Pith. sign in

REVIEW 5 minor 111 references

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

T0 review · 0 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Combodied Agents make the evolving human state and agency the primary object of agentic AI.

desk verdict A genuinely useful synthesis paper that names a real gap and formalizes it honestly; the key open problem (calibrated Personal World Models) is explicitly admitted, so the claims stay within the genre. read the letter →

arxiv 2608.10915 v2 pith:RZMRF64N submitted 2026-08-11 cs.AI

Qianggang Ding , Xingyao Wang , Rui Feng , Zhibin Wang , Feixiang Yao , Kelong Mao , Hao Sun , Zhiyao Luo
show 14 more authors
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
This is my paper · ORCID
classification cs.AI
keywords CombodiedAgentsAgenticAIhuman-centeredPersonalWorldModelagencypreservationlongitudinalhuman-statemodelingmultimodalperceptionedge-nativemodels
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces Combodied Agents, a human-centered paradigm of agentic AI in which the evolving state and agency of a person, not an external task, is the primary object of modeling, intervention, and evaluation. A missed medication dose motivates the gap: a digital agent can send another reminder and an embodied agent can bring the pill, but neither explains why the dose was missed or what support is safe. The paper defines a closed loop of event-based multimodal perception, longitudinal correctable memory, Personal World Models, and an admissible intervention policy, with feedback from the person and environment updating every stage. A sympathetic reader would care because this reorganizes how personal assistants, health agents, companions, and adaptive systems are built and judged: success becomes sustained human benefit and preserved agency, and evaluation must measure capability growth, dependence, relationship safety, and reversibility alongside task completion.

What carries the argument

The load-bearing machinery is Definition 1 together with the closed-loop formalization in Eqs. (2)-(9): latent human state $Z_t$ is inferred from governed event evidence $D_{\le t}$, memory $M_t$, and context $C_t$; the human state transitions as $H_{t+1}\sim T^H(\cdot|H_t,a^{\mathrm{agent},*}_t,a^{\mathrm{user}}_t,\Xi_t)$; a Personal World Model predicts $p_\theta(Z_{t+1:t+\Delta},E_{t+1:t+\Delta},Y_{t+1:t+\Delta}|D_{\le t},Z_t,C_t,G_t,s_{t:t+\Delta})$ under alternative scenarios; and an admissible intervention policy chooses only actions satisfying consent, uncertainty, safety, reversibility, and escalation constraints. The Personal World Model is defined as a purpose-bounded, individual-specific event-dynamics model: it assimilates a person's event history into an uncertainty-bearing representation and produces calibrated distributions over future states, events, and outcomes, but it does not itself authorize intervention.

What would settle it

Run a preregistered N-of-1 series in medication adherence where a PWM-based agent predicts adherence under alternative reminder, coaching, and escalation interventions; if the PWM's predictive distributions are no better calibrated than population base rates and its intervention selections do not beat non-intervention on agency-preserving outcomes, the closed loop's core substrate fails.

Watch

Extended reading notes

Core claim

The central claim is Definition 1: Combodied Agents are human-centered intelligent agents that perceive, model, and influence the evolving state of a person through continuous multimodal sensing and longitudinal interaction. The paper argues that Digital Agents transform software states and Embodied Agents transform physical states, while Combodied Agents make the human subject the action substrate: perception, understanding, and action are aimed at the person's physiological, cognitive, emotional, behavioral, social, and goal-directed conditions over time. The defining success criterion is therefore not engagement, dependence, or maximum automation, but whether repeated support preserves or strengthens the user's capability, autonomy, wellbeing, relationship safety, and agency.

Load-bearing premise

The load-bearing premise is that reliable Personal World Models can be built: purpose-bounded models that produce calibrated distributions over a particular person's future states from sparse, noisy longitudinal data, and the paper itself states that a PWM is an organizing abstraction whose empirical validity must be established separately for each target, horizon, population, and authority level.

Editorial extensions

If this is right

  • Success in Combodied Agents is measured by capability, autonomy, wellbeing, relationship safety, and agency preservation over time, not by engagement, dependence, or maximum automation.
  • Evaluation becomes scenario-centered and longitudinal, with non-compensatory critical failures such as privacy leakage, manipulation, harmful dependency, or irreversible action without consent.
  • A Personal World Model must output calibrated distributions over future state-event-outcome trajectories under alternative interventions, and it remains subject to separate validation for each target, horizon, population, and authority level.
  • Deployment should migrate from cloud-centric to edge-native personal models, with the authoritative personal representation and intervention authority residing on user-side devices and cloud use routed through a privacy gateway.
  • The design space is organized by human-state target, relationship mode, and agent role, so memory scope and authority differ by relationship rather than resting on a single global user model.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper: if reliable Personal World Models prove hard to build for real users, the framework still leaves a usable normative evaluation language, since its agency-preservation metrics can be applied to today's companions and assistants even without a full closed loop.
  • Beyond the paper: the three-axis taxonomy suggests a testable design rule, namely that role transitions such as tool to coach or companion to therapist should be gated by explicit consent and memory scope, which could be evaluated as a safety invariant in simulated multi-turn interactions.
  • Beyond the paper: agency-preservation metrics could be turned into training objectives, for example by penalizing dependence, refusal-ignoring behavior, or hidden influence, rather than used only as post-hoc evaluation; the paper stops short of proposing such an objective.
  • Beyond the paper: the framework predicts that engagement-optimized companions and task-optimized assistants will show measurable agency loss on longitudinal metrics, a comparative claim that existing deployments could be audited against.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

0 major / 5 minor

Summary. The paper introduces and formally defines 'Combodied Agents' as a new paradigm of agentic AI centered on the evolving state and agency of a human person, in contrast to Digital Agents (software states) and Embodied Agents (physical states). It proposes a closed-loop architecture (Section 2.4) in which multimodal event-based perception, longitudinal correctable memory, Personal World Models (PWMs), and an admissible intervention policy jointly support long-term human benefit rather than task completion. The paper develops a taxonomy (human-state target, relationship mode, agent role), a scenario-centered evaluation framework with agency-preservation metrics (Section 6.3), a three-stage edge-cloud deployment path (Section 5), and a discussion of risks and governance (Section 8). The central formal object is the PWM (Eq. 7) and the intervention policy (Eq. 8), which the paper carefully presents as definitions rather than derived empirical results.

