研究与技术Research & Technology

把临床科学,写进每一次对话。Clinical science, built into every conversation.

Pebble 的“温柔”背后,是一套认真的工程与临床方法,以及对前沿研究的持续跟读。这一页讲讲:我们怎么做、正在探索什么,以及我们站在哪些研究的肩膀上。Behind Pebble's gentleness is serious engineering and clinical method — and a habit of reading the frontier closely. Here's how we work, what we're exploring, and the research we build on.

技术方法How Pebble works

不是“套壳聊天机器人”,是一套有临床骨架的系统。Not a chatbot wrapper — a system with a clinical spine.

以下是已经在产品里运行的能力。These capabilities are live in the product today.

关系概念化层A formulation layer

每一轮对话都由个案概念化驱动——先理解“此刻正在发生什么”,再决定怎么回应,而不是逐句应答。Every turn is driven by a case formulation — we first understand what's happening right now, then choose how to respond, rather than replying line by line.

意图分诊与动态路由Intent triage & routing

先判断你此刻更需要被倾听、想要方法,还是在求助,再动态决定回应的方式与边界。We first sense whether you need to be heard, want a method, or are reaching for help — then route the response accordingly.

统一对话智能体One agent, every channel

信件、文字、语音共用同一套人格、记忆与安全底线;从打字切到说话,对话不断线。Letters, text, and voice share one persona, memory, and safety floor — switch from typing to talking without losing the thread.

纵深安全流水线Safety, by architecture

分层危机识别 + 输出守卫 + 隐私优先。安全是贯穿系统的架构,不是事后加的过滤器。Layered crisis detection, output guarding, privacy-first — safety runs through the architecture, it isn't a filter bolted on after.

评测驱动Evaluation-driven

每一次措辞改动都在真实模型上评测、由独立评委盲评、过回归门。质量与安全,我们都不做取舍。Every change in wording is tested on real models, blind-reviewed by independent judges, and gated by regression tests. We trade off neither quality nor safety.

跨会谈记忆与状态Memory & state over time

记得你上次聊到哪里,并追踪状态随时间的变化——让陪伴有连续性,而不是每次从零开始。Remembers where you left off and tracks how your state shifts over time — so support has continuity, not a cold start each visit.

我们的语音,不太一样Our voice is different

多数 AI 还在读你的字,Pebble 已经开始听你的“语气”。Most AI reads your words. Pebble has begun to hear your tone.

情绪,常常藏在“怎么说”里,而不只在“说了什么”。我们已经做出语音韵律感知,并在真实对话里看到了效果——从语速、停顿、语气的起伏中,捕捉文字之外的情绪信号。这让 Pebble 的语音陪伴,不只是“能说话”,而是更接近“听得懂”。Emotion often lives in how something is said, not only in the words. We've built voice-prosody sensing — and seen it work in real conversations — reading the signals in pace, pauses, and intonation that text alone can't carry. So Pebble's voice doesn't just speak; it comes closer to understanding.

语音韵律感知 · 已落地,持续进化Voice-prosody sensing · shipped & evolving
研发中 · 实验沙盒In the lab

我们正在把边界,一点点往前推。Pushing the edge, deliberately.

这些是我们正在攻的方向——还没全部进产品,但每一项都朝着“更懂人、更负责任”的目标走。These are the problems we're actively working on — not all in the product yet, but each one aimed at care that understands more, and is more responsible.

