ComBodied Agents: a New Paradigm of Human-Centric Agentic AI

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Qianggang Ding, Xingyao Wang, Rui Feng, Zhibin Wang, Feixiang Wang, Kelong Mao, Hao Sun, Zhiyao Luo, Jiankai Tang, Lei Li, Jiadong Guo, Minheng Ni, Weicong Lin, Chenxi Yang, Hongxiang Gao, Zhenghua Chen, Yang Bai, Min Wu, Jun Cheng, Huazhu Fu, Dacheng Tao, Bang Liu
• Venue: arXiv: cs.AI
• Published: 2026-08-11
• Collected: 2026-08-12
• URL: http://arxiv.org/abs/2608.10915v1
• PDF: https://arxiv.org/pdf/2608.10915v1
• DOI: UNKNOWN
• Registry group: academic_apis_and_preprints
• Evidence role: canonical_academic_index
• Language: en
• Country / region: UNKNOWN

Plain-Language Brief

The text introduces a new paradigm called ComBodied Agents, which aims to bridge the gap between digital and embodied agents in AI by focusing on understanding and supporting individual human state trajectories over time. The framework uses various tools like software, sensors, wearables, robots, and human services to provide personalized support based on an individual's evolving state.

What the Document Says

• The text discusses the need for a new paradigm in Agentic AI that bridges the gap between digital and embodied agents.
• Combodied Agents are introduced as a human-centered approach that focuses on perceiving, modeling, predicting, and supporting individual human state trajectories over time.
• The framework uses various tools like software, sensors, wearables, robots, and human services for intervention.
• The text mentions a closed loop system involving event-based multimodal perception, longitudinal memory, Personal World Models, and an admissible intervention policy.

Key Concepts Mentioned

• Agentic AI
• Combodied Agents
• digital agents
• embodied agents
• personal assistants
• health agents
• AI companions
• adaptive human-AI systems
• event-based multimodal perception
• longitudinal memory
• Personal World Models
• admissible intervention policy

Claims Made by the Document

• Combodied Agents can provide personalized support based on an individual's evolving state.

Evidence Used

• The text describes the use of various tools for intervention and the closed loop system.

Methods / Approach

• Event-based multimodal perception
• Longitudinal memory
• Personal World Models
• admissible intervention policy

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

The text does not specify any limitations of the proposed paradigm as stated by the authors.

Open Questions

• What specific tools will be used for each component of the closed loop system?
• How will the framework handle privacy and consent issues?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The text mentions various tools but does not specify which ones will be used for each component of the closed loop system. It also does not discuss privacy and consent issues.

Raw Abstract / Excerpt

After an older adult misses a medication dose, a software agent can send another reminder and an embodied agent can bring the medication. Yet neither explains whether the person forgot, is confused, has side effects, or deliberately refused, nor what support is appropriate. This reveals a structural gap in Agentic AI: Digital Agents primarily transform software states, while Embodied Agents transform physical states; neither makes a person's evolving state and agency the primary object of modeling, intervention, and evaluation. We introduce Combodied Agents, a human-centered paradigm that perceives, models, predicts, and supports individual human-state trajectories over time, using software tools, sensors, wearables, robots, and human services as action channels rather than end goals. We unify fragmented capabilities across personal assistants, health agents, AI companions, and adaptive human--AI systems into a closed loop: event-based multimodal perception reconstructs meaningful personal events; longitudinal, correctable memory provides temporal context; Personal World Models estimate future personal states and outcomes under alternative decisions and interventions; and an admissible intervention policy selects proportionate support under consent, uncertainty, safety, reversibility, and user control. Feedback from the person and environment updates the loop. Rather than requiring an exhaustive Human Digital Twin, the framework uses purpose-bounded, uncertainty-aware, user-correctable representations. We organize the design space by human-state targets, relational contexts, and agent roles, and propose scenario-centered evaluation, agency-preservation metrics, benchmark requirements, edge-native personal models, and governance directions. Combodied Agents shift Agentic AI from external task completion toward sustained human benefit.