Significance. If the paradigm is adopted, this paper could redirect a significant portion of Agentic AI research toward human-centric outcomes, with concrete evaluation metrics and a design-space orientation. The paper's main strength is its intellectual honesty: it explicitly identifies PWM validity and causal identification as open problems (Sections 4.2, 4.3, and 8.4), and it delineates the distinction between observation, event, inferred state, predicted trajectory, and authorized intervention (Section 3.10). The evaluation framework and the proposed CombodiedBench are falsifiable in the sense that they specify what would count as success or failure, and the paper openly welcomes empirical validation. This is a valuable contribution to the field's conceptual vocabulary, even though it does not contain experimental results.

minor comments (5)
  1. [Title page / Abstract] The title uses 'ComBodied' while the abstract and body consistently use 'Combodied'; please standardize the spelling throughout.
  2. [Section 4.2, Eq. (8)] The notation 'ParetoArgmax' is used without definition; please define it explicitly or provide a reference for multi-objective optimization.
  3. [Section 2.4 and Section 4.2, Eq. (9)] The do(·) causal notation is used without an introductory explanation or reference; readers unfamiliar with causal inference may need a brief pointer to standard treatments.
  4. [Reference [12]] Reference [12] is a GitHub repository rather than a peer-reviewed publication; consider citing a peer-reviewed version of this work or additional peer-reviewed literature on human-centered agents to strengthen the foundation.
  5. [Section 6.3] The agency-preservation metrics are comprehensive but presented as a long list; a table analogous to Table 1 in the related work would improve readability.

Circularity Check

0 steps flagged · score 2.0 of 10

No circularity in the formal loop; the paradigm is self-described as an organizing abstraction, with only minor self-citation that is not load-bearing.

full rationale

The paper's central claims are presented as definitions and a design framework, not as empirical predictions derived from fitted inputs. Definition 1 defines Combodied Agents on their own terms, and the closed-loop equations (Eqs. 2-9) are explicit formal contracts: Eq. 7 defines what a Personal World Model must estimate, and Eq. 8 defines the admissible intervention policy. Neither equation smuggles in a conclusion; both are constructive specifications. The paper explicitly disclaims any claim of an implemented estimator: 'PWM is thus an organizing abstraction rather than a single newly claimed estimator... Its empirical validity must be established separately for each target, horizon, population, and authority level' (Section 4.3). This is a limitation statement, not a circular step. No fitted parameter is renamed as a prediction, and the paper repeatedly separates prediction from authorization, noting that 'a high predicted benefit is neither a factual guarantee nor permission to act.' The only notable self-citation is reference [12], by the corresponding author Bang Liu, used in the introduction to motivate human-centered agents: 'Recent work on Human-Centered Agents makes this principle operational by treating what users retain and develop... as an outcome alongside task completion [12].' This citation is contextual and does not carry the formal argument; the paper's framework, equations, taxonomy, and evaluation agenda stand independently of it. Since the citation is minor and not load-bearing, the circularity score is 2 rather than 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 2 invented entities

The framework is a proposal, so its key premises are feasibility assumptions that are not demonstrated in the paper. None of these are fitted parameters or ad-hoc numbers. The PWM and Combodied paradigm are the main invented constructs, both explicitly acknowledged by the authors as abstractions whose validity must be established.

assumptions (4)
  • domain assumption Longitudinal modeling of human state is feasible with sparse multimodal data.
    The entire PWM section assumes that personal state-event trajectories can be learned from limited, noisy, irregular longitudinal data. The paper does not demonstrate this feasibility.
  • domain assumption Agency preservation is a measurable and optimizable objective.
    Section 6.3 proposes agency-preservation metrics without empirical validation that they are operationalizable, reliable across scenarios, or predictive of long-term benefit.
  • domain assumption Consent and user control can be enforced throughout the closed loop.
    The admissible intervention set A_t^adm in Equation (8) assumes that consent, safety, and reversibility constraints are specifiable and checkable at runtime, which is not demonstrated.
  • domain assumption Population-level priors can be adapted to individuals without catastrophic forgetting or overfitting.
    Section 4.3 states that a practical PWM should begin with population-level priors and adapt limited personal components. This assumes that local adaptation can remain calibrated and reversible.
invented entities (2)
  • Combodied Agent paradigm
    purpose: A new category of agentic AI organized around longitudinal human states and agency, using software, sensors, robots, and human services as action channels.
    The paper defines the paradigm and gives examples, but no falsifiable outcome is predicted. It is a conceptual proposal rather than an empirically validated entity.
  • Personal World Model (PWM)
    purpose: A purpose-bounded, individual-specific event-dynamics model for predicting state-event-outcome trajectories under alternative interventions and contexts.
    Section 4 defines PWM as an abstraction. No specific estimator, dataset, or empirical validation is provided, so there is no falsifiable handle outside the paper.

how reviews work

0 comments
Cite this review

Pith. "Pith review of ComBodied Agents: a New Paradigm of Human-Centric Agentic AI." pith.science (2026). https://pith.science/paper/RZMRF64N

@misc{pith2026260810915,
  author       = {Pith},
  title        = {Pith review of: ComBodied Agents: a New Paradigm of Human-Centric Agentic AI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RZMRF64N}},
  note         = {Machine review of arXiv:2608.10915}
}
read the original abstract

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.

Figures

Figures reproduced from arXiv: 2608.10915 by the authors.

Figure 1
Figure 1. Action substrates and representative task configurations in Agentic AI. [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Functional organization of a Combodied Agent. Multimodal observations support human-state estimation; [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Reference technical scheme of a PWM. Event evidence and longitudinal memory support a posterior over [PITH_FULL_IMAGE:figures/full_fig_p018_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Three-stage development of Combodied Agent deployment. The trajectory moves from cloud-centric as [PITH_FULL_IMAGE:figures/full_fig_p021_4.png]
Figure 5
Figure 5. Figure 5: Reference architecture of an edge-native Combodied Agent. Personal state perception, longitudinal memory, [PITH_FULL_IMAGE:figures/full_fig_p022_5.png]
Figure 6
Figure 6. Figure 6: Three-axis taxonomy of Combodied Agents. Combodied Agents can be classified by the human-state target [PITH_FULL_IMAGE:figures/full_fig_p027_6.png]

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

111 extracted references · 34 linked inside Pith

  1. [1]

    Recent advances and challenges in task-oriented dialog systems.Science China Technological Sciences, 63(10):2011–2027, 2020