纵向治疗状态建模Longitudinal state modeling把每一次对话看作一段旅程的一部分,跨会谈刻画临床状态的走向与变化。Treating each conversation as part of a longer arc, modeling how clinical state moves across sessions.
偏好学习Preference learning让模型从“什么样的回应更有帮助”里持续学习,而不是只追求“听起来顺”。Letting the model learn from what actually helps, not just what sounds smooth.
自动化技能评测Automated skill evaluation用语音与语言技术,自动评估共情、反映、引导等咨询技能——把“手艺”变成可测量的东西。Using speech and language tech to score skills like empathy, reflection, and guidance — turning craft into something measurable.
对话状态追踪Dialogue state tracking在多轮对话里更稳地记住关键信息与目标,让长对话不“忘事”、不跑偏。Holding key facts and goals steadily across many turns, so long conversations don't drift or forget.
诚实说明:以上为探索中的方向,任何能力在进入产品之前,都要先过安全与质量的关。In fairness: these are works in progress. Nothing ships until it clears our safety and quality bar.
参考文献References

我们站在这些研究的肩膀上。We build on this body of research.

这里列出对我们的临床与工程思考有影响的部分公开文献——从认知行为疗法的循证基础,到大模型心理治疗的最新架构与评测方法。A selection of the public literature that shapes our clinical and engineering thinking — from the evidence base of CBT to the newest architectures and evaluation methods for LLM-assisted care.

