit environmental grounding and deliberative multi-agent reasoning are essential for safe, contextually appropriate LLM-based elderly care.