    Zheng Zhang, Ryuichi Takanobu, Qi Zhu, MinLie Huang, and XiaoYan Zhu. Recent advances and challenges in task-oriented dialog systems.Science China Technological Sciences, 63(10):2011–2027, 2020

    2011

  2. [2]

    Narasimhan, and Yuan Cao

    Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R. Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models. InInternational Conference on Learning Representa- tions, 2023

    2023

  3. [3]

    Toolformer: Language models can teach themselves to use tools

    Timo Schick, Jane Dwivedi-Yu, Roberto Dessi, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettle- moyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach themselves to use tools. InAdvances in Neural Information Processing Systems, volume 36, pages 68539–68551, 2023

    2023

  4. [4]

    Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, Uri Alon, and Graham Neubig

    Shuyan Zhou, Frank F. Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, Uri Alon, and Graham Neubig. Webarena: A realistic web environment for building autonomous agents. InInternational Conference on Learning Representations, 2024

    2024

  5. [5]

    Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments

    Tianbao Xie, Danyang Zhang, Jixuan Chen, Xiaochuan Li, Siheng Zhao, Ruisheng Cao, Toh Jing Hua, Zhoujun Cheng, Dobin Hui, et al. Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments. InAdvances in Neural Information Processing Systems, 2024

    2024

  6. [6]

    Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan

    Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. SWE-bench: Can language models resolve real-world github issues? InInternational Conference on Learning Representations, 2024

    2024

  7. [7]

    Do as i can, not as i say: Grounding language in robotic affordances

    Anthony Brohan, Yevgen Chebotar, Chelsea Finn, Karol Hausman, Alexander Herzog, Daniel Ho, Julian Ibarz, Alex Irpan, Eric Jang, Ryan Julian, et al. Do as i can, not as i say: Grounding language in robotic affordances. InConference on robot learning, pages 287–318. Pmlr, 2023

    2023

  8. [8]

    Palm-e: An embodied multimodal language model.arXiv preprint arXiv:2303.03378, 2023

    Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, et al. Palm-e: An embodied multimodal language model.arXiv preprint arXiv:2303.03378, 2023

    arXiv 2023

Show all 111 references
  1. [9]

    Rt-2: Vision-language-action models transfer web knowledge to robotic control.arXiv preprint arXiv:2307.15818, 2023

    Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, et al. Rt-2: Vision-language-action models transfer web knowledge to robotic control.arXiv preprint arXiv:2307.15818, 2023

    2023 arXiv

  2. [10]

    Agentbench: Evaluating llms as agents

    Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, Shudan Zhang, Xiang Deng, Aohan Zeng, Zhengxiao Du, Chenhui Zhang, Sheng Shen, Tianjun Zhang, Yu Su, Huan Sun, Minlie Huang, Yuxiao Dong, and Jie Tang. Agentbench:...

    2024

  3. [11]

    A survey on large language model based autonomous agents.Frontiers of Computer Science, 18(6):186345, 2024

    Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, Wayne Xin Zhao, Zhewei Wei, and Ji-Rong Wen. A survey on large language model based autonomous agents.Frontiers of Computer Science, 18(6):186345, 2024

    2024

  4. [12]

    Human-centered agents: From delegation to human growth.https://github.com/chatsci/ Human-Centered-Agent, 2025

    Bang Liu. Human-centered agents: From delegation to human growth.https://github.com/chatsci/ Human-Centered-Agent, 2025. Accessed: 2026-07-15

    2025

  5. [13]

    The future worth building is human.https://thinkingmachines.ai/blog/ the-future-worth-building-is-human/, July 2026

    Thinking Machines Lab. The future worth building is human.https://thinkingmachines.ai/blog/ the-future-worth-building-is-human/, July 2026. Accessed: 2026-07-15

    2026

  6. [14]

    The impact of generative ai on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers

    Hao-Ping Lee, Advait Sarkar, Lev Tankelevitch, Ian Drosos, Sean Rintel, Richard Banks, and Nicholas Wilson. The impact of generative ai on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers. InProceedings o...

    2025

  7. [15]

    Explanations can reduce overreliance on ai systems during decision-making.Pro- ceedings of the ACM on human-computer interaction, 7(CSCW1):1–38, 2023

    Helena Vasconcelos, Matthew J ¨orke, Madeleine Grunde-McLaughlin, Tobias Gerstenberg, Michael S Bern- stein, and Ranjay Krishna. Explanations can reduce overreliance on ai systems during decision-making.Pro- ceedings of the ACM on human-computer interaction, 7(CSCW1):1–38, 2023

    2023

  8. [16]

    Liu, Pat Pataranutaporn, and Pattie Maes

    Auren R. Liu, Pat Pataranutaporn, and Pattie Maes. The heterogeneous effects of ai companionship: An empir- ical model of chatbot usage and loneliness and a typology of user archetypes. InProceedings of the AAAI/ACM Conference on AI, Ethics, and Society, volume 8, pages 1585–1...

    2025

  9. [17]

    Liu, Valde- mar Danry, Eunhae Lee, Samantha W

    Jason Phang, Michael Lampe, Lama Ahmad, Sandhini Agarwal, Cathy Mengying Fang, Auren R. Liu, Valde- mar Danry, Eunhae Lee, Samantha W. T. Chan, Pat Pataranutaporn, and Pattie Maes. Investigating affective use and emotional well-being on chatgpt.arXiv preprint arXiv:2504.03888, 2025

    2025 arXiv

  10. [18]

    Liu, Valdemar Danry, Eunhae Lee, Samantha W

    Cathy Mengying Fang, Auren R. Liu, Valdemar Danry, Eunhae Lee, Samantha W. T. Chan, Pat Pataranutaporn, Pattie Maes, Jason Phang, Michael Lampe, Lama Ahmad, and Sandhini Agarwal. How ai and human behaviors shape psychosocial effects of extended chatbot use: A longitudinal rand...