临床与心理治疗基础Clinical & psychotherapy foundations
  1. Beck, J. S. (2020). Cognitive Behavior Therapy, Third Edition: Basics and Beyond. Guilford Publications.
  2. Butler, A. C., Chapman, J. E., Forman, E. M., & Beck, A. T. (2006). The empirical status of cognitive-behavioral therapy: A review of meta-analyses. Clinical Psychology Review, 26(1), 17–31.https://doi.org/10.1016/j.cpr.2005.07.003
  3. Brown, J. (2015). Specific Techniques Vs. Common Factors? Psychotherapy Integration and its Role in Ethical Practice. American Journal of Psychotherapy, 69, 301–316.https://doi.org/10.1176/appi.psychotherapy.2015.69.3.301
  4. Bodenmann, G., Kessler, M., Kuhn, R., Hocker, L., & Randall, A. K. (2020). Cognitive-Behavioral and Emotion-Focused Couple Therapy: Similarities and Differences. Clinical Psychology in Europe, 2(3), e2741.https://doi.org/10.32872/cpe.v2i3.2741
  5. Hill, C. E. (2020). Helping Skills: Facilitating Exploration, Insight, and Action. American Psychological Association.
AI 对话智能体与临床证据Conversational agents & clinical evidence
  1. Heinz, M. V., Mackin, D. M., Trudeau, B. M., Bhattacharya, S., Wang, Y., Banta, H. A., Jewett, A. D., Salzhauer, A. J., Griffin, T. Z., & Jacobson, N. C. (2025). Randomized Trial of a Generative AI Chatbot for Mental Health Treatment. NEJM AI, 2(4).https://doi.org/10.1056/AIoa2400802
  2. Sohn, J.-S., Ha, B.-G., Park, S., Kim, J.-J., Lee, E., Oh, H., Lee, S., & Kim, E. (2026). Systematic review and meta analysis of chatbots in the management of depressive and anxiety symptoms. Npj Digital Medicine, 9(1), 377.https://doi.org/10.1038/s41746-026-02566-w
  3. Beatty, C., Malik, T., Meheli, S., & Sinha, C. (2022). Evaluating the Therapeutic Alliance With a Free-Text CBT Conversational Agent (Wysa): A Mixed-Methods Study. Frontiers in Digital Health, 4, 847991.https://doi.org/10.3389/fdgth.2022.847991
  4. Prochaska, J. J., Vogel, E. A., Chieng, A., Kendra, M., Baiocchi, M., Pajarito, S., & Robinson, A. (2021). A Therapeutic Relational Agent for Reducing Problematic Substance Use (Woebot): Development and Usability Study. Journal of Medical Internet Research, 23(3), e24850.https://doi.org/10.2196/24850
  5. Therabot for the treatment of mental disorders. Nature Mental Health. (n.d.). Retrieved July 15, 2026, from https://www.nature.com/articles/s44220-025-00439-x
大模型、治疗推理与架构LLMs, therapeutic reasoning & architecture
  1. Rollwage, M., McFadyen, J., Juchems, K., Balogh, A., Pisupati, S., Mircea, M.-T., Hauser, T. U., Prichard, G., & Harper, R. (2026). A cognitive layer architecture to support large-language model performance in psychotherapy interactions. Nature Medicine, 32(5), 1717–1725.https://doi.org/10.1038/s41591-026-04278-w
  2. Sun, X., De Wit, J., Li, Z., Pei, J., El Ali, A., & Bosch, J. A. (2025). Script-Strategy Aligned Generation: Aligning LLMs with Expert-Crafted Dialogue Scripts and Therapeutic Strategies for Psychotherapy. Proceedings of the ACM on Human-Computer Interaction, 9(7).https://doi.org/10.1145/3757655
  3. Zhang, M., Eack, S. M., & Chen, Z. (2026). Preference Learning Unlocks LLMs' Psycho-Counseling Skills. In Findings of the Association for Computational Linguistics: ACL 2026 (pp. 10729–10750). Association for Computational Linguistics.https://doi.org/10.18653/v1/2026.findings-acl.521
  4. Sinha, V., Guttal, P., Katike, P. D. R., Sinha, V., Ndawula, G., Yoon, L., Kleinsmith, A., & Gaur, M. (2026). Where do LLMs Fall Short in CBT-Guided Affective Reasoning? (arXiv:2607.02885). arXiv.https://doi.org/10.48550/arXiv.2607.02885
  5. Liu, C., Zhang, S., Ma, C., Tao, Y., Yang, M., & Hu, B. (2026). DMT-CBT: Longitudinal Therapeutic State Modeling for CBT Counseling (arXiv:2606.03132). arXiv.https://doi.org/10.48550/arXiv.2606.03132
评测与行为自动编码Evaluation & automated coding
  1. Flemotomos, N., Martinez, V. R., Chen, Z., Singla, K., Ardulov, V., Peri, R., Caperton, D. D., Gibson, J., Tanana, M. J., Georgiou, P., Van Epps, J., Lord, S. P., Hirsch, T., Imel, Z. E., Atkins, D. C., & Narayanan, S. (2021). Automated evaluation of psychotherapy skills using speech and language technologies. Behavior Research Methods, 54(2), 690–711.https://doi.org/10.3758/s13428-021-01623-4
  2. Ali, S., Zhu, J., Guo, A., Ye, X. N., Gu, Q., Wolff, J., Cooper, C., Melamed, O. C., Selby, P., & Rose, J. (n.d.). Automated Coding of Counsellor and Client Behaviours in Motivational Interviewing Transcripts: Validation and Application.
  3. Madani, N., & Srihari, R. (2025). ESC-Judge: A Framework for Comparing Emotional Support Conversational Agents. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (pp. 16048–16065). Association for Computational Linguistics.https://doi.org/10.18653/v1/2025.emnlp-main.811
对话状态追踪与临床决策支持Dialogue state & clinical decision support
  1. Jacqmin, L., Rojas Barahona, L. M., & Favre, B. (2022). “Do you follow me?”: A Survey of Recent Approaches in Dialogue State Tracking. Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, 336–350.https://doi.org/10.18653/v1/2022.sigdial-1.33
  2. Zeng, J., & Nakano, Y. (2026). Schema-Guided Response Generation using Multi-Frame Dialogue State for Motivational Interviewing Systems. In Findings of the Association for Computational Linguistics: ACL 2026 (pp. 41493–41524). Association for Computational Linguistics.https://doi.org/10.18653/v1/2026.findings-acl.2063
  3. Modular Clinical Decision Support Networks (MoDN) — Updatable, interpretable, and portable predictions for evolving clinical environments. PLOS Digital Health. (n.d.). Retrieved July 23, 2026, from https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000108
神经科学基础Neuroscience foundations
  1. Torrico, T. J., & Abdijadid, S. (2026). Neuroanatomy, Limbic System. In StatPearls. StatPearls Publishing.http://www.ncbi.nlm.nih.gov/books/NBK538491/