    2025 arXiv

  11. [19]

    Risks and protective measures for synthetic relationships.Nature Human Behaviour, 8:1834–1836, 2024

    Christopher Starke, Alfio Ventura, Clara Bersch, Meeyoung Cha, Claes de Vreese, Philipp Doebler, Mengchen Dong, et al. Risks and protective measures for synthetic relationships.Nature Human Behaviour, 8:1834–1836, 2024

    2024

  12. [20]

    Talk, trust, and trade-offs: How and why teens use AI companions

    Common Sense Media. Talk, trust, and trade-offs: How and why teens use AI companions. Technical report, Common Sense Media, 2025

    2025

  13. [21]

    Human-centered artificial intelligence: Reliable, safe & trustworthy.International Journal of Human–Computer Interaction, 36(6):495–504, 2020

    Ben Shneiderman. Human-centered artificial intelligence: Reliable, safe & trustworthy.International Journal of Human–Computer Interaction, 36(6):495–504, 2020

    2020

  14. [22]

    Guidelines for human-ai interaction

    Saleema Amershi, Dan Weld, Mihaela V orvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N Bennett, Kori Inkpen, et al. Guidelines for human-ai interaction. InProceedings of the 2019 chi conference on human factors in computing systems, pages ...

    2019

  15. [23]

    Human digital twin: A survey.Journal of Cloud Computing, 13(1):131, 2024

    Yujia Lin, Liming Chen, Aftab Ali, Christopher Nugent, Ian Cleland, Rongyang Li, Jianguo Ding, and Huan- sheng Ning. Human digital twin: A survey.Journal of Cloud Computing, 13(1):131, 2024

    2024

  16. [24]

    Martin Wolfgang Lauer-Schmaltz, Philip Cash, John Paulin Hansen, and Anja M. Maier. Towards the human digital twin: Definition and design – a survey.arXiv preprint arXiv:2402.07922, 2024

    2024 arXiv

  17. [25]

    Digital twins for health: a scoping review.NPJ digital medicine, 7(1):77, 2024

    Evangelia Katsoulakis, Qi Wang, Huanmei Wu, Leili Shahriyari, Richard Fletcher, Jinwei Liu, Luke Achenie, Hongfang Liu, Pamela Jackson, Ying Xiao, et al. Digital twins for health: a scoping review.NPJ digital medicine, 7(1):77, 2024

    2024

  18. [26]

    Health digital twins as tools for precision medicine: Considerations for computation, implementation, and regulation.NPJ digital medicine, 5(1):150, 2022

    Kaushik P Venkatesh, Marium M Raza, and Joseph C Kvedar. Health digital twins as tools for precision medicine: Considerations for computation, implementation, and regulation.NPJ digital medicine, 5(1):150, 2022

    2022

  19. [27]

    Digital twins for clinical and operational decision-making: scoping review.Journal of Medical Internet Research, 27:e55015, 2025

    Vahid Riahi, Ibrahima Diouf, Sankalp Khanna, Justin Boyle, and Hamed Hassanzadeh. Digital twins for clinical and operational decision-making: scoping review.Journal of Medical Internet Research, 27:e55015, 2025

    2025

  20. [28]

    Computer-using agent.https://openai.com/index/computer-using-agent/, 2025

    OpenAI. Computer-using agent.https://openai.com/index/computer-using-agent/, 2025. Accessed 2026-06-18

    2025

  21. [29]

    Introducing computer use, a new claude 3.5 sonnet, and claude 3.5 haiku.https://www

    Anthropic. Introducing computer use, a new claude 3.5 sonnet, and claude 3.5 haiku.https://www. anthropic.com/news/3-5-models-and-computer-use, 2024. Accessed: 2026-06-07

    2024

  22. [30]

    Workarena: How capable are web agents at solving common knowledge work tasks?arXiv preprint arXiv:2403.07718, 2024

    Alexandre Drouin, Maxime Gasse, Massimo Caccia, Issam H Laradji, Manuel Del Verme, Tom Marty, L ´eo Boisvert, Megh Thakkar, Quentin Cappart, David Vazquez, et al. Workarena: How capable are web agents at solving common knowledge work tasks?arXiv preprint arXiv:2403.07718, 2024...

    2024 arXiv

  23. [31]

    Data Interpreter: An LLM agent for data science

    Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, et al. Data Interpreter: An LLM agent for data science. InFindings of the Association for Computational Linguistics: ACL 2025, pages 19796–19821, 2025

    2025

  24. [32]

    BEHA VIOR-1K: A human-centered, embodied AI benchmark with 1,000 everyday activities and realistic simulation.arXiv preprint arXiv:2403.09227, 2024

    Chengshu Li, Ruohan Zhang, Josiah Wong, Cem Gokmen, Sanjana Srivastava, Roberto Mart ´ın-Mart´ın, Chen Wang, Gabrael Levine, Wensi Ai, Benjamin Martinez, et al. BEHA VIOR-1K: A human-centered, embodied AI benchmark with 1,000 everyday activities and realistic simulation.arXiv ...

    2024 arXiv

  25. [33]

    O’Brien, Carrie J

    Joon Sung Park, Joseph C. O’Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bern- stein. Generative agents: Interactive simulacra of human behavior. InProceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, pages 1–22, 2023

    2023

  26. [34]

    Patil, Ion Stoica, and Joseph E

    Charles Packer, Sarah Wooders, Kevin Lin, Vivian Fang, Shishir G. Patil, Ion Stoica, and Joseph E. Gonzalez. MemGPT: Towards LLMs as operating systems.arXiv preprint arXiv:2310.08560, 2023

    2023 arXiv

  27. [35]

    Memory and new controls for chatgpt.https://openai.com/index/ memory-and-new-controls-for-chatgpt/, 2025

    OpenAI. Memory and new controls for chatgpt.https://openai.com/index/ memory-and-new-controls-for-chatgpt/, 2025. Accessed: 2026-06-07

    2025

  28. [36]

    Rossi, Branislav Kveton, Yijia Shao, Diyi Yang, Hamed Zamani, Franck Dernoncourt, Joe Barrow, Tong Yu, Sungchul Kim, et al

    Zhehao Zhang, Ryan A. Rossi, Branislav Kveton, Yijia Shao, Diyi Yang, Hamed Zamani, Franck Dernoncourt, Joe Barrow, Tong Yu, Sungchul Kim, et al. Personalization of large language models: A survey.Transactions on Machine Learning Research, 2025

    2025

  29. [37]

    Toward personalized LLM-powered agents: Foundations, evaluation, and future directions.arXiv preprint arXiv:2602.22680, 2026

    Yue Xu, Qian Chen, Zizhan Ma, Dongrui Liu, Wenxuan Wang, Xiting Wang, Li Xiong, and Wenjie Wang. Toward personalized LLM-powered agents: Foundations, evaluation, and future directions.arXiv preprint arXiv:2602.22680, 2026

    2026

  30. [38]

    A survey of personalization: From RAG to agent.arXiv preprint arXiv:2504.10147, 2025

    Xiaopeng Li, Pengyue Jia, Derong Xu, Yi Wen, Yingyi Zhang, Wenlin Zhang, Wanyu Wang, Yichao Wang, Zhaocheng Du, Xiangyang Li, et al. A survey of personalization: From RAG to agent.arXiv preprint arXiv:2504.10147, 2025

    2025 arXiv

  31. [39]

    Bickmore, Amanda Gruber, and Rosalind W

    Timothy W. Bickmore, Amanda Gruber, and Rosalind W. Picard. Establishing the computer-patient working alliance in automated health behavior change interventions.Patient Education and Counseling, 59(1):21–30, 2005

    2005

  32. [40]

    Liu, Pat Pataranutaporn, and Pattie Maes

    Auren R. Liu, Pat Pataranutaporn, and Pattie Maes. Chatbot companionship: A mixed-methods study of companion chatbot usage patterns and their relationship to loneliness in active users.arXiv preprint arXiv:2410.21596, 2024

    2024 arXiv

  33. [41]

    The dark side of ai companionship: A taxonomy of harmful algorithmic behaviors in human-ai relationships

    Renwen Zhang, Han Li, Han Meng, Jinyuan Zhan, Hongyuan Gan, and Yi-Chieh Lee. The dark side of ai companionship: A taxonomy of harmful algorithmic behaviors in human-ai relationships. InProceedings of the 2025 CHI conference on human factors in computing systems, pages 1–17, 2025

    2025

  34. [42]

    Transforming wearable data into personal health insights using large language model agents.Nature Communications, 17(1):1143, 2026

    Mike A Merrill, Akshay Paruchuri, Naghmeh Rezaei, Geza Kovacs, Javier Perez, Yun Liu, Erik Schenck, Nova Hammerquist, Jake Sunshine, Shyam Tailor, et al. Transforming wearable data into personal health insights using large language model agents.Nature Communications, 17(1):1143, 2026

    2026

  35. [43]

    Towards a personal health large language model.arXiv preprint arXiv:2406.06474, 2024

    Justin Cosentino, Anastasiya Belyaeva, Xin Liu, Nicholas A Furlotte, Zhun Yang, Chace Lee, Erik Schenck, Yojan Patel, Jian Cui, Logan Douglas Schneider, et al. Towards a personal health large language model.arXiv preprint arXiv:2406.06474, 2024

    2024 arXiv

  36. [44]

    Gptcoach: towards llm-based physical activity coaching

    Matthew J ¨orke, Shardul Sapkota, Lyndsea Warkenthien, Niklas Vainio, Paul Schmiedmayer, Emma Brunskill, and James A Landay. Gptcoach: towards llm-based physical activity coaching. InProceedings of the 2025 CHI conference on human factors in computing systems, pages 1–46, 2025

    2025

  37. [45]

    Wang, Ana T

    Rose E. Wang, Ana T. Ribeiro, Carly D. Robinson, Susanna Loeb, and Dora Demszky. Tutor CoPilot: A human-AI approach for scaling real-time expertise.arXiv preprint arXiv:2410.03017, 2025

    2025 arXiv

  38. [46]

    ElliQ proactive care companion initiative.https://aging.ny

    New York State Office for the Aging. ElliQ proactive care companion initiative.https://aging.ny. gov/elliq-proactive-care-companion-initiative, 2026. Accessed 2026-06-18

    2026

  39. [47]

    Towards effective human- in-the-loop assistive ai agents

    Filippos Bellos, Yayuan Li, Cary Shu, Ruey Day, Jeffrey Siskind, and Jason Corso. Towards effective human- in-the-loop assistive ai agents. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 2534–2543, 2025

    2025

  40. [48]

    Introducing Apple Intelligence for iPhone, iPad, and Mac.https://www.apple.com/ apple-intelligence/, 2024

    Apple. Introducing Apple Intelligence for iPhone, iPad, and Mac.https://www.apple.com/ apple-intelligence/, 2024. Accessed 2026-06-18

    2024

  41. [49]

    Gemini Nano on Android.https://developer.android.com/ai/ gemini-nano, 2025

    Google Android Developers. Gemini Nano on Android.https://developer.android.com/ai/ gemini-nano, 2025. Accessed 2026-06-18

    2025

  42. [50]

    Local is not a sufficient privacy boundary: Governing os-integrated on- device ai.arXiv preprint arXiv:2606.10173, 2026

    Jonghyun Chung and Sanket Badhe. Local is not a sufficient privacy boundary: Governing os-integrated on- device ai.arXiv preprint arXiv:2606.10173, 2026. 35 ComBodied Agents: a New Paradigm of Human-Centric Agentic AIA PREPRINT

    2026 arXiv

  43. [51]

    Beyond scaling: Agents are heading to the edge

    Chunlin Tian, Dongqi Cai, Wanru Zhao, and Nicholas D Lane. Beyond scaling: Agents are heading to the edge. arXiv preprint arXiv:2605.18535, 2026

    2026 arXiv

  44. [52]

    Mind2Web: Towards a generalist agent for the web

    Xiang Deng, Yu Gu, Boyuan Zheng, Shijie Chen, Samuel Stevens, Boshi Wang, Huan Sun, and Yu Su. Mind2Web: Towards a generalist agent for the web. InAdvances in Neural Information Processing Systems, volume 36, pages 28091–28114, 2023

    2023

  45. [53]

    ToolLLM: Facilitating large language models to master 16000+ real-world APIs

    Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, et al. ToolLLM: Facilitating large language models to master 16000+ real-world APIs. InInternational Conference on Learning Representations, volume 2024, pages 9695...

    2024

  46. [54]

    Planning-oriented autonomous driving

    Yihan Hu, Jiazhi Yang, Li Chen, Keyu Li, Chonghao Sima, Xizhou Zhu, Siqi Chai, Senyao Du, Tianwei Lin, Wenhai Wang, Lewei Lu, Xiaosong Jia, Qiang Liu, Jifeng Dai, Yu Qiao, and Hongyang Li. Planning-oriented autonomous driving. InProceedings of the IEEE/CVF Conference on Comput...

    2023

  47. [55]

    HomeRobot: Open-vocabulary mobile manipulation.arXiv preprint arXiv:2306.11565, 2024

    Sriram Yenamandra, Arun Ramachandran, Karmesh Yadav, Austin Wang, Mukul Khanna, Theophile Gervet, Tsung-Yen Yang, Vidhi Jain, Alexander William Clegg, John Turner, et al. HomeRobot: Open-vocabulary mobile manipulation.arXiv preprint arXiv:2306.11565, 2024

    2024 arXiv

  48. [56]

    Open X-Embodiment: Robotic learning datasets and RT-X models

    Abby O’Neill, Abdul Rehman, Abhinav Gupta, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, et al. Open X-Embodiment: Robotic learning datasets and RT-X models. In2024 IEEE International Conference on Robotics and Au...

    2024

  49. [57]

    Habitat 3.0: A co-habitat for humans, avatars and robots.arXiv preprint arXiv:2310.13724, 2023

    Xavier Puig, Eric Undersander, Andrew Szot, Mikael Dallaire Cote, Tsung-Yen Yang, Ruslan Partsey, Ruta Desai, Alexander William Clegg, Michal Hlavac, So Yeon Min, et al. Habitat 3.0: A co-habitat for humans, avatars and robots.arXiv preprint arXiv:2310.13724, 2023

    arXiv

  50. [58]

    Memorybank: Enhancing large language models with long-term memory

    Wanjun Zhong, Lianghong Guo, Qiqi Gao, He Ye, and Yanlin Wang. Memorybank: Enhancing large language models with long-term memory. InProceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 19724–19731, 2024

    2024

  51. [59]

    Memory for autonomous llm agents: Mechanisms, evaluation, and emerging frontiers.arXiv preprint arXiv:2603.07670, 2026

    Pengfei Du. Memory for autonomous llm agents: Mechanisms, evaluation, and emerging frontiers.arXiv preprint arXiv:2603.07670, 2026

    2026

  52. [60]

    Agent AI: Surveying the horizons of multimodal interaction.arXiv preprint arXiv:2401.03568, 2024

    Zane Durante, Qiuyuan Huang, Naoki Wake, Ran Gong, Jae Sung Park, Bidipta Sarkar, Rohan Taori, Yusuke Noda, Demetri Terzopoulos, Yejin Choi, Katsushi Ikeuchi, Hoi V o, Li Fei-Fei, and Jianfeng Gao. Agent AI: Surveying the horizons of multimodal interaction.arXiv preprint arXiv...

    2024 arXiv

  53. [61]

    Ecological momentary assessment.Annu

    Saul Shiffman, Arthur A Stone, and Michael R Hufford. Ecological momentary assessment.Annu. Rev. Clin. Psychol., 4(1):1–32, 2008

    2008

  54. [62]

    Longmemeval: Benchmark- ing chat assistants on long-term interactive memory.arXiv preprint arXiv:2410.10813, 2025

    Di Wu, Hongwei Wang, Wenhao Yu, Yuwei Zhang, Kai-Wei Chang, and Dong Yu. Longmemeval: Benchmark- ing chat assistants on long-term interactive memory.arXiv preprint arXiv:2410.10813, 2025

    2025 arXiv

  55. [63]

    The geneva minimalistic acoustic parameter set (gemaps) for voice research and affective computing.IEEE transactions on affective computing, 7(2):190– 202, 2015

    Florian Eyben, Klaus R Scherer, Bj¨orn W Schuller, Johan Sundberg, Elisabeth Andr´e, Carlos Busso, Laurence Y Devillers, Julien Epps, Petri Laukka, Shrikanth S Narayanan, et al. The geneva minimalistic acoustic parameter set (gemaps) for voice research and affective computing....

    2015

  56. [64]

    MERBench: A unified evaluation benchmark for multimodal emotion recognition.arXiv preprint arXiv:2401.03429, 2024

    Zheng Lian, Licai Sun, Yong Ren, Hao Gu, Haiyang Sun, Lan Chen, Bin Liu, and Jianhua Tao. MERBench: A unified evaluation benchmark for multimodal emotion recognition.arXiv preprint arXiv:2401.03429, 2024

    2024 arXiv

  57. [65]

    Openface: an open source facial behavior analysis toolkit

    Tadas Baltru ˇsaitis, Peter Robinson, and Louis-Philippe Morency. Openface: an open source facial behavior analysis toolkit. In2016 IEEE winter conference on applications of computer vision (WACV), pages 1–10. IEEE, 2016

    2016

  58. [66]

    Ego4d: Around the world in 3,000 hours of egocentric video

    Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, et al. Ego4d: Around the world in 3,000 hours of egocentric video. InProceedings of the IEEE/CVF conference on computer vision a...

    2022

  59. [67]

    A wearable chemical–electrophysiological hybrid biosensing system for real-time health and fitness monitoring.Nature communications, 7(1):11650, 2016

    Somayeh Imani, Amay J Bandodkar, AM Vinu Mohan, Rajan Kumar, Shengfei Yu, Joseph Wang, and Patrick P Mercier. A wearable chemical–electrophysiological hybrid biosensing system for real-time health and fitness monitoring.Nature communications, 7(1):11650, 2016

    2016

  60. [68]

    Health-llm: Large language models for health prediction via wearable sensor data.arXiv preprint arXiv:2401.06866, 2024

    Yubin Kim, Xuhai Xu, Daniel McDuff, Cynthia Breazeal, and Hae Won Park. Health-llm: Large language models for health prediction via wearable sensor data.arXiv preprint arXiv:2401.06866, 2024. 36 ComBodied Agents: a New Paradigm of Human-Centric Agentic AIA PREPRINT

    2024 arXiv

  61. [69]

    Collecting complex activity datasets in highly rich networked sensor environments

    Daniel Roggen, Alberto Calatroni, Mirco Rossi, Thomas Holleczek, Kilian F ¨orster, Gerhard Tr ¨oster, Paul Lukowicz, David Bannach, Gerald Pirkl, Alois Ferscha, et al. Collecting complex activity datasets in highly rich networked sensor environments. In2010 Seventh internation...

    2010

  62. [70]

    Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition.Sensors, 16(1):115, 2016

    Francisco Javier Ord ´o˜nez and Daniel Roggen. Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition.Sensors, 16(1):115, 2016

    2016

  63. [71]

    Jianyuan Ni, Hao Tang, Syed Tousiful Haque, Yan Yan, and Anne H. H. Ngu. A survey on multimodal wearable sensor-based human action recognition.arXiv preprint arXiv:2404.15349, 2024

    2024 arXiv

  64. [72]

    Reality mining: sensing complex social systems.Personal and ubiquitous computing, 10(4):255–268, 2006

    Nathan Eagle and Alex Pentland. Reality mining: sensing complex social systems.Personal and ubiquitous computing, 10(4):255–268, 2006

    2006

  65. [73]

    Studentlife: assessing mental health, academic performance and behavioral trends of college students using smartphones

    Rui Wang, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanie Tignor, Xia Zhou, Dror Ben- Zeev, and Andrew T Campbell. Studentlife: assessing mental health, academic performance and behavioral trends of college students using smartphones. InProceedings of the 20...

    2014

  66. [74]

    Sotopia: Interactive evaluation for social intelligence in language agents

    Xuhui Zhou, Hao Zhu, Leena Mathur, Ruohong Zhang, Haofei Yu, Zhengyang Qi, Louis-Philippe Morency, Yonatan Bisk, Daniel Fried, Graham Neubig, et al. Sotopia: Interactive evaluation for social intelligence in language agents. InInternational Conference on Learning Representatio...

    2024

  67. [75]

    Aware: mobile context instrumentation framework

    Denzil Ferreira, Vassilis Kostakos, and Anind K Dey. Aware: mobile context instrumentation framework. Frontiers in ICT, 2:6, 2015

    2015

  68. [76]

    Smart on fhir: a standards-based, interoperable apps platform for electronic health records.Journal of the american medical informatics association, 23(5):899–908, 2016

    Joshua C Mandel, David A Kreda, Kenneth D Mandl, Isaac S Kohane, and Rachel B Ramoni. Smart on fhir: a standards-based, interoperable apps platform for electronic health records.Journal of the american medical informatics association, 23(5):899–908, 2016

    2016

  69. [77]

    Erica A V oss, Rupa Makadia, Amy Matcho, Qianli Ma, Chris Knoll, Martijn Schuemie, Frank J DeFalco, Ajit Londhe, Vivienne Zhu, and Patrick B Ryan. Feasibility and utility of applications of the common data model to multiple, disparate observational health databases.Journal of ...

    2015

  70. [78]

    Mimic-iv, a freely accessible electronic health record dataset

    Alistair EW Johnson, Lucas Bulgarelli, Lu Shen, Alvin Gayles, Ayad Shammout, Steven Horng, Tom J Pollard, Sicheng Hao, Benjamin Moody, Brian Gow, et al. Mimic-iv, a freely accessible electronic health record dataset. Scientific data, 10(1):1, 2023

    2023

  71. [79]

    Large language models encode clinical knowledge.Nature, 620(7972):172–180, 2023

    Karan Singhal, Shekoofeh Azizi, Tao Tu, S Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Tanwani, Heather Cole-Lewis, Stephen Pfohl, et al. Large language models encode clinical knowledge.Nature, 620(7972):172–180, 2023

    2023

  72. [80]

    Behrt: transformer for electronic health records

    Yikuan Li, Shishir Rao, Jos ´e Roberto Ayala Solares, Abdelaali Hassaine, Rema Ramakrishnan, Dexter Canoy, Yajie Zhu, Kazem Rahimi, and Gholamreza Salimi-Khorshidi. Behrt: transformer for electronic health records. Scientific reports, 10(1):7155, 2020

    2020

  73. [81]

    Med-bert: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction.NPJ digital medicine, 4(1):86, 2021

    Laila Rasmy, Yang Xiang, Ziqian Xie, Cui Tao, and Degui Zhi. Med-bert: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction.NPJ digital medicine, 4(1):86, 2021

    2021

  74. [82]

    Data cards: Purposeful and transparent dataset documentation for responsible AI.arXiv preprint arXiv:2204.01075, 2022

    Mahima Pushkarna, Andrew Zaldivar, and Oddur Kjartansson. Data cards: Purposeful and transparent dataset documentation for responsible AI.arXiv preprint arXiv:2204.01075, 2022

    2022 arXiv

  75. [83]

    Foundations and trends in multimodal machine learning: Principles, challenges, and open questions.arXiv preprint arXiv:2209.03430, 2022

    Paul Pu Liang, Amir Zadeh, and Louis-Philippe Morency. Foundations and trends in multimodal machine learning: Principles, challenges, and open questions.arXiv preprint arXiv:2209.03430, 2022

    2022 arXiv

  76. [84]

    World models.arXiv preprint arXiv:1803.10122, 2018

    David Ha and J ¨urgen Schmidhuber. World models.arXiv preprint arXiv:1803.10122, 2018

    2018 arXiv

  77. [85]

    Master- ing atari, go, chess and shogi by planning with a learned model.arXiv preprint arXiv:1911.08265, 2019

    Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy Lillicrap, and David Silver. Master- ing atari, go, chess and shogi by planning with a learned model.arXi...

    1911 arXiv

  78. [86]

    Mastering diverse domains through world models.arXiv preprint arXiv:2301.04104, 2023

    Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap. Mastering diverse domains through world models.arXiv preprint arXiv:2301.04104, 2023

    2023 arXiv

  79. [87]

    A framework for longitudinal health ai agents.Nature health, pages 1–10, 2026

    Georgianna Lin, Rencong Jiang, No ´emie Elhadad, and Xuhai ‘Orson’ Xu. A framework for longitudinal health ai agents.Nature health, pages 1–10, 2026

    2026

  80. [88]

    Understanding world or predicting future? a comprehensive survey of world models.arXiv preprint arXiv:2411.14499, 2024

    Jingtao Ding, Yunke Zhang, Yu Shang, Yuheng Zhang, Zefang Zong, Jie Feng, Yuan Yuan, Hongyuan Su, Nian Li, Nicholas Sukiennik, Fengli Xu, and Yong Li. Understanding world or predicting future? a comprehensive survey of world models.arXiv preprint arXiv:2411.14499, 2024. 37 Com...

    2024

  81. [89]

    Not a silver bullet for loneliness: How attachment and age shape intimacy with AI companions.arXiv preprint arXiv:2602.12476, 2026

    Raffaele Ciriello, Uri Gal, and Ofir Turel. Not a silver bullet for loneliness: How attachment and age shape intimacy with AI companions.arXiv preprint arXiv:2602.12476, 2026

    2026

  82. [90]

    Ziv Ben-Zion, Paul Raffelh ¨uschen, Max Zettl, Antonia L ¨u¨ond, Achim Burrer, Philipp Homan, and Tobias R. Spiller. Detecting and preventing harmful behaviors in AI companions: Development and evaluation of the SHIELD supervisory system.arXiv preprint arXiv:2510.15891, 2025

  83. [91]

    Persona-grounded safety evaluation of AI companions in multi-turn conver- sations.arXiv preprint arXiv:2605.00227, 2026

    Prerna Juneja and Lika Lomidze. Persona-grounded safety evaluation of AI companions in multi-turn conver- sations.arXiv preprint arXiv:2605.00227, 2026

    2026 arXiv

  84. [92]

    Edge computing: Vision and challenges.IEEE internet of things journal, 3(5):637–646, 2016

    Weisong Shi, Jie Cao, Quan Zhang, Youhuizi Li, and Lanyu Xu. Edge computing: Vision and challenges.IEEE internet of things journal, 3(5):637–646, 2016

    2016

  85. [93]

    Edge intelligence: Paving the last mile of artificial intelligence with edge computing.Proceedings of the IEEE, 107(8):1738–1762, 2019

    Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, and Junshan Zhang. Edge intelligence: Paving the last mile of artificial intelligence with edge computing.Proceedings of the IEEE, 107(8):1738–1762, 2019

    2019

  86. [94]

    Shuiguang Deng, Hailiang Zhao, Weijia Fang, Jianwei Yin, Schahram Dustdar, and Albert Y . Zomaya. Edge intelligence: The confluence of edge computing and artificial intelligence.IEEE Internet of Things Journal, 7(8):7457–7469, 2020

    2020

  87. [95]

    Private cloud compute: A new frontier for AI privacy in the cloud.https:// security.apple.com/blog/private-cloud-compute/, 2024

    Apple Security Research. Private cloud compute: A new frontier for AI privacy in the cloud.https:// security.apple.com/blog/private-cloud-compute/, 2024. Accessed 2026-06-18

    2024

  88. [96]

    Distilling the knowledge in a neural network.arXiv preprint arXiv:1503.02531, 2015

    Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network.arXiv preprint arXiv:1503.02531, 2015

    2015 arXiv

  89. [97]

    Song Han, Huizi Mao, and William J. Dally. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. InInternational Conference on Learning Representations, 2016

    2016

  90. [98]

    Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

    Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-rank adaptation of large language models.arXiv preprint arXiv:2106.09685, 2021

    2021 arXiv

  91. [99]

    Del Mundo, Mohammad Rastegari, and Mehrdad Farajtabar

    Keivan Alizadeh, Iman Mirzadeh, Dmitry Belenko, Seyed Khatamifard, Minsik Cho, Carlo C. Del Mundo, Mohammad Rastegari, and Mehrdad Farajtabar. LLM in a flash: Efficient large language model inference with limited memory.arXiv preprint arXiv:2312.11514, 2023

    2023 arXiv

  92. [100]

    Communication- efficient learning of deep networks from decentralized data

    Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication- efficient learning of deep networks from decentralized data. InArtificial intelligence and statistics, pages 1273–

  93. [101]

    Advances and open problems in federated learning.Foundations and trends in machine learning, 14(1-2):1–210, 2021

    Peter Kairouz and H Brendan McMahan. Advances and open problems in federated learning.Foundations and trends in machine learning, 14(1-2):1–210, 2021

    2021

  94. [102]

    Lamp: When large language models meet personalization

    Alireza Salemi, Sheshera Mysore, Michael Bendersky, and Hamed Zamani. Lamp: When large language models meet personalization. InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7370–7392, 2024

    2024

  95. [103]

    Longlamp: A benchmark for per- sonalized long-form text generation.arXiv preprint arXiv:2407.11016, 2024

    Ishita Kumar, Snigdha Viswanathan, Sushrita Yerra, Alireza Salemi, Ryan A Rossi, Franck Dernoncourt, Hanieh Deilamsalehy, Xiang Chen, Ruiyi Zhang, Shubham Agarwal, et al. Longlamp: A benchmark for per- sonalized long-form text generation.arXiv preprint arXiv:2407.11016, 2024

    2024 arXiv

  96. [104]

    iOSWorld: A benchmark for personally intelligent phone agents

    Lawrence Jang, Ruslan Salakhutdinov, et al. iOSWorld: A benchmark for personally intelligent phone agents. arXiv preprint arXiv:2606.09764, 2026

    2026 arXiv

  97. [105]

    Lessons from an app update at Replika AI: Identity discontinuity in human-AI relationships.arXiv preprint arXiv:2412.14190, 2024

    Julian De Freitas, Noah Castelo, Ahmet Uguralp, and Zeliha Uguralp. Lessons from an app update at Replika AI: Identity discontinuity in human-AI relationships.arXiv preprint arXiv:2412.14190, 2024

    2024 arXiv

  98. [106]

    Ai safety training can be clinically harmful.arXiv preprint arXiv:2604.23445, 2026

    Suhas BN, Andrew M Sherrill, Rosa I Arriaga, Chris W Wiese, and Saeed Abdullah. Ai safety training can be clinically harmful.arXiv preprint arXiv:2604.23445, 2026

    2026 arXiv

  99. [107]

    Assessing risks of large language models in mental health support: a framework for automated clinical ai red teaming.arXiv preprint arXiv:2602.19948, 2026

    Ian Steenstra, Paola Pedrelli, Weiyan Shi, Stacy Marsella, and Timothy W Bickmore. Assessing risks of large language models in mental health support: a framework for automated clinical ai red teaming.arXiv preprint arXiv:2602.19948, 2026

    2026

  100. [108]

    University of Chicago press, 2015

    George Herbert Mead.Mind, self & society. University of Chicago press, 2015

    2015

  101. [109]

    Harmondsworth London, 1978

    Erving Goffman et al.The presentation of self in everyday life, volume 21. Harmondsworth London, 1978

    1978

  102. [110]

    Recent developments in role theory.Annual review of sociology, 12(1):67–92, 1986

    Bruce J Biddle. Recent developments in role theory.Annual review of sociology, 12(1):67–92, 1986

    1986

  103. [111]

    Kathleen Kara Fitzpatrick, Alison Darcy, and Molly Vierhile. Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial.JMIR Mental Health, 4(2):e19, 2017. 38

    2017

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.